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| author | ShelbySolomonDarnell | 2024-10-17 12:24:26 +0300 |
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| committer | ShelbySolomonDarnell | 2024-10-17 12:24:26 +0300 |
| commit | 00cba4b9a1e88891f1f96a1199320092c1962343 (patch) | |
| tree | 270fd06daa18b2fc5687ee72d912cad771354bb0 /gnqa/paper2_eval/data/responses | |
| parent | e0b2b0e55049b89805f73f291df1e28fa05487fe (diff) | |
| download | gn-ai-master.tar.gz | |
Diffstat (limited to 'gnqa/paper2_eval/data/responses')
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diff --git a/gnqa/paper2_eval/data/responses/gpt4o/cs_aging_responses.json b/gnqa/paper2_eval/data/responses/gpt4o/cs_aging_responses.json new file mode 100644 index 0000000..25d5e17 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/gpt4o/cs_aging_responses.json @@ -0,0 +1,3222 @@ +{ + "1": { + "results": { + "completion": { + "id": "chatcmpl-AIFgRqvOB8PnpNpKMnpdr80oxf2MI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The main genetic factors that influence aging include:\n\n1. **Genomic Instability**: Aging is associated with the accumulation of DNA damage and the dysregulation of repair mechanisms, leading to genomic instability [9].\n\n2. **Telomere Attrition**: The shortening of telomeres, which are protective caps at the ends of chromosomes, is a significant factor in aging [9], [10].\n\n3. **Epigenetic Alterations**: Changes in epigenetic marks, which regulate gene expression without altering the DNA sequence, play a crucial role in aging [2], [4], [9], [10].\n\n4. **Deregulated Nutrient Sensing**: The pathways that sense and respond to nutrients become deregulated with age, affecting longevity [10].\n\n5. **Mitochondrial Dysfunction**: Mitochondria, the energy-producing organelles in cells, become less efficient with age, contributing to the aging process [10].\n\n6. **Cellular Senescence**: The process by which cells lose the ability to divide and function properly is a hallmark of aging [9], [10].\n\n7. **Loss of Proteostasis**: The ability of cells to maintain protein homeostasis declines with age, leading to the accumulation of damaged proteins [9], [10].\n\n8. **Stem Cell Exhaustion**: The decline in the regenerative capacity of stem cells contributes to aging [10].\n\nThese factors collectively shape the complex genetic landscape of aging, influencing the expression of aging phenotypes and lifespan [7].", + "role": "assistant" + } + } + ], + "created": 1728913555, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 312, + "prompt_tokens": 1050, + "total_tokens": 1362 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "3117c019-7311-53ae-8ab1-927ca822c709", + "score": 0.698876044120671, + "metadata": { + "text": "It is undisputed that genetic factors influence aging. In a remarkable", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "7ada6b55-99c2-5e20-bf96-d153f927256c", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "a9434032-4a9d-54f8-a7a6-16110d1b3118", + "score": 0.697221577167511, + "metadata": { + "text": "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x\n 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link-\ning environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048\n 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity \nand aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "title": "2018 - Sex Differences in Aging Genomic Instability.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "extraction_id": "0104338d-cc9c-538f-be29-8343a64da37d", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "a0672677-71ad-5603-8427-a0648eec407f", + "score": 0.6917247176170349, + "metadata": { + "text": "Clinical Genetics and Genomics of Aging", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "4ea8424f-1cd8-569c-a1df-3f0f54206e70", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "c1b5a31a-066d-571b-af1f-db746d9d17f6", + "score": 0.684185266494751, + "metadata": { + "text": "standing the cause and mechanisms of aging is imperative in\nassisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin-\nation of genetic and environmental factors. Previous twin stud-\nies have shown that the genetic contribution to general human\nlongevity is about 2030% [ 4,5], whereas environmental factors\nin human aging and longevity still account for the largest effect.\nEpigenetic factors influence the regulation of gene expres-", + "title": "2016 - Progress on the role of DNA methylation in aging.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "extraction_id": "bcb3c620-b960-5af6-95ea-13215c31672e", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "score": 0.6728764376901522, + "metadata": { + "text": "Recent developments on the genetics of aging can be seen as several streams\nof effort. In general, humans show a relatively modest ( <50%) heritability of", + "title": "2001 - The genetics of aging.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "extraction_id": "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "2d0a20b8-4196-5451-9d99-282f82234464", + "score": 0.6651506823121736, + "metadata": { + "text": "effect genetic variants on human longevity. Aging 2, 612620.\nYu, C.E., Seltman, H., Peskind, E.R., Galloway, N., Zhou, P.X., Rosenthal, E., Wijsman,\nE.M., Tsuang, D.W., Devlin, B., Schellenberg, G.D., 2007. Comprehensive analysis\nof APOE and selected proximate markers for late-onset Alzheimers disease:\npatterns of linkage disequilibrium and disease/marker association. Genomics", + "title": "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "version": "v0", + "chunk_order": 109, + "document_id": "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "extraction_id": "210aa417-372c-5bf6-b961-e281a1817458", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "8bcb7ae0-ac45-5b4c-8a4b-626564e8ec11", + "score": 0.6649871668227227, + "metadata": { + "text": "factors shape a complex scenario for which clear answers\nof the regulation of longevity have been dicult to distill.\nWith the discovery of genetic factors underlying aging in\nexperimental laboratory models, forays into the genetic\nregulation of these properties have rapidly expanded,\nuncovering conserved mechanisms across diverse metazoa\nthat inuence expression of aging phenotypes and lifespan.\nYet, the story gets muddled in that these factors are often", + "title": "2021 - Footprints in the Sand Deep Taxonomic Comparisons in Vertebrate Genomics to Unveil the Genetic Programs of Human Longevity.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "0dc45abe-ab02-5b07-9916-7093b53323c0", + "extraction_id": "34223e0e-590c-5f26-b120-b7250cd91b99", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "score": 0.6641277475204816, + "metadata": { + "text": "In addition to aging- and CR-related genes, another\nsource of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations.\nMany longevity-associated genes are related to spe-", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "d59d7882-333d-5576-86ab-3cfa6354b946", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "d811de8c-b666-5bb5-b0eb-a9b17fa16a8e", + "score": 0.6602960026440634, + "metadata": { + "text": "tion for decades, the underlying molecular genetic\ncauses of and responses to aging remain an area of\nactive study. Research from model systems hascharacterized a range of physiological and molecular\nphenotypes associated with aging. These include genomic\ninstability caused by accumulation of DNA damage,\ndysregulation of repair mechanisms, and telomere attri-\ntion; epigenetic alterations; dysregulation of transcription;\nloss of proteostasis; cellular senescence; and deregulated", + "title": "2017 - Genome-wide transcriptomics of aging.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "1a2a3737-b0a6-58b9-908f-50753241a309", + "extraction_id": "c7d6d597-a9c7-5db2-888d-5f9297f0af47", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + }, + { + "id": "081e12f9-359c-5a2c-b740-714d637367d3", + "score": 0.6599280440850244, + "metadata": { + "text": "143\nThe molecular bases of ageing are multi factorial, but there are nine distinctive \nfeatures related to this process, which include genomic instability, telomere shorten-\ning, de-regulated nutrient sensing, mitochondrial dysfunction, cellular senescence, \nstem cell exhaustion, altered cellular senescence, loss of proteostasis and a change \nin the patterns of epigenetic modifications [4, 5].\n Epigenetics andAgeing\nEpigenetics is considered as a dynamic interface between the genome and the envi-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 986, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "517379dd-d351-5e9a-8e78-72e543bb2945", + "associatedQuery": "What are the main genetic factors that influence aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIFgbQ1pPISWJITI2YHN6poeJtTyV", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genes affect the aging process in humans through complex interactions and pathways. Research has shown that single genes can regulate aging in model organisms, indicating that aging can be genetically manipulated [2]. Hundreds of genes have been identified that modulate longevity in these organisms, and some of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, have been shown to affect longevity across different species, suggesting that some mechanisms of aging are evolutionarily conserved [3]. \n\nIn humans, it has been more challenging to identify specific longevity candidate genes, but studies have shown that certain genes associated with aging in model organisms are evolutionarily conserved and may be relevant to human aging [5]. Additionally, approximately 4% of genes analyzed in a study of postmortem human brain tissue showed significant age-related expression changes, indicating that these genes play central roles in processes like synaptic plasticity, vesicular transport, and mitochondrial function [8]. \n\nOverall, while aging is a complex process involving multiple genes and their interactions with the environment, genetic studies in model organisms provide insights that may be applicable to understanding human aging [9].", + "role": "assistant" + } + } + ], + "created": 1728913565, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 226, + "prompt_tokens": 1287, + "total_tokens": 1513 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "0ae63c75-df5f-59b0-9561-30d5115f0f74", + "score": 0.7094772631241535, + "metadata": { + "text": "potentially associated with human ageing. For eachgene, a description compiled from the studies that linkthe gene to ageing is provided. It should be noted thatour focus is on genes that might affect the ageingprocess, rather than individual age-related pathologies;\ngenes affecting multiple, even if not all, age-related", + "title": "2012 - Human Ageing Genomic Resources Integrated.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "5f554cc7-c94d-5fbd-9567-528499663ed6", + "extraction_id": "25e9d8a3-54ac-5412-8efb-3b56d93f363f", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "c258bd44-b5b3-5eb5-9c72-60b365f18f81", + "score": 0.7014725146044777, + "metadata": { + "text": "showing that single genes can regulate aging in modelorganisms demonstrate that aging can be geneticallymanipulated (Finch and Ruvkun, 2001; Kenyon, 2010).Hundreds of genes that modulate longevity have nowbeen identified in model organisms (de Magalha es et al.,2009a). In some cases (e.g., in worms), mutations insingle genes can extend lifespan by almost 10-fold (Ayy-adevara et al., 2008). Nonetheless, aging is a complexprocess that derives not from single genes but from theinteractions of multiple genes", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "68b12e10-d643-570a-aa63-eda62de81928", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "score": 0.6974861206122577, + "metadata": { + "text": "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "0671ed95-2909-54ac-baea-b156ee0ea8f8", + "score": 0.6964022698162645, + "metadata": { + "text": "key genes and pathways important in aging; geneticstudies of heritable diseases that cause the appearanceof premature aging in affected people; physiological ex-Introductionperiments that relate the pace of aging to caloric intake;Is aging the final act in the script of developmental biol-and advances in human genetics, as well as cell andogy? The characteristic changes that are part and parcelmolecular biology leading to an understanding of theof aging appear similar to developmentally regulatedbasis of", + "title": "1999 - Molecular Biology of Aging.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "6f122d3a-d8ca-598f-8767-c059a941cef3", + "extraction_id": "66c6e911-2b6e-5d79-9e13-9f0e370c6fa6", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "650300e1-898c-56e2-9358-0bb6625b0073", + "score": 0.694001967787392, + "metadata": { + "text": "shown that genes associated with aging and/or longevity inmodel organisms are evolutionary conserved in terms of havingmore homologues than predicted by chance (Budovsky \net \n \nal \n.,2007, 2008) and exhibiting slower molecular evolution rates (de\nMagalhes & Church, 2007). Therefore, it is now clear that atleast some genes identified in model organisms may be relevantto human aging.\nTo allow researchers to focus specifically on human aging,", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "cc78a209-081f-5e0c-877a-36b85d5105a6", + "score": 0.6935852352627984, + "metadata": { + "text": "expression of certain genes have an effect upon longevity.\nAlthough similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until\nnow there has been a lack of large transcriptional profiles\nfrom the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "title": "2013 - Gene expression changes with age in skin.pdf", + "version": "v0", + "chunk_order": 49, + "document_id": "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "extraction_id": "18e9f96b-9af8-59bf-9323-084df9a4f9b6", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "a3859151-a94e-5085-8e7a-3c1d50716319", + "score": 0.6925199581647007, + "metadata": { + "text": "complex.108,109Studies on models such as the yeast Sac-\ncharomyces cerevisiae110the nematode Caenorhabditis\nelegans,111the fly Drosophila melanogaster,112-114the\nmouse Mus musculus,115and humans116show that single\ngene mutations can contribute to the initiation of aging andinduce premature aging syndromes. There are, however, nospecial genes that can cause aging-associated damages. Themanifestation of aging is mostly due to the failure of main-tenance and repair mechanisms.\n117,118", + "title": "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", + "extraction_id": "39cc0d86-f175-5fe3-990f-8471c7395ba4", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "d53018ae-0881-5ef4-9c49-48623e8aa342", + "score": 0.6913864200480938, + "metadata": { + "text": "on model organisms [3] or have been confined to specificaging-associated disorders such as progeria syndromes\n[4]. A study of postmortem human brain tissue from 30\nindividuals aged 26 to 106 years [5] showed that approxi-\nmately 4% of approximately 11,000 genes analyzed show\na significant age-related expression change (1.5-fold or\nmore) in individuals aged >40 years. These genes were\nreported to play central roles in synaptic plasticity, vesi-\ncular transport, and mitoch ondrial function. Another", + "title": "2013 - Gene expression changes with age in skin.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "extraction_id": "dfb687b2-f1ff-5e22-8a67-4a1db9ebeb3c", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "a4773f1a-f2d3-5950-a81e-d22357e97a0f", + "score": 0.6908473144951683, + "metadata": { + "text": "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "68b12e10-d643-570a-aa63-eda62de81928", + "associatedQuery": "How do genes affect the aging process in humans?" + } + }, + { + "id": "91530253-7015-5b7a-bb7b-506ddeb3c3f8", + "score": 0.6904643546981388, + "metadata": { + "text": "[2] L. Partridge, D. Gems, Mechanisms of ageing: public or private? Nat. Rev.\nGenet. 3 (2002) 165 175.\n[3] A.M. Leroi, et al., What evidence is there for the existence of individual\ngenes with antagonistic pleiotropic effects? Mech. Ageing Dev. 126 (2005)421429.\n[4] S.N. Austad, Is aging programmed? Aging Cells 3 (2004) 249 251.\n[5] V.D. Longo, J. Mitteldorf, V.P. Skulachev, Opinion: programmed and\naltruistic ageing, Nat. Rev. Genet. 6 (2005) 866 872.", + "title": "2007 - Characterization of a bidirectional promoter shared between two human.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "e9a81a0d-52b4-5ced-be05-130eed317085", + "extraction_id": "a5581fce-8af9-5131-a94b-8fce7fbf9557", + "associatedQuery": "How do genes affect the aging process in humans?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIFgiWkzt5opfBd5VTvAKGVKegG8y", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "To slow down genetic aging, several lifestyle choices can be beneficial:\n\n1. **Dietary Restriction (DR)**: Reducing food intake without causing malnutrition has been shown to extend lifespan in various organisms. This approach improves healthspan by influencing nutrient-sensing signaling networks and metabolism [1].\n\n2. **Healthy Diet and Physical Exercise**: Engaging in regular physical activity and maintaining a healthy diet can slow down aging and prevent or modify many chronic diseases prevalent in older adults. This contributes to successful aging, allowing individuals to achieve physical, social, and mental well-being [4].\n\n3. **Caloric Restriction**: This is considered a reasonable anti-aging intervention, as it has been shown to slow aging in multiple species, including yeast, nematodes, fruit flies, and rodents [6].\n\n4. **Exercise**: While exercise may not retard aging in all tissues, it can contribute to molecular-level aging retardation, which is beneficial for overall health and longevity [10].\n\nThese lifestyle choices, when combined, can help mitigate the effects of genetic aging and promote a longer, healthier life.", + "role": "assistant" + } + } + ], + "created": 1728913572, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 222, + "prompt_tokens": 1158, + "total_tokens": 1380 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "7460a40c-8723-5de9-9f2e-c781f4872f1f", + "score": 0.6213953680215888, + "metadata": { + "text": "as diabetes, cancer and neurodegenerative disorders\n[1, 2]. Environmental and genetic interventions can\nameliorate the effects of aging, with nutrition,\nnutrient-sensing signaling networks and metabolism\nplaying evolutionarily conserved roles [1, 3 5]. Diet-\nary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di-\nverse model and non-model organisms [3, 6]. DR\ninduces a remarkably broad-spectrum improvement in", + "title": "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "extraction_id": "21efa872-9d89-5dee-9dd1-27dcaa1208cf", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "d78564d5-d785-554a-bb2c-d71917ccfe19", + "score": 0.6209702676823817, + "metadata": { + "text": "limiting exposure to exogenous genotoxins and by suppressing metabolism thereby producing fewer reactive species. However, DNA damage, like caloric restriction, can also elicit a protective survival response that promotes longevity and healthy aging. Recently, the use of sirolimus in mice was found to extend their life span and de -\nlay the development of conditions associated with aging, including cancer.\n1 Sirolimus is one of pre -", + "title": "2009 - DNA Damage, Aging, and Cancer.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "630c29c7-1dd7-509e-9b6b-b4af98b4ea48", + "extraction_id": "b03f4297-85f4-5011-8dcf-ec169d3051d3", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "4bf7307d-d8a0-5594-b0b5-487fe0f265ca", + "score": 0.6070101442042038, + "metadata": { + "text": "Longev. Heal. 2, 10 (2013).\n7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. \nAging Cell18, e12852 (2019). [PubMed: 30548460] \n8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications \nfor studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] \n9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "version": "v0", + "chunk_order": 132, + "document_id": "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "extraction_id": "30ba3324-6e19-58c2-9e32-508f827af3e5", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "da620f88-db92-5267-af81-d6b548e9f29c", + "score": 0.6065477548078396, + "metadata": { + "text": "can be slowed down to some extent by eating a healthy diet and taking physical exercise, and many of the chronic diseases prevalent in older adults are either preventable or modi able with healthy lifestyle habits. Thus, older adults \ncan experience successful aging that allows them to achieve physical, social and mental well - being over the life course and to participate in society. \n Much research has been conducted in recent years to", + "title": "2012 - Systems Biology Approaches to Nutrition.pdf", + "version": "v0", + "chunk_order": 7631, + "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "extraction_id": "791bae8d-8d24-5873-b611-9c289591d11d", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "c96b67f8-ad31-50fd-b053-07b127938ef2", + "score": 0.5996953070254556, + "metadata": { + "text": "During the past century, remarkable progress has been \nmade in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "title": "2020 - Mitonuclear genomics and aging.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "extraction_id": "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "a4e0cb76-8950-5471-a3c1-1ed43094fdf3", + "score": 0.599365091286009, + "metadata": { + "text": "13,14\nPrior studies have identified dozens of genetic and environ -\nmental modifiers of chronological or replicative longevity, some of which are now known to function similarly to modulate life span in multicellular eukaryotes.\n15-17 One example of such a con -\nserved longevity intervention is dietary restriction, which has been shown to slow aging in many different species including yeast, nematodes, fruit flies and rodents,\n18,19 and most recently", + "title": "2011 - A genomic analysis of chronological longevity.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "a2e69cf7-8475-55f6-8fab-a572c12de9f0", + "extraction_id": "d7daf4ea-f57a-5f7b-b6f7-afae08c35b45", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "1da274d3-c789-5af5-a8b5-72cdc9a01899", + "score": 0.5979077398153468, + "metadata": { + "text": "Genetic studies have shown that aging can be slowed in\nmutants that are defective in a wide range of cellularprocesses (such as mitochondrial function, chromatin regu-\nlation, insulin signaling, tran scriptional regulation, and\ngenome stability). This indicates that aging is a complex\nprocess driven by diverse molecular pathways and biochem-\nical events. As such, a powerful approach to study aging is touse systems biology, which allows a multitude of factors", + "title": "2004 - A Transcriptional Profile of Aging.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "4ab656a7-9656-526b-94e1-422875409b44", + "extraction_id": "b382fe8a-0267-5515-ac4b-07be55420040", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "5fc33fac-ab39-5ec1-9fb9-dcaa93a595d3", + "score": 0.5952795921349406, + "metadata": { + "text": "Dietary interventions, including starvation and protein\ndeprivation, can also alter patterns of DNA methyla-\ntion, potentially in a long-lasting manner [42, 43],\nincluding transgenerationally [26, 44].\nDietary, genetic and pharmacological interventions\nthat improve health during aging and extend lifespan\ninduce long-lasting changes in gene expression that\nmediate their effects. Here we have asked if and how\nage-related DNA methylation, transcription and lipid", + "title": "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "extraction_id": "6364d669-4b96-5d2f-8ce8-526b065dce72", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "321d14fd-f2ae-5904-b502-dae3491cd370", + "score": 0.5949770034856521, + "metadata": { + "text": "in yeast , Drosophila, and C. elegans is able to slow aging and increase lifespan [252-255]. Follow -up stud ies \nout of Richard Millers laboratory reproduced these findings in mice fed a diet with rapamycin incorporated \n[256, 257]. These studies suggested that inhibiting mTOR via rapamycin could delay age-associated diseases \nand extend lifespan in mammals. A subsequent study replicated these findings by genetically manipulating a", + "title": "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "version": "v0", + "chunk_order": 107, + "document_id": "815d7f3e-e219-502f-aba0-57a68ae787d3", + "extraction_id": "86f9502b-7a3a-501f-9053-8af1d37043b4", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + }, + { + "id": "4c3d343d-d443-5bb4-a9ef-dd1eecaf9fac", + "score": 0.5949385343473715, + "metadata": { + "text": "appears to retard aging at the molecular level as indi-cated by the gene expression analysis? Most likely,aging retardation at the molecular level by exercise isnot observed in all tissues, including some that maylimit lifespan. For example, if exercise does not reduceaging rates in replicative tissues, then it will not retardage-related tumor onset, which tends to limit maxi-mum lifespan. Another possibility relates to the obser-vation that wheel running decreased to an average 680m/day at 33 mo of age", + "title": "2003 - Lifelong voluntary exercise in the mouse prevents.pdf", + "version": "v0", + "chunk_order": 83, + "document_id": "24d4f270-f45b-5830-84f9-b1e5bcd3c070", + "extraction_id": "e6c82594-27ba-5754-a106-69ae8b5e72ae", + "associatedQuery": "What lifestyle choices can help slow down genetic aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIFgqiJDPdSbdoRhIXU84YMtAnqaJ", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Scientists study the genetics of aging in animals using a variety of approaches and model organisms. Here are some key methods and models mentioned in the context:\n\n1. **Animal Model Systems**: Rodents, such as rats and mice, are indispensable for molecular biological studies on aging due to their practical advantages and similarities in aging processes to humans [1]. \n\n2. **Tissue-Specific Studies**: Recent genomic studies have been performed on specialized mammalian tissues, both post-mitotic (e.g., heart, nervous system) and mitotic (e.g., liver), to understand the tissue-specific effects of aging. These studies also explore how caloric restriction affects age-related transcriptional changes, which are tissue- or species-specific [2].\n\n3. **Laboratory Models**: Common laboratory models for studying aging include Caenorhabditis elegans (nematode worm) and Mus musculus (mice), which have shorter lifespans than humans, allowing for more rapid observation of aging processes [5].\n\n4. **Transgenic Mouse Strains and Interventional Studies**: These studies have identified evolutionarily conserved pathways involved in lifespan regulation and common denominators of aging across different organisms [6].\n\nThese methods and models help scientists uncover the genetic and molecular mechanisms underlying aging, providing insights that can be applied to understanding human aging and developing potential interventions.", + "role": "assistant" + } + } + ], + "created": 1728913580, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 274, + "prompt_tokens": 1348, + "total_tokens": 1622 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "91375d45-be1d-5c54-8d0f-a9b1dded69bb", + "score": 0.6931690573692322, + "metadata": { + "text": "for molecular biological studies on aging. Although material \nfrom humans should be employed where possible, for prac- \ntical reasons animal model systems like rats and mice are \nindispensible. There is evidence that, provided their health sta- \ntus and husbandry is optimal, rodents age much in the same \nway as humans do (Burek 1978). For studying certain funda- \nmental processes, such as the occurrence of various types of \nDNA rearrangement, lower organisms and cell lines can also", + "title": "1989 - Genetic instability and aging theories, facts, and future perspectives.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "4d5b1800-b676-5865-a555-09ea740cc14a", + "extraction_id": "b0531531-f629-512b-9835-24cc870b4ef3", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "a32e8775-583f-5827-a590-b7058b255d26", + "score": 0.6927486245185969, + "metadata": { + "text": "Until now most of the genomic studies of invertebrate\nmodels have been performed on whole animals. Several\nstudies, however, recently performed on specialized\nmammalian tissues, either post-mitotic (heart or nervous\nsystem) or mitotic (liver), show that the effects of aging\nare tissue-specific [19-25]. In addition, effects of caloric\nrestriction on age related transcriptional changes are also\ntissue- or species-specific [19].\nTo better understand the aging process in invertebrate", + "title": "2006 - Specific age related signatures in Drosophila body parts.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "24f073af-ef97-5ba3-9923-9a7d958bd411", + "extraction_id": "efba6890-9b12-567c-b3f0-4e6ff5c6e9c4", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "aba78d88-b097-52fe-8246-66301e39cdd5", + "score": 0.687142349822246, + "metadata": { + "text": "opportunities for assessing the efcacy of interventions onaging.\nWhen considering the advantages and disadvantages of\ndogs as a model for geroscience research, it is useful tonote that the vast majority of mammalian studies on thebasic biology of aging are performed in a relatively small\nnumber of inbred mouse strains. Typical average lifespan\nfor most of these mouse strains is approximately 23 years,", + "title": "2016 - The dog aging project translational geroscience in companion.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "extraction_id": "9c8bc002-4f7d-5c53-9736-70f59a6ee518", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "741dc9f2-2e8e-5fe3-9e6f-806a5a93213b", + "score": 0.6835690140724182, + "metadata": { + "text": "[14] Gerstbrein, B., Stamatas, G., Kollias, N., Driscoll, M. In vivo spec-\ntrofluorimetry reveals endogenous biomarkers that report health-\nspan and dietary restriction in Caenorhabditis elegans . Aging Cell \n2005 , 4: 127-137. \n[15] Kennedy, B.K. The genetics of ageing: insight from genome-wide \napproaches in invertebrate model organisms. J. Intern. Med. 2008 , \n263: 142-152. \n[16] Kenyon, C., Chang, J., Gensch, E., Rudner, A., Tabtiang, R. A C.", + "title": "2009 - MicroRNAs in C. elegans Aging Molecular Insurance for Robustness.pdf", + "version": "v0", + "chunk_order": 101, + "document_id": "dff49223-ac74-5419-a190-a0c7f43a5ee5", + "extraction_id": "c8d6f90d-a25c-590a-a546-4500df09aa28", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "0916cf4a-a863-5c5d-b687-2ae5fa80bac0", + "score": 0.6824588775634766, + "metadata": { + "text": "the DNA level leads to changes in gross phenotype, we must \nnow look downstream at changes in gene expression associ -\nated with genetic variation, aging, and ARD.\nComparison With Laboratory Models of Aging\nLaboratory models typically used to study aging, such as \nCaenorhabditis elegans (nematode worm) and Mus musculus \n(mice), have drastically shorter life spans than our own \n(~3 wk [ 51] and ~3 y [ 52], respectively, vs a 122 y maxi -\nmum for humans thus far; [ 53]). In some respects, these", + "title": "2012 - Genomics and Successful Aging Grounds for Renewed.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "6d2b82c3-4256-562a-9b23-ff7c71e9fd93", + "extraction_id": "3d18e792-3d83-5cc3-b9ab-309322ecf55d", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "b3e0de69-763f-5f19-aeb7-ea1df79a143b", + "score": 0.678830087184906, + "metadata": { + "text": "ing studies on invertebrate models of aging, long-lived mam-mals, transgenic mouse strains, and interventional studies, have led to the identification of evolutionarily conserved path-\nways involved in life span regulation, as well as common de-\nnominators of aging in different organisms.\n4 In this review, the \npathophysiological roles of these aging mechanisms, including \noxidative stress, mitochondrial dysfunction, impaired resis-", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "bfeb5c38-4fa6-5df5-90ce-63204deba3a8", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "e58a6718-dfef-58f6-9417-4abd793fe74d", + "score": 0.6787930727005005, + "metadata": { + "text": "chain triglyceride oil on life span of genetically heterogeneous mice. J. Gerontol. A. Biol. Sci. \nMed. Sci. 68, 616 (2013). [PubMed: 22451473] \n24. Yuan R, Peters LL & Paigen B Mice as a mammalian model for research on the genetics of aging. \nILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 52, 415 (2011).\n25. Saul MC, Philip VM, Reinholdt LG & Chesler EJ High-diversity mouse populations for complex \ntraits. Trends Genet. 35, 501514 (2019). [PubMed: 31133439]", + "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "version": "v0", + "chunk_order": 139, + "document_id": "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "extraction_id": "396683f9-b2e3-5942-bec8-f96fa798c341", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "71eb66cb-130c-5183-ba9e-038637582775", + "score": 0.6782771348953247, + "metadata": { + "text": "lowing the discovery of genes and pathways involved inanimal lifespan extension, human research has focusedon the corresponding candidate human genes withgenetic, genomic and epigenetic studies into ageingand longevity. The designs of these studies differwith respect to the selection of naturally occurringphenotypes and the study populations, which includepopulation-based, patient-based, family-based andexposure-based cohorts.\nStudies into human age-related disease phenotypes", + "title": "2011 - Genomics of human longevity.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "2e038219-fdaa-506f-9cd3-51379054130e", + "extraction_id": "89586b79-902d-5e2b-9b8a-b7a8c4971783", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "a0aa0b47-91a6-5f3e-b8a2-9ccdfcd79865", + "score": 0.6779917074446865, + "metadata": { + "text": "Animal studies as stalking horses for human biogerontology. For\nthe most part, studies on the biology of aging are as difficult and imprac-tical in humans as are studies of health insurance in rodents. It is fairlyCopyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research?\nhttp://www.nap.edu/catalog/9995.html", + "title": "2001 - Demography in the age of genomics.pdf", + "version": "v0", + "chunk_order": 1759, + "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "extraction_id": "94acf45b-980d-5273-8a09-5d748c94a51b", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + }, + { + "id": "322613d7-921b-5e2e-b410-57ab4acc4130", + "score": 0.6763045384702373, + "metadata": { + "text": "review of the evidence for genotype-dependent eects on lifespan. Ageing Res.\nRev. 11, 254270. doi: 10.1016/j.arr.2011.12.006\nTurturro, A., Witt, W. W., Lewis, S., Hass, B. S., Lipman, R. D., and Hart, R. W.\n(1999). Growth curves and survival characteristics of the animals used in the\nbiomarkers of aging program. J. Gerontol. Ser. Biol. Sci. Med. Sci 54, B492B501.\ndoi: 10.1093/gerona/54.11.b492\nVertti-Quintero, N., Berger, S., Solvas, X. C. I, Statzer, C., Annis, J., Ruppen,", + "title": "2021 - Lifespan-Associated Gene Expression Signatures of Recombinant BXD Mice Implicates Coro7 and Set in Longevity.pdf", + "version": "v0", + "chunk_order": 106, + "document_id": "6277f22c-f56d-51a7-add1-1fe7674dda74", + "extraction_id": "e3eb627c-15f4-5713-92a4-e92a891b7136", + "associatedQuery": "How do scientists study the genetics of aging in animals?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIFgxEMof4wOrO9UyQT5bEPH8q8fk", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, specific genes have been linked to longer lifespans. Research has identified more than 700 genes that regulate lifespan in model organisms, with many of these genes and their associated pathways, such as the insulin/IGF1/GH pathway, shown to affect longevity across different model organisms [2]. Additionally, variants near genes such as CHRNA3/5 and APOE have been found to have age- and sex-related effects on human lifespan [7]. Genome-wide association studies have also identified loci associated with exceptional human longevity [10].", + "role": "assistant" + } + } + ], + "created": 1728913587, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 109, + "prompt_tokens": 1456, + "total_tokens": 1565 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9defe0af-80a1-56da-90df-551fd55baa13", + "score": 0.7708674209792973, + "metadata": { + "text": "genes analyzed for their possible association with human lon-gevity (http://genomics.senescence.info/genes/longevity.html).All longevity association studies in humans we could find by thetime of the latest update were added to this list. These includestudies reporting negative results, which we see as essentialsince many genes display population-specific associations withlongevity.\nFig. 1 From the main page of the Human Ageing", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "score": 0.7568767215683043, + "metadata": { + "text": "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "2f28f34e-bf5e-57d6-8a8c-dd946f574906", + "score": 0.7510410844038624, + "metadata": { + "text": "Exceptional Longevity\nOne approach to identifying genes associated with low mortality is to\nexamine the genes of those who survive to the oldest ages. Several studieshave examined gene frequencies among centenarians or nonagenariansand compared them with frequencies at younger ages. Since changes ingene frequencies are more rapid when mortality rates are high, cross-sectional comparisons must be adjusted for differences in mortality amongcohorts.", + "title": "2001 - Demography in the age of genomics.pdf", + "version": "v0", + "chunk_order": 855, + "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "extraction_id": "0e71cb8b-4647-56bb-9b5f-d14d0ea2ff50", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "b3e21ac9-8df8-5119-a769-a9da82db78da", + "score": 0.7497157156806331, + "metadata": { + "text": "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. \nPLoS Genet. 2015. https://doi.org/10.1371/journal.pgen.\n 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, \netal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat \nGenet. 2015;47:7029.\n 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. \n2017;70:17789.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 167, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "ead14808-bfb7-5e32-9830-28efaae71151", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "c2234f77-2268-57d0-a227-e931fc4802c1", + "score": 0.7427925178059914, + "metadata": { + "text": "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479\nINCREASES in longevity of the general population world -\nwide are an unprecedented phenomenon with significant \nhealth and social impact. Although environmental factors \nhave led to an increase in life span, there is ample evidence \nthat genetic factors are involved in extreme longevity both \nin humans (17) and in other organisms (8). The protective \ngenetic factors that lead to longevity are likely to involve", + "title": "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "8e452186-a71c-5b62-81b2-7681c87c8e1d", + "extraction_id": "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "cc78a209-081f-5e0c-877a-36b85d5105a6", + "score": 0.7383499897509832, + "metadata": { + "text": "expression of certain genes have an effect upon longevity.\nAlthough similar aging processes are likely to operateacross multiple species [30], it has been much more diffi-cult to identify longevity candidate genes in human studies[30]. A key question in human aging is to what extent asignature of aging may be detectable across tissues. Until\nnow there has been a lack of large transcriptional profiles\nfrom the same human individuals in multiple tissues. TheMuTHER study provides ins ight into the human aging", + "title": "2013 - Gene expression changes with age in skin.pdf", + "version": "v0", + "chunk_order": 49, + "document_id": "5c121bbb-57b8-51cc-8461-effa1bfd87b9", + "extraction_id": "18e9f96b-9af8-59bf-9323-084df9a4f9b6", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "726417dd-f626-5197-966d-6a6ad25ff718", + "score": 0.7361661949058668, + "metadata": { + "text": "4. Joshi, P. K. et al. Variants near CHRNA3/5 and APOE have age- and sex-\nrelated effects on human lifespan. Nat. Commun. 7, 11174 (2016).\n5. Pilling, L. C. et al. Human longevity is in uenced by many genetic variants:\nevidence from 75,000 UK Biobank participants. Aging 8, 547560 (2016).\n6. Deelen, J. et al. Genome-wide association meta-analysis of human longevity\nidenti es a novel locus conferring survival beyond 90 years of age. Hum. Mol.\nGenet. 23, 4420 4432 (2014).", + "title": "2017 - Genome-wide meta-analysis associates HLA.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "3a565ba9-ee5b-5596-b870-ce8c055cb1f1", + "extraction_id": "c302507d-508e-5093-a877-1cec20f8440e", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "300f0303-caec-52b9-852b-8e67cec5d326", + "score": 0.7353308954637722, + "metadata": { + "text": "79-91. \n[97] Smith, E.D.; Kennedy, B.K.; Kaeberlein, M. Genome-wide \nidentification of conserved longevity genes in yeast and worms . \nMech. Ageing Dev. , 2007 , 128(1), 106-11. \n[98] Chen, D.; Pan, K.Z.; Palter, J.E.; Kapahi, P. Longevity determined \nby developmental arrest genes in Caenorhabditis elegans. Aging \nCell, 2007 , 6(4), 525-33. \n[99] Curran, S.P.; Ruvkun, G. Lifespan regulation by evolutionarily \nconserved genes essential for viability . PLoS Genet. , 2007 , 3(4), \ne56.", + "title": "2012 - Genome-Wide RNAi Longevity Screens in Caenorhabditis elegans.pdf", + "version": "v0", + "chunk_order": 160, + "document_id": "7589fec2-e893-5a4d-9f0c-09abb35858ab", + "extraction_id": "f160f818-03bf-5b4e-b1f4-bfbd3b0bfb99", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "025a94a9-595e-56f6-8c03-89ccea15a22c", + "score": 0.7336123148433987, + "metadata": { + "text": "9. vB Hjelmborg J, Iachine I, Skytthe A, Vaupel JW, McGue M, et al. (2006)\nGenetic influence on human lifespan and longevity. Hum Genet 119: 312321.doi:10.1007/s00439-006-0144-y.\n10. Sebastiani P, Perls TT (2012) The genetics of extreme longevity: lessons from the\nnew England centenarian study. Front Genet 3: 277. doi:10.3389/fgene.2012.00277.11. Perls TT, Wilmoth J, Levenson R, Drinkwater M, Cohen M, et al. (2002) Life-", + "title": "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", + "version": "v0", + "chunk_order": 110, + "document_id": "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", + "extraction_id": "eed176e2-226d-5638-b10c-4813cfbbd213", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + }, + { + "id": "68e705e1-54a1-578a-98ee-0c76b02ccf79", + "score": 0.7321038405833352, + "metadata": { + "text": "39. Fortney K, Dobriban E, Garagnani P, etal. Genome-wide scan informed by \nage-related disease identifies loci for exceptional human longevity. PLoS Genet. 2015;11:e1005728. doi:10.1371/journal.pgen.1005728\n 40. Beekman M, Nederstigt C, Suchiman HE, et al. Genome-wide asso-\nciation study (GWAS)-identified disease risk alleles do not compromise \nhuman longevity. Proc Natl Acad Sci U S A. 2010;107:1804618049. \ndoi:10.1073/pnas.1003540107", + "title": "2017 - Four Genome-Wide Association Studies Identify New.pdf", + "version": "v0", + "chunk_order": 154, + "document_id": "c10653f6-b3d7-5b92-9271-ab8fcc7905a7", + "extraction_id": "52557433-1cf1-5441-b44d-361963a61eca", + "associatedQuery": "Are there specific genes that have been linked to longer lifespans?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIFh26X5nul0obtiAeqSkHmHNgJoq", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Telomeres affect the aging process in several ways:\n\n1. **Telomere Shortening**: Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unreplicated on the lagging strand [1]. This shortening is a natural part of the aging process and is exacerbated by genotoxic stress, such as oxidative damage [1].\n\n2. **Cellular Senescence and Apoptosis**: The shortening of telomeres can induce processes such as apoptosis (programmed cell death) and cellular senescence (a state where cells stop dividing), which affect the health and lifespan of an individual [2]. When telomeres become critically short, they trigger a DNA damage response, leading to cellular senescence or apoptosis [4].\n\n3. **Genome Stability**: Telomeres ensure the stability of the genome and protect chromosomes from incorrect actions by the DNA repair machinery [3]. When telomeres are too short, they can no longer form protective structures, leading to genome instability and potentially contributing to aging [8].\n\n4. **Cancer Prevention**: Short telomeres limit the number of cell cycles, which is important for preventing the onset of cancer. However, this also contributes to the aging process as cells enter a state of permanent cell cycle arrest (senescence) [7].\n\n5. **Telomerase Activity**: The enzyme telomerase can maintain telomere length, but its activity varies over the lifespan and between cell types, tissues, and species [1]. In most human somatic cells, telomerase activity is limited, which contributes to telomere shortening and aging [4].\n\nOverall, telomere shortening acts as a biological clock that limits cellular replication, contributing to aging and age-related diseases [6].", + "role": "assistant" + } + } + ], + "created": 1728913592, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 378, + "prompt_tokens": 1222, + "total_tokens": 1600 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "53508a9e-d064-58a3-a4f9-0785470a1462", + "score": 0.7356500625610352, + "metadata": { + "text": "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "title": "2018 - Sex Differences in Aging Genomic Instability.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "extraction_id": "396708f1-aa0a-571e-a8d3-7cb8404e9502", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "b532d055-ab02-5326-8eb4-67e7277a92b8", + "score": 0.73097825050354, + "metadata": { + "text": "that shorten their length with progressing age. This shortening of telomeres is the \nresult of the absence of the activity of an enzyme called telomerase, and in turn it \ninduces several processes, such as apoptosis, senescence, or oncogenic transforma-\ntion of somatic cells, affecting the health and lifespan of an individual [42]. Human \ntelomere shortening has been mostly studied in leukocytes and linked not only to \nageing and life expectancy [43] but also to age-related diseases, including cardio-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1721, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "41b98643-1948-519b-8b27-ab0fa4041048", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "65fb74aa-f3c3-5c80-919f-329169db982f", + "score": 0.7253162264823914, + "metadata": { + "text": "nization may directly affect telomere attrition, resulting in accelerated replicative \nsenescence and progeroid phenotypes [180].\nTelomeres are regions constituted by tandem repeats of non-coding DNA \nsequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. \nThis structure ensures the stability of the genome and protects the chromosomes \nfrom a wrong action of the DNA repair machinery [184] by allowing the formation \nof a chromatin loop called T-Loop [185].", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1084, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "d4afa45a-5efa-577b-822e-7a82c2f6508d", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "ab6a6bda-490d-5b7e-a715-3b9b4f89243f", + "score": 0.7144376252424013, + "metadata": { + "text": "Telomeres play a central role in cell fate and aging by adjusting the cellular response to stress and growth stimulation on thebasis of previous cell divisions and DNA damage. At least a few hundred nucleotides of telomere repeats must cap eachchromosome end to avoid activation of DNA repair pathways. Repair of critically short or uncapped telomeres by telomeraseor recombination is limited in most somatic cells and apoptosis or cellular senescence is triggered when too many uncappedtelomeres accumulate.", + "title": "2008 - Telomeres and Aging.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "61d9c326-d36e-55c1-a891-335dc943e70f", + "extraction_id": "55fd2e43-f58e-5d89-8730-7d82d3b6c44f", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "80a2162f-6208-5f97-a646-e8803d501f4e", + "score": 0.7132883413686396, + "metadata": { + "text": "ing (84). This process is believed to be the trigger for the aging \nprocess, according to the telomere theory (11, 85, 86). It is further \nsupported by Bodnar etal. who proved that telomere elongation caused by ectopic expression of telomerase avoids the senescence phenotype (87). His work relied on one of the earliest studies \nlinking telomere shortening to aging which was performed", + "title": "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "extraction_id": "016d8de2-949f-511e-a9e1-d2d5fd2bede5", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "f181e6da-58b6-5f26-87a2-355e25388673", + "score": 0.7043320536613464, + "metadata": { + "text": "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for\nits relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of\nreplication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]).\nEventually, this loss over cellular generations culminates in telomere crisis and a permanent state of", + "title": "2018 - Repetitive Fragile Sites Centromere Satellite DNA.pdf", + "version": "v0", + "chunk_order": 35, + "document_id": "262df0d6-ad68-544a-88ed-b4568f305858", + "extraction_id": "3b0cb0ab-421d-54d7-9816-c6a2e6f1ac68", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "6d0cccc5-3ed7-507e-9f7a-6035badacc00", + "score": 0.6978059771292286, + "metadata": { + "text": "and consequently lose telomeric sequences, thereby limiting the number of cell cycles, which is\nimportant for preventing the onset of cancer. Cells perceive critically short telomeres as persistentDNA damage. This activates the DNA damage responses, including cell cycle checkpoints, which\nultimately leads to a permanent cell cycle arrest (cellular senescence). Senescence protects from\ncancer but contributes to the aging process (37).", + "title": "2016 - Genome Integrity in Aging.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "extraction_id": "5179130e-5fa6-5979-ba68-270e546e43d7", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "72b978c7-44fc-530d-a1d2-eaffaf2c8782", + "score": 0.6957833585610019, + "metadata": { + "text": "When the telomeres shorten, this loop is no longer able to form \nand in turn, the epigenetic regulation is changed to activation of the TPE-OLD genes. This happens before the telomeres reach \nthe critical length that causes activation of DDR, thus leading to \nanother earlier possible effect of telomere shortening on aging (138, 139). Interestingly, a following study by Kim etal. showed \nthat one of the TPE-OLD sensitive genes is hTERT, the core reverse transcriptase component of telomerase (140). This is", + "title": "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "extraction_id": "9fafad4c-f208-53e0-b2ac-f10569429a5e", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "0faa4fb9-efa7-5e92-8fe4-5e28c51dbee4", + "score": 0.694516658782959, + "metadata": { + "text": "to maintain proliferation potential (94). Cells with mutated telomerase exhibited irregular morphology and short telomeres, but these changes did not cause deadly damage and determinate senescence (95). One hypothesis connects aging to telomere \nerosion through the transcription of subtelomeric genes. Genes \nlocated in subtelomeric regions are affected by transcriptional silencing which was found to change in an age-related manner. \nKim et al. (96) found that silencing of genes in subtelomeric", + "title": "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "extraction_id": "016d8de2-949f-511e-a9e1-d2d5fd2bede5", + "associatedQuery": "How do telomeres affect the aging process?" + } + }, + { + "id": "b1383516-a23e-5048-9cf3-944b5142e16b", + "score": 0.6876281108745372, + "metadata": { + "text": "evidence implicates telomere shortening in cellularsenescence. Telomeres consist of repetitive nucleotides e q u e n c e s( T T A G G G )a tt h ee n d so fm a m m a l i a nc h r o -mosomes, that preserve chromosome stability andintegrity by preventing deterioration or fusion withneighboring chromosomes (76) (Central Illustration ).JACC VOL. 69, NO. 15, 2017 Paneni et al .\nAPRIL 18, 2017:1952 67 The Aging Cardiovascular System1957", + "title": "2017 - The Aging Cardiovascular System.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", + "extraction_id": "82798504-5de9-513c-b3df-09968387cd42", + "associatedQuery": "How do telomeres affect the aging process?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIFhETjzplVDZqcInKYA6bobssz1r", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "DNA repair plays a significant role in aging by maintaining the integrity and stability of the nuclear genome. Impairment of DNA repair mechanisms can result in accelerated aging and/or cancer [2]. As organisms age, endogenous sources of genotoxins increase, DNA repair capacity declines, and levels of DNA damage and mutations increase [2]. This accumulation of DNA damage is associated with aging phenotypes, as DNA damage can activate cellular responses that contribute to aging [6]. The DNA damage theory of aging suggests that genomic instability, caused by accumulated DNA damage, plays a causal role in aging [5]. Additionally, the burden of DNA lesions is greater in older mammals compared to younger ones, indicating that DNA repair is crucial for mitigating the effects of aging [5].", + "role": "assistant" + } + } + ], + "created": 1728913604, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 150, + "prompt_tokens": 1179, + "total_tokens": 1329 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "566bd0c9-262e-543e-8934-1af5fa9edef5", + "score": 0.7609542096514323, + "metadata": { + "text": "Effect of age on DNA repair\nResearch over the past decades suggest that many steps\nin DNA metabolism are altered with age in a variety\nof tissues and animal models (56,57). The relation of DNArepair to aging has been studied by measuring the ability\nof cells from organisms of various life spans to repair\nDNA damage and by experiments that have comparedthe ability of cells from young and old organisms to repair\nDNA damage. Interest was peaked by the original", + "title": "2007 - Caloric restriction and genomic stability.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "76c08863-1522-519b-8da6-65a872418fee", + "extraction_id": "a563be97-fd42-50ba-8a26-3e1ca3b738db", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "b8c3720d-f697-5d2f-9728-49b7489d6509", + "score": 0.7479147911071777, + "metadata": { + "text": "BI87CH14_Niedernhofer ARI 18 May 2018 15:1\nSUMMARY POINTS\n1. Evolutionarily conserved DNA repair pathways maintain the integrity and stability of\nthe nuclear genome. Impairment of DNA repair mechanisms results in accelerated agingand/or cancer.\n2. Evidence in humans and model organisms supports the conclusions that with age\n(a) endogenous sources of genotoxins increase, ( b) DNA repair capacity declines, and\n(c) levels of DNA damage and mutations increase.", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 180, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "44047f31-85e4-587c-ba58-8c3494fb7d52", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "9180d1c5-31b6-533e-bf2e-4b367dc2097d", + "score": 0.7460238488898381, + "metadata": { + "text": "Several lines of evidence suggest that DNA repair capacity might decrease with age. However,it should be noted that measuring DNA repair in tissues is challenging and that the validity ofsurrogate markers of repair capacity is not well established. For example, a reduction in expression\nof DNA repair genes/proteins is not proven to impact DNA repair. Frequently, the reduction in", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "e3e52327-4a23-5003-b418-dafdcdcae82c", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "ca253ce9-4661-5ca2-bf17-3a86ef3eff1d", + "score": 0.7436333894729614, + "metadata": { + "text": "improved DNA repair. Finally, there should be a plausible mechanism by which DNA damage\ncan drive aging. Here, we review the evidence currently supporting each of these predictions.\nEVIDENCE THAT DNA DAMAGE INCREASES WITH AGE\nSources of Damage Increase with Age\nThe free radical theory of aging posits that aging is caused primarily by oxidative damage in-\ncurred by ROS that chemically modify critical cellular biomolecules (13). This theory has evolved", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "b934a2a9-a672-5d65-9d0d-bbc36652a148", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "494f865d-a7b6-5978-9b02-d5e628952a9d", + "score": 0.7432990823209846, + "metadata": { + "text": "All rights reservedKeywords\nDNA damage, aging, mutations, senescence, DNA damage response, DNA\nrepair\nAbstract\nThe nuclear genome decays as organisms age. Numerous studies demon-\nstrate that the burden of several classes of DNA lesions is greater in older\nmammals than in young mammals. More challenging is proving this is acause rather than a consequence of aging. The DNA damage theory of\naging, which argues that genomic instability plays a causal role in aging,", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "2b406c50-28e1-5b8c-a39d-a26db15f8aaa", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "a1370bf9-13f2-5c98-9d9d-9dfead21ebd7", + "score": 0.7399291461642843, + "metadata": { + "text": "repaired; otherwise the genome would soon become saturated with\ndamage and life would cease. There is some evidence that DNA damage\naccumulates with age in some tissues ( Maslov et al., 2013 ), but the exact\nnature of the damage remains unclear. Indeed, even these low levels of\nspontaneous DNA damage may represent a steady state due to continu-\nous repair and induction of new damage. However, DNA damage can\ncause certain aging phenotypes by activating cellular responses, such", + "title": "2017 - Mutation and catastrophe in the aging genome.pdf", + "version": "v0", + "chunk_order": 25, + "document_id": "7ae205a2-e002-5e8b-bbf1-ea96ab599b37", + "extraction_id": "eb91e436-a1bb-5d10-b648-07224b9e5bff", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "8d2bc107-4d94-5dd8-8f67-b593aecc0478", + "score": 0.7375595789420483, + "metadata": { + "text": "36:1049-1062.\n66. Hasty P, Vijg J: Accelerating aging by mouse reverse genetics:\na rational approach to understanding longevity. Aging Cell\n2004, 3:55-65.\n67. Bohr VA: Deficient DNA repair in the human progeroid dis-\norder, Werner syndrome. Mutat Res 2005, 577:252-259.\n68. Nouspikel T, Hanawalt PC: DNA repair in term inally differenti-\nated cells. DNA Repair 2002, 1:59-75.\n69. Nouspikel T, Hanawalt PC: When parsimony backfires: neglect-\ning DNA repair may doom neurons in Alzheimer's disease.", + "title": "2007 - Decline of nucleotide excision repair capacity in aging.pdf", + "version": "v0", + "chunk_order": 201, + "document_id": "c9bb2ba2-a001-5c1b-8be8-d1c184924362", + "extraction_id": "a0e59df7-6a34-5f03-af2e-82bdc0edacb9", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "4db748ed-7063-50e5-b42c-cb6fa3ecd9a2", + "score": 0.7375421524047852, + "metadata": { + "text": "DNA repair. In the latterdifficult to arrive at a strict, experimentally useful defini-context, most premature aging syndromes are causedtion of aging. Factors implicated in organismal declineby mutations in genes encoding proteins involved inin genetic models might not play a role in the normalDNA repair ( Karanjawala and Lieber, 2004 ). Accumula-aging processes. A related difficulty is that prematuretion of mutations in critical genes may be one generalaging models fail to recapitulate all aspects of", + "title": "2005 - DNA Repair, Genome Stability.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "e658e73b-2494-5fa3-ae39-9f4933bc037b", + "extraction_id": "5ea2fb27-ddd7-50b4-b318-39ca71f1c7e2", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "4521b426-a67e-51e4-bc63-b6da5fab60cf", + "score": 0.7356736502452893, + "metadata": { + "text": "escape the repair process and accumulate in the genome, impacting several processes and\naging [67,145147].\nThere is little evidence of association between DNA repair improvement and life-\ntime expansion [ 148,149], thus, indicating that such mechanism seems to have evolved\nto maintain DNA stabilityand therefore healthonly until reproductive age, without\nany regard for the fate of the individual in old age, both in terms of quality and length of", + "title": "2023 - Genomic Instability Evolutionary Footprints on Human Health.pdf", + "version": "v0", + "chunk_order": 101, + "document_id": "59dec4a5-f80a-5a82-b55a-b6b1b33b907f", + "extraction_id": "57e201b2-a357-5cff-9555-49955299669e", + "associatedQuery": "What role does DNA repair play in aging?" + } + }, + { + "id": "4c627903-8a25-5db0-8a60-1850a924a27b", + "score": 0.7347921449193834, + "metadata": { + "text": "with age, and DNA repairtween different tissues. These differences likely reflectdefects can cause phenotypes resembling prematurefunctional characteristics of those tissues, such as mi-aging. We discuss how cellular DNA damage re-totic rate, transcriptional activity, metabolism, and thesponses may contribute to manifestations of aging.action of specific DNA repair systems.We review Sir2, a factor linking genomic stability, me-Reactive Oxygen Species: An Important Sourcetabolism, and aging. We conclude", + "title": "2005 - DNA Repair, Genome Stability.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "e658e73b-2494-5fa3-ae39-9f4933bc037b", + "extraction_id": "67128b6e-9bd6-53fe-b1e7-d0721db8619d", + "associatedQuery": "What role does DNA repair play in aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIFhM7HonwMIv1KCdMHKw9gGzAYlV", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, genetic research can potentially lead to treatments that slow down aging. Several pieces of evidence from the context support this possibility:\n\n1. The discovery of genetic markers for slow aging in humans suggests that understanding these genes could pave the way for therapeutic interventions for age-related maladies, including cancers, neurodegeneration, and metabolic syndrome [4].\n\n2. Research indicates that manipulating aging-related genes through various means, such as diet, lifestyle, and pharmaceuticals, could dramatically improve human health and lead to the development of drugs against age-related diseases [7].\n\n3. Advances in molecular biology, such as CRISPR/Cas9, are expected to clarify aging processes and identify new potential therapeutic targets, which could be crucial for developing treatments that slow aging [6].\n\n4. The use of senolytic drugs, which target senescent cells, has shown promise in halting biological aging in mice, and trials are underway to test their effectiveness in humans [3].\n\n5. There is a suggestion that interventions targeting DNA methylation and other genetic modifications could prevent age-related diseases and promote longevity, highlighting the potential of genetic research in developing therapeutic strategies against aging [10].\n\nOverall, while the research is still ongoing and some findings are speculative, there is significant potential for genetic research to contribute to treatments that slow down the aging process.", + "role": "assistant" + } + } + ], + "created": 1728913612, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 263, + "prompt_tokens": 1182, + "total_tokens": 1445 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9da7c5dc-0deb-577c-bb22-83f987bd76dd", + "score": 0.7105742269412156, + "metadata": { + "text": "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections.\nProspective Epidemiologic Surveys that Include Genetic Information\nSome epidemiologic cohort studies of populations have collected", + "title": "2001 - Demography in the age of genomics.pdf", + "version": "v0", + "chunk_order": 784, + "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "extraction_id": "e3014138-3d5b-58bc-a1a5-5ac6f04cac1c", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "78733c6a-d870-5154-9128-eb66291fa967", + "score": 0.7009225248190731, + "metadata": { + "text": "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "68b12e10-d643-570a-aa63-eda62de81928", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "3c636897-c47e-505d-9203-306124b73e0e", + "score": 0.6825295686721851, + "metadata": { + "text": "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic\ndrugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to\ntest the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi-\nple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage.\nFuture Prospects: Developing Interventions through DNA Repair", + "title": "2020 - Protecting the Aging Genome.pdf", + "version": "v0", + "chunk_order": 57, + "document_id": "bb774030-2570-5596-b2ab-b8f57ff81086", + "extraction_id": "e5067ce2-69a6-5433-bed4-b95daeaa691e", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "43cba086-7f03-529f-bcd0-6483202bf3c7", + "score": 0.6815123354937958, + "metadata": { + "text": "and potentially important genetic markers for slow aging\nhave been found in humans (Suh et al. 2008). Elucidating\nthe function of such genes is believed to enable decipher-\ning the core of the aging process, answer to what extentthe process is conserved, and pave the way for therapeutic\ninterventions of age-related maladies, including cancers,\nneurodegeneration, and metabolic syndrome (Guarente\n2011).\nThe identity of the virtual gerontogenes so far discov-", + "title": "2012 - Peroxiredoxins, gerontogenes linking.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "2eaad7ba-b6ae-5382-ba79-84609080b53e", + "extraction_id": "38ebdc6a-8e8e-5472-a3ed-9a0f06591474", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "de7c30f6-cce9-563d-83f4-809f2aab781b", + "score": 0.67976732970671, + "metadata": { + "text": "discover specific genes that directly influence how quickly people age, beyond diseases. If such\ngenes exist, their effects were too small to be detected in this study. The next step will be to\nexpand the study to include more participants, which will hopefully pinpoint further genomic regions\nand help disentangle the biology of ageing and disease.\nDOI: https://doi.org/10.7554/eLife.39856.002", + "title": "2019 - Genomics of 1 million parent lifespans.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "f68b939c-847b-5eac-8926-24713ae43478", + "extraction_id": "ff0adc7c-70ff-5b14-ba7d-a9dda60fac80", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "4eb34c07-921b-55bb-98eb-ff013bb2ace0", + "score": 0.6764809278868523, + "metadata": { + "text": "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in\nmolecular biology and cell culture approaches (for instance Clustered Regularly Interspaced\nShort Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes\nacross species.\nAn improved understanding of epigenetic mechanisms affecting longevity will be\ndeciding crucial step towards the identication of new potential therapeutic targets. In", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 146, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "f20fd517-5f05-53ca-93a5-916bc891ad92", + "score": 0.6719373855665463, + "metadata": { + "text": "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 170, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "8650652a-1765-563b-a98e-2e9336bcf29a", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "265126e3-2a4d-518f-93cf-21a201747eef", + "score": 0.669631520553646, + "metadata": { + "text": "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant \na new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705.\n 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov-\nery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190.\n 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1774, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "822571e2-b05d-5e17-9eaa-431151851111", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "afc304d1-dd43-55ec-811d-27ca27fc4e5d", + "score": 0.6661873459816027, + "metadata": { + "text": "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction,\nappear to extend not only life span, but also health span\n(Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does\nappear that it is possible to experimentally slow the rate of aging.\nStill, in each case, aging does continue on as if there is some", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "b9f038dd-97af-51ea-bb32-d73bf66c3dcb", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + }, + { + "id": "1c77b8dc-2fd6-5e3d-9cf0-5585e7c9fb57", + "score": 0.6659484108717209, + "metadata": { + "text": "genetic modification. Currently, emerging evidence suggeststhat certain interventions (e.g. CR, dietary supplementation andchemical drugs) can prevent age-related diseases and promote\nlongevity, at least in part, through reversing the aberrant age-\nassociated changes in DNA methylation, suggesting the greatpotential of DNA methylation in therapeutic strategies againstage-related diseases ( Figure 1B ).However, to further understand the roles of DNA methyla-", + "title": "2016 - Progress on the role of DNA methylation in aging.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "extraction_id": "8829c724-73ff-582b-ab94-c9f1a906cfd5", + "associatedQuery": "Can genetic research lead to treatments that slow down aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIFhRRoCeBcURczVYxgfGYsJd8loh", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Mitochondrial DNA (mtDNA) influences aging through several mechanisms:\n\n1. **Oxidative Damage**: Mitochondria are crucial for energy production and are highly susceptible to oxidative damage. The accumulation of oxidative lesions in mtDNA is a significant source of age-related damage [1].\n\n2. **Mutations and Lifespan**: Mutations in mtDNA can reduce lifespan. These mutations can aggravate aging and impair brain development, indicating a direct link between mtDNA mutations and the aging process [2].\n\n3. **Mitochondrial Dysfunction**: Aging is associated with mtDNA mutations, which contribute to mitochondrial dysfunction. This dysfunction is linked to age-related diseases and metabolic disorders, further influencing lifespan [4].\n\n4. **Genetic Instability**: The mutation rate for mtDNA is significantly higher than for nuclear DNA. These mutations can compromise mitochondrial functions, such as electron transport and oxidative phosphorylation, leading to declines in ATP levels and increased production of reactive oxygen species, which further damage both nuclear and mitochondrial DNA [9].\n\nOverall, mtDNA influences aging by accumulating mutations and oxidative damage, leading to mitochondrial dysfunction and contributing to age-related physiological decline.", + "role": "assistant" + } + } + ], + "created": 1728913617, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 239, + "prompt_tokens": 1385, + "total_tokens": 1624 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9ec5a15f-8232-5e79-a78d-64eeba35747f", + "score": 0.8005366086799949, + "metadata": { + "text": "In addition to nuclear DNA, mitochondrial DNA (mtDNA) also is affected by aging. Alterations in mitochondrial function and mito-chondrial-nuclear signaling occur during aging and have been linked to sex biases in aging and age-related diseases (28). Due to their role in energy production, mitochondria are at high risk of oxida-tive damage. Not surprisingly, accumulation of oxidative lesions is an important source of age-related mtDNA damage (29). In aged Wistar rats brains, DNA oxidation, as measured by", + "title": "2018 - Sex Differences in Aging Genomic Instability.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "extraction_id": "400784cf-bb7d-5bf8-b735-2142ebf7c953", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "39019881-9b6d-5111-87ea-71c413bdf4ff", + "score": 0.7608792412543074, + "metadata": { + "text": "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569.\n20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G,\nFreyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA\nmutations aggravate ageing and can impair brain development. Nature.\n2013;501(7467):412 5.\n21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H.\nNeutral mitochondrial heteroplasmy and the influence of aging. Hum Mol\nGenet. 2011;20(8):1653 9.", + "title": "2017 - Independent impacts of aging.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "d1d0b9ce-f827-5dfb-8e39-d87a9ca52f6d", + "extraction_id": "1f0b6363-a045-53aa-a124-4cf89e61fc26", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "1a9d5c26-f606-5cb5-98ee-4120de3fbd1a", + "score": 0.7512575611846916, + "metadata": { + "text": "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial \nDNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/\ns12864-017-4287-0.\n 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema-\ntopoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. \n2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 783, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "c8db1d28-f6c2-5896-95ec-bb01159ba483", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "e0d41918-20fb-53f0-ac63-cd079c6dce1e", + "score": 0.7381081801089105, + "metadata": { + "text": "other studies, the risk for metabolic disorders is highly associated with age-related \ndiseases that affect lifespan, and interestingly these conditions exhibit mitochon-\ndrial dysfunction [73].\nAging is a complex process as a time-dependent progressive loss of physiologi-\ncal integrity, leading to impaired function and increased vulnerability to death [74], \nand as we described above, aging is highly associated with mtDNA mutations; in", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 530, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "385c192b-a416-5208-9615-20111ce782aa", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "ef3be4e1-4cbc-5b61-a286-9b759df08cd9", + "score": 0.735212868620116, + "metadata": { + "text": "mt, and overall mitonuclear genomic compatibility. \nGiven the uncertainty of mtDNA mutation accumulation in driving the natural aging process, it is plausible that mito -\nchondrial communication may be a significant evolutionarily conserved force that influences lifespan and/or healthspan.\nAcknowledgements Funding was provided by the American Federa-\ntion for Aging Research (AFAR), the National Institute on Aging (T32", + "title": "2020 - Mitonuclear genomics and aging.pdf", + "version": "v0", + "chunk_order": 148, + "document_id": "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "extraction_id": "381cc064-9970-5dcd-b959-c52a8e487fe7", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "4206977e-23df-5307-8d8a-cb2ed7b33595", + "score": 0.7351043601303022, + "metadata": { + "text": "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. \n2014;306:E58191.\n 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA \nquantity and quality in humans. BMC Genomics. 2017;18:890.\n 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial \nfunction in aging. Mech Ageing Dev. 2010;131:45162.\n 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 607, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "ef9463cd-cf21-527f-ae4a-3df211c78435", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "7095cdbb-852e-541e-884b-a9e67c2c790c", + "score": 0.7326218108124084, + "metadata": { + "text": "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging.\nMol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028\nSymer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G.\nand Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic\ninstability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5\nSzabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "title": "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "version": "v0", + "chunk_order": 300, + "document_id": "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "extraction_id": "5cbace8d-e538-5531-9311-ea9726ad2f15", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "512e09e3-c880-5bed-9071-bfa84d80a5a3", + "score": 0.7307272014672046, + "metadata": { + "text": "than ones that affect mitochondrial DNA12,57,58,71.So,this is an important reason for favouring\nnuclear DNA as the ultimate damage target in natural ageing. Nevertheless, it is conceivable that\nwhen mutations occur in the mitochondrial genome, mutant-protein production could increase\nthe inefficiency of the mitochondrial respiratory chain, thereby resulting in more reactive oxygenspecies, which would then damage nuclear and mitochondrial DNA further.", + "title": "2004 - Ageing, repetitive genomes and DNA.pdf", + "version": "v0", + "chunk_order": 30, + "document_id": "1772d596-16a3-547a-9f76-2cb658e89746", + "extraction_id": "9b7b2005-857b-5379-ba5c-3a8c6fd6c891", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "2e2de9a7-2e83-5f46-a4b7-08eddcd37baa", + "score": 0.7305692869764141, + "metadata": { + "text": "generation animals as they grow older.Mitochondrial DNAGenetic instability outside of the nuclear genome mightalso contribute to aging (reviewed in Lee et al., 1997;Wallace et al., 1998). The mutation rate for mitochondrialDNA (mtDNA) is 10- to 20-fold greater than for nuclearDNA, and it is believed that mtDNA mutations may com-promise mitochondrial functions in different ways (Fig-ure 4). First, defects in electron transport and oxidativephosphorylation could lead to declines in ATP levelsand the NAD:NADH", + "title": "1999 - Molecular Biology of Aging.pdf", + "version": "v0", + "chunk_order": 64, + "document_id": "6f122d3a-d8ca-598f-8767-c059a941cef3", + "extraction_id": "68c55aac-d8fa-5287-a420-2bb83a2c159e", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + }, + { + "id": "c35761f9-58e8-526b-94fd-96ed7599cb53", + "score": 0.7301242190673481, + "metadata": { + "text": "of the human aging process(Corral-Debrinski et al., 1992; Soong et al., 1992;Wei etal., 1996b), and it has been demonstrated that certain pointmutations of mitochondrial DNA accumulate in the aginghuman brain (Zhang et al., 1993; Liu et al., 1997). However,thefunctionalimplicationsofthesendingsarecontroversial(Hayashietal.,1994).Tocomplicatethematterfurther,Takaiand co-workers discuss the possibility that the commonage-associated changes in human and mouse", + "title": "1998 - Neurodegeneration and Aging Role.pdf", + "version": "v0", + "chunk_order": 30, + "document_id": "694c44f3-6025-5a2c-9c72-d9c5f16c8b85", + "extraction_id": "21db434d-3cf5-5ff1-8257-0941ebe74822", + "associatedQuery": "How does mitochondrial DNA influence aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIFhaX8SGHyXXi1vHCPCMEW3CahGu", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, there are known genetic mutations that cause premature aging. Specifically, mutations in the LMNA gene are associated with Hutchinson-Gilford Progeria Syndrome, a disease characterized by premature aging [4]. Additionally, mutations in the RECQL4 gene are linked to Rothmund-Thomson syndrome, which also involves premature aging [4]. These genetic disorders highlight the connection between genome integrity and premature aging [7].", + "role": "assistant" + } + } + ], + "created": 1728913626, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 84, + "prompt_tokens": 1643, + "total_tokens": 1727 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a9f7eda5-1b64-507e-95dd-07c81f2d603b", + "score": 0.7192796468734741, + "metadata": { + "text": "logical phenomena is often facilitated by the \nstudy of genetic mutants, and, in the case of \nhumans, genetic disorders. Accordingly, a search \nwas made, over the years, for genetic disorders \ncharacterized by premature aging. If DNA dam- \nage and repair has anything to do with aging it \nshould be evidenced in such individuals. Martin \n(1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. \nAbout 21 feahares are considered as markers for", + "title": "1993 - Genomic Damage and Its Repair.pdf", + "version": "v0", + "chunk_order": 127, + "document_id": "d049f302-a130-5ee4-a1b5-5091605d5173", + "extraction_id": "64063108-0ff2-54e5-9801-bc1c49cbdee4", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "882149e3-8186-5577-a2a7-79f2659ff9b4", + "score": 0.6718193683839645, + "metadata": { + "text": "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA\n45:3545; 1959.\n[316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of\naging. Mech. Ageing Dev. 123:907915; 2002.\n[317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res.\n447:117135; 2000.\n[318] Martin, G. M. Genetic syndromes in Man with potential relevance to the\npathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "title": "2007 - Trends in oxidative aging theories.pdf", + "version": "v0", + "chunk_order": 379, + "document_id": "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "extraction_id": "752c6f1a-0c4d-5419-86cd-687d2aed7817", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "da4e59b7-d5b6-5992-9607-f6697c8f5276", + "score": 0.6711906790733337, + "metadata": { + "text": "19\n 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. \n2017;94:3440.\n 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. \nDNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777.\n 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation \naccumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 164, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "ead14808-bfb7-5e32-9830-28efaae71151", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "4841d806-98b4-513e-94a2-714df6c896f5", + "score": 0.6635691523551941, + "metadata": { + "text": "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria \nSyndrome: A premature aging disease caused by LMNA gene mutations. \nAgeing Res. Rev. 33, 1829. \n144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and \nrelated RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. \n145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? \nAgeing Res. Rev. 33, 3651. \n146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "title": "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "version": "v0", + "chunk_order": 137, + "document_id": "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "extraction_id": "d620ea24-4422-5636-86f5-0943371a4a18", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "fc10c968-3108-5c4b-a49c-cb0feabd18c5", + "score": 0.6526112945444175, + "metadata": { + "text": "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542.\n[2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and\nRose, M.R. (1996) Metabolic aspects of the trade-off between\nfecundity and longevity in Drosophila melanogaster ,Physiol.\nZool. 69, 11751195.\n[3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "title": "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "extraction_id": "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "eb8b89de-422a-5e9e-9ac8-60af4cd718c2", + "score": 0.6519961953163147, + "metadata": { + "text": "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "title": "2002 - Large genome rearrangements as a primary cause of aging.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "8a8926dc-2360-5a54-b586-8acc34e51c32", + "extraction_id": "8f1a0875-8179-5d45-abc0-bbd4c9ac8da5", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "34e6b3c4-63bf-5198-ab09-2a7200a7c19a", + "score": 0.6518602578723288, + "metadata": { + "text": "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA\nmutations and deletions were the driving force behind the observed accelerated aging phenotypes(18).\nTHE LINK BETWEEN NUCLEAR GENOME INTEGRITY\nAND PREMATURE AGING\nThe notion that the majority of currently identied progeria syndromes originate from defects\nin genome maintenance highlights the importance of the condition of DNA in the process of", + "title": "2016 - Genome Integrity in Aging.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "extraction_id": "17b26647-4659-5f2d-a9b0-7c122d4b5d1a", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "beed04cc-28c7-5dc7-b334-51226a217439", + "score": 0.6509696453231796, + "metadata": { + "text": "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based\npremature aging,NatMed. 2005;11:780 785.\n13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when\nrepairgetsold,NatMed. 2005;11:718 719.\n14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De\nSandreGiovannoli A,LvyN.HGPSandrelatedpremature aging\ndisorders: Fromgenomicidentification tothefirsttherapeutic \napproaches, MechAgeingDev.2008;129:449 459.\n15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "title": "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "version": "v0", + "chunk_order": 83, + "document_id": "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "extraction_id": "72beba0d-8c77-5aa9-82ac-ddf6a19355ac", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "badf3a36-1f99-58aa-b80c-725eccf4e8f3", + "score": 0.6501748561859131, + "metadata": { + "text": "Nature Genetics | Volume 55 | February 2023 | 268279 278\nArticle https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA \nrepair defects in disease and premature aging. Am. J. Hum. Genet. \n105, 237257 (2019).\n22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry \n50, 23212329 (2011).", + "title": "2023 - Genome-wide RNA polymerase stalling.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "78812a12-8d31-5159-8367-b0d38e5bc84b", + "extraction_id": "31088092-778f-59e0-a9de-5ec25c241aab", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "c35d1f43-c3bd-5cac-ae4d-937be35f1121", + "score": 0.6501638889312744, + "metadata": { + "text": "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al.,\nPremature aging in mice decient in DNA repair and transcription, Science\n296 (2002) 12761279.\n[37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A.\nReijo Pera, Genetic markers of ovarian follicle number and menopause in\nwomen of multiple ethnicities, Hum. Genet. 131 (2012) 17091724.", + "title": "2016 - Menopause Genome stability as new paradigm.pdf", + "version": "v0", + "chunk_order": 145, + "document_id": "564dead1-2737-572f-860c-f00de4d0395e", + "extraction_id": "0855231d-cb95-540c-a3dd-c93729efb34c", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIFhez5FFXsDDkyj8CmiEuE5k6YSr", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent discoveries in the genetics of aging include the identification of a number of genes capable of altering the aging process significantly in animal models and even in some humans [2]. Additionally, recent efforts have focused on isolating aging mutants through mutagenesis experiments to determine the mechanistic basis for unusual life spans, leading to the discovery of genes that can either enhance or reduce life span [4]. These findings contribute to a growing understanding of the genetic factors influencing aging and longevity.", + "role": "assistant" + } + } + ], + "created": 1728913630, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 93, + "prompt_tokens": 1336, + "total_tokens": 1429 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c96b67f8-ad31-50fd-b053-07b127938ef2", + "score": 0.7399749314799959, + "metadata": { + "text": "During the past century, remarkable progress has been \nmade in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "title": "2020 - Mitonuclear genomics and aging.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "extraction_id": "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "1c4286b6-ede2-568b-9c18-b1e99ede17a6", + "score": 0.7397668361663818, + "metadata": { + "text": "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "7ada6b55-99c2-5e20-bf96-d153f927256c", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "e09c33ea-4139-5cc2-9cf5-a40045f26a0c", + "score": 0.7192355608712308, + "metadata": { + "text": "Recent developments on the genetics of aging can be seen as several streams\nof effort. In general, humans show a relatively modest ( <50%) heritability of", + "title": "2001 - The genetics of aging.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "extraction_id": "76bae746-eabf-51ed-a01f-d32ecc89c11b", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "f7120061-9773-5f74-9760-5442d49fbaae", + "score": 0.7119012475013733, + "metadata": { + "text": "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "title": "2009 - Garland_and_Rose_Experimental_Evolution.pdf", + "version": "v0", + "chunk_order": 4645, + "document_id": "496faa7f-9623-5ab7-9816-7c3755abb3aa", + "extraction_id": "5aa7f5b9-df70-54ec-a95c-dcaefa3b617f", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "d0e74ffd-034d-5e0e-86b6-4cf0de57d774", + "score": 0.7118854098388321, + "metadata": { + "text": "One approach that has become increasingly common in the characterization of the ge-netics of aging is to isolate aging mutants, usually from mutagenesis experiments, andthen to determine the mechanistic basis for the unusual life span in the mutants. Thisapproach has led to the discovery of genes that can enhance (e.g., Maynard Smith 1958;Lin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span(e.g., Pearl and Parker 1922). Most of the large-effect mutants affecting aging", + "title": "2009 - Experimental_Evolution.pdf", + "version": "v0", + "chunk_order": 4645, + "document_id": "34821353-1b74-5ee2-ac39-66dd46f145bf", + "extraction_id": "c9d59e72-f068-58da-be7a-71b2f51a23f3", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "217c3592-1622-503f-a140-fd1452083301", + "score": 0.7038054642793272, + "metadata": { + "text": "genetics of aging I. What is aging? Frontiers in Genetics. doi:10.3389/fgene.2012.00134.\nr\nose, Michael \nr\n., Anthony D. Long, Laurence D. Mueller, Cristina L. \nr\nizza, Kennedy C. Matsagas, \nLeeF. Greer, and Bryant \nvilleponteau. 2009. \ne\nvolutionary nutrigenomics. In The future of aging, eds. \nG. M. Fahy, M. D. West, L. S. Coles, and S. B. \nh\narris. Berlin: Springer.\nr\nushton, J. \np\nhillippe. 1995. Race, evolution, and behavior: A life history approach. New Brunswick, NJ: \nTransaction \np\nublishers.", + "title": "2015 - Great Is Their Sin.pdf", + "version": "v0", + "chunk_order": 251, + "document_id": "e5ae9710-3049-5327-82e4-e6626eb670c2", + "extraction_id": "44c57701-0d0e-5ef8-afa1-ea3a6c4742d6", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "b3e21ac9-8df8-5119-a769-a9da82db78da", + "score": 0.7030582008923185, + "metadata": { + "text": "informed by age-related disease identifies loci for exceptional human longevity. Li H, editor. \nPLoS Genet. 2015. https://doi.org/10.1371/journal.pgen.\n 15. Polderman TJC, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, \netal. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat \nGenet. 2015;47:7029.\n 16. Cellerino A, Ori A.What have we learned on aging from omics studies? Semin Cell Dev Biol. \n2017;70:17789.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 167, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "ead14808-bfb7-5e32-9830-28efaae71151", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "fd811aec-6e33-5078-83d5-b68bd59b5a61", + "score": 0.7030342817306519, + "metadata": { + "text": "eries that have inspired thousands of researchers across the world \nto study aging, and we acknowledge the wider significance of the \ncreation of a field that has the potential to transform human health.\nGenetics\nAging is influenced by genetic factors. It may be surprising to know \nthat as recently as the 1970s and 1980s, the concept of modulating \nDownloaded from https://academic.oup.com/biomedgerontology/article/76/7/e85/6145792 by guest on 15 October 2023", + "title": "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "extraction_id": "dd4a6239-2e79-5b99-89ef-3e4939b87805", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "de7c30f6-cce9-563d-83f4-809f2aab781b", + "score": 0.7005462050437927, + "metadata": { + "text": "discover specific genes that directly influence how quickly people age, beyond diseases. If such\ngenes exist, their effects were too small to be detected in this study. The next step will be to\nexpand the study to include more participants, which will hopefully pinpoint further genomic regions\nand help disentangle the biology of ageing and disease.\nDOI: https://doi.org/10.7554/eLife.39856.002", + "title": "2019 - Genomics of 1 million parent lifespans.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "f68b939c-847b-5eac-8926-24713ae43478", + "extraction_id": "ff0adc7c-70ff-5b14-ba7d-a9dda60fac80", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + }, + { + "id": "a9434032-4a9d-54f8-a7a6-16110d1b3118", + "score": 0.6990343928337097, + "metadata": { + "text": "males: what are the molecular and evolutionary causes? Aging Cell. 2007;6:225233. doi:10.1111/j.1474-9726.2007.00279.x\n 63. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: link-\ning environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593610. doi:10.1038/nrm4048\n 64. Sen P, Shah PP, Nativio R, Berger SL. Epigenetic mechanisms of longevity \nand aging. Cell. 2016;166:822839. doi:10.1016/j.cell.2016.07.050", + "title": "2018 - Sex Differences in Aging Genomic Instability.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "8cfb5529-7f0c-58fc-b6e4-b3ee800fb72f", + "extraction_id": "0104338d-cc9c-538f-be29-8343a64da37d", + "associatedQuery": "What recent discoveries have been made about the genetics of aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIFhiQpAn2q80Ivtr6ZFWK3Q6aNNm", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Epigenetic changes affect aging through several mechanisms:\n\n1. **Alterations in Chromatin Structure**: During aging, there are various epigenetic alterations such as the accumulation of histone variants, changes in chromatin accessibility, loss of histones and heterochromatin, and imbalances in activating/repressing histone modifications. These changes can affect transcription and translation processes, impacting cellular function [1].\n\n2. **Epigenetic Drift**: As individuals age, epigenetic changes accumulate, leading to a phenomenon known as epigenetic drift. This drift results in the cumulative loss of gene regulation over time, impairing cellular and tissue function. It is suggested that this disruption may also play a role in the development of age-related diseases, such as cancer [3].\n\n3. **DNA Damage and Chromatin Remodeling**: It is suggested that epigenetic alterations during aging are largely triggered by DNA damage. This damage leads to chromatin remodeling and redistribution of chromatin modifiers, which are recruited away from their normal sites to engage in DNA repair [4].\n\n4. **Increased Expression of Pro-aging Genes**: Epigenetic dysregulation can lead to increased expression of pro-aging genes, such as the cell-cycle inhibitor p16, which drives cell senescence. This contributes to increased transcriptional noise and decreased coordination of gene expression, further contributing to organismal aging [10].\n\n5. **Potential for Reversibility**: Despite these changes, there is potential reversibility in epigenetic modifications, offering opportunities to alter the trajectory of age-related diseases. This highlights the plasticity of aging and the potential for interventions that could slow down the aging process [7].\n\nOverall, epigenetic changes play a crucial role in the aging process by affecting gene expression, cellular function, and the development of age-related diseases.", + "role": "assistant" + } + } + ], + "created": 1728913634, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 370, + "prompt_tokens": 904, + "total_tokens": 1274 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "e97baf15-d14f-563f-875f-a2a6bd314ad4", + "score": 0.765143417273266, + "metadata": { + "text": "Figure 1. Epigenetics of aging and aging-relate d diseases. During aging, various ep igenetic alterations occur including \naccumulation of histone variants, change s in chromatin accessibility mediated by chromatin remodeling complexes, loss \nof histones and heterochroma tin, imbalance of activating /repressing histone modifications and aberrant expres-\nsion/activity of miRNAs. These deregulations can affect transcrip tion and, subsequently, transl ation, as well as the stabi-", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 150, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "8db25d5e-25bd-5873-a53d-3815badbfd32", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "38dc7635-499d-52ba-a35f-f57ba8f8df22", + "score": 0.7465935349464417, + "metadata": { + "text": "ment of 5 years corresponded to a 21% increased risk of\nmortality overall [7]. Thus, predictions of epigenetic\nagemay be an indication of an individual s biological\nstate of aging.\nBeyond these examples of advanced epigenetic aging, a\ncomplementary but unanswered question is whether\nepigenetic clocks can also be slowed. Epigenetic aging\nstudies in humans have not thus far been well suited to\naddress questions of slowed aging, given the lack of\nwell-documented interventions that enhance health or", + "title": "2017 - Epigenetic aging signatures in mice livers.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "b20b11a6-1490-51b8-9218-c441a2e65ba7", + "extraction_id": "8e77571a-60ea-5e53-877d-08169e86d553", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "975250c9-7b4b-5ce5-b907-606ca6c70641", + "score": 0.743276834487915, + "metadata": { + "text": "al., 2005 ). The epigenetic changes that accumulated with age had a dramatic effect on gene expression, thus the authors propos e that a so-called epigenetic drift accompanies the aging process. Epigenetic modifications can result in the cumulative loss of gene regulation over time, ultimately impairing cellular and tissue function. Further, recent data sugge st that epigenetic disruption of tissue specific stem and progenitor cells may play a role in cancer development (Feinberg et al., 2006 ). The", + "title": "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "extraction_id": "6c8b334f-2e14-5099-85fe-93f9ed6046ad", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "7fad6aa1-d4d9-585c-bdbd-394a9552ec0e", + "score": 0.7432393584704795, + "metadata": { + "text": "epigenetic changes during aging are currentlyunknown (Fig. 3). It has been suggested thatthe epigenetic alterations are largely triggered\nby DNA damage (reviewed in Oberdoerffer\nand Sinclair 2007). In this scenario, randomlyoccurring DNA damage leads to chromatin\nremodeling and to redistribution of chromatin\nmodiers within the genome with modiersbeing recruited away from their normal sites\nso that they can engage in the repair of the", + "title": "2010 - Higher-order Genome Organization.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "91339298-860e-57d0-b58d-5a4571b4fc2b", + "extraction_id": "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "b43c1348-b982-59c2-9685-af7bb9fd0c4a", + "score": 0.7407828790182737, + "metadata": { + "text": "Epigenetic Dysregulation with Age", + "title": "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "version": "v0", + "chunk_order": 150, + "document_id": "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "extraction_id": "42a32702-d380-56e7-8309-6ab1efbcd479", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "33f6a665-bb01-5c9f-9325-0f9acf312b54", + "score": 0.7407828790182737, + "metadata": { + "text": "Epigenetic Dysregulation with Age", + "title": "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "extraction_id": "eb735cbe-b7f5-56cb-8cd6-1f2fb2b990a4", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "0aede05b-f0dd-595a-a11d-acac0970d25d", + "score": 0.7407643044450478, + "metadata": { + "text": "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing\nattention. The potential reversibility of these epigenetic\nchanges that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases.\n8\nThis is especially important given the remarkable plasticityof aging.\n9,10In the literature, age-associated epigenetic alter-\nations have been identified by epigenome-wide association", + "title": "2016 - Epigenetic drift in the aging genome a ten-year.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "52f09ef3-4e4c-538f-909c-d28eb72d91f3", + "extraction_id": "2f6d20f0-addc-51e8-979d-1aac7ac26694", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "5e3a0748-9dc0-55b1-ac4d-d8b2291fa297", + "score": 0.7391665798177299, + "metadata": { + "text": "in gene transcription and, as a consequence, translation as well as the stabilization or\ndegradation of molecular factors. While mechanisms underlying aging-related pathologies\nremain to be elucidated in detail, various studies demonstrate an epigenetic component.\nIn fact, the aforementioned epigenetic modications were shown to play essential roles\nin diseases including inammation, cancer, osteoporosis, neurodegenerative diseases,\nand diabetes.", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 144, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "c35ad17b-fe97-5ce5-bae1-59fd08201a7b", + "score": 0.7377009391784668, + "metadata": { + "text": "PLoS Biology | www.plosbiology.org August 2007 | Volume 5 | Issue 8 | e201 1759\nEpigenetic Dysregulation with Age", + "title": "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "extraction_id": "0a77e94f-85de-5ab3-b23f-129b08839502", + "associatedQuery": "How do epigenetic changes affect aging?" + } + }, + { + "id": "dea115e3-3d9b-5d08-a604-ab227fcd1b71", + "score": 0.7310480097248561, + "metadata": { + "text": "and increased expression of proaging genes such as the cell-cycle inhibitor p16, which drives cell senescence. Additional consequences of epigenetic dys-regulation include increased transcriptional noise and decreased coordination of gene expression that contributes to organismal aging.\nCell148, January 20, 2012 2012 Elsevier Inc. 53", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 110, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "0e274732-b0df-53b8-999b-30b798af92e2", + "associatedQuery": "How do epigenetic changes affect aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIFhpW2QcT6L6LqU3pI7kcz7hsxkv", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The gene FOXO3 plays a significant role in human longevity. Multiple studies have shown a strong association between variations in the FOXO3 gene and increased lifespan. For instance, the FOXO3A genotype has been strongly linked with human longevity, as demonstrated in studies by Willcox et al. (2008) and confirmed in various populations, including German and Southern Italian centenarians [1], [2], [3]. The FOXO3 locus is associated with extreme longevity in humans, particularly among centenarians [5]. Additionally, specific variants at the FOXO3 locus have been identified that respond to cellular stress, which may contribute to their role in promoting longevity [8].", + "role": "assistant" + } + } + ], + "created": 1728913641, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 138, + "prompt_tokens": 1654, + "total_tokens": 1792 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "77c88648-7807-5606-8793-4389378a82fd", + "score": 0.7730233073234558, + "metadata": { + "text": "27 Willcox, B. J. et al. 2008 FOXO3A genotype is\nstrongly associated with human longevity. Proc. Natl\nAcad. Sci. USA 105, 13 98713 992. ( doi:10.1073/\npnas.0801030105 )\n28 Flachsbart, F., Caliebe, A., Kleindorp, R., Blanche, H.,\nvon Eller-Eberstein, H., Nikolaus, S., Schreiber, S. &\nNebela, A. 2009 Association of FOXO3A variationwith human longevity conrmed in GermanGenomics of human longevity P . E. Slagboom et al. 41", + "title": "2011 - Genomics of human longevity.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "2e038219-fdaa-506f-9cd3-51379054130e", + "extraction_id": "7c183ae5-f10e-5f0c-962e-32135887b3bd", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "9c463b71-be3a-5f01-bc6f-d1d29b7a162f", + "score": 0.7644128343812936, + "metadata": { + "text": "3. Willcox BJ, Donlon TA, He Q et al (2008) FOXO3A genotype is\nstrongly associated with human longevity. Proc Natl Acad Sci\nUSA 105(37):1398713992. doi: 10.1073/pnas.0801030105\n4. Anselmi CV, Malovini A, Roncarati R et al (2009) Association of\nthe FOXO3A locus with extreme longevity in a southern Italian\ncentenarian study. Rejuvenation Res 12(2):95104. doi: 10.1089/\nrej.2008.0827\n5. Flachsbart F, Caliebe A, Kleindorp R et al (2009) Association of\nFOXO3A variation with human longevity conrmed in German", + "title": "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "version": "v0", + "chunk_order": 64, + "document_id": "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "extraction_id": "5cc56e3b-53ab-5299-814d-014e2ed31d2f", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "2f98af09-5895-545a-b36f-c05b70beee07", + "score": 0.7606979751170903, + "metadata": { + "text": "are, in fact, part of the same insulin/IGF1/GH pathway(Fig. 1) that modulates lifespan across organisms (Ke-nyon, 2010). A strong association between FOXO3 and\nhuman longevity has been reported (Willcox et al., 2008)and subsequently validated in other populations (forreview, see Kenyon, 2010). FOXO3 was also associatedAGING GENES AS TARGETS FOR DRUG DISCOVERY 95", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "d59d7882-333d-5576-86ab-3cfa6354b946", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "c6e1f317-e421-5f6b-ab4e-034f1aa94ba1", + "score": 0.756262748686945, + "metadata": { + "text": "Biogerontology 11:28797\n117. Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, et al. 2008. FOXO3A genotype is strongly associated\nwith human longevity. Proc. Natl. Acad. Sci. USA 105:1398792\n118. Soerensen M, Dato S, Christensen K, McGue M, Stevnsner T, et al. 2010. Replication of an association\nof variation in the FOXO3A gene with human longevity using both case-control and longitudinal data.\nAging Cell 9:101017\n119. Mardis ER. 2011. A decades perspective on DNA sequencing technology. Nature 470:198203", + "title": "2013 - Genome Instability and Aging.pdf", + "version": "v0", + "chunk_order": 200, + "document_id": "71e08916-8cc8-5d96-8c06-4461b972b54d", + "extraction_id": "3091bce3-8eb6-593d-8a92-ee3570e8e9a9", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "34dfec26-9828-56c8-be82-69eb114fa9e3", + "score": 0.7560266110912939, + "metadata": { + "text": "FOXO3 locus is associated with extreme longevity in humans (centenarians) [2, \n58, 59].\nNRF/SKN-1 activates the expression of genes involved in protecting the cell in \nresponse to ROS, toxins, and metabolic changes through mTOR and insulin/IGF \nsignaling, and it is also dysregulated later in life [60, 61]. Increasing the levels of \nL. Garca-Velzquez and C. Arias", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "68deea31-59de-5665-9c97-df57d72d0b52", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "6dd65017-bb91-5a1a-9d85-c1c1cfcd5780", + "score": 0.7414736747741699, + "metadata": { + "text": "A. 2003;100:406671. https://doi.org/10.1073/pnas.2628028100.\n 24. van den Akker EB, Deelen J, Slagboom PE, Beekman M. Exome and whole genome \nsequencing in aging and longevity. Adv Exp Med Biol. 2015;847:12739. https://doi.\norg/10.1007/978-1-4939-2404-2_6.\n 25. Flachsbart F, etal. Association of FOXO3A variation with human longevity confirmed in \nGerman centenarians. Proc Natl Acad Sci U S A. 2009;106:27005. https://doi.org/10.1073/\npnas.0809594106.\nA. Garca-Venzor and E. A. Mandujano-Tinoco", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 750, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "7555b8ec-cf4e-54a4-b654-6ae7e63d150c", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "160acccd-d5c5-5e54-8f88-ada1d413e91b", + "score": 0.7410400355482136, + "metadata": { + "text": "X.L., 2009. Genetic association of FOXO1A and FOXO3A with longevity trait in\nHan Chinese populations. Hum. Mol. Genet. 18, 48974904.\nLunetta, K.L., DAgostino Sr., R.B., Karasik, D., Benjamin, E.J., Guo, C.Y., Govindaraju,\nR., Kiel, D.P., Kelly-Hayes, M., Massaro, J.M., Pencina, M.J., Seshadri, S., Murabito,\nJ.M., 2007. Genetic correlates of longevity and selected age-related phenotypes:", + "title": "2011 - A genome-wide association study confirms APOE as the major gene influencing.pdf", + "version": "v0", + "chunk_order": 97, + "document_id": "63b27b06-db2c-5542-9b1a-cb9ebe64d339", + "extraction_id": "210aa417-372c-5bf6-b961-e281a1817458", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "aceb74e0-8b79-587f-9dd0-e260eeb90ab5", + "score": 0.732019305229187, + "metadata": { + "text": "theFOXO3 locus is not surprising, since this locus was previously\nreported in the longevity GWA study from the CHARGE con-\nsortium\n7, from which many cohorts are included in these meta-\nanalyses. So far, three functional longevity-associated variants\nhave been identi ed at the FOXO3 locus (rs2802292, rs12206094,\nand rs4946935). For all of them, an allele-speci c response to\ncellular stress was observed. Consistently, the longevity-associated\nalleles of all three variants were shown to induce FOXO3", + "title": "2019 - A meta-analysis of genome-wide association.pdf", + "version": "v0", + "chunk_order": 88, + "document_id": "9d36fc35-9708-5d1a-9514-9ce3469d7591", + "extraction_id": "68c41fe5-4413-5cfc-846b-a0097f994bcd", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "049ee89e-2f05-595b-9112-725976cb4ab3", + "score": 0.7297561925794976, + "metadata": { + "text": "exceptional longevity with no significant genetic contribution. Interestingly, the \nauthors found that FOXO3A, a longevity allele, may not be related to healthy aging \nphenotype [29].\nAging is a complex process usually accompanied by the onset of different dis-\neases like neurodegenerative disorders (Alzheimers disease and Parkinsons dis-\nease), cardiovascular illnesses, and cancer. The study of the genetic basis of these \naging-related diseases is another approach in the study of the genomic basis of", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 654, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "bdfc934a-d31b-57e4-9a78-15c719049c4f", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + }, + { + "id": "f6636c31-1105-5ea2-9b3b-ae8b21e08bee", + "score": 0.7286234498023987, + "metadata": { + "text": "centenarians. Proc Natl Acad Sci USA 106(8):27002705. doi: 10.\n1073/pnas.0809594106\n6. Li Y, Wang WJ, Cao H et al (2009) Genetic association of\nFOXO1A and FOXO3A with longevity trait in Han Chinese\npopulations. Hum Mol Genet 18(24):48974904. doi: 10.1093/\nhmg/ddp459\n7. Soerensen M, Dato S, Christensen K et al (2010) Replication of\nan association of variation in the FOXO3A gene with human\nlongevity using both case-control and longitudinal data. AgingCell 9(6):10101017. doi: 10.1111/j.1474-9726.2010.00627.x", + "title": "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "extraction_id": "5cc56e3b-53ab-5299-814d-014e2ed31d2f", + "associatedQuery": "What is the role of the gene FOXO3 in longevity?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIFhuoRML5l0E69TztcoQUZAgCOF2", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The environment interacts with genes to influence aging through several mechanisms:\n\n1. **Signaling Pathways and Gene Expression**: Environmental factors can trigger signaling pathways and modulate gene expression, impacting aging. For example, certain genes have varying effects on lifespan depending on environmental factors like diet [1].\n\n2. **Epigenetic Modifications**: Environmental factors can lead to changes in DNA methylation, acetylation, or deacetylation of histones, which are epigenetic modifications that influence gene expression. These changes can result in cellular damage and accelerated aging, such as the shortening of telomeres [2], [6].\n\n3. **Epigenetics as a Link**: Epigenetic modifications provide a potential link between the environment, disease, and aging. This suggests that specific environmental factors might directly induce specific epigenetic changes, which could be targeted for interventions aimed at improving healthspan or promoting healthy aging [3].\n\n4. **Physiological Capacity and Longevity**: Environmental factors, along with protective genetic alleles, contribute to an individual's physiological capacity, indirectly determining healthy lifespan and longevity. For instance, caloric restriction and smoking have opposite effects on the rate of aging [4].\n\n5. **Plasticity of Aging**: Aging is a plastic process that can be manipulated by both genetic and environmental factors. Understanding these interactions can help identify targets for anti-aging therapies, potentially through diet, lifestyle, and pharmacological interventions [5], [10].\n\nOverall, the interaction between the environment and genes is complex and involves multiple pathways and mechanisms that collectively influence the aging process.", + "role": "assistant" + } + } + ], + "created": 1728913646, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 321, + "prompt_tokens": 1106, + "total_tokens": 1427 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a4773f1a-f2d3-5950-a81e-d22357e97a0f", + "score": 0.7851045387858702, + "metadata": { + "text": "of multiple genes with each other and withthe environment. Evidence from animal systems showsa major impact of the environment on aging, yet envi-ronmental manipulations of aging act through genesand proteins, usually by triggering signaling pathwaysand modulating gene expression. In fact, some geneshave been shown in model organisms to have varyingeffects on lifespan depending on diet (Heikkinen et al.,2009). Genes that can regulate aging in model organ-isms cannot be directly applied to humans through", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "68b12e10-d643-570a-aa63-eda62de81928", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "3d657599-d2c8-518d-aee3-46c0643a88ec", + "score": 0.7439095087159265, + "metadata": { + "text": "Several studies show the influence of the environment on the ageing process [24]. \nEnvironmental factors may affect homeostasis and lead to the development of dis-\neases, thus affecting the quality of life in older age [25]. They also produce cellular \ndamage, which causes an accelerated shortening of the telomeres at the genetic \nlevel, accompanied by changes in DNA methylation, acetylation or deacetylation \nof histones, among others. Altogether, these changes induce an aberrant gene", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1707, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "53f4a434-3d4b-5ae5-a788-9b32c94fc0ab", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "49127379-fac4-525a-bf90-5c3bae66860a", + "score": 0.7258800431561457, + "metadata": { + "text": "changes are generated during the aging process. For a long time it\nhas been believed that epigenetic modications occurring during\naging may depend on environmental factors. This idea is attractive\nbecause, if true, epigenetics could provide a link between the\nenvironment, disease and aging. It also opens the possibility of\ntargeted intervention aimed, for example, at improving healthspan\nor healthy aging. Thus, the rst question is whether specic\nenvironmental factors can directly induce specic epigenetic", + "title": "2009 - The role of epigenetics in aging and age-related diseases.pdf", + "version": "v0", + "chunk_order": 63, + "document_id": "f7b452fc-0115-5582-b0c0-c2829f090e9d", + "extraction_id": "5a231e39-0927-56e5-970c-e86d69490a69", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "7ce9af40-0bf8-58e1-ad7c-cd55ba0a7cf8", + "score": 0.7208803677296558, + "metadata": { + "text": "In addition, environmental factors influence the\norganism s ability to withstand the increase in entropy\nwith aging: for example, caloric restriction and\nsmoking can exert opposite effects on the rate ofaging (Colman et al. 2009 ; Fraser and Shavlik 2001 ).\nBoth protective alleles and a benevolent environment\ncontribute to excess physiological capacity, which in\nturn indirectly determines an individual s healthy life\nspan and longevity (Martin et al. 2007 ). The well-", + "title": "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "ed31486c-a651-5894-bd96-21fbd78f2646", + "extraction_id": "677eccb2-c51a-59dc-9690-7d02c51fbb7a", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "3f37774f-e56b-5350-93e8-371948bf3e23", + "score": 0.7131877328995574, + "metadata": { + "text": "to humans through ge-netic manipulations for numerous legal, ethical, andtechnical reasons. If we could understand how the envi-ronment modulates these aging-related genes, we mightbe able to create antiaging therapies applicable to hu-mans, potentially through diet, lifestyle, and even phar-macological interventions. Therefore, understanding ge-nome-environment interactions in the context of agingcan be a powerful approach to identify attractive targetsfor drug design.", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 19, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "68b12e10-d643-570a-aa63-eda62de81928", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "3466f905-760d-5d0b-a3e1-b39f506e6289", + "score": 0.7015897035598805, + "metadata": { + "text": "ing human life span have been identified [2,3]. At the\nsame time, there is a growing realization that environ-\nmental factors are major contributors to aging and age-\nassociated illness. Epigenetics is the study of chemical\nmodifications of the genome, heritable by cell progeny,\nand it has been an attractive target for studies of aging\nand environmentally influenced disease. Several groups\nhave shown differences in DNA methylation - a covalent", + "title": "2015 - Age and sun exposure-related widespread.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "acf06062-9ca8-50be-a543-ef3b34ad6ad3", + "extraction_id": "aa62f800-8e83-5033-889a-64ff3f453fca", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "3c369292-4b9c-5156-a80f-4b3301026f30", + "score": 0.7009334742466538, + "metadata": { + "text": "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries.\nAGING GENES AS TARGETS FOR DRUG DISCOVERY 91", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 67, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "c1b5a31a-066d-571b-af1f-db746d9d17f6", + "score": 0.6962083163137259, + "metadata": { + "text": "standing the cause and mechanisms of aging is imperative in\nassisting to suppress age-related diseases and promote healthylongevity. It is well-known that aging is influenced by a combin-\nation of genetic and environmental factors. Previous twin stud-\nies have shown that the genetic contribution to general human\nlongevity is about 2030% [ 4,5], whereas environmental factors\nin human aging and longevity still account for the largest effect.\nEpigenetic factors influence the regulation of gene expres-", + "title": "2016 - Progress on the role of DNA methylation in aging.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "e4cdc02f-4415-5638-aab8-f848b4d64a22", + "extraction_id": "bcb3c620-b960-5af6-95ea-13215c31672e", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "90f9e09f-f339-5d59-ae24-fcbdd2ca6ceb", + "score": 0.6893077677900942, + "metadata": { + "text": "known to affect the function of epigenetic regulators, this may be an example of how aging interacts\nwith our genome to inuence AD development.", + "title": "2018 - Genomics New Light on Alzheimer?s.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "940593d2-04c3-59b9-a5bf-976febbc6f71", + "extraction_id": "05bcb479-ca17-57eb-9674-1c2fecb5726c", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + }, + { + "id": "c44c36ad-fcca-540a-a4f3-3965e48e3948", + "score": 0.6884725465211217, + "metadata": { + "text": "consequently the incidence of age-related diseasessuch as heart disease, cancer, and neurodegenerativediseases, is projected to increase considerably in thecoming decades. Findings from model organisms haverevealed that aging is a surprisingly plastic processthat can be manipulated by both genetic and environ-mental factors. Here we review a broad range of find-ings in model organisms, from environmental to ge-netic manipulations of aging, with a focus on thosewith underlying gene-environment interactions", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "df213743-7428-59be-ba19-2563f8ce5c70", + "associatedQuery": "How does the environment interact with genes to influence aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIFi4Qsa1GjY5azJi3IYJdr8DLXln", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Senescent cells are cells that have stopped dividing and have entered a state of permanent growth arrest. They are characterized by an altered metabolism and the secretion of pro-inflammatory factors, a phenomenon known as the senescence-associated secretory phenotype (SASP) [1], [3]. These cells accumulate in tissues over time and are resistant to apoptosis, meaning they are not easily cleared from the body [9].\n\nSenescent cells contribute to aging and age-related diseases in several ways. They impact the tissue environment by secreting inflammatory cytokines, proteases, and growth factors, which can lead to chronic inflammation and tissue dysfunction [3], [4]. This chronic inflammation is a significant factor in the development of age-related degenerative diseases [1], [4]. Additionally, senescent cells can alter the tissue microenvironment, promoting the degeneration of organs and stem cell niches, and potentially stimulating cancer cell growth [6].\n\nThe accumulation of senescent cells is associated with various age-related pathologies, such as atherosclerosis, osteoarthritis, and Alzheimer's disease [5], [9]. Recent studies have shown that clearing senescent cells can prevent or delay tissue dysfunction and extend health span, highlighting their causative role in aging [5].", + "role": "assistant" + } + } + ], + "created": 1728913656, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 249, + "prompt_tokens": 1323, + "total_tokens": 1572 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "2f35de05-41ee-5471-870d-a4e663cf32f6", + "score": 0.6795354271488873, + "metadata": { + "text": "senescence, exhausting the ability for a tissue to regenerate after injury, impacting mitochondrial function,and inducing protein aggregation. Senescent cells have altered metabolism, and they can secreteproinammatory factors and alter the local tissue environment, thereby contributing to aging andage-related degenerative diseases. In addition, stem cell function can be impacted by DNA damage by bothcell autonomous and nonautonomous mechanisms. Proper function of mitochondria is dependent upongenome", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 124, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "4b00515d-e599-5ce1-84e3-012d7efe1a30", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "1efa76cb-2289-5dd3-9fa5-776083aa5cd5", + "score": 0.6562108602398132, + "metadata": { + "text": "[87] and the accumulation of senescent cells in human\ntissues with age has been implicated as a driver of aging-\nrelated diseases. Indeed, pharmacological approaches\ntargeting senescent cells, like senolytics, are a major and\ntimely area of research that could result in human clin-\nical applications [ 5,88]. It is imperative that we fully\nunderstand and deconstruct cellular senescence in order\nto target aging-related diseases. We hope that CellAge\nwill help researchers understand the role that CS plays", + "title": "2020 - A multidimensional systems biology.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "extraction_id": "95744ef5-34b9-5540-a5e5-01fd580539e6", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "9faa9b6b-6a97-5979-bf49-8bbdb4bb383d", + "score": 0.6410194467282955, + "metadata": { + "text": "An important source of inflammatory signals in aged organ-\nisms is thought to be the accumulation of senescent cells across\ntissues [ 5,7]. Indeed, accumulating evidence has shown that\nsenescent cells are characterized by a senescence-associatedsecretory phenotype [ 810], which includes a panoply of\npro-inflammatory cytokines, proteases, growth factors and\nmetabolites [ 10,11]. The impact of senescent cells on age-related\ninflammation, and their potential role as a target for pro-", + "title": "2022 - Functional genomics of inflamm-aging.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "435dc081-e3d1-52c5-93a1-caa11206422f", + "extraction_id": "1635dbe1-1dcb-5213-9446-74129d50c5f8", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "6d4a1a0b-2af3-5cc4-b7c0-a7223ce3edfa", + "score": 0.6345081140247575, + "metadata": { + "text": "senescent cells [150]. SASP factors exert their functions in either an autocrine or a \nparacrine manner and are responsible for the induction of the chronic inflammation \nand cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu-\nmulation of senescent cells in tissue is closely associated with aging-related dis-\neases. Recently, it was determined that senescent fibroblasts significantly increase \nthe expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 152, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "6a2a94de-cfc0-50eb-b50e-bf3a0f813c78", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "45f74737-847a-52c2-a0b9-bf9de335a7ce", + "score": 0.6322256922721907, + "metadata": { + "text": "atherosclerosis, osteoarthritis, sarcopenia, ulcer formation, cancer, and Alzheimer disease, which\nis suggestive of a causative role. However, the most convincing evidence that senescent cells causeaging comes from recent genetic (85) and pharmacologic studies (86) revealing that clearance of\nsenescent cells can prevent or delay tissue dysfunction and extend health span.\nSenescent cells induce autocrine, as well as paracrine, signaling by secretion of proinamma-", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 120, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "bd5fffd3-cf7a-5f67-b581-6cb803a48de4", + "score": 0.627122245602475, + "metadata": { + "text": "senescence can deplete both stem (5153) and stromal\n(10,11) cell pools. Moreover, because senescent cellspersist, they have the ability to alter the tissue micro-environment, and can therefore also promote the degen-eration of organs and stem cell niches (14,46). Finally,\nsenescent cells secrete factors such as matrix metallopro-\nteinase-3 (MMP-3), which favors extra-cellular matrixremodeling, promotes defects in epithelial cell dierentia-tion and stimulates cancer cell growth (46,54,55).", + "title": "2007 - Two faces of p53 aging and tumor suppression.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "b1ef905a-c145-5270-9110-ae6954ea3d72", + "extraction_id": "4a95fed4-61db-58e9-96d7-3a9dcf87ef7f", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "27d74137-3987-571d-87ab-2c12ec66d1f7", + "score": 0.6243986115218048, + "metadata": { + "text": "potential role of senescence in in vivo aging and disease has been difficult to assess and somewhat controversial \n[146]. However, recent studies have shown that senescent cells accumulate in normal arterial tissue over the \nlifespan of humans [147, 148]. Likewise, the accumulation of senescent cells has been reported in diseased \ntissues, such as atherosclerotic plaques [149] and abdominal aortic aneurysms [150]. Baker et al. showed that", + "title": "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "version": "v0", + "chunk_order": 67, + "document_id": "815d7f3e-e219-502f-aba0-57a68ae787d3", + "extraction_id": "10f1fcbd-35a6-507d-880f-1f3f303737ea", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "180adffa-397c-599b-adb3-64a7f464aaaa", + "score": 0.616710030036568, + "metadata": { + "text": "51. Jeyapalan JC, Ferreira M, Sedivy JM, Herbig U. 2007. Accumulation of senescent cells in mitotic tissue\nof aging primates. Mech. Ageing Dev. 128:3644\n52. Boyle J, Kill IR, Parris CN. 2005. Heterogeneity of dimer excision in young and senescent human dermal\nbroblasts. Aging Cell 4:24755\n53. Seluanov A, Mittelman D, Pereira-Smith OM, Wilson JH, Gorbunova V. 2004. DNA end joining\nbecomes less efcient and more error-prone during cellular senescence. PNAS 101:762429", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 211, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "029ae7be-b0ab-55f8-84a2-5a74681e454d", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "93b3cc74-a414-5097-802a-7dc2ad10171d", + "score": 0.6157262554331877, + "metadata": { + "text": "in many accelerated-aging mouse models and in a plethora of human age-associated pathologies,\nincluding osteoporosis, atherosclerosis, glomerular disease, diabetic venous ulcers, chronic ob-structive pulmonary disease and emphysema, osteoarthritis, herniated intervertebral discs, and\nvascular calcication (112). Senescent cells are resistant to apoptosis and accumulate exponen-\ntially with age as a consequence of inefcient clearance. Unlike apoptotic tissues, senescent tissues\n436 VermeijHoeijmakersPothof", + "title": "2016 - Genome Integrity in Aging.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "extraction_id": "102fcfb3-b333-5b67-ab94-08033f04ba5c", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + }, + { + "id": "3593241d-677d-5042-a1e9-dd92760a8c0e", + "score": 0.6138603321397117, + "metadata": { + "text": "wound healing [ 8], and immune clearance [ 9,10]. By\ncontrast, the gradual accumulation and chronic\npersistence of senescent cells with time promotes dele-\nterious effects that are considered to accelerate deterior-\nation and hyperplasia in aging [ 11]. Senescent cells\nsecrete a cocktail of inflammatory and stromal regula-\ntorsdenoted as the senescence-associated secretory\nphenotype, or SASP which adversely impact neighbor-\ning cells, the surrounding extracellular matrix, and other", + "title": "2020 - A multidimensional systems biology.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "extraction_id": "fe4ec57e-6ae7-59c4-b8fa-da73fe77ce96", + "associatedQuery": "What are senescent cells and how do they contribute to aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIFjcxBUYW02ZQfh6Ogs975bGNDi2", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, there are known lifestyle interventions that can positively impact genes related to aging. Dietary interventions, such as dietary restriction (DR) and calorie restriction, have been shown to alter patterns of DNA methylation and induce long-lasting changes in gene expression that improve health during aging and extend lifespan [1], [8]. These interventions can modify the epigenome, which is linked to the biology of aging [5]. Additionally, glucose restriction has been shown to extend human cellular lifespan through SIRT1-mediated epigenetic and genetic mechanisms [7].", + "role": "assistant" + } + } + ], + "created": 1728913752, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 108, + "prompt_tokens": 1348, + "total_tokens": 1456 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "5fc33fac-ab39-5ec1-9fb9-dcaa93a595d3", + "score": 0.7159391854983329, + "metadata": { + "text": "Dietary interventions, including starvation and protein\ndeprivation, can also alter patterns of DNA methyla-\ntion, potentially in a long-lasting manner [42, 43],\nincluding transgenerationally [26, 44].\nDietary, genetic and pharmacological interventions\nthat improve health during aging and extend lifespan\ninduce long-lasting changes in gene expression that\nmediate their effects. Here we have asked if and how\nage-related DNA methylation, transcription and lipid", + "title": "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "extraction_id": "6364d669-4b96-5d2f-8ce8-526b065dce72", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "4bf7307d-d8a0-5594-b0b5-487fe0f265ca", + "score": 0.700632593054551, + "metadata": { + "text": "Longev. Heal. 2, 10 (2013).\n7. Kreienkamp Ret al.Doubled lifespan and patient-like pathologies in progeria mice fed high-fat diet. \nAging Cell18, e12852 (2019). [PubMed: 30548460] \n8. Heilbronn LK & Ravussin E Calorie restriction and aging: review of the literature and implications \nfor studies in humans. Am. J. Clin. Nutr. 78, 361369 (2003). [PubMed: 12936916] \n9. Liang Yet al.Calorie restriction is the most reasonable anti-ageing intervention: a meta-analysis of", + "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "version": "v0", + "chunk_order": 132, + "document_id": "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "extraction_id": "30ba3324-6e19-58c2-9e32-508f827af3e5", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "afc304d1-dd43-55ec-811d-27ca27fc4e5d", + "score": 0.69575321934401, + "metadata": { + "text": "a medical intervention), without changing the fundamental rateof organismal aging. Nevertheless, it does seem that manyso-called longevity genes, as well as dietary restriction,\nappear to extend not only life span, but also health span\n(Kauffman et al., 2010; Luo et al., 2010 ). In that regard, it does\nappear that it is possible to experimentally slow the rate of aging.\nStill, in each case, aging does continue on as if there is some", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "b9f038dd-97af-51ea-bb32-d73bf66c3dcb", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "3fc1603d-dd9e-5bcf-96e6-6b927d344be1", + "score": 0.6918727571888421, + "metadata": { + "text": "As we describe above, a small but growing number ofinterventions has been shown to reproducibly increase\nlifespan in laboratory animals and, in a few cases, to also\ndelay or reverse age-related declines in multiple organsystems. These healthy aging interventions could, in prin-\nciple, be tested to determine whether they also increase\nlifespan and promote healthspan in dogs (Table 1). There\nare several questions that immediately present themselves\nwhen considering the design of a healthy aging interven-", + "title": "2016 - The dog aging project translational geroscience in companion.pdf", + "version": "v0", + "chunk_order": 50, + "document_id": "e841c6bd-78b8-56e1-b3dd-e2bcc8a0f590", + "extraction_id": "e433208e-665d-550c-b8e8-c9fb400f1159", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "7ca45b81-3f97-5b1b-9a84-84cfffc4cc08", + "score": 0.6913794485145646, + "metadata": { + "text": "be linked to the biology of stem cell quiescence and self-renewal.\nAlthough genetic and environmental interventions have clearly\nproven to be effective in prolonging life span, we postulate thatthose interventions, as well as the rejuvenating interventions\ndescribed above, are, in fact, acting primarily to modify theepigenome. Consistent with this, genetic interventions directlytargeting the epigenome can extend life span ( Greer et al.,\n2010 ). Studying aging and rejuvenation through the lens of", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 116, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "915ca931-d49d-5837-97fd-f06c145764d0", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "c96b67f8-ad31-50fd-b053-07b127938ef2", + "score": 0.6899251732252514, + "metadata": { + "text": "During the past century, remarkable progress has been \nmade in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "title": "2020 - Mitonuclear genomics and aging.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "extraction_id": "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "193d98c7-8d37-5f83-b1b2-84aee242f079", + "score": 0.6873417515166182, + "metadata": { + "text": "205. Li, Y.; Tollefsbol, T.O. p16INK4a Suppression by Glucose Restriction Contributes to Human Cellular Lifespan Extension through\nSIRT1-Mediated Epigenetic and Genetic Mechanisms. PLoS ONE 2011 ,6, e17421. [CrossRef]\n206. Daniel, M.; Tollefsbol, T.O. Epigenetic linkage of aging, cancer and nutrition. J. Exp. Biol. 2015 ,218, 5970. [CrossRef]\n207. Kapahi, P .; Kaeberlein, M.; Hansen, M. Dietary restriction and lifespan: Lessons from invertebrate models. Ageing Res. Rev. 2017 ,\n39, 314. [CrossRef]", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 259, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "9770f6f4-b86a-514f-9cce-c23d2963aeae", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "7460a40c-8723-5de9-9f2e-c781f4872f1f", + "score": 0.6848835553194155, + "metadata": { + "text": "as diabetes, cancer and neurodegenerative disorders\n[1, 2]. Environmental and genetic interventions can\nameliorate the effects of aging, with nutrition,\nnutrient-sensing signaling networks and metabolism\nplaying evolutionarily conserved roles [1, 3 5]. Diet-\nary restriction (DR), in which food intake is reducedwhile avoiding malnutrition, extends lifespan in di-\nverse model and non-model organisms [3, 6]. DR\ninduces a remarkably broad-spectrum improvement in", + "title": "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "extraction_id": "21efa872-9d89-5dee-9dd1-27dcaa1208cf", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "38c89363-89a1-56d5-82f2-28c19fa0fbcb", + "score": 0.6846686805685964, + "metadata": { + "text": "53. Mair W & Dillin A Aging and survival: the genetics of life span extension by dietary restriction. \nAnnu. Rev. Biochem. 77, 727754 (2008). [PubMed: 18373439] \n54. Masoro EJCaloric restriction-induced life extension of rats and mice: a critique of proposed \nmechanisms. Biochim. Biophys. Acta1790, 10401048 (2009). [PubMed: 19250959] \n55. Weindruch R, Walford RL, Fligiel S & Guthrie D The retardation of aging in mice by dietary", + "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "extraction_id": "bca61863-81b3-5ef7-850d-10cc9577a9e1", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + }, + { + "id": "b9240ab4-370f-5bc1-8c33-9755ab788aac", + "score": 0.682794431364705, + "metadata": { + "text": "In addition to genes associated with aging, research has\nfocused on identifying genes associated with the life-\nextending effects of CR. One method is to identify genesthat decrease or cancel out the life-extending effects of CRwhen mutated (Gems et al., 2002; Bishop and Guarente,2007). More than 100 such genes have been identified inmodel organisms (D. Wuttke, C. Vora, J. P. de Magalhes,unpublished observations). The growth hormone receptor(GHR) is the only gene so far identified in mammals that", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 68, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "13ca8905-ddbb-5437-b6a8-4012969daa43", + "associatedQuery": "Are there any known lifestyle interventions that can positively impact genes related to aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIFji2gbFHCW8aj8mLegsooXneEeb", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The 'epigenetic clock' is a molecular biomarker of aging that is based on the DNA methylation levels of specific CpG sites. These methylation patterns are highly correlated with an individual's chronological age, with a robust correlation coefficient of approximately 0.9 for individuals aged between 20 and 100 years [1]. The epigenetic clock serves as a reliable predictor of biological age, which refers to how well a person's body functions compared to their chronological age [2]. \n\nIn aging research, the epigenetic clock is used to estimate the biological age of cells, tissues, or organs by analyzing the methylation levels of select CpGs, often referred to as clock CpGs [8]. This estimated age, known as the epigenetic age, can indicate different aging rates between individuals with the same chronological age, providing insights into the biological basis of aging [9]. The epigenetic clock has been applied in various studies to understand the relationship between epigenetic aging and factors such as metabolism, and it is considered one of the most promising molecular estimators of biological age [6], [8].", + "role": "assistant" + } + } + ], + "created": 1728913758, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 225, + "prompt_tokens": 1260, + "total_tokens": 1485 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "e2522b52-d927-5c1a-8569-8fcb706ecc1e", + "score": 0.7772643102845974, + "metadata": { + "text": "vided one of the most reliable aging biomarkers. An epigenetic clock is a group of \nCpG sites with particular methylation patterns that are highly related to the chrono-\nlogical age of an individual. This correlation is very robust (r=0.9) for individuals \nbetween 20 and 100years. The epigenetic clock is a breakthrough discovery that \nwill allow novel experimental approaches to understand the biological basis of \naging [113]. For example, by using the epigenetic clock as a measure of cellular", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 729, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "a81cc7a6-0cc6-5909-9192-ac0fab26fbc2", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "c76f4517-c117-56e6-96b9-218f0fdae9f3", + "score": 0.7516819982718244, + "metadata": { + "text": "Epigenetic Clock\nChronological age is the number of years a person has lived, and biological or phys-\niological age refers to a measure of how well your body functions compared to your \nchronological age. Biological age is influenced by multiple factors (genes, lifestyle, \nbehavior, environment, among others) and correlates with mortality and health sta-\ntus. The epigenetic clock is one potentially reliable predictor of biological age.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1002, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "63c7bfe5-a409-5435-91ea-487534957b81", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "4edf498a-20de-593a-b301-73c799b07691", + "score": 0.7424924680599662, + "metadata": { + "text": "Background\nEpigenetic clocks are sets of CpG dinucleotides whose DNA methylation (DNAm) can\nbe used to accurately predict a person s chronological age [ 1]. In recent years, various\nepigenetic clocks have been developed [ 25]. Well-known examples are the clocks de-\nveloped by Hannum et al., trained on blood samples and containing 71 CpGs [ 2], and\nHorvath, a multi-tissue predictor consisting of 353 CpGs [ 3]. A popular application of", + "title": "2022 - Functional genomics analysis identifies.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "1fe1c748-9e73-51ba-8521-de924cc133d4", + "extraction_id": "6d7c1694-2c53-554c-9070-2db848fc5a42", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "99532996-c835-534a-b6e7-a2f95ec00e2c", + "score": 0.7356219291687012, + "metadata": { + "text": "An EpigeneticClock\nThe aging transcriptome could be used to gauge the physiological \nage of worms, and in that way serve as an epigenetic clock revealing \nhow much of life span has been spent and how much remains (23). \nMiddle-aged worms show an aging transcriptome half-way between \nthe aging expression profiles of young and old worms. This provides \nan independent way to assess the age of an animal independent of \nits life span. This is important as there are at least 2 explanations to", + "title": "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "version": "v0", + "chunk_order": 30, + "document_id": "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "extraction_id": "c6cc3d8b-3736-5fe8-a4ff-eb186679a37e", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "cb09d819-b809-5844-a111-5c7c7b9f9a99", + "score": 0.7303161025047302, + "metadata": { + "text": "The epigenetic aging clock measures the sum of all the age-related \npathways affecting cellular physiology in old age. The aging epigen-\netic clock is heavily enriched for germline- and intestinal-expressed \ngenes, but lack muscle- and neuronal-expressed genes (23, 25). \nExpression changes in the germline and intestine were expected as \nthere are massive changes in the morphology of gonad at the end of \nfertility and the intestine in old age. The aging transcriptome pro-", + "title": "2021 - Career Retrospective Tom Johnson?Genetics, Genomics.pdf", + "version": "v0", + "chunk_order": 33, + "document_id": "f3a26f44-f5af-5b2b-aa1c-aec2fd99f17e", + "extraction_id": "c6cc3d8b-3736-5fe8-a4ff-eb186679a37e", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "2d08a161-7a62-5d3f-b300-1ca93ee5b751", + "score": 0.7297257619932761, + "metadata": { + "text": "etic mouse aging and may be used to inform future studies in other model organisms and humans \nfocused on studying the relationship between epigenetic aging and metabolism.\nIntroduction\nEpigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These \nDNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE\n*For correspondence: \nkmozhui@uthsc.edu\nCompeting interest: See page \n22\nFunding: See page 22", + "title": "2021 -Mozhui- Epigenetic aging.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "d23daa43-4176-54e6-b3c3-b889843e92f1", + "extraction_id": "6dfd0c51-91dd-5bb3-b7ae-a9c86ea22c35", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "66c03d04-0af5-50e5-8d4a-9a645493db46", + "score": 0.7297257619932761, + "metadata": { + "text": "etic mouse aging and may be used to inform future studies in other model organisms and humans \nfocused on studying the relationship between epigenetic aging and metabolism.\nIntroduction\nEpigenetic clocks are widely used molecular biomarkers of aging (Horvath and Raj, 2018). These \nDNA methylation (DNAm) age predictors are based on the methylation levels of select CpGs that are RESEARCH ARTICLE\n*For correspondence: \nkmozhui@uthsc.edu\nCompeting interest: See page \n22\nFunding: See page 22", + "title": "2021 - Genetic loci and metabolic states associated with murine epigenetic aging.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "b82bd9e1-2373-577b-a942-164565eaca6b", + "extraction_id": "68ee1ea3-5caf-5df5-8efc-134943a456cb", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "35c83256-6072-5e6a-b15e-0cae1991b034", + "score": 0.7288637484474534, + "metadata": { + "text": "estimators epigenetic clocks; telomere length; transcriptomic-, proteomic-, and \nmetabolomic-based estimators; and composite biomarkers concluded that the epi-\ngenetic clock is the most promising molecular estimator of biological age [26]. \nEpigenetic age estimators are sets of CpGs (also known as clock CpGs) that are \ncoupled with a mathematical algorithm to estimate the age of a DNA source, such \nas cells, tissues, or organs. This estimated age, also referred to as epigenetic age or", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 333, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "8f22afaf-a5fb-5f44-9fc2-18d4aeceede7", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "39dfbf42-78ec-5b0a-8448-55f47c22830e", + "score": 0.7156729271606859, + "metadata": { + "text": "proved epigenetic clock. It should be noted that building\na biological age predictor is difficult since there is no\nclear definition of biological age. Nevertheless, one of\nthe essential features of biological age is its ability to in-\ndicate the different ageing rates between individuals with\nthe same chronological age. A previous study has re-\nported a number of CpG sites that show variation in the\nlongitudinal changing rates between individuals [ 40].", + "title": "2019 - Improved precision of epigenetic clock.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "556d0179-023f-581f-9c2d-febe4e75722f", + "extraction_id": "487cf1b1-1190-5d14-8b24-ba92f75aa6aa", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + }, + { + "id": "d5ae06ad-3d88-5c4f-972a-0510d2fc67f3", + "score": 0.7122355103492737, + "metadata": { + "text": "ranging from 0.15 to 0.19 [ 8,9]. Individuals with epigenetic clock estimates greater than\ntheir chronological age display age acceleration and have been shown to be at a\ngreater risk of all-cause mortality and multiple adverse health outcomes [ 10]. Conse-\nquently, identification of genetic and environmental contributors to the variation in\nthese measures in populations has become a major goal in the field [ 11].\nThe first generation of epigenetic aging clocks used penalized regression models to", + "title": "2021 - Genome-wide association studies identify.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "60c2e869-1fee-53ea-b332-26d9c2abc747", + "extraction_id": "53db6715-4f12-50ad-8fb9-acba4e2f4f37", + "associatedQuery": "What is the 'epigenetic clock,' and how is it used in aging research?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIFlJI3UdMQFpFb34IWrf77DCGGJY", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Researchers use model organisms like yeast and worms to study human aging due to their simpler genomes, short lifespans, and the ease with which they can be genetically and environmentally manipulated. These characteristics make them ideal for identifying and characterizing genes and signaling pathways involved in aging [3]. \n\nYeast, specifically Saccharomyces cerevisiae, is a highly informative model for aging studies because of its genetic tools and the ability to measure aging through replicative or chronological lifespan assays [2], [5]. Yeast has been extensively used to identify genes and interventions responsible for lifespan extension, providing insights into the aging processes of all eukaryotic organisms [10].\n\nSimilarly, the nematode Caenorhabditis elegans is another widely used model organism in biogerontology. Researchers study these organisms to understand whether the aging process is evolutionarily conserved and to what degree mechanisms in these simpler organisms can be indicative of aging mechanisms in humans [1], [6]. These model organisms help explore both genetic and environmental determinants of lifespan, contributing to hypotheses surrounding extended lifespan and healthspan [7].", + "role": "assistant" + } + } + ], + "created": 1728913857, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 220, + "prompt_tokens": 1188, + "total_tokens": 1408 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "f125dd77-ecd5-59aa-9cf0-ae89333159d2", + "score": 0.7750034928321838, + "metadata": { + "text": "the nematode Caenorhabditis elegans , and the budding yeast \nSaccharomyces cerevisiae , have emerged as the most widely \nused and, hence, best characterized, model organisms in bio-\ngerontology. \n When considering the use of simple eukaryotes to study \naging and age-related disease, it is pertinent to ask whether, and to what degree, the aging process is evolutionarily con-\nserved. Does a yeast cell age by the same mechanism(s) as a", + "title": "2007 - Longevity Genomics Across Species.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", + "extraction_id": "789baa84-292c-547a-8600-8568f34ea9de", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "35414229-a946-525c-b508-4b8f49a2702c", + "score": 0.769577466534897, + "metadata": { + "text": "Studies on the aging of mammals are rather limited by the long life\nspan of the commonly used model organisms. Thus, both nonverte-brate and invertebrate organisms, with their shorter life span and ease\nof genetic and environmental manipulations, gained popularity amongresearchers in the aging field as experimental models for aging studies.\nAmong them, budding yeast or Saccharomyces cerevisiae is a highly in-\nformative organismal model for aging studies with its genetic tools,", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "6b4e8da8-7d82-5531-b1a8-21935a6f4a51", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "1b3d84fb-c799-5d19-b3bd-a9032b7980fc", + "score": 0.743890903708068, + "metadata": { + "text": "Abstract\nCellular models such as yeasts are a driving force in biogerontology studies. Their simpler genome, short lifespans and vast\ngenetic and genomics resources make them ideal to characterise pro-ageing and anti-ageing genes and signalling pathways.Over the last three decades, yeasts have contributed to the understanding of fundamental aspects of lifespan regulation\nincluding the roles of nutrient response, global protein translation rates and quality, DNA damage, oxidative stress,", + "title": "2022 - Genome-wide screens in yeast models towards.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "be6ba237-7f13-5aac-8cb1-6a43e82d326b", + "extraction_id": "6977180a-bec7-5d39-ba97-b0fd664b6c96", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "5caecfbd-14ef-59e2-a281-2bc524ca0353", + "score": 0.7376859188079834, + "metadata": { + "text": "usually chosen for convenience rather than for specific features \napplicable to human aging. Hence, choosing the suitable animal model to answer the specific question we aim to understand is \nof high importance in these types of studies. Among the most \nprevalent aging model organisms are Saccharomyces cerevisiae , \nCaenorhabditis elegans, Drosophila melanogaster, and Mus mus -\nculus . As a single-celled organism, S. cerevisiae is easily grown,", + "title": "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "version": "v0", + "chunk_order": 23, + "document_id": "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "extraction_id": "fbb608d4-8b2b-502d-88cc-d1fcb0b89afb", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "c14402ec-2ad7-5857-9f09-39c71656bf0f", + "score": 0.7347005605697632, + "metadata": { + "text": "mammalian genes that affect aging than any other model organism. Aging in yeast is assayed primarily by\nmeasurement of replicative or chronological life span. Here, we review the genes and mechanisms implicated\nin these two aging model systems and key remaining issues that need to be addressed for their optimization.", + "title": "2012 - Replicative and Chronological Aging.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "496e387e-4278-5f74-8ecc-4edc1cee7dfe", + "extraction_id": "cb936ad1-1a4f-5b1c-89f1-f8991f059969", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "c103f3f8-b155-5787-bdd9-16f9d390369d", + "score": 0.7263469912513197, + "metadata": { + "text": "be more exaggerated in more distantly related species (such \nas the worm and mouse models). There are, however, simi -\nlarities between aged humans and aged model organisms; \nthey all tend to have decreasing overall fitness, and there -\nfore, studies using model organisms continue as they may \nbe at least indicative of some aging mechanisms in humans.\nExtensions to life span in model organisms are mostly \nassociated with disruption to fundamental metabolic path -", + "title": "2012 - Genomics and Successful Aging Grounds for Renewed.pdf", + "version": "v0", + "chunk_order": 49, + "document_id": "6d2b82c3-4256-562a-9b23-ff7c71e9fd93", + "extraction_id": "3d18e792-3d83-5cc3-b9ab-309322ecf55d", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "b19ebe3b-e87e-5cab-baef-24deddd303bb", + "score": 0.7236888408660889, + "metadata": { + "text": "eukaryote model organisms, namely yeast, worms, ies,and sh, as well as mice and rats, to explore both genetic\nand environmental determinants of lifespan. While these\nshort-lived models have each yielded a number of fasci-\nnating ndings and insights into hypotheses surrounding\nextended lifespan and healthspan, they may also haveconstrained this complex, multifactorial eld to areas in\nwhich they are best suited, most notably short-term inter-", + "title": "2016 - Unraveling the message insights into comparative genomics.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "0deba7bb-c27a-5d9e-b1b2-e48a5574882c", + "extraction_id": "0dc4e4f4-cd48-5ccd-991b-edd688533b5e", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "c32f3dbe-95d5-531a-9165-d4da7b2dc2a8", + "score": 0.7108061525465895, + "metadata": { + "text": "et al., 2010 ). These effects require an intact germline, andTable 2. Repositories and Tools for Aging Research\nModels Description Link/Reference\nYeast Saccharomyces genome database http://www.yeastgenome.org/\npublished lifespan data http://lifespandb.sageweb.org/ (McCormick et al., 2015 )\nWilcoxon rank sum test to test signicance of lifespan\ndifferenceshttp://data.kaeberleinlab.org/scripts/ranksum.php\nyeast outgrowth data analyzer (YODA) for chronological\nlifespan assayshttp://yoda.sageweb.org/", + "title": "2016 - Epigenetic Mechanisms of Longevity and Aging.pdf", + "version": "v0", + "chunk_order": 26, + "document_id": "588185a0-e157-552f-a304-4beefb85d398", + "extraction_id": "5ff88a27-4525-5e7f-99bd-e781cedd8005", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "91375d45-be1d-5c54-8d0f-a9b1dded69bb", + "score": 0.7103649377822876, + "metadata": { + "text": "for molecular biological studies on aging. Although material \nfrom humans should be employed where possible, for prac- \ntical reasons animal model systems like rats and mice are \nindispensible. There is evidence that, provided their health sta- \ntus and husbandry is optimal, rodents age much in the same \nway as humans do (Burek 1978). For studying certain funda- \nmental processes, such as the occurrence of various types of \nDNA rearrangement, lower organisms and cell lines can also", + "title": "1989 - Genetic instability and aging theories, facts, and future perspectives.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "4d5b1800-b676-5865-a555-09ea740cc14a", + "extraction_id": "b0531531-f629-512b-9835-24cc870b4ef3", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + }, + { + "id": "ae5be149-52ad-5854-b40a-c24374545cf0", + "score": 0.7083187103271484, + "metadata": { + "text": "short life span, and fully sequenced genome (20 ,21). Despite being uni-\ncellular, yeast has been an excellent model to identify and characterize\nconserved basic biological processes, including aging. Yeast has beenextensively used to identify genes and interventions responsible for lifespan extension and to gain insights into the aging processes of all eu-\nkaryotic organisms. In parallel, over the years, studies on invertebrate\norganisms, such as Drosophila melanogaster (flies) and Caenorhabditis", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "6b4e8da8-7d82-5531-b1a8-21935a6f4a51", + "associatedQuery": "How do researchers use model organisms like yeast or worms to study human aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIFlT2nob40QrExWjGMMqZ4fSc8yC", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, there are promising anti-aging therapies being developed based on genetic research. Several approaches are being explored:\n\n1. **Senolytic Drugs**: Research has shown that abolishing senescent cells through genetic manipulation or senolytic drugs can significantly halt biological aging in mice. Trials are underway to test the ability of senolytics to postpone age-associated pathologies in humans [3].\n\n2. **Genetic Discoveries in Aging**: A number of genes capable of altering the aging process have been identified in animal models and even in humans. This area of research is promising as it explores the association of multiple alleles with human exceptional longevity [6].\n\n3. **Manipulation of Aging-Related Genes**: There is potential in manipulating aging-related genes through diet, lifestyle, and pharmaceuticals to improve human health and develop drugs against age-related diseases such as cancer, heart disease, type 2 diabetes, obesity, and neurodegenerative diseases [8].\n\nThese developments indicate that genetic research is paving the way for potential anti-aging therapies.", + "role": "assistant" + } + } + ], + "created": 1728913867, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 208, + "prompt_tokens": 1345, + "total_tokens": 1553 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "78733c6a-d870-5154-9128-eb66291fa967", + "score": 0.7031545573792666, + "metadata": { + "text": "need to develop approaches and therapies targeting theaging process and age-related diseases (Butler et al.,2008). Delaying the process of aging, even slightly,would have profound social, medical and economic ben-efits (Olshansky et al., 2006; Butler et al., 2008). Forexample, slowing aging by a mere 7 years would cutmortality of age-related diseases by half at every age.Therefore, the potential benefits from research on thebasic biology and genetics of aging are unparalleled interms of improving quality", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "68b12e10-d643-570a-aa63-eda62de81928", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "9da7c5dc-0deb-577c-bb22-83f987bd76dd", + "score": 0.6974615815327716, + "metadata": { + "text": "raises the possibility of therapies to slow aging. Therefore the discoveryof a gerontogene with even very rare mutations that increased longevitywould cause speculation about future trends in mortality. However, thediscovery of such a gene would be relevant only to long-term (and, there-fore, very speculative) projections.\nProspective Epidemiologic Surveys that Include Genetic Information\nSome epidemiologic cohort studies of populations have collected", + "title": "2001 - Demography in the age of genomics.pdf", + "version": "v0", + "chunk_order": 784, + "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "extraction_id": "e3014138-3d5b-58bc-a1a5-5ac6f04cac1c", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "3c636897-c47e-505d-9203-306124b73e0e", + "score": 0.6919307892363904, + "metadata": { + "text": "Interestingly, when senescent cells are abolished either through genetic manipulation or via senolytic\ndrugs, biological aging is signicantly halted in mice [ 53,54]. Therefore, trials are now under way to\ntest the ability of senolytics to postpone age-associated pathologies in humans [ 55]. Notably, multi-\nple drugs are being pursued that either directly or indirectly impact DNA repair or the consequenceof DNA damage.\nFuture Prospects: Developing Interventions through DNA Repair", + "title": "2020 - Protecting the Aging Genome.pdf", + "version": "v0", + "chunk_order": 57, + "document_id": "bb774030-2570-5596-b2ab-b8f57ff81086", + "extraction_id": "e5067ce2-69a6-5433-bed4-b95daeaa691e", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "265126e3-2a4d-518f-93cf-21a201747eef", + "score": 0.6850976911012499, + "metadata": { + "text": "5. Goldman DP, etal. Substantial health and economic returns from delayed aging may warrant \na new focus for medical research. Health Aff (Millwood). 2013;32(10):1698705.\n 6. Esplin ED, Oei L, Snyder MP.Personalized sequencing and the future of medicine: discov-\nery, diagnosis and defeat of disease. Pharmacogenomics. 2014;15(14):177190.\n 7. Marian AJ.Clinical applications of molecular genetic discoveries. Transl Res. 2016;168:614.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1774, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "822571e2-b05d-5e17-9eaa-431151851111", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "dcc13291-f18b-5094-83b6-4609322bc242", + "score": 0.6829540322450489, + "metadata": { + "text": "J.L. Kirkland, Barriers to the Preclinical Development of Therapeutics that Target\nAging Mechanisms, J. Gerontol. A Biol. Sci. Med Sci. 71 (11) (2016) 1388 1394 .\n[2]D.J. Baker, B.G. Childs, M. Durik, M.E. Wijers, C.J. Sieben, J. Zhong, R.A. Saltness,\nK.B. Jeganathan, G.C. Verzosa, A. Pezeshki, K. Khazaie, J.D. Miller, J.M. van\nDeursen, Naturally occurringp16(Ink4a)-positive cells shorten healthy lifespan,\nNature 530 (7589) (2016) 184 189.", + "title": "2018 - Spontaneous DNA damage to the nuclear genome promotes senescence.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "08be7274-78a3-5e93-9e8c-3d4f6dbeacf9", + "extraction_id": "005e73b5-7a93-53ff-946c-735fb4588de5", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "1c4286b6-ede2-568b-9c18-b1e99ede17a6", + "score": 0.6753207832192489, + "metadata": { + "text": "series of recent breakthroughs, a number of genes capable ofaltering the aging process as a whole or at least to a largedegree have been identified in animal models and even a fewin humans (Finch & Ruvkun, 2001; de Magalhes, 2005; Kenyon,2005). Furthermore, multiple alleles have been examined fortheir association with human exceptional longevity (Vijg & Suh,2005). This is a fascinating and important area of research, yetthere are now so many genes being associated with aging andlongevity that keeping", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "7ada6b55-99c2-5e20-bf96-d153f927256c", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "2c5241f1-1655-5e36-a787-b966767b2534", + "score": 0.6704605018543647, + "metadata": { + "text": "pharmaceutical and other interventions for human aging based on research that starts with the genomic information required to sustain adaptation, and thus health, in older fruit flies [36-39]. \n \nNaturally, any such genomic short-cut to reverse-engineering the evolution of slowed aging from fruit flies to humans is fraught with potential for error. Such \nevolutionarily deep orthologies are sure to supply", + "title": "2009 - Adaptation, aging, and genomic information.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "54a993af-b86b-5cc3-a04b-bab03c244534", + "extraction_id": "c2a8f947-44f2-5100-99e5-9c3a2f1284e9", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "f20fd517-5f05-53ca-93a5-916bc891ad92", + "score": 0.6675249336615335, + "metadata": { + "text": "century. Manipulation of aging-related genes by diet,lifestyle, and pharmaceuticals could dramatically im-prove human health and could be used to develop drugsagainst age-related diseases such as cancer, heart dis-ease, type 2 diabetes, obesity, and neurodegenerativediseases. The hundreds of aging-related genes and genesrelated to CR already identified offer enormous oppor-tunities for target discovery (Fig. 2). Although aging-related genes cannot be modified in humans, under-standing how these can be", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 170, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "8650652a-1765-563b-a98e-2e9336bcf29a", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "69681eeb-6629-5091-b2b4-b4444e570913", + "score": 0.6667904853599408, + "metadata": { + "text": "[7] Hughes, S.E., Evason, K., Xiong, C., Kornfeld, K. Genetic and \npharmacological factors that influence reproductive aging in nema-\ntodes. PLoS Genet. 2007 , 3: e25. \n[8] Vijg, J., Campisi, J. Puzzles, promises and a cure for ageing. Na-\nture 2008 , 454: 1065-1071. \n[9] Rolland, Y., Czerwinski, S., Abellan Van Kan, G., Morley, J.E., \nCesari, M., Onder, G., Woo, J., Baumgartner, R., Pillard, F., Boirie, \nY., Chumlea, W.M., Vellas, B. Sarcopenia: its assessment, etiol-", + "title": "2009 - MicroRNAs in C. elegans Aging Molecular Insurance for Robustness.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "dff49223-ac74-5419-a190-a0c7f43a5ee5", + "extraction_id": "c8d6f90d-a25c-590a-a546-4500df09aa28", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + }, + { + "id": "5d8cc04f-7e13-5dbc-80c2-a35643954e9a", + "score": 0.6647262576711921, + "metadata": { + "text": "for the aging process during the 20th Century. Thissituation poses a fundamental challenge to anti-aging\nmedicine: how to develop effective therapies for a\ngenomically complex pathology. We propose such astrategy. As a rst step, we recommend the use of modelsystems in which signicant genetic intervention is not\nproscribed or impractical. Second, we propose that work", + "title": "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "extraction_id": "6c9e1997-bfe6-5708-a476-07c833eed8fa", + "associatedQuery": "Are there any promising anti-aging therapies being developed based on genetic research?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIFlaWmRr9Bl0RbY7T3uxkIANSmkA", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Caloric restriction and diet have significant impacts on the genetics of aging through various mechanisms:\n\n1. **Gene Expression and Lifespan Extension**: Caloric restriction (CR) has been shown to delay age-related gene-expression changes in mice and, to some extent, in flies. This suggests that CR may influence the genetic pathways associated with aging, potentially contributing to lifespan extension [4].\n\n2. **Epigenetic and Post-Translational Mechanisms**: In calorie-restricted rats, transcriptome analysis indicates that CR involves epigenetic and post-translational mechanisms, which are implicated in neuroprotection and aging. These mechanisms may alter genome function to promote increased health and lifespan [3], [5].\n\n3. **mTOR Pathway**: Caloric restriction is associated with decelerating mTOR-driven aging, which is a significant pathway involved in cellular growth and metabolism. By modulating this pathway, CR may influence the genetic regulation of aging processes [5].\n\n4. **Genomic and Epigenetic Approaches**: Nutritional modulation, including caloric restriction, can impact aging through genomic and epigenetic approaches. This suggests that diet can influence the genetic and epigenetic landscape, potentially affecting the aging process [6].\n\nOverall, caloric restriction and diet can modulate genetic pathways and mechanisms that are crucial for aging, potentially leading to increased lifespan and improved health during aging.", + "role": "assistant" + } + } + ], + "created": 1728913874, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 283, + "prompt_tokens": 1597, + "total_tokens": 1880 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "56e72d29-31c2-5096-b4f8-06c740bce06e", + "score": 0.7473972511944728, + "metadata": { + "text": "caloric restriction. Physiol. Genom. 17, 307 315.Van Remmen, H., Ward, W.F., Sabia, R.V ., Richardson, A., 1995. Gene\nexpression and protein degradation. In: Masoro, E.J. (Ed.), Handbook ofPhysiology. Section 11: Aging. Oxford University Press, New York, pp.\n171234.\nWeindruch, R., Walford, R.L., 1982. Dietary restriction in mice beginning at\n1 year of age: effect on life-span and spontaneous cancer incidence.Science 215, 1415 1418.S.R. Spindler / Mechanisms of Ageing and Development 126 (2005) 960 966 966", + "title": "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "extraction_id": "db18d213-92ed-5f3e-a242-60d3ed0ec8c8", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "2b081115-d36e-57ec-aedc-2fd9691bc5e9", + "score": 0.7285994291305542, + "metadata": { + "text": "extension by dietary restriction. Annu Rev Biochem 2008,\n77:727-54.\n8. Harper JM, Leathers CW, Austad SN: Does caloric restriction\nextend life iin wild mice? Aging Cell 2006, 5:441-9.\n9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the\neffect of caloric intake on mortality in mice. FASEB J 2003,\n17:690-2.\n10. Spindler SR, Mote PL: Screening candidate longevity therapeu-\ntics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "title": "2009 - Genes and gene expression modules associated with caloric.pdf", + "version": "v0", + "chunk_order": 276, + "document_id": "893ba204-2e69-563f-9046-7246ca61494f", + "extraction_id": "0b45ae60-562c-5e48-a1c1-9eb29614a63c", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "bb028469-8295-5657-8061-a715cb314a4a", + "score": 0.7226208613740898, + "metadata": { + "text": "analysis in calorie-restricted rats implicates epigenetic and post-translational\nmechanisms in neuroprotection and aging. Genome Biol. 2015;16:285.\n21. Gillespie ZE, Pickering J, Eskiw CH. Better living through chemistry: caloric\nrestriction (CR) and CR mimetics alter genome function to promote\nincreased health and lifespan. Front Genet. 2016;7:142.\n22. Jiang T, Liebman SE, Lucia MS, Phillips CL, Levi M. Calorie restriction modulates\nrenal expression of sterol regulatory element binding proteins, lipid", + "title": "2017 - Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism.pdf", + "version": "v0", + "chunk_order": 146, + "document_id": "fe573bb0-3d37-55e5-93fa-65b3fbc5f532", + "extraction_id": "a29560f5-a9e0-56d0-95f2-138eef516ef5", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "e01c4c58-342d-5369-89e6-98344af55000", + "score": 0.7175973653793335, + "metadata": { + "text": "Calorie restriction, a dietary regimen that extends \nthe lifespan of numerous organisms, also delays the \nmajority of age-related gene-expression changes in \nmice and, to a certain extent, in flies45,50. It is currently \nunclear whether the effect of calorie restriction on gene \nexpression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest \nthat there may be a causal link: Sir2 not only facilitates \nheterochromatin and promotes DNA stability, but is", + "title": "2007 - The role of nuclear architecture.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "extraction_id": "9655c555-838e-5cdf-85cf-13736c3cf028", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "1e116f55-36fd-525f-9950-9a1354c32f7b", + "score": 0.7175322602708507, + "metadata": { + "text": "Transcriptome analysis in calorie-restricted rats implicates epigenetic and post-\ntranslational mechanisms in neuroprotection and aging. Genome Biol. 16,2 8\n(2015).\n204. M. V. Blagosklonny, Calorie restriction: Decelerating mTOR-driven aging from cells to or-\nganisms (including humans). Cell Cycle 9, 683 688 (2010).\n205. D. K. Ingram, G. S. Roth, Calorie restriction mimetics: Can you have your cake and eat it,\ntoo? Ageing Res. Rev. 20,4 662 (2015).", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 322, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "91434549-bda3-5154-b089-28efed9c1089", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "b990eb0a-709a-500c-836e-83e202e0d6a6", + "score": 0.7143113613128662, + "metadata": { + "text": "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science\n289:21262128.\nMair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary\nrestriction and death in Drosophila. Science 301:17311733.\nMasoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev\n126:913922.\nMathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap-\nproaches. Mech Ageing Dev 127:584589.\nMeric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 214, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "5745c701-a549-51c3-adcc-b19c47436740", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "833c9ddc-ec27-5301-9c3b-025eab95e28c", + "score": 0.7139496803283691, + "metadata": { + "text": "Keywords: Caloric restriction; Short-term; Longevity; Cancer; Microarray; Affymetrix\nAging is widely assumed to result from the gradual age-\nrelated accumulation of essentially irreversible moleculardamage. In this context, CR is often viewed as preventing orslowing the accumulation of such damage, thereby slowingthe process of aging ( Bokov et al., 2004 ). This view is\nintuitively appealing, as it provides a straightforwardexplanation for the stochastic nature of aging and the onset", + "title": "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "extraction_id": "f4edcf22-ca64-5fe6-9bfa-f97a017d2d08", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "8151fbcb-f498-56a7-84ce-8af647fd2245", + "score": 0.7123688802279428, + "metadata": { + "text": "of short- and long-term caloric restriction effects in the liver of agingmice. Proc. Natl. Acad. Sci. U.S.A. 98, 10630 10635.Capstick, F., Brooks, B.A., Burns, C.M., Zilkens, R.R., Steinbeck, K.S.,\nYue, D.K., 1997. Very low calorie diet (VLCD): a useful alternative inthe treatment of the obese NIDDM patient. Diab. Res. Clin. Pract. 36,\n105111.\nChen, H., 2004. Gene expression by the anterior pituitary gland: effects of\nage and caloric restriction. Mol. Cell. Endocrinol. 222, 21 31.", + "title": "2005 - Rapid and reversible induction of the longevity, anticancer.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "0b1bf178-21e4-5382-97c9-c93cdc1a9e66", + "extraction_id": "381d5314-5aa1-525f-a2a5-352b70ad86fa", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "537cff80-380d-5c6a-a25a-04b32e9b1bd7", + "score": 0.7118311736533399, + "metadata": { + "text": "genomic effects of caloric restriction. Mech. Ageing Dev. 126 : 960 966 . \n Sun , H. , R.J. Bennett , and N. Maizels . 1999 . The Saccharomyces cerevisiae \nSgs1 helicase effi ciently unwinds G-G paired DNAs. Nucleic Acids Res. \n 27 : 1978 1984 . \n Thompson , L.H. , and D. Schild . 2002 . Recombinational DNA repair and human \ndisease. Mutat. Res. 509 : 49 78 .", + "title": "2008 - Longevity mutation in SCH9 prevents recombination.pdf", + "version": "v0", + "chunk_order": 182, + "document_id": "a6b022ba-653f-51d3-845a-dd6b3d61d4a4", + "extraction_id": "bc856edb-7526-5424-a822-47075459a607", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + }, + { + "id": "4f28b643-04a4-59e2-8226-ab050e698b64", + "score": 0.7113211154937744, + "metadata": { + "text": "L. & Spindler, S.\n \nR. Genomic profiling of short- and \nlong-term caloric restriction effects in the liver of aging mice. Proc. Natl Acad. Sci. \nUSA 98, 1063010635 (2001).\n62.\n \nHarman, D. The aging process. Proc. Natl Acad. Sci. USA 78, 71247128 \n(1981).\n63.\n \nvan der Pluijm I, G.\n \nG.et.al. Impaired genome maintenance suppresses the growth \nhormoneinsulin-like growth factor 1 axis in mice with cockayne syndrome. PLoS Biol. \n5, 2338 (2006).\n64.\n \nYang, H., Baur, J.", + "title": "2008 - DNA damage and ageing.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "f170e9cf-dfab-5758-ab23-08daff2af694", + "extraction_id": "554b2b00-d006-5b97-aeb1-70ec31482641", + "associatedQuery": "How do caloric restriction and diet impact the genetics of aging?" + } + } + ], + "kg_search_results": null + } + } + } +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/gpt4o/cs_diabetes_responses.json b/gnqa/paper2_eval/data/responses/gpt4o/cs_diabetes_responses.json new file mode 100644 index 0000000..870d3c0 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/gpt4o/cs_diabetes_responses.json @@ -0,0 +1,3222 @@ +{ + "1": { + "results": { + "completion": { + "id": "chatcmpl-AIFp3LBsYqGcLUvIYyVnKoJE32qw3", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genetic mutations in the insulin gene can significantly affect glucose metabolism in several ways:\n\n1. Mutations that result in mutant insulin or the inability to convert proinsulin to insulin can lead to glucose intolerance. This is because the proper conversion and function of insulin are crucial for maintaining normal glucose levels in the blood [1].\n\n2. Insulin gene mutations can cause early-onset diabetes, with marked differences in clinical presentation and metabolic status. These mutations can lead to the retention of insulin in the endoplasmic reticulum, causing stress and potentially leading to beta-cell apoptosis, which reduces insulin production and secretion [2], [10].\n\n3. Insulin-deficient conditions, such as those caused by certain mutations, can lead to severe hyperglycemia, as insulin is a central hormone for maintaining glucose homeostasis. Without sufficient insulin, glucose cannot be effectively taken up by cells, leading to elevated blood glucose levels [5].\n\n4. Some mutations may result in abnormal proinsulin folding, leading to an accumulation of misfolded proteins in the endoplasmic reticulum. This accumulation can cause endoplasmic reticulum stress and beta-cell apoptosis, further impairing insulin production and secretion [10].\n\nOverall, genetic mutations in the insulin gene can disrupt normal insulin synthesis and function, leading to impaired glucose metabolism and conditions such as diabetes.", + "role": "assistant" + } + } + ], + "created": 1728914089, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 266, + "prompt_tokens": 1393, + "total_tokens": 1659 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "111f4875-7ccd-502f-bd5b-5d4ee88e5af6", + "score": 0.7227188944816589, + "metadata": { + "text": "Mutations that result in mutant insulin or the \ninability to convert proinsulin to insulin result in gl ucose \nintolerance in some of these cases. Genetic defects \nin the insulin receptor or in the signal transduction \npathway of insulin have been demonstrated to result in \nhyperinsulinemia and modest hyperglycemia to severe \ndiabetes[1]. \nDisease of the exocrine pancreas\nDamage of the cells of the pancreas due to diffused \ninjury of the pancreas can cause diabetes. This damage", + "title": "2015 - Diabetes mellitus The epidemic of the century.pdf", + "version": "v0", + "chunk_order": 54, + "document_id": "e114dd28-fd39-56df-bdeb-8806474a6c10", + "extraction_id": "7d87ee73-2cc1-576c-8c0d-eb58479177b8", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "3f7cc31a-dc57-568a-a3b0-602280c56428", + "score": 0.7041979432106018, + "metadata": { + "text": "A, et al. Insulin gene mutations resulting in early-onset diabetes:\nmarked differences in clinical presentation, metabolic status, and\npathogenic effect through endoplasmic reticulum retention.\nDiabetes. 2010;59:653 61.\n21. Steele AM, Shields BM, Wensley KJ, Colclough K, Ellard S,\nHattersley AT. Prevalence of vascular complications among pa-\ntients with glucokinase mutations and prolonged, mild hyperglyce-\nmia. JAMA. 2014;311:279 86.22. Chakera AJ, Spyer G, Vincent N, Ellard S, Hattersley AT, Dunne FP.", + "title": "2019 - How Recent Advances in Genomics Improve Precision Diagnosis.pdf", + "version": "v0", + "chunk_order": 94, + "document_id": "42b64375-06af-5e09-9ae6-6bd0ecb782c7", + "extraction_id": "ebb48c39-f48a-5dce-a4dc-fcd34e6f17e1", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "3cb068b3-0761-5fd8-91bd-92744fa9ca9a", + "score": 0.6915489045247112, + "metadata": { + "text": "presumed glucose toxicity (34). The finding that a mutation\nof a single nucleotide in the gene encoding the glucokinase\nenzyme can result in NIDDM lends credibility to the hypoth-\nesis that inherited defects in insulin production contribute to\nNIDDM (6). Increased insulin demand of obesity and insulin\nresistance is accompanied by enhanced insulin biosynthesis,", + "title": "1994 - Isolation of the Human LIMTHomeodomain Gene Islet-1.pdf", + "version": "v0", + "chunk_order": 58, + "document_id": "c77b101b-9f78-5090-8be9-6f98d9380466", + "extraction_id": "b3b6cf5d-8cc3-5559-af5f-36780a303792", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "5d64fd7b-32d2-55ac-8586-c7ba9172a9ef", + "score": 0.6910686492919922, + "metadata": { + "text": "insulin synthesis and function while mutations in the insulin gene ( INS)\nobviously affect the key hormone made by pancreatic beta cells [62].\nATP synthesis defect (mitochondrial diabetes) and mutations in ATP-\nsensitive potassium channel subunits (channel-building Kir6.2 [po-\ntassium inwardly-rectifying channel, subfamily J, member 11;KCNJ11 ] and regulatory SUR1 [ATP-binding cassette transporter\nsubfamily C member 8], ABCC8 ) all affect insulin secretion [63].", + "title": "2016 - Dissecting diabetes metabolic disease.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "eee2f79d-e093-52fb-871a-798fd859235e", + "extraction_id": "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "551f3603-6a4c-51e6-b568-7ae2fc9e7b33", + "score": 0.6834791302680969, + "metadata": { + "text": "Insulin gene mutations \nInsulin is synthesized in 13-cells of the islets of Langerhans and is a \ncentral honnone that maintains glucose homeostasis. Insulin-deficient mice \ndie shortly after birth due to severe hyperglycemia.53 All cell types of the \nendocrine pancreas are present in insulin deficient mice suggesting that \ninsulin is not required for development and differentiation of the endocrine \npancreas. 53 \nNaturally occurring mutations in the insulin gene that result in the", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1118, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "ce0307a5-fae4-5b6d-9786-10619e49e075", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "2126e367-c1aa-56ae-aff4-0ba7e7070a22", + "score": 0.6803162097930908, + "metadata": { + "text": "Theprevalenceofgeneticmutationsaffectingthestructure\noftheinsulinmoleculeinthegeneralpopulationisunknown.\nUptothepresent,onlythosepatientsmanifestingthemutant\ninsulinsyndrome(5-8,36)withunusualorfamilialTypeII\ndiabeteshavebeenscreenedanddiscovered.Thus,mutantin-\nsulinspecieswithnormalorrelativelywell-preservedbinding\nandbiologicalactivitycharacteristics,andthereforenormal\nmetabolicclearances,areunlikelytobediscoveredbythisap-\nproachsincehyperinsulinemiawillbeabsentorsubtle.Future", + "title": "1986 - Diabetes due to secretion of a structurally abnormal insulin.pdf", + "version": "v0", + "chunk_order": 56, + "document_id": "5b884835-4cf4-5e80-a762-36582271e63e", + "extraction_id": "ffdee7b8-ff45-57bf-973a-ca03ba595d23", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "487ca988-cce2-5b92-a05f-2e1cd11efea3", + "score": 0.6698050498962402, + "metadata": { + "text": "at various steps, resulting in an impaired insulin action and potential \ndevelopment of extreme insulin resistant clinical conditions. Many mutations \nhave been identified in the insulin receptor gene. These mutations may lead \nto: \n Decreased insulin receptor biosynthesis \n Premature chain termination in extracellular or intracellular domain \n Accelerated receptor degradation \n Defect in the receptor transport to plasma membranes \n Decreased insulin binding affinity \n Impaired tyrosine kinase activity", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1336, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "508fd29c-5cf8-52bc-8bf2-5cebb1833cb1", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "fa07c029-ad6e-5768-97da-a4bc5aa4e44f", + "score": 0.6691384514909524, + "metadata": { + "text": "15. Steiner DF, Tager HS, Chan SJ, et al . Lessons learned from\nmolecular biology of insulin-gene mutations. Diabetes Care\n1990; 13: 600609.\n16. Vionnet N, Stoffel M, Takeda J, et al . Nonsense mutation in the\nglucokinase gene causes early-onset non-insulin-dependent\ndiabetes mellitus. Nature 1992; 356 : 721722.\n17. Sakagashira S, Sanke T, Hanabusa T, et al . Missense mutation of\namylin gene (S20G) in Japanese NIDDM patients. Diabetes\n1996; 45: 12791281.", + "title": "2001 - Genomic variation in pancreatic ion channel genes in Japanese type 2 diabetic patients.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "bff7795f-c109-5d7f-871d-ef1f4400a2c6", + "extraction_id": "2d845f58-a674-5e08-a68b-e8cfea8a9b84", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "644810c4-af08-5c60-b333-8c97ddadae8b", + "score": 0.6649872064590454, + "metadata": { + "text": "vating mutations in the gene encoding\nKir6.2 alter fetal and postnatal growthand also cause neonatal diabetes. J\nClin Endocrinol Metab 2006; 91(7):\n27822788.\n93. Stoy J, Edghill EL, Flanagan SE, et al.\nInsulin gene mutations as a cause\nof permanent neonatal diabetes. Proc\nNatl Acad Sci U S A 2007; 104(38):\n1504015044.\n94. Pulizzi N, Lyssenko V, Jonsson A, et al.\nInteraction between prenatal growth\nand high-risk genotypes in the devel-opment of type 2 diabetes. Diabetolo-\ngia2009; 52(5): 825829.", + "title": "2011 - Genome-wide association studies (GWAS) impact.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "086c6869-7c70-5364-9269-760267fb458d", + "extraction_id": "a4721e1b-273b-5042-bc20-cf5602f0955a", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + }, + { + "id": "ac0df77e-c676-552b-b742-1591cb18fbbb", + "score": 0.6630228161811829, + "metadata": { + "text": "(Edghill et al., 2008; Garin et al., 2010; Stoy et al., 2007). Hyperglycemia occurs due to decreased insulin biosynthe-sis, in which most of the reported missense heterozygous mutations are expected to cause an abnormal proinsulin folding. An accumulation of the misfolded protein in the en-doplasmic reticulum (ER) consequently occurs, resulting in ER stress and betacell apoptosis (Liu, Hodish, Rhodes, & Arvan, 2007). Our identified de novo novel variant in INS is expected to result in aberrant proinsulin", + "title": "2019 - The clinical and genetic characteristics of permanent neonatal diabetes PNDM in the state of Qatar.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "90268a68-3926-535e-952e-735e206eb3ab", + "extraction_id": "d340209d-0773-5cfc-b0d1-e714f2ebe9f2", + "associatedQuery": "How do genetic mutations in the insulin gene affect glucose metabolism?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIFpDYOJMy59ARMmvejZqYaNW81X4", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The most common genetic loci associated with an increased risk of Type 2 diabetes include:\n\n1. **TCF7L2** [2], [5], [7]\n2. **KCNJ11** [2], [5]\n3. **PPARG** [2], [5]\n4. **CDKAL1** [2], [5], [7]\n5. **CDKN2A/CDKN2B** [2], [5], [7]\n6. **IGF2BP2** [2], [5], [7]\n7. **SLC30A8** [2], [5], [7]\n8. **FTO** [2], [5]\n9. **HHEX** [2], [5], [7]\n\nThese loci have been identified and replicated in multiple studies as being associated with an increased susceptibility to Type 2 diabetes.", + "role": "assistant" + } + } + ], + "created": 1728914099, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 185, + "prompt_tokens": 1627, + "total_tokens": 1812 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "47558743-2803-51a7-856b-8f6606712d08", + "score": 0.7865780846383527, + "metadata": { + "text": "novel risk loci for type 2 diabetes. Nature 2007, 445(7130) :881-885.5. Gaulton KJ, Willer CJ, Li Y, Scott LJ, Conneely KN, Jackson AU, Duren WL,\nChines PS, Narisu N, Bonnycastle LL, et al:Comprehensive association\nstudy of type 2 diabetes and related quantitative traits with 222\ncandidate genes. Diabetes 2008, 57(11) :3136-3144.\n6. Hu C, Zhang R, Wang C, Wang J, Ma X, Lu J, Qin W, Hou X, Bao Y, Xiang K,\net al:PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX,", + "title": "2012 - Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 67, + "document_id": "f5096148-3f85-57c1-8414-2f240ea42068", + "extraction_id": "e61efd89-f844-5c3a-98b9-1a827b58b507", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "score": 0.7847404479980469, + "metadata": { + "text": "ly associated with type 2 diabetes: TCF7L2, KCNJ11, \nand PPARG .\n5-7 However, in 2007, a number of novel \ngenetic variants ( CDKAL1, IGF2BP2, the locus on \nchromosome 9 close to CDKN2A/CDKN2B, FTO, \nHHEX, SLC30A8, and WFS1)8-14 were shown to in -\ncrease susceptibility to type 2 diabetes in repro -\nducible studies. Furthermore, a recent meta-analy -\nsis identified six novel variants ( JAZF1, CDC123/\nCAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia -\nbetes.\n15", + "title": "2008 - Clinical Risk Factors, DNA Variants.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "extraction_id": "2643b341-8c50-5cea-af36-86a8b070a80e", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "225792f4-c56b-5139-8bec-d5d1d393a6b2", + "score": 0.7845839986453071, + "metadata": { + "text": "2009. There are now at least 19 loci containing genes that increase\nrisk of T2D, including PPARG [27], KCNJ11 [27], KCNQ1 [28,29],\nPLoS Genetics | www.plosgenetics.org 1 February 2010 | Volume 6 | Issue 2 | e1000847", + "title": "2010 - A Genome-Wide Association Study Identifies.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "0301881d-40dd-5343-b22e-927d58c2cb2a", + "extraction_id": "f5b0ecdc-fdf2-5ac3-bebb-9c9ff5863935", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "8b718138-167a-50b0-afb7-4b507abc05ff", + "score": 0.7839787006378174, + "metadata": { + "text": "et al. Association between type 2 diabetes loci and measures of fatness. PLoS One 5,\ne8541 (2010).\n22 Ng, M. C., Park, K. S., Oh, B., Tam, C. H., Cho, Y. M., Shin, H. D. et al. Implication of\ngenetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and\nFTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 57,22262233 (2008).\n23 Thorsby, P. M., Midthjell, K., Gjerlaugsen, N., Holmen, J., Hanssen, K. F., Birkeland, K. I.", + "title": "2012 - Association between type 2 diabetes genetic susceptibility loci and visceral and subcutaneous fat area as determined by computed tomography.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "b86d3101-f383-520b-8360-7d80bc7ec6fa", + "extraction_id": "e0bbfc0e-ae79-568c-b704-96febad87d6f", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "e3cbe02b-9a3c-5b66-a5fb-d9d75b5db3f9", + "score": 0.7799915307757718, + "metadata": { + "text": "Genome-wide association studies validated these old culprits of T2D and expanded them to include\nhundreds of single-nucleotide variants (SNVs) that represent more than 150 genomic loci that are\nassociated with T2D, insulin secretion, and insulin resistance [ 11]. Besides TCF7L2 ,PP ARG , and\nKCNJ11 loci, the most replicated T2D susceptibility variants identied in GWASs were found in and\naround CDKN2A/2B ,IGF2BP2 ,SLC30A8 ,CDKAL1 and FTO genes [ 1215]. The variants that are most", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "aba850e8-8c0d-5256-b2ba-fa1dfc221114", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "f3ce8455-f123-5840-8a50-da7885c7e18d", + "score": 0.7798925042152405, + "metadata": { + "text": "Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 2008;40:638-45.\n20. Dupuis J, Langenberg C, Prokopenko \nI, et al. New genetic loci implicated in fasting glucose homeostasis and their im -\npact on type 2 diabetes risk. Nat Genet 2010;42:105-16.\n21. Qi L, Cornelis MC, Kraft P, et al. Ge -\nnetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010;19:2706-15.", + "title": "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "3d629777-f1b6-5450-94ef-56736e5a4e10", + "extraction_id": "8a28c11f-e0d2-526b-ac85-2f2fbf054fc5", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "dfba6b2e-1531-5ac4-a41d-aa4a6d76d7e0", + "score": 0.7754033207893372, + "metadata": { + "text": "multiple loci associated with susceptibility to type 2 diabetes, includ-\ningTCF7L2 (transcription factor 7-like 2), which had been originally\nidentied by a large-scale association mapping prompted by prior\nevidence of linkage in that area2,SLC30A8 (solute carrier family 30\nmember 8), HHEX (haematopoietically expressed homeobox),\nCDKAL1 (CDK5 regulatory subunit associated protein 1-like 1),\nCDKN2A/B (cyclin-dependent kinase inhibitor 2A/B) and IGF2BP2\n(insulin-like growth factor 2 mRNA-binding protein 2)37.", + "title": "2008 - SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "78702b1e-0f14-5757-b967-9bcb7852f6ac", + "extraction_id": "706cb4a1-57c4-5b63-9d4e-4a7ea027a8f1", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "78d81651-7215-596a-b128-37e429dc7edb", + "score": 0.7752246856689453, + "metadata": { + "text": "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 .\n74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are \nassociated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102.\n75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which \ncommon variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. \n76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "title": "2010 - Diabetes in Asia.pdf", + "version": "v0", + "chunk_order": 128, + "document_id": "0be842b8-7f69-503b-baed-c336e5c834d6", + "extraction_id": "11faf4fe-7b71-562e-9901-c428ab20b285", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "b0d3a09d-36a3-5c6e-a110-3fccddaa74b7", + "score": 0.7739214531755884, + "metadata": { + "text": "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3].\nObesity is the greatest risk factor for type 2 diabetes\nmellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF \n \n release, free fatty acids, etc.). Both", + "title": "2003 - Genome-wide screen in obese pedigrees with type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "335a3c08-14d3-5511-ab84-340e64c6f993", + "extraction_id": "1110f7b4-ab5a-5b41-b37d-a992b29cb20c", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + }, + { + "id": "8469faae-c6c9-5fd4-8437-870eef394dd1", + "score": 0.7739064962975443, + "metadata": { + "text": "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes\nn engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in \nthe Botnia subjects (P = 0.006).\nOf the 16 loci that have been associated with \ntype 2 diabetes previously,8-15 we showed that 11 \n TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, \nCDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX \nwere associated with an enhanced risk of future", + "title": "2008 - Clinical Risk Factors, DNA Variants.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "extraction_id": "0aae948a-50f9-568a-b0dc-5960a2d2ceaa", + "associatedQuery": "What are the most common genetic loci associated with an increased risk of Type 2 diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIFpJNprqmrM6nedwSTz4Aw1PacbM", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genome-wide association studies (GWAS) help in identifying diabetes-related genes by examining associations between a large number of single-nucleotide polymorphisms (SNPs) and the disease, such as type 2 diabetes mellitus (T2DM) [1]. GWAS have successfully identified genetic loci associated with T2DM, providing insights into the genetic risk factors and biological pathways underlying the disease [2], [4]. These studies have identified hundreds of SNPs associated with T2DM, which can uncover previously unexpected disease pathways and contribute significantly to our understanding of the genetics of diabetes [5], [9]. Additionally, meta-analyses of GWAS data have improved the ability to detect new loci by combining findings from multiple studies, thereby increasing statistical power [2], [10]. Despite challenges such as the modest effect sizes of individual variants and stringent significance thresholds, GWAS remain a powerful tool for identifying genetic loci for complex diseases like diabetes [7], [10].", + "role": "assistant" + } + } + ], + "created": 1728914105, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 194, + "prompt_tokens": 1163, + "total_tokens": 1357 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b6827ec6-aa43-53e3-8d00-19e802bc3010", + "score": 0.7417203408992395, + "metadata": { + "text": "BMC Medical Genomics 2009, 2:72 http://www.biomedcentral.com/1755-8794/2/72\nPage 2 of 8\n(page number not for citation purposes)Background\nGenome-wide association study (GWAS) offers unbiased\nways to examine association of more than a million singlenucleotide polymorphisms (SNPs) with disease [1]. Sev-eral GWAS have indentified novel genomic regions influ-encing risk for type 2 diabetes mellitus (T2DM) [2-6].However, the challenge remains to prioritize SNPs from", + "title": "2009 - Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "4b1a56e7-6821-5504-b6da-27dcdf57c6a5", + "extraction_id": "e2b46a32-6616-55ad-8511-31ee8f9cce45", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "9abaf02e-eee2-504d-be20-d589cb9a3164", + "score": 0.740890405800653, + "metadata": { + "text": "GWAS have successfully identified genetic loci associ-\nated with a variety of conditions such as type 2 diabetes2\nand coronary disease.35The large number of statistical\ntests required in GWAS poses a special challenge because\nfew studies that have DNA and high-quality phenotypedata are sufficiently large to provide adequate statisticalpower for detecting small to modest effect sizes.\n6Meta-\nanalyses combining previously published findings have im-proved the ability to detect new loci.", + "title": "2009 - Cohorts for Heart and Aging Research in Genomic.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "9534989a-a5a5-52d8-95b8-0ad2926f228c", + "extraction_id": "746e7837-d0f3-5a73-bfef-adfd748e35d6", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "a1e3ca85-6fd1-5364-87c5-442c3f96ba74", + "score": 0.7397869294691324, + "metadata": { + "text": "diabetes mellitus6,7. However, the traditional GWAS ignored a\nlarge number of loci with moderate effects, because of the strin-gent signi cance thresholds used.\nGene-based analysis takes a gene as a basic unit for\nassociation analysis. As this method can combine genetic\ninformation given by all the SNPs in a gene to obtain moreinformative results\n8, it is being used as a novel method com-\nplementing SNP-based GWAS to identify disease susceptibilitygenes. Notably, this method can increase our chance of nd-", + "title": "2014 - Identification of novel risk genes associated with type 1 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "97fe33b0-a6c7-59b6-bd34-05528e77293f", + "extraction_id": "4b1681f4-4088-5b15-a704-040e35e31080", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "263ea999-9662-5518-a606-939f69d09f90", + "score": 0.7390662651975833, + "metadata": { + "text": "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk \nfactors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par -\nticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "title": "2020 - Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "5dd7d700-03db-595d-b1a5-beca77f9579e", + "extraction_id": "2c601441-443d-5c47-95bb-6343378dd5dc", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "53c3668c-95f8-5fb9-b978-e4c03ddfa40f", + "score": 0.7377198714697428, + "metadata": { + "text": "that a genome-wide approach could uncover \npreviously unexpected disease pathways.\nIn early 2007, GW AS provided by far the \nbiggest increment to date in our knowledge of \nthe genetics of this common health problem.\nSix new gene regions identified\nTogether, the six recent GW AS papers \nprovide convincing evidence for six new \ngene regions involved in type\n \n2 diabetes1621; \na seventh publication describes how one \nof these variants alters BMI and represents by far the best example of an association", + "title": "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "extraction_id": "aa94128a-99f6-59f3-b5fa-33ac97b858d5", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "7fd80e84-ec0c-564c-8e8b-278b8c622abb", + "score": 0.734627821009525, + "metadata": { + "text": "Abstract\nGenome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a\ncommon complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target\ntissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories", + "title": "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "extraction_id": "9369222f-e125-58c0-8f2b-cf5daa867f77", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "9afcf9a9-3abf-5441-a711-55e25f1ef9b7", + "score": 0.733059688392859, + "metadata": { + "text": "mechanisms of DR remain poorly understood.\nA genome-wide association study (GWAS) is a powerful tool\nto identify genetic loci for complex diseases, and a large number\nof genetic loci for the susceptibility to various diseases, such astype 2 diabetes, have been successfully identified through GWAS\n(69). GWAS for DR have been performed, but most of the studies\nonly reported suggestive signals with no replication ( 5)b e c a u s e\nof their limited sample sizes. Recently, several loci with genome-", + "title": "2021 - Genome-wide association studies identify two novel loci.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "7131256d-7d55-597d-aac5-a62956736923", + "extraction_id": "fc9812ae-7b35-5dac-af9b-6d60f4faaa54", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "ad7955f2-824c-59f8-8357-6ee201756ec9", + "score": 0.7320249894864858, + "metadata": { + "text": "kidney disease, several loci have been identi ed and validated,\nbut the results were quite heterogenic across different popula-\ntions and depended on the type of diabetes and stage of disease.\nThe major bene t of GWAS results is to be found in the in-\ncreased understanding of disease mechanism and identi ca-\ntion of novel pathways and possibly new therapeutic targets.Follow-up studies are important in order to identify variants\nwith speci c biological effect and may provide important", + "title": "2015 - Genome-wide studies to identify risk factors for kidney disease.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "3e696b99-6306-5429-bce9-8d04a2471b2d", + "extraction_id": "92bd58f8-6770-5c1c-8202-19b08bd57df8", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "5488da5b-5efa-55cd-92c3-a0d77e587fce", + "score": 0.7298565128402866, + "metadata": { + "text": "Abstract\nGenome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally per -", + "title": "2020 - Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease..pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "f0385a45-ad3e-5813-ab1f-b3e227d5164b", + "extraction_id": "2341dbc6-8084-5d51-a52e-f8f667b79bbb", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + }, + { + "id": "7f17fa56-1b7a-5d51-a111-3c74b31a5821", + "score": 0.7289577343749328, + "metadata": { + "text": "linkage or association data. But, none of these studies include in\nthe analysis existing data from GWAs.\nFinally, a recent study identied additional susceptibility loci\nfor type 2 diabetes by performing a meta-analysis of three\npublished GWAs.21As acknowledged by the authors, GWAs\nare limited by the modest effect sizes of individual common\nvariants and the need for stringent statistical thresholds. Thus,\nby combining data involving 10,128 samples, the authors found", + "title": "2009 - Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 63, + "document_id": "0fd2b5c8-9bda-5cc8-adb4-231d3842d50f", + "extraction_id": "0c5401ea-2a43-5578-af0b-6ad1e818fa42", + "associatedQuery": "How does genome-wide association studies (GWAS) help in identifying diabetes-related genes?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIFpPxGF7qh3CvkVYK1PXW0J3bHPe", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The HLA region plays a significant role in the genetic predisposition to Type 1 diabetes. The major genetic susceptibility determinants for Type 1 diabetes have been mapped to the MHC class II genes, specifically HLA-DQB1 and HLA-DRB1 [7]. These genes are part of the highly polymorphic HLA class II genes, which play the most important single role in susceptibility to Type 1 diabetes [3]. The class II genes encoding HLA-DR and HLA-DQ, along with one or more additional genes within the HLA region, confer most of the genetic risk for Type 1 diabetes [4]. The contribution of HLA genes to Type 1 diabetes susceptibility accounts for about 44% of the genetic risk [6]. However, variation at these loci alone cannot explain all of the genetic association and linkage of the MHC with Type 1 diabetes, indicating that other genes within the MHC region may also affect Type 1 diabetes risk [3].", + "role": "assistant" + } + } + ], + "created": 1728914111, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 201, + "prompt_tokens": 1519, + "total_tokens": 1720 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "f654b4ad-31d8-528c-800d-000a0f56438a", + "score": 0.7919327020645142, + "metadata": { + "text": "conferred by specic alleles, genotypes, and haplotypes ofthe HLA class II (and class I) genes. There are currentlyabout 50 non-HLA region loci that also affect the type 1diabetes risk. Many of the assumed functions of thenon-HLA genes of interest suggest that variants at theseloci act in concert on the adaptive and innate immunesystems to initiate, magnify, and perpetuate /H9252-cell destruc-", + "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "version": "v0", + "chunk_order": 118, + "document_id": "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "extraction_id": "c584fa40-ecb0-57e0-8bd1-6761ae3b4d27", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "8480cb40-fb38-5f5e-98c5-41eb2b3bdad2", + "score": 0.7839834094047546, + "metadata": { + "text": "II HLA gene associated with type 1 diabetes maps to the 240-kbregion near HLA-B. Diabetes 49: 22172221, 2000.\n303. Nejentsev S, Howson JM, Walker NM, Szeszko J, Field SF.\nLocalization of type 1 diabetes susceptibility to the MHC class Igenes HLA-B and HLA-A. Nature 450: 887892, 2007.\n304. Nejentsev S, Walker N, Riches D, Egholm M, Todd JA. Rare\nvariants of IFIH1, a gene implicated in antiviral responses, protectagainst type 1 diabetes. Science 324: 387389, 2009.", + "title": "2011 - Type 1 Diabetes Etiology, Immunology.pdf", + "version": "v0", + "chunk_order": 535, + "document_id": "3c9823cd-3615-53b6-96c8-b7d2123d3eb0", + "extraction_id": "36ba6e4a-46c7-5a84-89ca-2b6e2a3c2a2c", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "5157af28-d09f-5bbb-8984-61da49bed642", + "score": 0.7792037725448608, + "metadata": { + "text": "Although the highly polymorphic HLA class II genesclearly play the most important single role in susceptibilityto type 1 diabetes, variation at these loci alone cannotexplain all of the evidence of genetic association andlinkage of the MHC with type 1 diabetes. To better denegenes within the MHC that may affect type 1 diabetes riskand would therefore merit further studies, the T1DGCundertook a comprehensive study of the genetics of theclassic 4-Mb MHC region. More than 3,000 SNPs and 66microsatellite", + "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "version": "v0", + "chunk_order": 21, + "document_id": "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "extraction_id": "508a0315-d23a-506d-bf66-b5c355576f96", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "5c650a7a-98f1-5119-b66f-5a93db18faec", + "score": 0.77757328748703, + "metadata": { + "text": "age to type 1 diabetes in the HLA region and suggestive evidence at a small number of other regions in the genome. In general, the emerging picture from linkage studies is that the class II genes encoding HLA-DR and HLA-DQ, as well as one or more additional genes within the HLA re -\ngion, confer most of the genetic risk for type 1 dia -\nbetes. Genes outside the HLA region also con -\ntribute to the risk of type 1 diabetes, but their individual contributions are much smaller than that of HLA.", + "title": "2009 - Genetics of Type 1A Diabetes.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "7a98f456-6c43-5e9e-b404-31122159eab8", + "extraction_id": "79b78db5-d77a-51bd-84ff-14b558534df8", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "0fa5241d-e039-55b3-ba8c-aa14d0125967", + "score": 0.7742921113967896, + "metadata": { + "text": "Benkalha and Polychronakos, 2008 ). Other genetic loci ( Table 1) are\nbelieved to in uence population-level risk for T1D, although it is poorly\nunderstood how these non-HLA loci contribute to disease susceptibility\n(Ram et al., 2016a ).\n2.1. Human leukocyte antigen (HLA)\nThe association between T1D and the HLA complex was rst de-\nmonstrated in 1973 following observation of an increased frequency ofHL-W15 (HLA antigen) in T1D patients compared to controls ( Singal", + "title": "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "extraction_id": "65402f67-0066-5d04-98c6-5e7b16087495", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "f159c8b5-357c-57f6-98e4-5d5436f59925", + "score": 0.7736279964447021, + "metadata": { + "text": "cyte Antigen (HLA) gene region in immune regulation, and ready availability of\nserologic markers, led investigators to discover the association between certainHLAalleles and T1D in the early 1970s (33,130,158). The global importance of\ntheHLAonT1Dhassincebeenconrmedingenome-widescansforlinkage:All\nsuchscansperformedtodateshowamajorlocusatthe HLA(28,32,36,78,119).\nThefractionofallgeneticrisk,whichcanbeattributedtothecontributionof HLA\ngenes to T1D susceptibility, is about 44%, with a \nSof3.4 (160).", + "title": "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 26, + "document_id": "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "extraction_id": "548d7a8d-5314-53f3-b646-108a4b1ac2f2", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "277e2627-b99d-5b35-ae45-1fbaa2bf0710", + "score": 0.7721036076545715, + "metadata": { + "text": "The major histocompatibility complex (MHC) on chromosome 6\nis associated with susceptibility to more common diseases than\nany other region of the human genome, including almost all dis-\norders classified as autoimmune. In type 1 diabetes the major\ngenetic susceptibility determinants have been mapped to the\nMHC class II genes HLA-DQB1 andHLA-DRB1 (refs 13), but\nthese genes cannot completely explain the association between\ntype 1 diabetes and the MHC region411.Owing to the regions", + "title": "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "3887995f-fa61-5472-b0a2-90b7b39592c2", + "extraction_id": "0ed848eb-6e55-5fab-82d0-f10b3c83d98f", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "69fb55b3-37a8-5fb6-9916-2ab5be15a0a8", + "score": 0.7700294256210327, + "metadata": { + "text": "The HLA class I A locus a ects susceptibility to type 1 diabetes. Hum. Immunol. 63,\n657 664. pii). https://doi.org/S0198885902004214 .\nNoble, J.A., Valdes, A.M., Cook, M., Klitz, W., Thomson, G., Erlich, H.A., 1996. The role of\nHLA class II genes in insulin-dependent diabetes mellitus: molecular analysis of 180\nCaucasian, multiplex families. Am. J. Hum. Genet. 59, 1134 1148 .\nNoble, J.A., Valdes, A.M., Thomson, G., Erlich, H.A., 2000. The HLA class II locus DPB1", + "title": "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 147, + "document_id": "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "extraction_id": "dd2e688a-2f06-5911-9eb8-13c5d935885e", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "3ccbc6c4-a2a3-53ab-b904-c4d5875e2e2c", + "score": 0.7676813006401062, + "metadata": { + "text": "to type 1diabetes susceptibility, including within the MHC itself.Currently, there are over 50 non-HLA regions that signi-cantly affect the risk for type 1 diabetes (http://www.t1dbase.org). Many of these regions contain interesting,but previously unrecognized, candidate genes. A few re-gions contain genes of unknown function or no knownannotated genes, suggesting roles for long-distance generegulatory effects, noncoding RNAs, or unknown mecha-nisms. Against a background of ever-improving knowledgeof the", + "title": "2010 - Genetics of Type 1 Diabetes What\u2019s Next.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "261cbb40-ed6b-554c-a70d-db6b9f14cf74", + "extraction_id": "3e3ff61a-86ee-5e2a-9b14-9aa3b06937ff", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + }, + { + "id": "2b1f2a05-4693-595c-94c0-fea40e19539c", + "score": 0.7664471864700317, + "metadata": { + "text": "the 240-kb region near HLA-B. Diabetes 49,22172221 (2000).\n6. Lie, B. A. et al. The predisposition to type 1 diabetes linked to the human leukocyte\nantigen complex includes at least one non-class II gene. Am. J. Hum. Genet. 64,\n793800 (1999).\n7. Valdes, A. M. et al. Extended DR3 D6S273-HLA-B haplotypes are associated\nwith increased susceptibility to type 1 diabetes in US Caucasians. Tissue Antigens\n65,115119 (2005).\n8. Valdes, A. M., Erlich, H. A. & Noble, J. A. Human leukocyte antigen class I B and C", + "title": "2007 - Localization of type 1 diabetes susceptibility to the MHC Class 1 Genes.pdf", + "version": "v0", + "chunk_order": 49, + "document_id": "3887995f-fa61-5472-b0a2-90b7b39592c2", + "extraction_id": "92a54171-9f94-51ea-83cb-11698b1f0c21", + "associatedQuery": "What is the role of the HLA region in the genetic predisposition to Type 1 diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIFpUuEUTWxzzcta8xK3fjxfSUNPx", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genetic differences contribute to variations in diabetes prevalence among different populations in several ways:\n\n1. **Genetic Heterogeneity**: Different genes may be responsible for the development of type 2 diabetes mellitus (T2DM) in different populations, indicating genetic heterogeneity. Even within the same ethnic group, different genes might be responsible for different subtypes of diabetes, such as those with predominating failure in insulin secretion or insulin resistance [3].\n\n2. **Ethnicity-Specific Genetic Variants**: The pathogenesis of T2DM involves genetic variants in candidate genes, and these variants can show ethnicity-specific differences. The interactions between genes involved in insulin signaling and secretory pathways play a crucial role in determining an individual's susceptibility to T2DM [4].\n\n3. **Single Nucleotide Polymorphisms (SNPs) and Haplotypes**: Minute genetic differences, such as SNPs and haplotypes, are used to explain patterns of disease between populations. These genetic markers are understood as the basis for biological differences between populations, which can affect diabetes prevalence [5].\n\n4. **Risk Allele Frequencies**: Human populations exhibit substantial variation in risk allele frequencies, which contributes to different susceptibilities to diseases, including diabetes, among populations [7].\n\n5. **Insulin Secretion Capacity**: In some populations, such as the Asian population, genetic influences on the prevalence of type 2 diabetes are mainly related to insulin secretion capacity. This suggests that genes associated with insulin secretion capacity are particularly relevant in contributing to ethnic differences in diabetes prevalence [10].\n\nOverall, genetic differences, including variations in specific genes, SNPs, and risk allele frequencies, contribute to the observed variations in diabetes prevalence among different populations. These genetic factors interact with environmental and lifestyle factors to influence diabetes risk.", + "role": "assistant" + } + } + ], + "created": 1728914116, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 361, + "prompt_tokens": 1133, + "total_tokens": 1494 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "49748fe8-4351-5cd1-8367-957a160a59d9", + "score": 0.7598252522893328, + "metadata": { + "text": "of diabetes when compared to the native population while not necessar-ily different from populations where they origi-nate from. Risk factors for diabetes appear to be similar between populations, mostly insulin resistance, obesity, and sedentary lifestyle with possible genetic differences contributing to the increased susceptibility. Some data suggest a greater prevalence of microvascular complica-", + "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "version": "v0", + "chunk_order": 2135, + "document_id": "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "extraction_id": "61fb4dd8-1428-5add-8c41-9ec2459ffd5a", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "80ad1f9c-4f67-5a68-9446-1f692b23f324", + "score": 0.7463788167721416, + "metadata": { + "text": "nants of type 2 diabetes between immigrant and native populations. Some studies in South Asian (Indian) populations suggest that genetic differ-ences may exist [ \n17 , 30 ], but larger studies are \nneeded to get better insight into this issue. \n Prevalence Estimates \n The prevalence of diabetes in minorities is affected by ethnicity and country of residence. In one study in the UK [ \n59 ], standardized preva-", + "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "version": "v0", + "chunk_order": 2155, + "document_id": "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "extraction_id": "090365f1-32e0-5adc-b589-b9331e0630a0", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "5fd9c60a-410f-5782-90a9-03d377a5f72b", + "score": 0.7383460182444477, + "metadata": { + "text": "majority of cases it is difficult to replicate the findingsin other populations. One of the major problems in thesearch for genes responsible for common forms ofdiabetes is the genetic heterogeneity of the diseasewith different genes responsible for the developmentof T2DM in different populations. Furthermore, evenwithin the same ethnic group, different genes may beresponsible for different subtypes of diabetes (for in-stance with predominating failure in insulin secretionor insulin resistance). This is", + "title": "2005 - Type 2 diabetes mellitus from genes to disease.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "52687a38-6a4b-51d2-aafa-812c76981dfe", + "extraction_id": "73278198-67af-5556-9414-86580dd07c48", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "d02a16ce-c62e-537d-9d32-266018c70415", + "score": 0.7162386392426089, + "metadata": { + "text": "across different races or populations but show ethnicity-\nspecific differences. The pathogenesis of T2D involves \ngenetic variants in the candidate genes. The interactions \nbetween the genes involved in insulin signaling and secre -\ntory pathways are believed to play an important role in \ndetermining an individuals susceptibility towards T2D. \nTherefore, the present study was initiated to examine the \ndifferences, if any, in the contribution of polymorphisms", + "title": "2016 - Association of genetic variants in INS (rs689), INSR (rs1799816) and PP1G.G (rs1799999) with type 2 diabetes (T2D) a case\u2013control study in three ethnic groups from N.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "5fe7c5f4-a209-56be-8504-c08073335c3b", + "extraction_id": "4cbd4dfc-da8e-5432-b844-5f70d6f3811d", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "684d1e26-b78a-5dde-b405-a79ee28087c3", + "score": 0.714162013356746, + "metadata": { + "text": "That is, the minute genetic differences discernable with SNPs, patterns of single nu-cleotides (A,G,T ,C), and other mutation analysis technologies are now used to explainpatterns of disease between populations, which are in turn understood as the basisfor biological differences between the populations themselves. The case of diabetesgenetics research affords a more nuanced look at what is labeled genetic determinism.It is evident in diabetes research that SNPs and haplotypes, (an inherited pattern of\n99", + "title": "2007 - Bioethnic Conscription Genes, Race.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "d90126d9-fd87-5b38-87f7-08415f690836", + "extraction_id": "95f0e6f8-da7d-5997-ab8a-a1aad020c706", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "8445ab0a-2287-5537-ab3a-cb058205e944", + "score": 0.7116393871092431, + "metadata": { + "text": "-\ntion for disease classification. This genetic component may be specifically important when understanding the pathogenesis of diabetes in ethnic groups, when BMI [14, 15] and HbA1c [16] show distinct differences between ethnicities. Though applying patient-matched, genomic information is currently unrealistic for disease diagnosis, it may hold the key for revealing commonalities across ethnic and demographic groups when classifying diabetic onset, progression, and severity.", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 21, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "8d323598-fdf7-56cf-8290-be85929f0eaf", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "10c1db42-f724-5885-99e0-7637dfce63ca", + "score": 0.7018723697028273, + "metadata": { + "text": "particularly useful for understanding differences in dis-ease prevalence and drug response among differentpopulations. There is ample evidence that human popu-lations have different susceptibility to diseases, exhibit-ing substantial variation in risk allele frequencies [1].For example, genetic predisposition to asthma differsamong the differentially-admixed Hispanic populations\nof the United States, with the highest prevalence\nobserved in Puerto Ricans. Ge netic variants responsible", + "title": "2011 - Dating the age of admixture via wavelet.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "786cebc5-c3cc-586e-bdc0-e7bee67edc19", + "extraction_id": "a5c137e5-84d2-5d75-8191-fa6b0be3d39e", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "d29cdd31-d214-52cf-b236-be4de1182b26", + "score": 0.7005400866431026, + "metadata": { + "text": "populations and across countries. World-wide differences in prevalence of theforms of diabetes necessitates inclusion\nof currently understudied populationsfor the development of precision diag-nostics and therapeutics. As a result, theprecise subtype of diabetes a particularindividual is diagnosed with may vary indifferent populations based on subtypefrequency or genetic or dietary or life-style differences.\nThe communication strategy used by\nthe interventionalist and the patient s", + "title": "2020 - Precision Medicine in Diabetes.pdf", + "version": "v0", + "chunk_order": 182, + "document_id": "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "extraction_id": "9dc25bb6-787b-5e7a-af5d-d1353d122959", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "6fd138d2-6960-55fd-b656-05f4e84a0c6d", + "score": 0.6991221696400024, + "metadata": { + "text": "were positively associated with country level income [49].\nHowever, the drivers for the observed pattern with geographi-\ncal differences and varying time trends are still unclear.\nSusceptibility to type 1 diabetes denitely has a strong genetic\ncomponent (HLA genotype) [50], but the heterogeneity of type\n1 diabetes cannot be explained solely by the prevalence of\nsusceptibility genes [5153] . Thus, the reasons for changes in", + "title": "2014 - Diabetes in Europe An update.pdf", + "version": "v0", + "chunk_order": 35, + "document_id": "81e1fc53-6768-590f-9b47-9a5105b6ddb5", + "extraction_id": "fa58324a-e5b7-538e-9cbb-0549887a2154", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + }, + { + "id": "2771c343-be7b-51a2-a598-235647357416", + "score": 0.6972634611882842, + "metadata": { + "text": "twice higher than that of 2010 [3] .\n The genetic influences on the prevalence of type 2 diabetes i n the Asian population are \nmainly related to insulin secretion capacity [4] ; other genes involved in the risk of type 2 \ndiabetes are not substantially different in other ethnic groups [5] . The most relevant genes \ncontributing to ethnic differences are associated with insulin secretion capacity, and they are", + "title": "2016 - TRPV1 Gene Polymorphisms Are Associated with Type 2 Diabetes by Their Interaction with Fat Consumption in the Korean Genome Epidemiology Study.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "521db985-2ce8-56c3-aed7-b38ef41cce45", + "extraction_id": "8276c974-f60b-5f59-943d-94a635160d1d", + "associatedQuery": "How do genetic differences contribute to variations in diabetes prevalence among different populations?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIFpdRa9QE6LvKot0urXoNDlpAF5x", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The PPAR-gamma gene encodes a transcription factor that plays a crucial role in regulating insulin sensitivity and lipid metabolism, which are important in the context of diabetes [4]. PPAR-gamma influences insulin sensitivity partly by modulating circulating adiponectin levels, with PPAR-gamma agonists known to increase these levels [1]. This gene is a target for the thiazolidinedione class of oral antidiabetic agents, which are used to improve insulin sensitivity in patients with type 2 diabetes [4].\n\nVariants of the PPAR-gamma gene can significantly impact the risk and progression of type 2 diabetes. The Pro12Ala variant, for example, is a well-established genetic variant that modulates insulin sensitivity and increases the risk of type 2 diabetes [1], [4]. Other pathogenic PPARG variants, such as R194Q, A417V, R212W, P387S, M203I, and T356R, have been identified in patients with partial lipodystrophy and are associated with an increased risk of type 2 diabetes [7]. These variants can affect the gene's function in adipocyte differentiation and metabolism, thereby influencing the development of insulin resistance and type 2 diabetes [10].", + "role": "assistant" + } + } + ], + "created": 1728914125, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_e5e4913e83", + "usage": { + "completion_tokens": 250, + "prompt_tokens": 1427, + "total_tokens": 1677 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "6f046969-4e26-5dee-a310-cf32dc1f799c", + "score": 0.741815999638082, + "metadata": { + "text": "The transcription factor peroxisome-proliferator-\nactivated receptor gamma (PPAR g) is known to\ninuence insulin sensitivity, and acts partly via amodulation of the circulating adiponectin level\n(PPAR gagonists increase the adiponectin level)\n(Ref. 38). The PPAR gP12A SNP is a well-\nestablished genetic variant that modulates\ninsulin sensitivity and the risk of type 2 diabetes\n(Ref. 39). In a Chinese family study, Yang et al.demonstrated a genetic interaction between the", + "title": "2006 - Adiponectin, type 2 diabetes and the metabolic syndrome.pdf", + "version": "v0", + "chunk_order": 35, + "document_id": "6a46f7cf-e75b-5b72-b77b-7e0cc03f92d8", + "extraction_id": "4647b43a-e4a0-5e8a-9cf5-6bf33cd6e672", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "c909cc5c-6fdb-5646-8332-973a92ac9486", + "score": 0.7338819066356489, + "metadata": { + "text": "intricate regulation of PPAR signaling to pave the way to tailored therapies in patients with insulin resistance and T2D.\nKeywords PPARG genetic variants .Dominant-negative isoforms .Post-tranlational modifications .Adipose tissue\ndysfunctions .Drug responsiveness .Type 2 diabetes\nIntroduction\nPeroxisome proliferator activated receptor gamma (PPAR )\nis a ligand-activated transcription factor belonging to the nu-", + "title": "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "4ea83190-476d-5090-a461-abde1adccbc5", + "extraction_id": "2d610953-ea5c-5c01-ad19-60c607383da4", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "c3ac7ed2-1b42-5c87-9104-b6da2e33b30b", + "score": 0.7244109295484176, + "metadata": { + "text": "2 . A widespread \nGly482Ser polymorphism of PGC1 - (known as PPARGC1 ), a \ntranscriptional coactivator of a series of nuclear receptors includ-ing PPARG , has been associated with a 1.34 genotype relative risk \nof T2DM [93] . In this study, a test for interaction with the Pro12Ala variant in PPARG gave no indication for additive effects \non diabetes status. \n Other genes have been shown to be implicated in the genetic", + "title": "2010 - The Genetics of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 91, + "document_id": "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "extraction_id": "1df8f645-85c4-5832-8142-09bacafcd01d", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "02a160ba-95ee-5aa9-bc45-445b4706715b", + "score": 0.721716182129612, + "metadata": { + "text": "PPARG Peroxisome proliferator-activated receptor- gene. This gene is located on chromosome 3p25, and has been studied as a candidate genefor type 2 diabetes based on its role in adipocyte and lipid metabolism. The Pro12Ala variant in particular has been associated with adecrease in insulin sensitivity and a several-fold increased risk of type 2 diabetes. PPAR is a target for the thiazolidinedione class of oralantidiabetic agents", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 32, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "f8b79de5-3e0c-5495-b6c2-8a3be6138223", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "4e415210-bf41-542f-841c-4bb17622d2e6", + "score": 0.720844106623883, + "metadata": { + "text": "Genetic variation in the peroxisome proliferator-activated receptor (PPAR) and peroxisome proliferator-activated receptor gamma co-activator 1 (PGC1) gene families and type 2 diabetes. Ann Hum Genet 78:2332\nVimaleswaran KS, Radha V, Ghosh S, Majumder PP, Deepa R, Babu \nHN etal (2005) Peroxisome proliferator-activated receptor-gamma \nco-activator-1alpha (PGC-1alpha) gene polymorphisms and their \nrelationship to type 2 diabetes in Asian Indians. Diabetic Med 22:15161521", + "title": "2018 - Association of PGC-1\u03b1 gene with type 2 diabetes in three unrelated endogamous groups of North-West India (Punjab) a case-control and meta-analysis study.pdf", + "version": "v0", + "chunk_order": 116, + "document_id": "2faa21d2-146e-568a-b881-63201819e99a", + "extraction_id": "94ee1317-d606-5921-8175-a86da2fa95d6", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "8d7fb270-e23f-5d89-b75c-50b8fbd22fe8", + "score": 0.7202155160775486, + "metadata": { + "text": "Dali-Youcef N, et al. The Pro12Ala PPARgamma2 variant deter-\nmines metabolism at the gene-environment interface. Cell Metab.\n2009;9:88 98.\n53. Agostini M, Schoenmakers E, Mitchell C, Szatmari I, Savage D,\nSmith A, et al. Non-DNA binding, dominant-negative, human\nPPARgamma mutations cause lipodystrophic insulin resistance.\nCell Metab. 2006;4:303 11.\n54. Agostini M, Gurnell M, Savage DB, Wood EM, Smith AG,\nRajanayagam O, et al. Tyrosine agonists reverse the molecular", + "title": "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "4ea83190-476d-5090-a461-abde1adccbc5", + "extraction_id": "02cdfa1b-cc8f-5141-bde0-1079d252c6e8", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "9f62a8cf-a14f-5989-a899-cf1f525905bf", + "score": 0.719792144310944, + "metadata": { + "text": "associated with a marked increase in T2D risk in the general\npopulation, schematized in Fig. 1. The latter systematically\ntested all the possible PPAR protein variants by using a\nlarge-scale pooled functional assay based on a human macro-\nphage cell line. Using these in vitro data to train a classifier by\nsupervised machine learning, they identified six pathogenic\nPPARG variants (R194Q, A417V, R212W, P387S, M203I,\nand T356R) in patients with partial lipodystrophy [ 109].", + "title": "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "4ea83190-476d-5090-a461-abde1adccbc5", + "extraction_id": "4bdd6cdb-1f2a-585f-b08e-392a54c6dad8", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "818c1d6b-c1c1-570d-9e7a-87449fae279a", + "score": 0.7187401056289724, + "metadata": { + "text": "lipid metabolism, as well as insulin sensitivity and inflammatory pathways. These pleiotropic functions confer great relevance to\nPPAR in physiological regulation of whole-body metabolism, as well as in the etiology of metabolic disorders. Accordingly,\nPPARG gene mutations, nucleotide variations, and post-translational modifications have been associated with adipose tissue\ndisorders and the related risk of insulin resistance and type 2 diabetes (T2D). Moreover, PPAR alternative splicing isoforms", + "title": "2021 - PPAR\u03b3 and Diabetes Beyond the Genome and Towards Personalized Medicine.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "4ea83190-476d-5090-a461-abde1adccbc5", + "extraction_id": "2d610953-ea5c-5c01-ad19-60c607383da4", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "793e2430-fa2b-513a-a4ab-0c85a167de3f", + "score": 0.7186153102746433, + "metadata": { + "text": "the PPARgamma locus. Diabetes 2001;50:686 689\n12. Kahara T, Takamura T, Hayakawa T, et al.\nPPARgamma gene polymorphism is as-sociated with exercise-mediated changes\nof insulin resistance in healthy men. Me-\ntabolism 2003;52:209 212\n13. Franks PW, Luan J, Browne PO, et al.\nDoes peroxisome proliferator-activated\nreceptor gamma genotype (Pro12ala)\nmodify the association of physical activityand dietary fat with fasting insulin level?\nMetabolism 2004;53:11 16\n14. Memisoglu A, Hu FB, Hankinson SE, et al.", + "title": "2013 - Gene-Environment and Gene-Treatment.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "fe958fb1-5408-56ec-b102-ccf07b4bac2d", + "extraction_id": "a6b92963-2cf0-51a4-8686-ce3a7515d443", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + }, + { + "id": "7c375d6d-672d-594c-a56e-7391ed3e9daa", + "score": 0.7166055227337651, + "metadata": { + "text": "30. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in \nadipocyte differentiation are associated with increased risk of type 2 diabetes. \nProc Natl Acad Sci USA 111, 1312713132 (2014).\n 31. Majithia, A. R. et al. Prospective functional classification of all \npossible missense variants in PPARG . Nat. Genet. 48, 15701575 (2016).\n 32. Claussnitzer, M. et al. Leveraging cross-species transcription factor binding", + "title": "2018 - Refining the accuracy of validated target identification through coding variant.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "3362e616-f824-55fa-9b4d-3ee8dcf52ac0", + "extraction_id": "d96545e5-f3a0-5765-9b06-27a41219d3b9", + "associatedQuery": "What is the function of the PPAR-gamma gene in diabetes, and how do its variants impact the disease?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIFppDyOUKllFXSAk1UvPBBd5ythq", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "CRISPR/Cas9 technology can be used to study or treat genetic forms of diabetes in several ways:\n\n1. **Genome and Epigenome Editing**: CRISPR/Cas9 has revolutionized the ability to modify genomes and epigenomes, allowing researchers to target specific genes associated with Type 2 Diabetes (T2D) and manipulate cis-regulatory elements related to beta cell function and glucose homeostasis [1].\n\n2. **Gene Knockout Studies**: CRISPR has been used to knock out genes by introducing frameshift mutations, which result in protein depletion. This approach has been adopted to study several genes in beta cell lines and human embryonic stem cell-derived beta cells, providing insights into the genetic basis of diabetes [4].\n\n3. **Correction of Point Mutations**: CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method for this is CRISPR/Cas9-based homology-directed repair (HDR), which involves generating a Cas9-mediated cut near the mutation site and using a homologous donor template to introduce the intended nucleotide change [5].\n\n4. **Disease Modeling**: CRISPR/Cas9 can be employed in human pluripotent stem cells (hPSCs) to model diabetes and metabolic syndromes in vitro. This allows researchers to study the disease mechanisms and test potential treatments in a controlled environment [3].\n\n5. **Animal Models**: CRISPR/Cas9 has been used to create knockout and knock-in mouse models, such as leptin and leptin receptor knockout mice, which serve as tools for diabetes and obesity research. These models help in understanding the genetic factors contributing to diabetes and testing therapeutic interventions [7].\n\nOverall, CRISPR/Cas9 technology provides a powerful platform for both studying the genetic underpinnings of diabetes and developing potential gene therapies to treat the disease.", + "role": "assistant" + } + } + ], + "created": 1728914137, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 396, + "prompt_tokens": 1358, + "total_tokens": 1754 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "f42c0f84-d2a8-5bf9-89c2-3dd182bfb235", + "score": 0.7536211609840393, + "metadata": { + "text": "A variety of cellular and animal models have been developed and applied\nover the past few years to experimentally manipulate cis-regulatory\nelements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at\nwill. Unsurprisingly, CRISPR (epi)genome editing tools can and have\nbeen used to target putative T2D target genes [54] orcis-REs[55] in beta", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 79, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "57736895-897e-54e5-a735-aadcbd77cb63", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "1859f32b-8f5c-5c3c-9f4d-54193d37645d", + "score": 0.7519766836180293, + "metadata": { + "text": "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes.\nKeywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young,\nstem cells, mouse models\nINTRODUCTION\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise\nto 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "5f8a0ddd-a0c7-5151-9b6a-e0980bb94aa6", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "df30dab3-a490-5497-a079-2741f9039f87", + "score": 0.7391794919967651, + "metadata": { + "text": "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin\ngenes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is\nincreasingly becoming the favored choice for genome editing inhPSCs [16,52] .\n3.2. Employing hPSCs and genome editing tools to study diabetes\nand metabolic syndromes\nIn general, the strategy to carry out in vitro disease modeling of dia-", + "title": "2016 - Dissecting diabetes metabolic disease.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "eee2f79d-e093-52fb-871a-798fd859235e", + "extraction_id": "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "eadf2320-de70-5499-ade0-7aa9930ac091", + "score": 0.734539270401001, + "metadata": { + "text": "Due to its simplicity and adaptability, CRISPR has rapidly\nbecome the most popular genome editing tool available for the\nmammalian genome ( 50,63). Because NHEJ DNA repair often\nintroduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the\ndiabetes eld, CRISPR has also been adopted to study several\ngenes in bcell lines and in human ES-derived bcells ( 21,151,", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "99ccc9a2-865f-5d11-9b08-b26261d02fc9", + "score": 0.7254999443590687, + "metadata": { + "text": "samples ( 236).\nCRISPR technology has been used recently to correct point\nmutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC\nis CRISPR/Cas9-based homology-directed repair (HDR). Here, a\nCas9-mediated cut is generated adjacent to the site of interest. A\nhomologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "0a3e3095-4789-505a-96b7-123a05078e95", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "1f114642-3f77-5346-89e8-394c433f66ff", + "score": 0.722271203994751, + "metadata": { + "text": "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model\n(230,231). However, the use of iPSC is controversial and there\nare some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275).\nManipulation of hESC/iPSC cells via CRISPR-Cas9\ntechnology provides a platform for the correction of genomic\nmutations not only in diabetes but in other disease elds as well", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 139, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "57b9550d-0258-5a87-be57-976f471e5763", + "score": 0.7202445101883729, + "metadata": { + "text": "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265).\nUsing CRISPR-Cas9, leptin and leptin receptor knockout mice\nhave been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via\nHDR to achieve cell-speci c deletion of the gene ( 266).\nGenome Editing: Clinical Application\nin Diabetes\nAn important goal in genetic research is to identify the genetic", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 133, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "4b170851-2dbd-5c06-9e3a-188d30a00170", + "score": 0.7056117233269392, + "metadata": { + "text": "CRISPR-Cas9 epigenome editing enables high-throughput screening for functionalregulatory elements in the human genome. Nature Biotechnology 35(6):561 e568.\n[58] Hodson, D.J., Mitchell, R.K., Marselli, L., Pullen, T.J., Gimeno Brias, S.,\nSemplici, F., et al., 2014. ADCY5 couples glucose to insulin secretion in humanislets. Diabetes 63(9):3009 e3021 .\n[59] Zhou, Y., Park, S.-Y., Su, J., Bailey, K., Ottosson-Laakso, E., Shcherbina, L.,", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 135, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "4e73f54b-d265-594d-9fc1-9535a2d84672", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "83053df5-47ac-59da-9c30-69740a64372d", + "score": 0.6952319145202637, + "metadata": { + "text": "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System.\nJ Vis Exp JoVE (2017). doi: 10.3791/56260\n277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells:\nTools for Advancing Diabetes Disease Modeling and Beta-Cell Development.\nCurr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells\nFrontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 280, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "a36cee80-5961-55e5-8ea4-8d4e1bc501a9", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + }, + { + "id": "6f0adc7f-54ce-5a70-a2ea-153e074ccbdf", + "score": 0.6797996163368225, + "metadata": { + "text": "DNA donors as templates, it is possible the nCas9-RT will beable to convert all variants at once. This new technique, however,is still in early development, and its editing ef ciency and side-\neffects remain to be seen.FUTURE PROSPECTIVES\nRecent technological developments around CRISPR-Cas9 and itsderivative technologies, combined with advances in humancellular models, should accelerate our understanding of theinterplay between diabetes risk-associated genetic variants and", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 144, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "62d513ed-2dca-5f45-9da2-d847f92fc931", + "associatedQuery": "How can CRISPR/Cas9 technology be used to study or treat genetic forms of diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIFq1suHUy36hH4x2WAKqkv1fOq53", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genetic polymorphisms in the GLUT4 gene have been studied to determine their significance in relation to Type 2 diabetes. However, according to the provided context, none of the identified polymorphisms in the GLUT4 gene have been linked to or found to be associated with Type 2 diabetes across various populations [1]. This suggests that, despite investigations, there is no established connection between GLUT4 gene polymorphisms and the susceptibility to Type 2 diabetes.", + "role": "assistant" + } + } + ], + "created": 1728914149, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 93, + "prompt_tokens": 1423, + "total_tokens": 1516 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "bdc8f1de-8c90-5e28-8a21-a5bb0182cfe1", + "score": 0.7754814296386922, + "metadata": { + "text": "Effectors \nGlucose transporters. A number of polymorphisms have been identified in \nthe GLUT4 gene. None of them have been linked to or found to be \nassociated with type 2 diabetes in a variety of populations. 5960 Interestingly, \nan association was found between a polymorphism in the human GLUT! \ngene and type 2 diabetes60 that was significant for obese women. Regulation \nof GLUT4 protein expression in diabetes occurs in a strongly tissue-specific", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 434, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "0734af87-4854-5a0f-b10c-2ea89376cb87", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "bc93539a-df5f-5720-a4ce-0345fe4b66d4", + "score": 0.7438287444318837, + "metadata": { + "text": "M,XiangKS,etal.1996.Geneticcontri-bution of polymorphism of the GLUT1and GLUT4 genes to the susceptibilityto type 2 (non-insulin-dependent) dia-betes mellitus in different populations.Acta Diabetologica 33:19397\n141. Poulsen P, Kyvik KO, Vaag A, Beck-\nNielsen H. 1999. Heritability of type II(non-insulin-dependent) diabetes melli-tus and abnormal glucose toleranceapopulation-basedtwinstudy. Diabetolo-\ngia42:13945\n142. Pugliese A, Zeller M, Fernandez AJ,", + "title": "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 239, + "document_id": "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "extraction_id": "78e2a11a-4e89-5d14-b076-ef24c92b35b2", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "0a7e6fb3-bf85-5440-adeb-c66fca9d170a", + "score": 0.7292735737995176, + "metadata": { + "text": "A mutation in the Glut2 glucose transporter gene of a diabetic patientabolishes transport activity. J Biol Chem 269: 1776517767, 1994.\n36.Patel P, Bell GI, Cook JT, Turner RC, Wainscoat JS. Multiple\nrestriction fragment length polymorphisms at the GLUT2 locus: GLUT2haplotypes for genetic analysis of type 2 (non-insulin-dependent) diabetesmellitus. Diabetologia 34: 817821, 1991.\n37.Pereira MA, FitzerGerald SJ, Gregg EW, Joswiak ML, Ryan WJ,\nSuminski RR, Utter AC, Zmuda JM. A collection of Physical Activity", + "title": "2007 - Physical activity modifies the effect of SNPs in the SLC2A2 (GLUT2).pdf", + "version": "v0", + "chunk_order": 133, + "document_id": "6f5ced46-b777-563a-b644-432f4e7e2644", + "extraction_id": "276a7b90-6325-59c8-b8b2-77f855aa2553", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "a4973968-2510-5f08-8252-f2be85be3c42", + "score": 0.7235005992351904, + "metadata": { + "text": "NootherrecentassociationsofpolymorphismswithT2Dhavebeenreplicated\nto date (Table 5). However, a recent meta-analysis (106) identied some earlyreproducibilityofanassociationbetweenvariationin GLUT1andT2D,originally\nreportedin1988(104).Itislikelythatthisassociationhasnotbeenpursuedfurtherfor several reasons, but one possibility is a study that reported the rejection oflinkageto GLUT1athighlevelsofsignicance(46).However,linkagehaslimited", + "title": "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 155, + "document_id": "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "extraction_id": "51702d4a-735b-5bc4-98a4-d26bf1e58b40", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "4bab532c-8b73-54b8-905a-d7b070af1da8", + "score": 0.7182047274938725, + "metadata": { + "text": "mechanism by which type 2 diabetes is influenced remains to\nbe identified.\nThere have been several attempts to clarify the role of the\npolymorphism in SLC30A8 in the development of type 2\ndiabetes and the focus has been set on insulin secretion dueto the importance of ZnT-8 for insulin storage in the granulaof pancreatic cells.\nThe results are controversial, but there appears to be an\nassociation between the risk variant of rs13266634 and\nreduced insulin secretion. Interestingly, decreased insulin", + "title": "2009 - Zinc and Diabetes - clinical links and molecular mechanisms.pdf", + "version": "v0", + "chunk_order": 132, + "document_id": "72ab8458-928b-56b9-9547-1ba4b59dfab9", + "extraction_id": "a482defd-8d6a-5966-8ec1-5aa7e49c14f1", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "42eee55f-adfa-5a04-b3f0-a592b3b08a2c", + "score": 0.7140612772666659, + "metadata": { + "text": "glucose tolerance, suggesting a r ole for this polymorphism in the\nonset of GDM as well as type 2 diabetes mellitus ( 17). The switch on\nIRS-1 of the amino acid GLY972 Arg (rs1801278) impairs insulinsecretion, and a study on 1306 GDM patients and 1973 pregnantwomen without GDM found a signi cant association between the\npresence of this polymorphism and the risk of GDM ( 18).\nIntriguing results were generated by a study on the genetic", + "title": "2020 - Genetics and Epigenetics New Insight on Gestational Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 25, + "document_id": "301a7093-a33a-57c9-8979-58146c57ced1", + "extraction_id": "7d315f2c-43f0-587a-9370-e0f205d6c611", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "65c8f702-eee5-550e-bd63-78892b158c93", + "score": 0.710313873868355, + "metadata": { + "text": "tients the EUGENE2 study. Diabetologia 2008;51:816 820\n32. Kirchhoff K, Machicao F, Haupt A, et al. Polymorphisms in the TCF7L2,\nCDKAL1 and SLC30A8 genes are associated with impaired proinsulinconversion. Diabetologia 2008;51:597 601\n33. Nicolson TJ, Bellomo EA, Wijesekara N, et al. Insulin storage and glucose\nhomeostasis in mice null for the granule zinc transporter ZnT8 and studies\nof the type 2 diabetes-associated variants. Diabetes 2009;58:2070 2083", + "title": "2012 - Reduced Insulin Exocytosis in Human Pancreatic b-Cells.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "689e4fcc-99db-5798-8f1d-68c41d4638db", + "extraction_id": "e6e7fc9f-e4a4-5d51-9070-01ce34cffcd3", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "a6ef60db-d564-5f55-a31a-db893879ab14", + "score": 0.7092558492117108, + "metadata": { + "text": "is markedly reduced in glucose-unresponsive islets from ani-mal models of type 2 diabetes (51). In a previous study in PimaIndians, we found that ~5% of this population carries a mis-sense polymorphism in exon 3 of the GLUT2 gene (52), but\nthis polymorphism was not associated with the residual fast-ing plasma insulin concentration in the present study.Despite the fact that GLUT2 is an attractive candidate, it", + "title": "2000 - A High Fasting Plasma Insulin Concentration.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "77375e8f-ca8e-5bbb-b310-910dd82ade9c", + "extraction_id": "6aefb64e-b732-5742-90a4-f2aa43c8b866", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "b91a2e1b-eb4d-5e1b-a85c-46a8f394603c", + "score": 0.7054600295150275, + "metadata": { + "text": "polymorphisms in 24 DNA samples. Common variants were thengenotyped in 760 type 2 diabetic patients and 641 nondiabetic sub-jects. Genetic associations with diabetes-related phenotypes werealso analyzed.\nResults: Nine polymorphisms were identified, and four common poly-\nmorphisms [g. /H110021500C /H11022G, g./H110021062G /H11022C, g./H11002994C/H11022T, g./H11001408C/H11022A\n(Leu72Met)] were genotyped in a larger study. The genotype distri-butions of these four common polymorphisms in type 2 diabetes pa-", + "title": "2006 - Polymorphisms in the Ghrelin Gene Are Associated with Serum High-Density Lipoprotein.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "8ad4e1b5-9c29-5b20-bed8-afbf4a14862a", + "extraction_id": "00f6985d-f69b-50e3-b673-0ec508e6c025", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + }, + { + "id": "277f37e3-ee45-5619-b051-33d5ba95bd07", + "score": 0.7024430809949005, + "metadata": { + "text": "in turn, result in a defective or poorly expressed glucagonprotein and lead to decreased insulin secretion and conse-\nquently hyperglycaemia [ 48].\nThe current study identified, for the first time, several type\n2 diabetes-associated risk alleles associated with a higher riskof GDM, namely rs7957197 ( HNF1A ), rs10814916 ( GLIS3 ),\nrs3802177 ( SLC30A8 ) and rs7041847 ( GLIS3 ). These SNPs", + "title": "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "3b301dd1-17bd-5632-9a96-d6294c6d7650", + "extraction_id": "a228ec1a-de5b-5e0c-b24f-db8249be4053", + "associatedQuery": "What is the significance of genetic polymorphisms in the GLUT4 gene for Type 2 diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIFq5UdPTHMiHxXb8m3RyqvBs55fY", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "MicroRNAs (miRNAs) regulate gene expression related to diabetes by acting at the post-transcriptional level to control their target genes. They are involved in several crucial pathways associated with diabetes, including insulin secretion, cholesterol biosynthesis, fat metabolism, and adipogenesis [2]. miRNAs also play significant roles in pancreatic islet development, beta-cell dysfunction, insulin synthesis and secretion, and insulin resistance, which are key factors in the pathology of both Type 1 and Type 2 Diabetes Mellitus (T1DM and T2DM) [6]. Additionally, specific miRNAs have been implicated in the pathogenesis of diabetic complications, such as diabetic nephropathy, where miRNAs like miR-192, miR-216a, miR-217, and miR-377 are up-regulated [2]. These miRNAs can modulate the actions of growth factors and inflammatory factors, further influencing diabetic complications [5].", + "role": "assistant" + } + } + ], + "created": 1728914153, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 188, + "prompt_tokens": 1439, + "total_tokens": 1627 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c2fa8cbd-5f7f-5086-90ec-d1e5e6df0ee9", + "score": 0.736937733061589, + "metadata": { + "text": "MicroRNAs (miRNA) ar e single -stranded, small RNA molecules that act at the post -\ntranscriptional standard to regulate their target or source genes. Many biological processes \nare regulated by this Micro RNA. Since its discovery about two decades ago. It is correlated \nwith a com prehensive set of diseases and described by numerous miRNAs, including T2DM \nand cardiovascular diseases. Specifically, with respect to T2DM, micro RNA plays a", + "title": "2023 - Genetic Link Between Type 2 Diabetes Mellitus and Cardiovascular Disease.pdf", + "version": "v0", + "chunk_order": 45, + "document_id": "c54f9f64-7e6d-5186-a1de-d487ba9d19b8", + "extraction_id": "2211fc04-119d-534b-8de8-dfa4d1bfbf09", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "3cd8facc-0c2c-5a48-9f7c-cbd5685d914a", + "score": 0.7289735997946791, + "metadata": { + "text": "they can act as oncogenes or tumor suppressors (8, 29, 72). miRs are associated with the 341 \nregulation of genes relevant to insulin secre tion, cholesterol biosynthesis, fat metabolism and 342 \nadipogenesis, crucial pathways in the pathogene sis of diabetes (53, 114, 115). miRs have also 343 \nbeen implicated in TGF- signaling related to th e pathogenesis of diabetic nephropathy with key 344 \nmiRs such as miR-192, miR-216a, miR-217 and miR-377 being up-regula ted in glomerular 345", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "b1d2c95c-d639-5c75-8c52-278f1e187675", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "f35c5082-c877-5cdf-9ba8-a91dd72da2e8", + "score": 0.7277096238482499, + "metadata": { + "text": "Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM et al\n(2005) Microarray analysis shows that some microRNAs down-regulate large numbers of target mRNAs. Nature 433:769773\nLovis P, Roggli E, Laybutt DR, Gattesco S, Yang JY et al (2008)\nAlterations in microRNA expression contribute to fatty acid-induced pancreatic beta-cell dysfunction. Diabetes 57:27282736\nNadler ST, Stoehr JP, Schueler KL, Tanimoto G, Yandell BS et al", + "title": "2009 - Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.pdf", + "version": "v0", + "chunk_order": 100, + "document_id": "c3d2aced-4550-553f-abed-0d3a7ac1414f", + "extraction_id": "7d22ecdf-dd9f-53e9-aa2b-df81bd03c3bc", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "abbcafb6-f502-5648-a9a4-196466452564", + "score": 0.7271047126463582, + "metadata": { + "text": "Abstract\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the\nregulation of transcriptional activity within the pancreatic beta-cell. MicroRNAs (miRNAs) represent an important\ncomponent of regulatory control, and have proven roles in the development of human disease and control of glucose", + "title": "2013 - The miRNA Profile of Human Pancreatic Islets and BetaCells and Relationship to Type 2 Diabetes Pathogenesis.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "05e7f076-6b4a-5ab0-b4d0-28e4b6eeef8f", + "extraction_id": "65ad21df-f728-54b6-b329-9ed8793c33ce", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "8347a530-d264-5d7a-81f6-704f8ed7bf57", + "score": 0.7260474293175475, + "metadata": { + "text": "evidence demonstrates that miRNAs and lncRNAs can alsoregulate the expression of genes and modulate the actions of\ngrowth factors and inflammatory factors related to diabetic\ncomplications [ 8]. These reports have been described in sev-\neral reviews [ 8,8791] and are only briefly discussed here.\nNumerous recent reports have demonstrated abnormal ex-\npression of various miRNAs in renal, vascular and retinal cellsunder diabetic conditions, and in vivo models of related", + "title": "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "version": "v0", + "chunk_order": 88, + "document_id": "470f1f94-792d-5273-a88f-7e06084951c5", + "extraction_id": "593dfb70-8b55-5a74-abd5-446394a0bd23", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "f0bb404a-2062-584e-850d-cf49a1e0b4a7", + "score": 0.7203913282998289, + "metadata": { + "text": "In addition, miRNAs have been shown to be involved in T2DM. For example, miRNAs play major roles \nin pancreatic islet development, cell dysfunction, insulin synthesis and secretion and insulin \nresistance [148] . Studies based on miRNA microarray analysis have identified many different miRNAs \ninvolved in the pathology of both T1DM and T2DM; these miRNAs include mi R-375, miR -29, miR -9, \nmiR-124a, miR -195, miR -222, miR -126, miR -133a, miR -296, miR -96, miR -34a, miR -146b, miR -657,", + "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "version": "v0", + "chunk_order": 147, + "document_id": "ce4f171c-494c-53f2-a770-c3edd3561c40", + "extraction_id": "0cb154ce-660d-54fa-a31f-0391434a5470", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "a9695ed0-6f3d-5e79-ab99-514119637e0b", + "score": 0.7115685435370103, + "metadata": { + "text": "26. He Y , Ding Y , Liang B, Lin J, Kim TK, Yu H, Hang H, \nWang K. A Systematic Study of Dysregulated MicroRNA in \nType 2 Diabetes Mellitus. Int J Mol Sci. 2017:18.\n27. Dias S, Hemmings S, Muller C, Louw J, Pheiffer C. MicroRNA Expression Varies according to Glucose \nTolerance, Measurement Platform, and Biological Source. \nBiomed Res Int. 2017;2017:1080157.\n28. El Ouaamari A, Baroukh N, Martens GA, Lebrun P, Pipeleers D, van Obberghen E. miR-375 targets \n3'-phosphoinositide-dependent protein kinase-1 and", + "title": "2018 - MicroRNA profiling and their pathways in South African.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "b6bb090d-7176-59db-af04-582aa1d5cf10", + "extraction_id": "14577d73-d320-54dd-93f2-c55f986bc8bc", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "1d9d150b-27f9-55f7-8111-1f6de79a78bc", + "score": 0.7088165630320882, + "metadata": { + "text": "nucleotide RNA molecules that potentially regulate the\nexpression of thousands of genes. To understand therelationship between miRNA regulation and obesity-\ninduced diabetes, we quantitatively proled approximately220 miRNAs in pancreatic islets, adipose tissue, and liver\nfrom diabetes-resistant (B6) and diabetes-susceptible\n(BTBR) mice. More than half of the miRNAs proled wereexpressed in all three tissues, with many miRNAs in each\ntissue showing signicant changes in response to genetic", + "title": "2009 - Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "c3d2aced-4550-553f-abed-0d3a7ac1414f", + "extraction_id": "42c407dd-9f88-57b3-b47b-e467c486e3a4", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "5bf6de7b-8b41-5a32-a513-843f0f71c640", + "score": 0.70811406277251, + "metadata": { + "text": "11. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and\nfunction. Cell. 2004;116(2):281 97.\n12. Pirola L, Balcerczyk A, Tothill RW, et al. Genome-wide analysis\ndistinguishes hyperglycemia regulated epigenetic signatures of pri-\nmary vascular cells. Genome Res. 2011;21(10):1601 15.\n13.Cooper ME, El-Osta A. Epigenetics: mechanisms and implications\nfor diabetic complications. Circ Res. 2010;107(12):1403 13.Thispaper also provides a review of evidence pertaining to the role", + "title": "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "extraction_id": "767d65c7-b99d-5427-8f5a-4afa10669e11", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + }, + { + "id": "01d78f49-9996-58ea-b076-e352ff22461c", + "score": 0.7069891023059317, + "metadata": { + "text": "128. Diao X, Shen E, Wang X, Hu B. Differentially expressed microRNAs and their target genes in the hearts of streptozotocin-induced diabetic mice. Mol Med Rep (2011) 4:63340. doi:10.3892/mmr.2011.489 \n129. La Sala L, Cattaneo M, De Nigris V , Pujadas G, Testa R, Bonfigli AR, \net al. Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells. Cardiovasc Diabetol (2016) \n15:71. doi:10.1186/s12933-016-0390-9", + "title": "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf", + "version": "v0", + "chunk_order": 159, + "document_id": "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2", + "extraction_id": "9e010393-b98f-5f6c-a62d-fc0646ba8667", + "associatedQuery": "How do microRNAs regulate gene expression related to diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIFqHVwVjpSqrvqjfDgFColFbW31Q", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Studying the genetic basis of MODY (Maturity Onset Diabetes of the Young) has provided several important insights:\n\n1. **Genetic Heterogeneity**: MODY is caused by mutations in multiple genes, with at least 13 known genes implicated. The most prevalent mutations occur in the genes HNF1A, GCK, and HNF4A [3]. This genetic diversity leads to different subtypes of MODY, each with distinct clinical characteristics such as age of onset, pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifestations [3].\n\n2. **Inheritance Pattern**: MODY is inherited in an autosomal dominant manner, which means that it can be passed down through families. This inheritance pattern allows for the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies [2].\n\n3. **Clinical Presentation**: MODY typically presents in young adults, often before the age of 25, and is characterized by primary insulin secretion defects. It is not related to obesity or autoimmune processes, distinguishing it from other forms of diabetes like type 1 and type 2 diabetes [5].\n\n4. **Pathogenic Mechanisms**: Despite advances in understanding the molecular pathogenesis of MODY, there remain unknown genetic determinants in many patients with a MODY-like phenotype, suggesting additional locus heterogeneity and new pathogenic mechanisms yet to be discovered [4].\n\n5. **Impact on Treatment and Diagnosis**: Genetic testing for MODY can lead to more accurate diagnoses and tailored treatment plans. Many patients with MODY are currently undiagnosed or misdiagnosed with type 1 or type 2 diabetes, highlighting the importance of genetic testing in identifying this condition [7].\n\nThese insights underscore the complexity and variability of MODY, as well as the importance of genetic research in improving diagnosis and treatment strategies for this form of diabetes.", + "role": "assistant" + } + } + ], + "created": 1728914165, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 383, + "prompt_tokens": 1343, + "total_tokens": 1726 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "7d0ed0e8-6967-5dbe-b57e-f1e3a5821fd0", + "score": 0.8096703418794584, + "metadata": { + "text": "studying the highly familial MODY form of young - onset diabetes or other rare forms of monogenic diabetes. \n Table 12.2 The different subtypes of maturity - onset diabetes of the young ( MODY ). \n MODY \ntype Gene \nlocus Gene name Year of \ndiscovery Distribution Onset of \ndiabetes Primary \ndefect Severity of \ndiabetes Complications OMIM \n MODY1 20q HNF4A ( TCF14 ) 1996 Rare (2 3%) Adolescence/", + "title": "2010 - The Genetics of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "extraction_id": "6c0f6484-fb94-5583-8c4a-f707983ff29f", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "e3ec46a7-b0c9-567d-ba4a-e1c4f1341364", + "score": 0.8007671118239841, + "metadata": { + "text": "penetrance and early - onset diabetes, allows the collection of multigenerational pedigrees, making MODY an attractive model for genetic studies. MODY usually develops in thin young adults (usually before 25 years of age; in childhood, adolescence or young adulthood), and is associated with primary insulin - secretion defects [4,5] . The prevalence of MODY is estimated to be less than 1 2% of patients with T2DM, although it could represent as many as 5% of European cases of diabetes [4,25] . MODY is not", + "title": "2010 - The Genetics of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 31, + "document_id": "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "extraction_id": "6c0f6484-fb94-5583-8c4a-f707983ff29f", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "72000563-bea1-562e-b5d6-ea2c82f74d53", + "score": 0.7927499289959641, + "metadata": { + "text": "[2] . Mutations in 13 genes are known \nto cause MODY; the most prevalent are HNF1A , GCK \nand HNF4A [3, 4] . The MODY subtypes differ in age of \nonset of diabetes, the pattern of hyperglycemia, response to treatment, and associated extrapancreatic manifesta-tions \n[5] . As compared to type 2 diabetes, the clinical Key Words \n Best practice Genetic testing Healthcare providers Interview study Maturity onset diabetes of the young \n Abstract", + "title": "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "076f1140-8992-536f-832b-65ebdb9232a2", + "extraction_id": "c5c209f5-e4dc-5eb2-a9f0-536a686efa96", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "b1c43f5d-53c4-58e5-ac10-a90ecdd9d576", + "score": 0.7827710379821695, + "metadata": { + "text": "causal for MODY , although genetic or functional evidence of\nobvious pathogenicity is not fully compelling (Table 1).\nDespite these important advances in understanding the mo-\nlecular pathogenesis of MODY , the genetic determinants in\nmany patients with young-onset diabetes resembling a\nMODY-like phenotype remain unknown, suggesting addi-\ntional locus heterogeneity and new pathogenic mechanismsto be yet discovered. This has particularly been observed in", + "title": "2019 - How Recent Advances in Genomics Improve Precision Diagnosis.pdf", + "version": "v0", + "chunk_order": 42, + "document_id": "42b64375-06af-5e09-9ae6-6bd0ecb782c7", + "extraction_id": "7117f141-8841-5c11-ba1a-85039181b393", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "1555d1c2-53e4-5f7f-8411-7bb11d990eed", + "score": 0.7714792583715256, + "metadata": { + "text": "MODY Maturity Onset Diabetes of the Young. This is an uncommon form of diabetes, inherited as an autosomal dominant condition, and displaysa slow onset of symptoms. It generally presents before 25 years of age, is not related to obesity, and appears to have no autoi mmune\nbasis. Multiple forms of MODY have been characterised based on mutations affecting different genes involved in the control of -cellfunction, and display different degrees of disease severity\nContinued over page", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "4668a316-6c87-5039-b55b-47fe0b8fbc71", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "25e3417d-4e7e-595c-bec6-6f6e3d697ab4", + "score": 0.7695330381393487, + "metadata": { + "text": "Genetic Testing for MODY Public Health Genomics 2015;18:5259 \nDOI: 10.1159/00036796359 1 Singh R, Pearson ER: The importance of mak-\ning a genetic diagnosis of diabetes. Can J Dia-betes 2006; \n 30: 183190. \n 2 Ledermann HM: Is maturity onset diabetes at \nyoung age (MODY) more common in Europe than previously assumed? Lancet 1995; \n 345: \n 648.", + "title": "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "version": "v0", + "chunk_order": 106, + "document_id": "076f1140-8992-536f-832b-65ebdb9232a2", + "extraction_id": "3c09962c-226d-5271-a5c8-14f6327a079b", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "e479acca-9418-552b-98ae-edb6eb74ee6f", + "score": 0.767810651762246, + "metadata": { + "text": "Genetic Testing for MODY Public Health Genomics 2015;18:5259 \nDOI: 10.1159/00036796353symptoms present often at a relatively young age in pa-\ntients without overweight, who have a positive family his-tory. As compared to type 1 diabetes, progression may be less severe, and the required dosage of insulin low.\n Many patients with MODY are currently undiagnosed \nor misdiagnosed with type 1 or 2 diabetes mellitus [4] . In", + "title": "2015 - Current and Best Practices of Genetic Testing for Maturity.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "076f1140-8992-536f-832b-65ebdb9232a2", + "extraction_id": "039d8c39-8fe3-5183-b952-3e8a25510b86", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "b964fb31-cf7f-5d5d-9d73-d737daa96b8d", + "score": 0.7649612426757867, + "metadata": { + "text": "in 1992, through familial linkage analysis of French pedigreeswith early-onset, non-auto-immune, non-obese diabetes thatwas also called maturity-onset diabetes of the young (MODY)\n(Froguel et al., 1992 ). Mutations in GCK (encoding glucokinase)\nwere shown to cause a relatively benign form of MODY. Inciden-tally, it was the rst time that the direct causative effect of rela-\ntive insulin deciency was demonstrated in T2D, when insulin", + "title": "2015 - Rare and Common Genetic Events in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "641771c3-048c-5afb-a41c-05a883e99408", + "extraction_id": "587c2476-a12a-5e32-b0cf-ada54ee4a1db", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "847efd79-3919-5ec0-b5b3-9934cdb29c39", + "score": 0.7617053247514575, + "metadata": { + "text": "gene studies were under powered. However, studies of \nmonogenic forms of diabetes, specifically maturity onset \ndiabetes of the young 2 (MODY2), provided some of the \nfirst insights into the contribution of genetic variation to \nhyperglycemia observed during pregnancy and fetal \noutcomes. MODY2 is an autosomal dominant form of \nMODY due to mutations in glucokinase ( GCK ) [2527]. Table 1. Characteristics and treatment modalities of different forms of diabetes mellitus\nCharacteristics Treatment modalities", + "title": "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 19, + "document_id": "6d341cd2-ae56-5807-9aff-39298efc4d06", + "extraction_id": "318e6c28-16d2-5d59-b047-cfdc6b2e7301", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + }, + { + "id": "77d42dce-1bb6-577f-95f4-f8c7ece85c19", + "score": 0.7578971385955865, + "metadata": { + "text": "is variable, underlining that this disorder is genetically heterogeneous. \nTable 1. Definition of MODY \n Impaired glucose tolerance \n Age of onset <25 years \n Autosomal-dominant inheritance \nUsing genetic linkage and candidate gene approaches, mutations in \ngenes on chromosomes 2, 7, 12, 13, 19, and 20 have been linked to MODY \nand collectively may represent up to 3% of all patients with type 2 diabetes \n(Table 2). The gene on chromosome 7 (MODY2) encodes the glycolytic", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1054, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "d4fcf425-4ac9-5918-973a-fe6c422b19bc", + "associatedQuery": "What insights have been gained from studying the genetic basis of MODY (Maturity Onset Diabetes of the Young)?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIFqUmWTKdcimZ6Y2TYtW6SieUkPG", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The genes most commonly associated with an increased risk of developing diabetes, particularly type 2 diabetes, include:\n\n1. **CDKAL1, CDKN2A, CDKN2B** - These genes are linked to reduced insulin secretion via reduced beta-cell mass [1].\n2. **MTNR1B, TCF7L2, KCNJ11** - These genes are associated with beta-cell dysfunction [1].\n3. **FTO** - This gene is related to increased insulin resistance associated with obesity [1].\n4. **IRS1, PPARG** - These genes are related to increased insulin resistance unrelated to obesity [1].\n5. **IGF2BP2, HHEX, SLC30A8, WFS1** - These genes have been shown to increase susceptibility to type 2 diabetes in reproducible studies [3].\n6. **JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2** - These are additional variants identified in a recent meta-analysis as being associated with type 2 diabetes [3].\n7. **KCNQ1** - This gene is associated with susceptibility to type 2 diabetes in East Asian and European populations [6].\n\nThese genes have been identified through various genome-wide association studies (GWAS) and other genetic research efforts.", + "role": "assistant" + } + } + ], + "created": 1728914178, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 282, + "prompt_tokens": 1557, + "total_tokens": 1839 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "47e8bd94-fd61-57f2-b1d0-cc139d71936a", + "score": 0.767777144908905, + "metadata": { + "text": "of Diabetes \n Results of several genome-wide association stud-\nies (GWAS) have linked the following common gene variants with a 1520% increased risk of diabetes: reduced insulin secretion via reduce beta-cell mass (CDKAL1, CDKN2A, CDKN2B) and beta-cell dysfunction (MTNR1B, TCF7L2, KCNJ11) and increased insulin resistance related to obesity (FTO) and unrelated to obesity (IRS1, PPARG) [ \n11 ]. While most of the early studies", + "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "version": "v0", + "chunk_order": 2429, + "document_id": "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "extraction_id": "d7bd898b-1d46-557a-b065-f94fc5310b2a", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "437a7129-63b8-5f34-8273-2eef9535e987", + "score": 0.7428935170173645, + "metadata": { + "text": "gene are associated with NIDDM in Caucasians. Diabetes 1996 , 45, 825-831. \n46. Tarasov, A.I.; Nicolson, T.J. ; Riveline, J.P.; Taneja, T.K. ; Baldwin, S.A.; Baldwin, J.M.; \nCharpentier, G.; Gautier, J.F. ; Froguel, P.; Vaxillaire, M.; et al. A rare mutation in ABCC8/SUR1 \nleading to altered ATP-sensitive K+ channel activ ity and beta-cell glucose sensing is associated \nwith type 2 diabetes in adults. Diabetes 2008 , 57, 1595-1604.", + "title": "2010 - Pharmacogenetics of Anti-Diabetes Drugs.pdf", + "version": "v0", + "chunk_order": 184, + "document_id": "ffeebaf9-ff76-5751-9b8b-7a2a4a4f1dc3", + "extraction_id": "73e1aaff-7ef6-5ca2-9c94-23f5674a4f88", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "aa72551a-ac0c-5d7d-8057-34f229f68eb1", + "score": 0.7374500036239624, + "metadata": { + "text": "ly associated with type 2 diabetes: TCF7L2, KCNJ11, \nand PPARG .\n5-7 However, in 2007, a number of novel \ngenetic variants ( CDKAL1, IGF2BP2, the locus on \nchromosome 9 close to CDKN2A/CDKN2B, FTO, \nHHEX, SLC30A8, and WFS1)8-14 were shown to in -\ncrease susceptibility to type 2 diabetes in repro -\nducible studies. Furthermore, a recent meta-analy -\nsis identified six novel variants ( JAZF1, CDC123/\nCAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, and NOTCH2 ) that are associated with type 2 dia -\nbetes.\n15", + "title": "2008 - Clinical Risk Factors, DNA Variants.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "extraction_id": "2643b341-8c50-5cea-af36-86a8b070a80e", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "461b6f32-4dd8-5dc1-b69f-134f949fc021", + "score": 0.7339475154876709, + "metadata": { + "text": "CDKAL1 in uences insulin response and risk of type 2 diabetes. Nat Genet 2007; 39: 77075.\n69 Wu Y , Li H, Loos RJ, et al. Common variants in CDKAL1, CDKN2A/\nB, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 2008; 57: 283442.\n70 Sandhu MS, Weedon MN, Fawcett KA, et al. Common variants in \nWFS1 confer risk of type 2 diabetes. Nat Genet 2007; 39: 95153.", + "title": "2010 - Diabetes in Asia.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "0be842b8-7f69-503b-baed-c336e5c834d6", + "extraction_id": "11faf4fe-7b71-562e-9901-c428ab20b285", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "score": 0.7332150936126709, + "metadata": { + "text": "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood.\n12 Also, several", + "title": "2015 -precision-medicine-for-managing-diabetes.pdf", + "version": "v0", + "chunk_order": 27, + "document_id": "80949bab-d085-5f61-b98a-4bee043bc4e2", + "extraction_id": "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "78d81651-7215-596a-b128-37e429dc7edb", + "score": 0.7301425933837891, + "metadata": { + "text": "associated with susceptibility to type 2 diabetes mellitus. Nat Genet 2008; 40: 109297 .\n74 Unoki H, Takahashi A, Kawaguchi T, et al. SNPs in KCNQ1 are \nassociated with susceptibility to type 2 diabetes in East Asian and European populations. Nat Genet 2008; 40: 1098102.\n75 Lyssenko V, Lupi R, Marchetti P, et al. Mechanisms by which \ncommon variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 215563. \n76 Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA", + "title": "2010 - Diabetes in Asia.pdf", + "version": "v0", + "chunk_order": 128, + "document_id": "0be842b8-7f69-503b-baed-c336e5c834d6", + "extraction_id": "11faf4fe-7b71-562e-9901-c428ab20b285", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "b0d3a09d-36a3-5c6e-a110-3fccddaa74b7", + "score": 0.7266103245797854, + "metadata": { + "text": "type 2 diabetes or the inability to replicate linkage withdened loci. However, at least one susceptibility gene, namelyCAPN10, was found using a genome-wide scan approach [3].\nObesity is the greatest risk factor for type 2 diabetes\nmellitus, as it is known to induce insulin resistance via variousmechanisms ( TNF \n \n release, free fatty acids, etc.). Both", + "title": "2003 - Genome-wide screen in obese pedigrees with type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "335a3c08-14d3-5511-ab84-340e64c6f993", + "extraction_id": "1110f7b4-ab5a-5b41-b37d-a992b29cb20c", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "e6c0f12d-8136-5a16-b77c-88dd17c3a212", + "score": 0.7217490242869257, + "metadata": { + "text": "50 most cases of type 2 diabetes are thought to be \ndue to genetic variations that are more common but exert less e ect. In early studies, genetic variants in the peroxisome proliferator-activated receptor- gene (PPARG)\n51 and the ATP-sensitive potassium channel \nKir62 (KCNJ11) were reproducibly associated with type 2 diabetes.\n52 In Asian populations, the protective e ect of \nthe PPARG*A12Ala allele on insulin resistance and risk of type 2 diabetes was not consistently seen.\n53", + "title": "2010 - Diabetes in Asia.pdf", + "version": "v0", + "chunk_order": 19, + "document_id": "0be842b8-7f69-503b-baed-c336e5c834d6", + "extraction_id": "e99fe157-eda9-5e56-9ec9-8f428de2a161", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "d632d486-4e04-5c2d-9cf0-9d614453cab3", + "score": 0.7215678691864014, + "metadata": { + "text": "49. Cornelis MC, Qi L, Zhang C, et al. Joint e ects of common genetic\nvariants on the risk for type 2 diabetes in U.S. men and women ofEuropean ancestry. Ann Intern Med . 2009;150:541 550(in eng).\n50. Hu C, Zhang R, Wang C, et al. PPARG, KCNJ11, CDKAL1,\nCDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8are associated with type 2 diabetes in a Chinese population. PLoS\nOne. 2009;4:e7643 (in eng).\n51. Lin X, Song K, Lim N, et al. Risk prediction of prevalent diabetes in", + "title": "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "version": "v0", + "chunk_order": 183, + "document_id": "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "extraction_id": "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + }, + { + "id": "e1ba568f-cc08-549a-9c87-a23285c3b5dc", + "score": 0.7215024994908287, + "metadata": { + "text": "46. Sladek R, Rocheleau G, Rung J et al (2007) A genome-wide asso-\nciation study identifies novel risk loci for type 2 diabetes. Nature\n445:881 885\n47. Lauenborg J, Grarup N, Damm P et al (2009) Common type 2\ndiabetes risk gene variants associate with gestational diabetes. J\nClin Endocrinol Metab 94:145 150\n48. Florez JC, Jablonski KA, Bayley N et al (2006) TCF7L2 polymor-\nphisms and progression to diabetes in the Diabetes Prevention\nProgram. N Engl J Med 355:241 250", + "title": "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf", + "version": "v0", + "chunk_order": 106, + "document_id": "3b301dd1-17bd-5632-9a96-d6294c6d7650", + "extraction_id": "f6de8981-a79b-5817-b688-a20f76bff86c", + "associatedQuery": "What genes are most commonly associated with an increased risk of developing diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIFqbEtJpAtIpQor3Q5twvd1eoH61", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genetic testing can help predict a person's risk for diabetes in several ways:\n\n1. **Tailored Interventions**: Knowing an individual's genotype can allow for the development of personalized lifestyle intervention programs aimed at preventing or significantly delaying the onset of type 2 diabetes [1].\n\n2. **Role of Genetic Factors**: Genetic factors play a role in determining an individual's risk of developing diabetes, suggesting that genetic testing can help identify those at higher risk [2].\n\n3. **Genetic Risk Scores**: A genotype risk score can predict type 2 diabetes from a young age, as demonstrated in studies like the CARDIA study [6]. This score can help identify individuals who are at increased risk due to their genetic makeup.\n\n4. **Heritability and Risk Assessment**: Type 2 diabetes is heritable, and genetic testing can help identify individuals with a familial risk, which is increased by a factor of 2 to 6 compared to those without familial diabetes [7].\n\n5. **Improved Prediction and Stratification**: Genetic testing offers the potential for improved prediction and stratification of patients according to their risk, which can aid in selecting possible therapeutic targets [8].\n\n6. **Identification of Genetic Variants**: By genotyping specific single nucleotide polymorphisms (SNPs) associated with diabetes, genetic testing can improve the ability to detect who will ultimately develop the disease [9].\n\nOverall, genetic testing provides valuable insights into an individual's risk for diabetes, enabling more targeted prevention and management strategies.", + "role": "assistant" + } + } + ], + "created": 1728914185, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 303, + "prompt_tokens": 1059, + "total_tokens": 1362 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "28d6dfa3-28eb-537b-ad53-7d312f20fc88", + "score": 0.7473591566085815, + "metadata": { + "text": "genetic knowledge beyond its use for predic-tion of the individuals type 2 diabetes risk?One major advantage of knowing an at-riskpersons genotype could be to offer an individ-ually tailored lifestyle intervention program to\nprevent or, at least, to significantly retard the", + "title": "2009 - Pathomechanisms of Type 2 Diabetes Genes.pdf", + "version": "v0", + "chunk_order": 161, + "document_id": "cf8ec75c-8ffe-5baa-830d-ac7a4a5964bd", + "extraction_id": "9c49d40d-91d3-5f0d-8eaa-b3efa49ac200", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "score": 0.7451751232147217, + "metadata": { + "text": "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "71ebe60b-4807-5b6f-887a-2ab897a46039", + "score": 0.7436718940734863, + "metadata": { + "text": "(35).\nIf genetic tests are not helpful in the\nprediction and prevention of diabetes,they could have a role in discriminatingbetween type 1 and type 2 diabetes. Theepidemic of obesity (36) has made it moredifcult to distinguish diabetes type be-\ncause many children and young adultswith type 1 diabetes are also obese (37).Misclassi cation poses signi cant risks;\nan incorrect diagnosis of type 2 diabetes", + "title": "2016 - The Application of Genomics.pdf", + "version": "v0", + "chunk_order": 19, + "document_id": "2ec5c9c1-fe53-59ca-b36f-d360dfce0da5", + "extraction_id": "7aa2ab48-620b-5b30-b2de-103e103579ba", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "6cf756f6-bc3a-515a-a879-7270f663c516", + "score": 0.7371379137039185, + "metadata": { + "text": "geted at specific genetic mutations, it is likely that accompa-nying diagnostic tests for biomarkers will also become available to confirm whether the target biomarker is present.\nGenomic Analyses for Diabetes Risk", + "title": "2015 -precision-medicine-for-managing-diabetes.pdf", + "version": "v0", + "chunk_order": 26, + "document_id": "80949bab-d085-5f61-b98a-4bee043bc4e2", + "extraction_id": "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "59b0a653-0d03-582e-8fb5-009af723b984", + "score": 0.736946485547617, + "metadata": { + "text": "genes improves prediction of type 1 diabetes[published correction appears in Diabetologia. 2015;\n58(1):206]. Diabetologia . 2014; 57(12):2521 2529.\n57. Oram RA, Patel K, Hill A, Shields B, McDonald TJ,\nJones A, Hattersley AT, Weedon MN. A type 1\ndiabetes genetic risk score can aid discrimination\nbetween type 1 and type 2 diabetes in young adults.Diabetes Care . 2016; 39(3):337 344.\n58. Redondo MJ, Oram RA, Steck AK. Genetic risk", + "title": "2019 - Genetic Risk Scores for Diabetes Diagnosis.pdf", + "version": "v0", + "chunk_order": 246, + "document_id": "8c66aca1-d4ba-534d-a037-4273de340ee1", + "extraction_id": "ba3abde6-0fac-587f-976e-bd0e08c48ae3", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "9d44b00e-027f-557f-a851-e870605ea20f", + "score": 0.7363494634628296, + "metadata": { + "text": "10.2337/db13-1663.\n 20. Vassy JL, et al. A genotype risk score predicts type 2 diabetes from young \nadulthood: the CARDIA study. Diabetologia. 2012;55:26042612. doi: \n10.1007/s00125-012-2637-7.\n 21. Vassy JL, et al. Is genetic testing useful to predict type 2 diabe-tes? Best Pract Res Clin Endocrinol Metab. 2012;26:189201. doi: \n10.1016/j.beem.2011.09.002.\n 22. Khera AV, et al. Genome-wide polygenic score to identify a monogenic \nrisk-equivalent for coronary disease. bioRxiv. 2017. doi: 10.1101/218388.", + "title": "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "extraction_id": "e9c258eb-26f2-5e33-87a2-7ac5a5b29989", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "f0ca71ce-f2bb-54f2-a933-dc9c952f1eb8", + "score": 0.7260829426670955, + "metadata": { + "text": "Genotype Score for Prediction of Type 2 Diabetes\nn engl j med 359;21 www.nejm.org november 20, 2008 2209Type 2 diabetes mellitus is a m ajor \nhealth problem worldwide.1 Fortunately, \nits development can be prevented in many \ninstances,2 and persons at risk can be readily \nidentified with the measurement of a few com -\nmon risk factors.3-5 Type 2 diabetes is heritable, \nwith a risk for people with familial diabetes as \ncompared with those without familial diabetes that is increased by a factor of 2 to 6.", + "title": "2008 - Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "fb502e5b-7094-58aa-9508-103476a9c035", + "extraction_id": "e0f816e4-3c97-575e-8bbe-0e006c8c8e61", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "e32de26a-7ad6-51a9-860e-5df0b45d981d", + "score": 0.7240273356437683, + "metadata": { + "text": "risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets. As germ-line factors, genetic risk variants are present and amenable to study at all times be -", + "title": "2009 - Genetics of Type 1A Diabetes.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "7a98f456-6c43-5e9e-b404-31122159eab8", + "extraction_id": "d3fa98dd-b7be-5192-9a7c-71742b1b05e4", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "b677fe54-5f7e-5d87-a16d-6694578c6f2b", + "score": 0.7192047834396362, + "metadata": { + "text": "offers the opportunity to test whetherknowledge of these genetic loci canimprove our ability to detect who willultimately develop diabetes. To answerthis question, we genotyped 18 well-validated single nucleotide polymorph-isms that had previously been associat-\ned with diabetes in large genetics", + "title": "2010 - Cardiovascular Disease Risk Factors, Type 2 Diabetes Mellitus, and the Framingham Heart Study.pdf", + "version": "v0", + "chunk_order": 50, + "document_id": "134c506f-f66f-5a17-9e81-1f4c5923fe91", + "extraction_id": "5763fc63-1abb-5baf-b2ed-ad1b019bdb56", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + }, + { + "id": "530788ae-3a97-50d6-ad96-5463a3dc75e8", + "score": 0.7177377343177795, + "metadata": { + "text": "Comprehension of Genomic Risk for \nDiabetes Public Health Genomics 2014;17:95104 \nDOI: 10.1159/000358413101their results in-person from a genetic counselor were able \nto correctly indicate their genomic or lifetime risk score for T2DM and interpret their genomic risk, compared to 50% of participants receiving their results online. This finding aligns with reports that suggest genetic counsel-ing (though limited to reporting of test results in this study) improves patients accuracy of risk perception", + "title": "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "b2665466-da66-59f0-8581-a68131e924bf", + "extraction_id": "aafcb80d-7069-59da-8a21-d6a32f1a8820", + "associatedQuery": "How can genetic testing help predict a person's risk for diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIFqiY2VOktGY4xVSkvpvMDbynoMw", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Family genetics play a significant role in the likelihood of developing diabetes. Several studies and observations highlight this connection:\n\n1. Genetic factors are important in determining an individual's risk of developing diabetes [1].\n2. A family history of diabetes, particularly in first-degree relatives such as parents or siblings, is associated with a two- to fourfold increased risk of developing diabetes [3].\n3. Type 2 diabetes is strongly influenced by genetics, as evidenced by high concordance rates in identical twins, with studies showing a 58-75% concordance rate [5]. In some studies, the concordance rate is reported to be nearly 100% [6].\n4. The risk of developing type 2 diabetes is approximately 70% if both parents have the disease and about 40% if one parent is affected [7], [9].\n5. Type 2 diabetes clusters in families, and having a first-degree relative with the disease increases the lifetime risk significantly, up to 40% or more [9].\n\nOverall, these findings underscore the strong genetic component in the susceptibility to diabetes, particularly type 2 diabetes.", + "role": "assistant" + } + } + ], + "created": 1728914192, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 224, + "prompt_tokens": 1244, + "total_tokens": 1468 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "score": 0.755399763584137, + "metadata": { + "text": "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "03dbb574-1b16-5300-af34-08b82263388e", + "score": 0.7400701642036438, + "metadata": { + "text": "Metabolic Syndrome and Family History \nof Diabetes Public Health Genomics 2010;13:353359 357able difference in the odds between these 2 risk levels. \nThis table indicates that, compared with the average fa-milial risk, a moderate or high familial risk of diabetes increases the odds for each single component of the met-a b o l i c s y n d r o m e . T h e s e o d d s v a r y f r o m 1 . 1 9 ( 9 5 % C I : 0.881.61) to 1.53 (95% CI: 1.301.81).\n C o n c l u s i o n", + "title": "2010 - Family History of Diabetes and Prevalence.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "f16c4c6e-bb5f-5d4a-9945-8af4d0df19f4", + "extraction_id": "b91922c6-7b5b-5fa1-a740-4564ec4cfa36", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "13fa34fd-9bf6-5ae5-8a7e-e1998d56d084", + "score": 0.721138553551523, + "metadata": { + "text": "For type 2 diabetes, there have been a few \nstudies utilising a candidate-gene approach as well as genome-wide association studies, although some argue that genetic factors play only a minor role among Caribbean populations [ \n90 ]. A family history of diabetes in any rst- \ndegree relative (parent, sibling) or in a grandpar-ent is associated with a two- to fourfold increased risk of diabetes [ \n10 , 91 ]. A family history of dia-", + "title": "2017 - diabetes-mellitus-in-developing-countries-and-underserved-commun-2017.pdf", + "version": "v0", + "chunk_order": 1363, + "document_id": "8a9451b9-d7e8-5417-b6a5-5fd1b791cc4d", + "extraction_id": "5ae0e120-7064-5ced-84ff-e74fb0f90047", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "527419f1-075d-5d53-a8b5-1685952ecdb0", + "score": 0.7160833647201452, + "metadata": { + "text": "evidenced by a very high positive rate of family history of diabetes, and \ndrastically different prevalence in various ethnic groups. Therefore, there is \nno doubt that type 2 diabetes is a disease with a strong genetic influence. \nHowever, the prediction of the relative contribution of genetic influence and \nnumber of genes involved in the pathogenesis of the disease has changed in \nthe past few years. Initially, enthusiastic searches of diabetes genes were", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 638, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "40d292c1-03bc-5780-a2ae-9b0fe245f39c", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "3a807b66-fcae-5cae-b8ad-83a5c6815221", + "score": 0.7089239954948425, + "metadata": { + "text": "can decrease risk of diabetes.22 Diet may also play a role. High calorie diets, \nincluding those high in fat, and especially saturated fat, have been implicated \nin the development of type 2 diabetes?4-26 Family history is a very strong risk \nfactor for type 2 diabetes. A strong genetic component is suggested by the \n58-75% concordance rates for type 2 diabetes observed in identical twins \n(Table 3).3 \nTable 3. Estimated risk of developing type 2 diabetes by family history \nOne parent with type 2 diabetes", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 570, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "8e5322e6-a8a2-5d98-b87d-1ba3846d5fe1", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "b63c48dd-b954-56d4-bdfa-8ab135e7bf47", + "score": 0.7072950179997088, + "metadata": { + "text": "The fact that type 2 diabetes is a genetic disease is well\nknown to clinicians by how it occurs in families, and by\nthere being ethnic populations who are particularly high risk.\nThe genetic link was clearly shown more than two decades\nago by a famous study of identical twins in the U.K. that\nfound essentially a 100% concordance rate for this disease\nif one twin developed type 2 diabetes, then the other one\ninvariably developed it (9). However, this kind of study", + "title": "2005 - Pathogenesis of Type 2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "75b4ae7d-7abf-57b8-bda9-5b022d698ae6", + "extraction_id": "d62a1716-bd6a-5532-ab22-ee6e7ec4cf37", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "ee3d0900-a422-59cd-a6db-308f20052cc0", + "score": 0.7053035497665405, + "metadata": { + "text": "genetic factors play an important role in the susceptibility to T2D. The risk of the disease developing\nat some point of life is ~70% when both parents are diabetic and ~40% when one parent has T2D [ 4].\nFurthermore, latest data show that more than 400 genetic risk variants at 250 loci for T2D have been\nGenes 2018 ,9, 374; doi:10.3390/genes9080374 www.mdpi.com/journal/genes", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "f6b9d6b9-a60b-56f5-9727-d90d43efe0ac", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "2aa9f009-ae05-5c93-ac3a-58b1f516d844", + "score": 0.7022915659969124, + "metadata": { + "text": "36 Herder C, Roden M. Genetics of type 2 diabetes: pathophysiologic \nand clinical relevance. Eur J Clin Invest 2011; 41: 67992.\n37 Dabelea D, Hanson RL, Lindsay RS, et al. Intrauterine exposure \nto diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships. Diabetes 2000; 49: 220811.\n38 Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes \nsusceptibility loci identi ed through large-scale association analysis. Nat Genet 2010; 42: 57989.", + "title": "2011 - Type 2 diabetes across generations from pathophysiology to prevention and management.pdf", + "version": "v0", + "chunk_order": 151, + "document_id": "0f49b102-1d7e-5702-af30-35e5f2ed93a6", + "extraction_id": "baec13ec-c42b-51b4-9974-8ef1c2d10ddc", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "353dc970-3106-5bbe-8a58-d65d13e5e6ee", + "score": 0.7021226705385859, + "metadata": { + "text": "long follow-up. Type 2 diabetes and impaired glucose tolerance (IGT) \ncluster in families. Thus, most patients have a positive family history, and \nthe lifetime risk for developing type 2 diabetes is increased up to 40% (more \nthan five times the background rate) by having a first degree relative with the \ndisease. If both parents have type 2 diabetes the risk to the offspring may be \nas high as 70%. Available evidence supports a polygenic mode of \ninheritance with a considerable environmental input. 1", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 938, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "5a2221e0-dabc-523c-8358-3e43789e8f7a", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + }, + { + "id": "6c14eef8-bb27-503a-9523-9e7a16d71021", + "score": 0.7017937898635864, + "metadata": { + "text": "Genetic factors\nType 2 diabetes has a strong genetic component and most Asian patients have a rst-degree relative with diabetes.\n48,49 Much progress has been made in our \nunderstanding of the genetics of this disease. Importantly, most of the loci originally associated with diabetes in European populations have been replicated in Asian populations. Whereas monogenic forms of diabetes result from rare genetic mutations with large e ects, \nsuch as those seen in maturity-onset diabetes of young people,", + "title": "2010 - Diabetes in Asia.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "0be842b8-7f69-503b-baed-c336e5c834d6", + "extraction_id": "e99fe157-eda9-5e56-9ec9-8f428de2a161", + "associatedQuery": "What role do family genetics play in the likelihood of getting diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIFqrzKmzcOBxhh6XTfMBqYsubXv7", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, lifestyle changes can affect genetic risk factors for diabetes. Evidence from both observational studies and clinical trials suggests that lifestyle factors can modify the genetic effects on the risk of developing type 2 diabetes mellitus (T2DM) [1]. Lifestyle modifications, such as improvements in dietary quality and increased physical activity, have been shown to be particularly effective in preventing and managing type 2 diabetes, even in individuals with a high genetic risk [6]. Furthermore, behavioral changes can substantially mitigate the diabetogenic effects of individual or multiple risk alleles, indicating that the adverse effects of some established T2D-associated loci may be greatly attenuated by appropriate lifestyle changes [9].", + "role": "assistant" + } + } + ], + "created": 1728914201, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 132, + "prompt_tokens": 1075, + "total_tokens": 1207 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a1c71566-1d75-551a-8588-9a05436545dc", + "score": 0.7656337758520989, + "metadata": { + "text": "of a given genetic variant is modified by the environ -\nmental milieu (and vice versa). Evidence that lifestyle factors modify the genetic effects on T2DM risk has been \ngenerated from both observational studies and clinical \ntrials82. However, genetic background might also affect \nthe individuals response to lifestyle interventions83. In \naddition, replication data are sparse, and comprehensive, \nlarge-scale studies have failed to provide a compelling", + "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "extraction_id": "751ccb98-2846-5ca7-8ab8-2684100c28fa", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "fe89ba68-d709-5494-bcdc-82d81e1498d1", + "score": 0.7589735984802246, + "metadata": { + "text": "genetic risk for diabetes may not moti-vate improvements in lifestyle behaviors.Indeed, knowledge of increased geneticrisk for diabetes may decrease motiva-tion to modify behavior in genetic fatal-ists (83).\nDiet recommendations optimized to\nthe individual have been shown to re-duce postprandial glycemic excursionsto a greater extent than standard\napproaches in healthy individuals (84).Meal compositions that induce the most\nfavorable glycemic pro les have been", + "title": "2020 - Precision Medicine in Diabetes.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "extraction_id": "0504a937-6b88-5004-a13e-5e9c3073eaf6", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "799f3578-a7ac-551f-b84a-b9fb3be53040", + "score": 0.7469942569732666, + "metadata": { + "text": "diabetes regardless of the underlying\ngenetic risk. This contrasts with theextensive epidemiological evidence sug-gesting that the relationship of lifestylewith obesity is dependent on genetic risk(7881); however, with few exceptions\n(e.g., [74]), analyses in large randomizedcontrolled trials have failed to show thatthese same genetic variants modifyweight loss in response to lifestyle in-tervention (82). It is also important to\nrecognize that knowledge of increased", + "title": "2020 - Precision Medicine in Diabetes.pdf", + "version": "v0", + "chunk_order": 135, + "document_id": "0ad5b2de-d782-5d43-b294-bff5c7befd2d", + "extraction_id": "0504a937-6b88-5004-a13e-5e9c3073eaf6", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "54ff4672-bf7f-5158-b228-ca3d45e0cb0d", + "score": 0.7338041067123413, + "metadata": { + "text": "Genetic factors appear to play a role in determining an individuals risk of developing diabetes. It is hoped", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "be87703d-e7b2-5db5-9983-5412e09a57ba", + "score": 0.7328479290008545, + "metadata": { + "text": "suggested to attenuate its negative e ect on metabolic pro le, \nbody weight, and diabetes risk ( Franks et al., 2007 ; Kilpelainen et al., 2008 ; Lindi et al., 2002 ; Ruchat et al., 2010 ) ( \n Table 1 ). \nThe notion that lifestyle modi cation can eliminate the increased \nrisk for development of T2DM in subjects with genetic suscepti-bility is also supported by ndings of Barwell et al. (2008) who", + "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "extraction_id": "93638ea5-6d1f-5b6a-9629-798804de24dd", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "89339b65-325f-588f-9f25-761124f0012f", + "score": 0.7295693916339058, + "metadata": { + "text": "proven particularly effective for preven-tion and management of type 2 diabetes.For example, improvement in dietaryquality, in conjunction with other lifestylemodications like increased physical ac-tivity, was shown to be more effectivethan pharmacological treatment in pre-vention of diabetes in individuals at highrisk (1). Further, lifestyle modicationmay mitigate the risk associated with thestrongest known diabetes risk loci (2).While the existence of environmental in-uences on genetic risk (and vice", + "title": "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "extraction_id": "6283c124-b479-5050-86ca-dc42390147a1", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "fe35615a-6df7-548c-b313-4abca69b1e2d", + "score": 0.7234389997553531, + "metadata": { + "text": "who is lean, genetic risk factors are more\nlikely to be present than in someone who\nis obese and develops the disease or that\nweight loss enhances the genetic risk ofdiabetes.\nGenetic analyses performed in clinical\ntrials involving intensive lifestyle modi -\ncation provide an important adjunct to\nthe epidemiological literature on gene-\nlifestyle interactions in type 2 diabetes.On one hand, a major advantage of ran-\ndomized controlled trials is that interac-", + "title": "2013 - Gene-Environment and Gene-Treatment.pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "fe958fb1-5408-56ec-b102-ccf07b4bac2d", + "extraction_id": "ee6a4bf3-6f68-58e7-a96f-c879b5269694", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "68a382e9-85e0-548c-910e-5f24cb48f9c8", + "score": 0.7233020663261414, + "metadata": { + "text": "Lifestyle behaviors and genetic loci have clear and distinguishable effects on \nT2D risk; however, the pattern of disease occurrence within and between popula-tions that differ in their genetic and environmental underpinnings suggests T2D is caused in part by the interaction between adverse lifestyle behaviors and the genetic profile of an individual. For many, this seems a reasonable assumption, but there is little robust empirical evidence supporting the presence of such interactions.", + "title": "2011 - Interaction Between Exercise and Genetics.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "c36db75e-4b76-540d-9efb-d0e156e61541", + "extraction_id": "ed6dcfee-8273-5512-8fb4-fc51a9c921da", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "6b83f0af-1145-5679-9dae-0f645771d25d", + "score": 0.7165352871098956, + "metadata": { + "text": "this occurs. Findings to date, however, indicate\nthat behavioral changes can substantially\nmitigate diabetogenic and obesogenic effects\nof individual or multiple risk alleles, which\nhas much broader clinical and public health\nimplications.We have seen considerable progress in our\nunderstanding of the role that both environ-\nment and genetics play in the development of\nT2D. Recent work suggests that the adverse\neffect of some established T2D-associated\nloci may be greatly attenuated by appropriate", + "title": "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "extraction_id": "89bf4316-d0cc-5310-a45e-1dd8b8aefe1b", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + }, + { + "id": "1b364e28-08e2-5813-b066-7ce37eeb36cf", + "score": 0.7156190872192383, + "metadata": { + "text": "Susceptibility to obesity and diabetes is deter-\nmined by both genetic and lifestyle factors.Suggestive evidence of genelifestyle interac-\ntion (Box 33.3) in the development of common\ndiseases such as obesity and type 2 diabetes wasrst provided by descriptive epidemiological\nstudies such as migration studies that compare\nthe disease risk between genetically related pop-ulations who live different lifestyles. A classicalexample is the comparison of the risk of obesity", + "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "version": "v0", + "chunk_order": 4331, + "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c", + "extraction_id": "3bf3c6a7-de03-5114-bad8-d53fd76d0fba", + "associatedQuery": "Can lifestyle changes affect genetic risk factors for diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIFqw6zPQKQT7tNlJNiyf2dx560ep", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent breakthroughs in understanding the genetic causes of diabetes include several key advancements:\n\n1. The number of genetic regions causally implicated in both type 1 and type 2 diabetes has increased to 11 each, marking significant progress in identifying genetic factors associated with these conditions [1].\n\n2. Genome-wide association studies have been particularly successful in uncovering genetic determinants of complex diseases like diabetes. These studies have identified a number of novel disease-associated variants, which have been largely replicated by independent research groups, highlighting their reliability and significance [2].\n\n3. Advances in technology, such as the use of single nucleotide polymorphism (SNP) chips, have enabled the analysis of up to a million genetic variations. This technological leap has greatly enhanced our understanding of the genetic basis of complex, multifactorial forms of diabetes, bringing it closer to the understanding of rarer, single-gene disorders [5].\n\n4. Recent studies have yielded six new replicating gene regions associated with type 2 diabetes, providing new insights into the genetic architecture of the disease and suggesting potential directions for future research [8].\n\nThese breakthroughs represent a significant leap forward in the genetic understanding of diabetes, driven by technological advancements and collaborative research efforts.", + "role": "assistant" + } + } + ], + "created": 1728914206, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_e5e4913e83", + "usage": { + "completion_tokens": 240, + "prompt_tokens": 1212, + "total_tokens": 1452 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9250b1a6-26d8-5c38-840f-547a9647e809", + "score": 0.7339037219343364, + "metadata": { + "text": "understanding of the genetic basis of diabetes, and the\nadvances of recent months are arguably the most important\nmade since the role of the HLA region was recognised in type1 diabetes. The number of genetic regions causally implicated\nis now 11 each for type 1 and type 2 diabetes [ 19], and is set\nto rise further. The bewildering pace of new discovery standsin stark contrast to the slow progress that characterised the\nprevious two decades, with a total combined output of three", + "title": "2007 - Genetic studies of diabetes following the advent of the genome-wide association study where do we go from here.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "7b96d9b2-6494-5c20-9693-dc146a4e347c", + "extraction_id": "1a155200-3610-528f-a51d-b2f27562037a", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "9d55a0b9-d125-587d-b21e-f4bd55b8de28", + "score": 0.7038693008500767, + "metadata": { + "text": "It has proven to be challenging to isolate the genes underlying the genetic\ncomponents conferring susceptibility to type 1 and type 2 diabetes. Unlike\nprevious approaches, genome-wide association studies have extensively\ndelivered on the promise of uncovering genetic determinants of complexdiseases, with a number of novel disease-associated variants being largelyreplicated by independent groups. This review provides an overview of these\nrecent breakthroughs in the context of type 1 and type 2 diabetes, and", + "title": "2011 - Genome-wide association studies (GWAS) impact.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "086c6869-7c70-5364-9269-760267fb458d", + "extraction_id": "cf06774a-9e13-59fd-9652-d5013ef83387", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "0372a2d5-28c0-5369-8f05-18f7124eb4ae", + "score": 0.6993317187230768, + "metadata": { + "text": "The history of diabetes genetics traces human genetic research more broadly.Initially, only a few polymorphic genetic markers were known, and these werestudiedinpopulation-basedassociationstudies.Withthedevelopmentofgenome-wide maps for family-based linkage analysis and of positional cloning, attentionturned to monogenic forms of disease. The application of family-based linkagemethods to common forms of diabetes, however, met with less clear success.More recently, with progress in genome sequencing and", + "title": "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 22, + "document_id": "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "extraction_id": "238129d2-439f-5a25-8e86-297e7a69d81c", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "5b134bfd-6af3-5189-b144-57bf70c2cf20", + "score": 0.6979157328605702, + "metadata": { + "text": "the elucidation of the wide spectrum of genes that \nplayed a role in the molecular mechanism of diabetes \ndevelopment[142-144]. However , despite the vast flow of \ngenetic information including the identification of many \ngene mutations and a large array of single nucleotide \npolymorphisms (SNPs) in many genes involved in the \nmetabolic pathways that affect blood glucose levels, \nthe exact genetic mechanism of diabetes remains \nelusive[145,146]. Evidently, a major complication is the", + "title": "2015 - Diabetes mellitus The epidemic of the century.pdf", + "version": "v0", + "chunk_order": 78, + "document_id": "e114dd28-fd39-56df-bdeb-8806474a6c10", + "extraction_id": "6b04dc27-e7ff-53c8-9021-a3cdb5415059", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "3cd5df03-7c2b-585c-a3bb-67dc0e1c615c", + "score": 0.6874925680462988, + "metadata": { + "text": "confirmed genes for type 2 diabetes and six for type 1(Fig. 1). At last, it seems, our understanding of the genetic\nbasis of complex, multifactorial forms of diabetes is catching\nup with that of rarer, single-gene disorders.\nThis leap in knowledge is the result of major advances in\ntechnology plus an improved understanding of patterns of\nhuman genetic variation. Using single nucleotide polymor-\nphism (SNP) chips it is now possible to analyse up to a million", + "title": "2007 - Genetic studies of diabetes following the advent of the genome-wide association study where do we go from here.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "7b96d9b2-6494-5c20-9693-dc146a4e347c", + "extraction_id": "1a155200-3610-528f-a51d-b2f27562037a", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "9b04e578-bfe5-5f3c-8556-aac26d6429cc", + "score": 0.6869953274726917, + "metadata": { + "text": "make dissection of the black box of genetics of diabetespossible in the near future, but at this point, apart fromthe pro les that distinguish between type 1 and type 2\ndiabetes and a limited number of speci c variants that\nidentify small subgroups of patients (MODY), genetics has\nnot been successful in further differentiating subclasses ofdiabetes.\nResearch Gaps\nAfter consideration of the known genetic associations\nwith diabetes risk, consensus developed that the eld is", + "title": "2017 - Differentiation of Diabetes by Pathophysiology.pdf", + "version": "v0", + "chunk_order": 30, + "document_id": "9cfaef1e-fb60-5c2b-94f0-632c89b2eb16", + "extraction_id": "a9accd40-eb89-5595-bf27-b6b82b49f4d4", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "f3c6864c-7c06-5a61-bdda-d5730821c237", + "score": 0.6792303706132924, + "metadata": { + "text": "studies provide new insights into type 2diabetes aetiology. Nat Rev Genet 2007;8:657662\n11. Grant RW, Moore AF, Florez JC. Genetic\narchitecture of type 2 diabetes: recentprogress and clinical implications. Diabe-tes Care 2009;32:11071114\n12. Dupuis J, Langenberg C, Prokopenko I,", + "title": "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "version": "v0", + "chunk_order": 106, + "document_id": "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "extraction_id": "40190f1d-aad5-5d71-b5ba-78331d5e3abb", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "81e7ee8d-adb5-5fd7-a3b1-1f6bfb059974", + "score": 0.6752230122552518, + "metadata": { + "text": "early results have been excellent, yielding six \nnew replicating gene regions.\nHere I discuss the insights into type\n \n2 \ndiabetes genetics that have been provided by \nthese new findings. I consider where diabe -\ntes genetic studies might go from here, and \npresent a perspective that may be applicable \nto other common traits. I also briefly discuss \nthe wider implications that surround the \nidentification of a common gene that predis -\nposes to type", + "title": "2007 - Genome\u2013wide association studies provide new insights into type 2 diabetes aetiology..pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "2ad9b6c6-56ed-5ba6-ad88-c1a6777f5196", + "extraction_id": "cd034e2b-72bd-5cda-a456-48cf17ead1bf", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "score": 0.6748439863417168, + "metadata": { + "text": "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + }, + { + "id": "23321ca3-f73d-5542-a6c0-1133c3d3e9e5", + "score": 0.673834641347252, + "metadata": { + "text": "24. Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl. Acad. Sci. USA 114, \n23012306 (2017).\n 25. Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 diabetes susceptibility loci. eLife 7, \ne31977 (2018).\n 26. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, \n14151425 (2015).", + "title": "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "extraction_id": "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "associatedQuery": "What recent breakthroughs have been made in understanding the genetic causes of diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIFr5iGpAfX0NjS78CDO8cOuBpwUj", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genes influence how our bodies respond to sugar and insulin in several ways:\n\n1. **Insulin Secretion and Resistance**: Genes are directly related to insulin secretion and indirectly influence insulin resistance by interacting with other genes. This interaction supports the idea that environmentally triggered insulin resistance can interact with genetically programmed beta-cell dysfunction, leading to diabetes [1].\n\n2. **Beta-Cell Function**: Many genetic variants primarily affect beta-cell function, development, or survival, rather than insulin sensitivity. This suggests that genetic factors play a significant role in how beta cells respond to sugar and insulin [2].\n\n3. **Expression in Metabolically Relevant Tissues**: Genes affecting insulin sensitivity are often expressed in peripheral insulin-sensitive tissues, such as the liver and adipose tissue, and their expression can be responsive to metabolic status. For instance, a high-fat diet can decrease the expression of several of these genes, indicating a genetic influence on how the body responds to dietary changes [3].\n\n4. **Regulation by Metabolic Conditions**: The expression of certain genes can be altered by conditions such as fasting and feeding, which are known to affect peripheral insulin sensitivity. This suggests that genetic regulation can influence how the body responds to changes in sugar and insulin levels [4].\n\n5. **Genetic Variation and Insulin Response**: Specific genetic variations, such as those in the GIPR gene, can influence glucose and insulin responses to an oral glucose challenge, highlighting the role of genetic differences in individual responses to sugar intake [9].\n\nOverall, genetic factors can influence both the secretion of insulin and the body's sensitivity to it, affecting how we metabolize sugar and respond to dietary changes.", + "role": "assistant" + } + } + ], + "created": 1728914215, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 334, + "prompt_tokens": 1215, + "total_tokens": 1549 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "3bbf736e-7d8b-5e67-a4bf-e1ae28738bf3", + "score": 0.6845700112754098, + "metadata": { + "text": "genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. Thisseems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with geneticallyprogrammed bcell dysfunction to precipitate diabetes.\nCitation: Jain P, Vig S, Datta M, Jindel D, Mathur AK, et al. (2013) Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS\nONE 8(1): e53522. doi:10.1371/journal.pone.0053522", + "title": "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "extraction_id": "9369222f-e125-58c0-8f2b-cf5daa867f77", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "ccf2d9af-4dca-5021-9c9d-301f817f80e4", + "score": 0.6621851526388447, + "metadata": { + "text": "have been the subject of most follow-up studies to date.Specifically, we examined acute changes in expression of\nthese genes in response to feeding and fasting and\nlonger term changes in the expression of these genes inresponse to a diet high in fat and sugar, recognized as a\ncritical environmental risk factor for type 2 diabetes.\nIt has been hypothesized that most of the new genetic\nvariants affect -cell function, development or survival\nbut not insulin sensitivity [6]. Consistent with this,", + "title": "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "98564dd2-424b-557a-a539-022508283567", + "extraction_id": "c9f74729-056d-556f-8aa8-e0f7a7bd6b66", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "d580609b-d24b-5718-ab63-0e6088c8bfeb", + "score": 0.6557703428542205, + "metadata": { + "text": "or survival. However, we also found evidence that most\nof the genes could have potential roles in other\nmetabolically-relevant tissues. Genes affecting insulinsensitivity may be expected to be expressed in peripheralinsulin sensitive tissues, such as liver and adipose tissue,\nand be responsive to metabolic status. Consumption of a\nhigh fat diet was associated with a tendency for the ex-\npression of several of these genes to be decreased. Simi-larly, many of the genes were regulated by feeding and", + "title": "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "version": "v0", + "chunk_order": 49, + "document_id": "98564dd2-424b-557a-a539-022508283567", + "extraction_id": "c9f74729-056d-556f-8aa8-e0f7a7bd6b66", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "3f90af62-9a1d-5ac2-b5ee-a616857b34df", + "score": 0.6532995311439328, + "metadata": { + "text": "secretion versus insulin sensitivity). We also sought todetermine whether any of these genes are regulated by\nconditions known to alter the expression of metabolic-\nally relevant genes. We examined the expression of thesegenes under fasting and non-fasting conditions (e.g. in\nresponse to insulin), which might be altered if they affect\nperipheral insulin sensitivity. Consumption of diets high\nin fats and sugars is associated with risk of developing\ntype 2 diabetes [34] and many genes that are critical for", + "title": "2013 - Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "98564dd2-424b-557a-a539-022508283567", + "extraction_id": "a9ec4c4f-b038-52d2-90db-7bee1ef1f78c", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "c171a147-2cf6-5340-82d4-caa63cdafbbd", + "score": 0.6383192374325708, + "metadata": { + "text": "regulating sugar metabolism. Moreover, genes that were", + "title": "2015 - Gestational Diabetes Alters Offspring DNA.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "59b51d61-b2c4-540b-a2fb-4c56badb26c1", + "extraction_id": "af8de1bb-e71e-514f-a5eb-59f37498028e", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "81eb21fb-488a-5b08-b883-cd8780110c66", + "score": 0.6332761267855103, + "metadata": { + "text": "Figure 2: The role of type 2 diabetes genes in insulin secretion\nPancreatic -cell genes associated with type 2 diabetes are in italics. G6P=glucose-6-phosphate. Adapted from Florez JC. Newly identi ed loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia 2008; 51: 110010, by kind permission of the author and Springer Science + Business Media. \nPositive calorie balance\nCycle A++\nCycle B Liver fat\n Insulin suppression of", + "title": "2010 - Diabetes in Asia.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "0be842b8-7f69-503b-baed-c336e5c834d6", + "extraction_id": "510b7c7b-ccbb-5d0d-b654-e1dbcf859cb7", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "9b60d258-714a-5e70-b2fa-b0a29fc0d672", + "score": 0.6329711298363738, + "metadata": { + "text": "tive Glis3 expression, which in turn drive increased levels of beta cell \napoptosis and senescence. Genetic susceptibility could be replicated \nby elevated levels of dietary fat. Transcriptional analysis of human \nislets identified the same genetic networks at play. Together, these \nfindings demonstrate both the important role of genetic variation in \nbeta cells for diabetes susceptibility and a mechanism by which the \nWestern diet may contribute to the growing diabetes epidemic.\nRESULTS", + "title": "2016 - Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "5b239c51-7b4c-58e0-acca-2061593fe317", + "extraction_id": "af25ae45-0c5f-5492-86d0-734eb0fbac12", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "dd3348a8-1f07-5e6d-8ba0-3c6c263c0799", + "score": 0.6316082101101761, + "metadata": { + "text": "associated with fasting proinsulin levels and provides new insights into the \npathophysiology of type 2 diabetes. Diabetes 60, 26242634 (2011).\n65. Saxena, R. etal. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142148 (2010).\n66. Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet. 42, 441447 (2010).", + "title": "2021 - Interpreting type 1 diabetes risk.pdf", + "version": "v0", + "chunk_order": 176, + "document_id": "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "extraction_id": "0bd0fae3-9297-5da2-b3ae-21190af84094", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "9080e28b-1c0d-5bfa-8698-7ae677aa64ed", + "score": 0.6283100468927605, + "metadata": { + "text": "38. Saxena R, Hivert M, Langenberg C, Tanaka T, Pankow JS, et al. (2010) Genetic\nvariation in GIPR influences the glucose and insulin responses to an oral glucose\nchallenge. Nat Genet 42: 142148. doi:10.1038/ng.521.\n39. Neale BM, Sham PC (2004) The future of association studies: gene-based\nanalysis and replication. Am J Hum Genet 75: 353362. doi:10.1086/423901.\n40. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, et al. (2007)", + "title": "2010 - Common Inherited Variation in Mitochondrial Genes.pdf", + "version": "v0", + "chunk_order": 288, + "document_id": "9a5c8cba-06cb-5280-871f-1bbe128c3dc4", + "extraction_id": "930831e1-56c3-5ef6-b847-2f25f2567032", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + }, + { + "id": "c2f1a416-7f04-55b0-b19b-8a8aa858b801", + "score": 0.6278498394578802, + "metadata": { + "text": "Nature Reviews | EndocrinologyFactors that aect insulin secretion and action\n Body weight\n Level of physical activity Smoking Heavy alcohol consumption Genetic predisposition Geneenvironment interaction\nPositive risk prole Negative risk prole\nNormoglycaemia/uni03B2-cell dysfunction and insulin resistanceAdipose tissue Skeletal muscle LiverInsulin-mediated \nglucose production /uni2191Insulin-mediated glucose uptake /uni2193 Insulin-mediated glucose uptake /uni2193\nHyperglycaemia\n Epigenetics", + "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "extraction_id": "8248ba2b-335c-53ce-afbe-f31b68507443", + "associatedQuery": "How do genes influence how our bodies respond to sugar and insulin?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIFrBAew5HsqHnMUVkuc9dpSmo0io", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, there are specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes.\n\nFor Type 1 diabetes, genome-wide association studies have identified over 50 loci associated with an increased genetic risk. Several candidate genes within these regions have been suggested or identified, although the exact molecular mechanisms by which they contribute to islet cell inflammation and beta cell destruction are not fully understood [1].\n\nFor Type 2 diabetes, specific genetic markers have also been identified. Some of the loci associated with an increased risk include TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX [9]. Additionally, markers such as TCF7L2 and CAPN10 have been strongly associated with the risk of developing Type 2 diabetes [8].\n\nThese findings indicate that while both types of diabetes have genetic components, the specific markers and loci associated with each type differ, reflecting their distinct pathophysiological mechanisms.", + "role": "assistant" + } + } + ], + "created": 1728914221, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 223, + "prompt_tokens": 1479, + "total_tokens": 1702 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "263dc0cb-dfa0-5ee2-b927-f9a196294d46", + "score": 0.7546218183862092, + "metadata": { + "text": "Genes signifying increased risk for both type 1 and type 2 dia-betes have been identified. Genomewide association studies have identified over 50 loci associated with an increased genetic risk of type 1 diabetes. Several T1D candidate genes for increased risk of developing type 1 diabetes have been sug-gested or identified within these regions, but the molecular basis by which they contribute to islet cell inflammation and beta cell destruction is not fully understood.\n12 Also, several", + "title": "2015 -precision-medicine-for-managing-diabetes.pdf", + "version": "v0", + "chunk_order": 27, + "document_id": "80949bab-d085-5f61-b98a-4bee043bc4e2", + "extraction_id": "f53ccf4e-f47f-5b44-8b41-f7068efc8be3", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "988cae28-e149-5190-8ff0-6ecce8d001bc", + "score": 0.7422494445890533, + "metadata": { + "text": "Genetics of Type 2 Diabetes Chapter 12\n197400 multiallelic markers (short tandem repeats or microsatellites, \nwith a density of 1 marker/10 cmol) allows identi cation of \npolymorphic markers showing strong allele identity by descent in diabetic family members (i.e. allele sharing in sibships is signi -\ncantly higher than 50%). Once identi ed, such susceptibility \ngenes for diabetes may then be positionally cloned in the intervals of linkage.", + "title": "2010 - The Genetics of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "extraction_id": "2610c9c1-5e75-528e-98d8-c4a543ea2f89", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "ca9e53b7-6e51-5ae6-9ef4-8f2f5f40acb5", + "score": 0.7348826904629888, + "metadata": { + "text": "3. Katsarou, A. etal. Type 1 diabetes mellitus. Nat. Rev. Dis. Primers 3, 17016 (2017).\n4. Onengut-Gumuscu, S. etal. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. \n47, 381386 (2015).\n5. Barrett, J. C. etal. Genome-wide association study and meta-analysis find that over 40 \nloci affect risk of type 1 diabetes. Nat. Genet. 41, 703707 (2009).", + "title": "2021 - Interpreting type 1 diabetes risk.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "extraction_id": "254be2dd-1b4f-5cf9-af93-dbf3d5867510", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "61f523a8-f466-5148-afba-6400c44ed278", + "score": 0.7347316145896964, + "metadata": { + "text": "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes\nn engl j med 359;21 www.nejm.org november 20, 2008 2229(Fig. 3). An increase in the BMI and a concomi -\ntant decrease in insulin sensitivity during the \n8-year period were consistent findings, with no differences between subjects at high and low genetic risk (Fig. 3A and 3B). However, subjects with a high genetic risk did not increase their insulin secretion (disposition index) to compen -", + "title": "2008 - Clinical Risk Factors, DNA Variants.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "extraction_id": "640f3749-a2bf-5b6b-adab-72ce7f029a28", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "151d8a78-8aa8-5024-8e15-54fba4f1857b", + "score": 0.7289990565479112, + "metadata": { + "text": "and genetic markers to improve the prediction of type 2 diabetes: theEPIC-Potsdam Study. Diabetes Care . 2009;32:2116 2119 (in eng).\n56. Cauchi S, Meyre D, Durand E, et al. Post genome-wide association\nstudies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. PLoS One . 2008;3(5):\ne2031 .\n57. Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA\nvariants, and the development of type 2 diabetes. N Engl J Med .\n2008;359:2220 2232 (in eng).", + "title": "2018 - Quantitative Relationship Between Cumulative Risk Alleles Based.pdf", + "version": "v0", + "chunk_order": 186, + "document_id": "d585896e-1c32-51cb-827d-e4fd3b3943f3", + "extraction_id": "6db9f25e-36fd-51c0-be36-6dfacd963b1b", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "f692be48-b905-5463-8101-22eaf14e6405", + "score": 0.7246813506267845, + "metadata": { + "text": "etically expressed homeobox variant (rs1111875) on type 2 diabetes risk. Molecular Genetics \nand Metabolism , 102 (2), 194199. \n Watanabe, R. M., Black, M. H., Xiang, A. H., Allayee, H., Lawrence, J. M., & Buchanan, T. A. (2007). \nGenetics of gestational diabetes mellitus and type 2 diabetes. Diabetes Care , 30 (Suppl. 2), \nS134S140. \n Williams, M. A., Qiu, C., Dempsey , J. C., & Luthy , D. A. (2003). Familial aggregation of type 2", + "title": "2011 - Shared Genomics of Type 2 and Gestational Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 232, + "document_id": "bef0cabe-0bca-5715-9ffc-0b825744fbcf", + "extraction_id": "41fefdf5-447e-556e-b95f-c132bdea7c41", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "48c93a37-d0d5-51de-b2d1-5c6122c01ab1", + "score": 0.7246804021394162, + "metadata": { + "text": "markers, genetic markers do not change with disease progression.Dimas and collaborators examined the association of 37 establishedT2D susceptibility loci and indices of proinsulin processing, insulin\nsecretion, and insulin sensitivity in 58,614 nondiabetic subjects [6].\nCluster analysis classi ed the risk loci into ve major categories on the\nbasis of their association with glycemic phenotypes. The rst cluster\nwas characterized by the effects of the risk alleles of PPARG ,KLF14 ,", + "title": "2019 - Biomarkers for type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 66, + "document_id": "c8ee94fc-f9bc-5a32-9524-9d1d9cf37159", + "extraction_id": "bc4717c3-d353-5f44-9513-50634f8d5196", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "82debd98-f2fe-51aa-931c-63e11249de7b", + "score": 0.7228839296906133, + "metadata": { + "text": "recently, meta-analysis of GWAS data involving African \nAmerican type 2 diabetes patients identified similar loci \nto the previous studies with the addition of two novel \nloci, HLA-B and INS-IGF[157]. These results provide \nstrong evidence of common genetic determinants \nincluding common specific genes that are linked to \ndiabetes. A small list of specific genetic markers seem \nstrongly associated with the risk of developing type 2 \ndiabetes including the TCF7L2[158] and CAPN10[159,160]", + "title": "2015 - Diabetes mellitus The epidemic of the century.pdf", + "version": "v0", + "chunk_order": 86, + "document_id": "e114dd28-fd39-56df-bdeb-8806474a6c10", + "extraction_id": "7cfe9f29-a0ee-56d3-be3b-1b238a43bc07", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "8469faae-c6c9-5fd4-8437-870eef394dd1", + "score": 0.7217622896857948, + "metadata": { + "text": "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes\nn engl j med 359;21 www.nejm.org november 20, 2008 2231MPP subjects (P = 0.001) and from 0.79 to 0.83 in \nthe Botnia subjects (P = 0.006).\nOf the 16 loci that have been associated with \ntype 2 diabetes previously,8-15 we showed that 11 \n TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, \nCDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX \nwere associated with an enhanced risk of future", + "title": "2008 - Clinical Risk Factors, DNA Variants.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "extraction_id": "0aae948a-50f9-568a-b0dc-5960a2d2ceaa", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + }, + { + "id": "387e1774-0250-5c72-b11c-069bdf3ef9ea", + "score": 0.7195725179228165, + "metadata": { + "text": "Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes\nn engl j med 359;21 www.nejm.org november 20, 2008 2227(Fig. 1B), whereas impaired fasting glucose or \nimpaired glucose tolerance developed in 313 of 2039 subjects (15.4%).\nClinical Factors Predicting Incidence \nof Diabetes\nIn both the MPP and Botnia studies, a family his -\ntory of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of tri -", + "title": "2008 - Clinical Risk Factors, DNA Variants.pdf", + "version": "v0", + "chunk_order": 57, + "document_id": "0018610a-9c86-5e2d-a27d-f66cf4f8519d", + "extraction_id": "38bacfcd-d182-5220-b8bc-18f6c74b14a8", + "associatedQuery": "Are there specific genetic markers that can indicate a higher risk for Type 1 versus Type 2 diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIFrIc4qRPbtDcHbcrNOicZwU9hKr", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "New gene therapies have the potential to cure or treat diabetes through several innovative approaches:\n\n1. **Gene-Based Therapies**: Advances in understanding the biological mechanisms that maintain glucose homeostasis and the molecular defects leading to chronic hyperglycemia could lead to the development of gene-based therapies. These therapies aim to target specific genetic factors involved in diabetes, potentially offering more precise treatment options [3].\n\n2. **In Vivo Gene Therapy**: This approach involves directly inserting a vector containing the desired gene into the patient. It is considered a promising therapeutic strategy for type 1 diabetes, although challenges remain in developing safe and effective vectors [9].\n\n3. **Inducing Islet Neogenesis**: Gene therapy techniques, such as betacellulin gene therapy, have been shown to induce islet neogenesis in the liver and reverse diabetes in mice. This suggests that gene therapy can stimulate the body to produce insulin-producing cells, offering a potential cure for diabetes [10].\n\nThese strategies highlight the potential of gene therapies to address the underlying genetic causes of diabetes and restore normal insulin production and glucose regulation.", + "role": "assistant" + } + } + ], + "created": 1728914228, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 217, + "prompt_tokens": 1323, + "total_tokens": 1540 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "59cce584-cd38-52d1-bdaa-d5500175eefb", + "score": 0.6679459810256958, + "metadata": { + "text": "unraveling the pathophysiological mechanisms of this disease, identifying \ncandidate diabetic genes, and discovering and testing new therapeutic agents. \nThe classical rodent models of diabetes allow unbiased discovery, while the \nnew models made by genetic manipulation allow testing of the role of \nspecific genes and tissues. Experimental animal models are an irreplaceable \nresource for diabetes research and are hastening the progress towards the \ngoals of better treatment, prevention, and cure.", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 773, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "a5ae065c-371f-5459-830b-7a34891ca091", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "d37e62ab-6261-5f14-8423-3b6e2574422e", + "score": 0.6633472838012757, + "metadata": { + "text": "is absence of reliable methods for generating specific celltypes,immunologicalrejectionofthetransplantedcells,anddifficulty in purification of specific lineages [55]. Furtherconcernsincludetheuncontrolledproliferationofthetrans-planted embryonic stem cells into a specific type, once theyaretransplanted[56].Still,despiteofitsmanifoldlimitationsboth scientific and ethical, the application of stem cell tech-nologyholdsimmenseprospectsintreatmentofdiabetes.\n6. Gene Therapy in Diabetes", + "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "version": "v0", + "chunk_order": 62, + "document_id": "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "extraction_id": "6b2ac076-ee4b-53b3-b49b-1d15f46e6a98", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "f4e8a3c8-0b85-5595-8917-933aced8b3ba", + "score": 0.6623994309425187, + "metadata": { + "text": "T ogether, these discoveries will continue to improve our \nunderstanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to \nchronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene -\n based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "title": "2010 - The Genetics of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 258, + "document_id": "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "extraction_id": "c4de4c07-4749-5401-bbf3-16988c132852", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "cb7178a0-7015-555c-801a-cd2d258cf3dc", + "score": 0.6559618711471558, + "metadata": { + "text": "Greatstrideshavebeenmadeclinicallyintheprevention,\ndevelopment,andtreatmentofthediseasebutnotherapeuticmethod have been completely successful till date. With newtechnologies revolutionizing the treatment possibilities, thesearch for an effective medication is not far ahead. Theextensive research leading to the discovery of the pathwaygenes contributing to the development of the disease andthe sequencing of complete genomes have revolutionized\nthe diabetes research. The development of the techniques", + "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "extraction_id": "48643e77-c5b4-5042-8f08-82c986d9f5b2", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "d5963c8e-686f-52f5-a6de-b978d5c40e20", + "score": 0.6545608455466263, + "metadata": { + "text": "into different genetic levels of disease categories, from which pre-\nvention or treatment methods could be provided accordingly [ 4].\nFor example, some forms of diabetes are directly related to a change\nin a single gene [ 34]. Some patients who are diagnosed with type 1\ndiabetes can now be tested for one of monogenic diabetes. The\nappropriate treatment for these patients is not injecting insulin, but\ngiving oral sulfonylureas [ 34]. Moreover, it is now well understood", + "title": "2018 - A theoretical framework for interaction of nursing discipline.pdf", + "version": "v0", + "chunk_order": 35, + "document_id": "4c90f95f-3365-522e-9eb4-9ea002beddb2", + "extraction_id": "abf78c3a-ad53-5c86-979d-2d9d176a51a4", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "0b4a495d-fdee-515a-a524-d9415b17f97e", + "score": 0.6466418504714966, + "metadata": { + "text": "pp .430435,2003.\n[58] M. Zalzman, S. Gupta, R. K. Giri et al., Reversal of\nhyperglycemia in mice by using human expandable insulin-\nproducing cells differentiated from fetal liver progenitor cells,Proceedings of the National Academy of Sciences of the United\nStatesofAmerica ,vol.100,no .12,pp .72537258,2003.\n[59] H.-S. Jun and J.-W. Yoon, Approaches for the cure of type 1\ndiabetes by cellular and gene therapy, Current Gene Therapy ,\nvol.5,no.2,pp.249262,2005.", + "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "version": "v0", + "chunk_order": 124, + "document_id": "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "extraction_id": "168e94e9-e8c2-547c-878a-1e5306564193", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "13b73999-262c-50e1-b668-2d5f7ca02067", + "score": 0.6438010334968567, + "metadata": { + "text": "transgenics. It is likely that animal models will play an importantrole in the eventual cure of human diabetes mellitus. \nCompeting interests \nNone declared. \nReferences \n1Sima AAF, Shafrir E, eds. \nAnimal Models of Diabetes: A Primer. \nAmsterdam: Harwood Academic Publishers, 2000.\n2British Union for the Abolition of Vivisection. Home page. Available\nfrom: http://www.buav.org.\n3Patterson C. \nEternal Treblinka. Our Treatment of Animals and the\nHolocaust \n. New York: Lantern Books, 2002.\n4Regan T.", + "title": "2005 - Animal models of diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "extraction_id": "3dca156c-64c4-577f-b0a6-069de0f31234", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "1299cc23-f6b0-5801-bead-b46ac90bc3a8", + "score": 0.6380507946014404, + "metadata": { + "text": "Third, this view of diabetes pathogenesis is consistent with\nthe growing portfolio of available therapies. We have agents\nand interventions that can prevent or ameliorate diabetesthrough, for example, beneficial effects on islet function\n(e.g. sulfonylureas), obesity (weight loss), insulin resistance\n(e.g. exercise), fuel partitioning (e.g. thiazolidinediones) andmicrobiome content (metformin, possibly). Just as diabetes\nrisk alleles influence metabolic phenotype through pushing", + "title": "2017 - Painting a new picture of personalised medicine for diabetes.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "e226b2b1-0bc4-5d79-b931-ad47f21be045", + "extraction_id": "1cd3076d-af86-55d7-903c-9065bc640af0", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "3201da93-5a34-5164-8bf4-c98d32019019", + "score": 0.6354519724845886, + "metadata": { + "text": "aprospectivetherapeuticapproachfortype1diabetes[59].\nThein vivogene therapy is the method of choice as\na therapeutic strategy because it is simpler and the vectorcontaining the desired gene is directly inserted into thepatient, but the development of safe (not toxic to host)and effective vectors remains as a challenging task for genetherapist. Presently, the strategies for in vivotherapy involve", + "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "version": "v0", + "chunk_order": 67, + "document_id": "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "extraction_id": "6b2ac076-ee4b-53b3-b49b-1d15f46e6a98", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + }, + { + "id": "0418b345-7005-5d7d-a79f-570fb61bd14b", + "score": 0.6338961938681498, + "metadata": { + "text": "betacellulin gene therapy induces islet neogenesis in the liver\na n dr e v e r s e sd i a b e t e si nm i c e , Nature Medicine ,v o l .9 ,n o .5 ,\npp.596603,2003.\n[73] S. Ferber, A. Halkin, H. Cohen et al., Pancreatic and duode-\nnal homeobox gene 1 induces expression of insulin genes inliver and ameliorates streptozotocin-induced hyperglycemia,\nNature Medicine ,vol.6,no .5,pp .568572,2000.\n[74] P.A.Halban,S.E.Kahn, A.Lernmark,andC.J.Rhodes,Gene\nandcell-replacementtherapyinthetreatmentoftype1diabetes.", + "title": "2015 - Recent Trends in Therapeutic Approaches for Diabetes Management A Comprehensive Updat.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "ec4921c2-af14-56cc-aed3-65f8ea236bde", + "extraction_id": "168e94e9-e8c2-547c-878a-1e5306564193", + "associatedQuery": "How can new gene therapies potentially cure or treat diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIFrQL8Phj0Q41oiXeOOZsabJcULg", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Monogenic and polygenic diabetes are distinct forms of diabetes with different genetic underpinnings:\n\n1. **Monogenic Diabetes**: This form of diabetes results from a mutation in a single gene that is highly penetrant, meaning it has a strong effect on the individual who carries it [1], [6]. Monogenic diabetes is often associated with defects in beta-cell function, leading to a decrease in the number or function of these cells [2]. It is typically characterized by early onset, often before the age of 25, and can include conditions like Maturity-Onset Diabetes of the Young (MODY) [5]. Monogenic diabetes is relatively rare, representing about 2%-5% of diabetes cases [2].\n\n2. **Polygenic Diabetes**: In contrast, polygenic diabetes results from the combined effect of multiple genetic variants, each contributing a small effect, along with environmental and lifestyle factors [1], [6]. This form of diabetes is more common and is the predominant mode of inheritance in type 2 diabetes [7]. The genetic variants involved in polygenic diabetes do not have as strong an effect individually as those in monogenic diabetes, but together they contribute to the disease risk in the presence of other factors like obesity and sedentary lifestyle [3].\n\nIn summary, monogenic diabetes is caused by a single gene mutation with a strong effect, while polygenic diabetes involves multiple genes with smaller effects combined with environmental influences.", + "role": "assistant" + } + } + ], + "created": 1728914236, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 289, + "prompt_tokens": 1172, + "total_tokens": 1461 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c38627c6-0dd1-5dec-aeb6-ea1edb924480", + "score": 0.7862402200698853, + "metadata": { + "text": "to improve diagnosis. Monogenic vs. polygenic diabetes \nMonogenic and polygenic diabetes are traditionally considered distinct, with \nmonogenic diabetes resulting from one highly penetrant variant in one gene in a given \nindividual, and polygenic diabetes resulting from the contribution of several variants with \nsmaller effects in the context of environmental/lifestyle factors. In T1D, autoimmune \ndysfunction is the prominent mechanism, with variation in the major histocompatibility", + "title": "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf", + "version": "v0", + "chunk_order": 79, + "document_id": "e315a891-ba59-57e9-856b-602544375324", + "extraction_id": "29df597d-e40d-5bc8-8ee0-89141d8e7fc0", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "d704148a-88c0-58fe-810a-89b767a1f53b", + "score": 0.7451968789100647, + "metadata": { + "text": "represent about 2%-5% of diabetes patients. Mono -\ngenic diabetes results primarily from gene defects that \nlead to a decrease in beta cell number or function. \nMonogenic diabetes genes were identified using linkage \nstudies or code for proteins that directly affected \nglucose homeostasis. The majority of genes responsible \nfor monogenetic diabetes code for either transcription \nfactors that participate in the control of nuclear gene \nexpression or proteins that are located on the cell", + "title": "2015 - Diabetes mellitus The epidemic of the century.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "e114dd28-fd39-56df-bdeb-8806474a6c10", + "extraction_id": "e119acfb-4ad6-515e-a1bb-7796d283befc", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "7bbf950d-cbf8-5221-8ea6-b3571fab4fad", + "score": 0.7153393030166626, + "metadata": { + "text": "diabetic patients inwhom rare, highly\npenetrant mutations ofasingle gene\ncause their diabetes (13). While com -\nmon variants ofthese genes that make a\nsmall contribution topolygenic diabetes\nmay also exist (13), thevariants causing\nmonogenic diabetes have limited util-\nityinpharmacogenetics duetotheir low\nallele frequency. Thevast majority oftype\n2diabetes patients have polygenetic forms\nofthedisease that typically also require a\npermissive environment (e.g., obesity, sed-", + "title": "2007 - Pharmacogenetics of metformin response a step in the path toward personalized medicine.pdf", + "version": "v0", + "chunk_order": 31, + "document_id": "12344230-0ed1-516f-bf2d-9c6e71ac76b5", + "extraction_id": "c66bcb9f-15af-5843-9e9c-168e8cf230d0", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "35efeaf0-c6b6-509e-9426-d23c8727164f", + "score": 0.7151061296463013, + "metadata": { + "text": "diabetes exist along more of a continuum than previously appre -\nciated. Therefore, knowledge about monogenic diabetes not only \nprovides opportunities for etiology-based treatment of the minori-\nty of individuals with highly penetrant variants, but also informs \nbroader understanding of diabetes etiology.\nTypes of monogenic diabetes\nMaturity-onset diabetes of the young\nMODY comprises most monogenic diabetes cases, with classical \ncharacteristics of young diagnosis age, family history of diabe -", + "title": "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "extraction_id": "38df3fac-cb86-5e74-b270-1e1e9e12dcdb", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "55e16624-4a02-5fba-bbe7-a07db8559401", + "score": 0.7041106643742732, + "metadata": { + "text": "Monogenic Diabetes \n Monogenic diabetes is a class of diabetes associated with genetic defects in beta - cell function. They are frequently associated with early onset of hyperglycemia (typically before 25 years of age). Three common forms of mono-genic diabetes include maturity - onset diabetes of the", + "title": "2012 - Systems Biology Approaches to Nutrition.pdf", + "version": "v0", + "chunk_order": 9661, + "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", + "extraction_id": "8d7fefe4-325f-5c64-9fee-0587c545d5ab", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "854afd34-91ed-5817-b24e-1fd5894261f3", + "score": 0.7016476810960592, + "metadata": { + "text": "HNF4A-MODY and requires genetic testing to diagnose. Here \nwe will describe monogenic diabetes types, etiologies, diagnosis, \nmanagement, and strategies to improve diagnosis.\nMonogenic versus polygenic diabetes\nMonogenic and polygenic diabetes are traditionally considered \ndistinct, with monogenic diabetes resulting from one highly pene -\ntrant variant in one gene in a given individual and polygenic diabe -\ntes resulting from the contribution of several variants with smaller", + "title": "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "extraction_id": "5a39ee4b-ba00-56d6-ba6c-0edeac3b4f2e", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "a74728c6-2903-5035-afd9-0f6a0f0c295e", + "score": 0.7016175389289856, + "metadata": { + "text": "Monogenic inheritance is caused by mutation of a single gene. There are \nsome well-defined monogenic rodent models. In humans, monogenic obesity \nand diabetes exist as well, but are extremely rare. \nPolygenic inheritance is the result of multiple contributing genes and \nis the predominant mode of inheritance in human type 2 diabetes. Multiple \npolygenic animal models are also available. However, even in monogenic \nanimal models, genetic background plays an important influence. For", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 741, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "c6bf083c-f045-55e2-9eae-ff96a4ceea4c", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "5b06a49e-7ef9-558a-b402-11866c555cd5", + "score": 0.6908171772956848, + "metadata": { + "text": "(Mendelian) that may also cause type 2 diabetes (Yang & Chan, 2016). More than twenty genes highly expressed in pancreatic cells have been identified within these mono-genic subtypes (AlkortaAranburu et al., 2014). Recently, two national surveys revealed that most patients with mono-genic diabetes are likely to be unrecognized and misdiag-nosed as type 1 or type 2 diabetes (Delvecchio et al., 2017; Johansson et al., 2017). Genetic diagnosis leads to improved treatment, better prediction of disease", + "title": "2019 - HDAC4 mutations cause diabetes and induce \u2010cell FoxO1 nuclear exclusion.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "7e4028b2-d5c1-5ddc-a06d-fd4f767d0f39", + "extraction_id": "7f53ea65-79ed-5207-9397-68b6d14bc19c", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "fedbf66e-cfd2-52bb-b9db-393d815aade7", + "score": 0.690174400806427, + "metadata": { + "text": "Key words:\ndiabetes, gene, polygenic, monogenic\nIntroduction\nDiabetes is one of the most common metabolic disor -\nders. It is estimated that the number of diabetes pa -\ntients worldwide has already exceeded 200 million\n[92]. This creates a need to understand the etiology ofthe disease, genetic and enviromental factors influ -\nencing development of diabetes. Diabetes is a group\nof metabolic diseases that are characterized by ele -\nvated glucose level. Poorly controlled or undiagnosed", + "title": "2005 - Type 2 diabetes mellitus from genes to disease.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "52687a38-6a4b-51d2-aafa-812c76981dfe", + "extraction_id": "d79047d9-58d0-5440-b63c-e648b5df5538", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + }, + { + "id": "c48920f3-1236-5921-b2a1-f09edba1e7ec", + "score": 0.689247727394104, + "metadata": { + "text": "2 \n 1.1.2 Introduction \nMonogenic diabetes is caused by a single defect in one of over 40 genes1,2. Since \nMODY (maturity onset diabetes of the young) was named by Fajans for the T2D -like \npresentation in young people with an autosomal dominant pattern of inheritance3,4, our \nunderstanding of phenotypic and genetic heterogeneity in monogenic diabetes has \nincreased. The major monogenic diabetes categories are MODY, neon atal diabetes", + "title": "2021 - Genomic Medicine in Diabetes Improving the Diagnostic Rate of Monogenic Diabetes.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "e315a891-ba59-57e9-856b-602544375324", + "extraction_id": "29df597d-e40d-5bc8-8ee0-89141d8e7fc0", + "associatedQuery": "What is the difference between monogenic and polygenic diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIFrXDhQoyphIy4Ti77mFez47y0g5", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Studying the DNA of people with diabetes helps scientists find better treatments or cures in several ways:\n\n1. **Identification of Genetic Determinants**: By performing genetic profiling on diabetic patients, scientists can identify genetic determinants that define the targets of current and future therapies. This leads to the development of therapies that are more specific to the genetic makeup of individuals with diabetes [1].\n\n2. **Understanding Disease Mechanisms**: Genetic studies improve our understanding of the biological mechanisms that maintain glucose homeostasis and reveal molecular defects leading to chronic hyperglycemia. This knowledge can lead to the development of more specifically targeted antidiabetic drugs or even gene-based therapies [4].\n\n3. **Pharmacogenetics**: Pharmacogenetic testing can be used to predict therapeutic responses to different classes of drugs for each patient, allowing for more personalized treatment plans [4].\n\n4. **Discovery of New Therapeutic Targets**: A greater understanding of the genetic and epigenetic basis of diabetes can enable the discovery of new therapeutic targets, potentially leading to novel treatments for diabetes and its complications [3].\n\n5. **Stratification of Diabetes Subclasses**: By analyzing DNA variations and their interactions with environmental factors, scientists can stratify type 2 diabetes into subclasses. This stratification allows for more effective treatment strategies tailored to specific genetic and lifestyle interactions [8].\n\n6. **Identification of Key Genetic Elements**: Genetic studies can identify key genetic elements that determine susceptibility to diabetes, disease progression, and responsiveness to specific therapies. This information helps in identifying novel targets for future interventions [9].\n\nOverall, studying the DNA of people with diabetes provides critical insights that drive the development of more effective and personalized treatments.", + "role": "assistant" + } + } + ], + "created": 1728914243, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 337, + "prompt_tokens": 1130, + "total_tokens": 1467 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "e7d89095-ef66-5c11-982c-879791dd14a4", + "score": 0.7071230586097814, + "metadata": { + "text": "by performing a genetic profile on diabetic patients (pharmacogenetics). \nFurthermore, identification of genetic determinants of diabetic patients will \nbetter define the targets of current and future therapies, and will lead to \ntherapies that are more specific for their genetic constitutes. \nSUMMARY \nWith the advancement of the Human Genome Project, we enter the \nera of a sequence-based biology. Some progress has been made in the", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 685, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "a19924b0-a834-5100-8b24-6b57dcddb82a", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "c21caf96-f04a-551d-92b2-f4ff084d43c8", + "score": 0.6807519983797087, + "metadata": { + "text": "Todate,studiesofdiabeteshaveplayedamajorroleinshapingthinkingabout\nthegeneticanalysisofcomplexdiseases.Basedontrendsingenomicinformationandtechnology,combinedwiththegrowingpublichealthimportanceofdiabetes,diabetes will likely continue to be an important arena in which methods will bepioneeredandlessonslearned.Itiswithgreatenthusiasmthatwelookforwardtothis effort, and with avid curiosity we await to see whether the lessons of todaywill be supported by the data of tomorrow.", + "title": "2003 - The Inherited Basis of Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 172, + "document_id": "7b85b290-d711-55d5-9b1e-b06e4d6f14a2", + "extraction_id": "2aa8d99c-99d7-55de-aa2a-c24a46ea9058", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "fceee048-359b-5854-b45d-5531b9374ce8", + "score": 0.6783680511493665, + "metadata": { + "text": "DNA code. Therefore, greater unders tanding of the epigenetic basis of disease could enable the 576 \ndiscovery new therapeutic targets for the treat ment of numerous human diseases including 577 \ndiabetes and its complications. 578 \n 579 \n580", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "919cb859-8f47-5930-8713-090520be523f", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "f4e8a3c8-0b85-5595-8917-933aced8b3ba", + "score": 0.6781570709300941, + "metadata": { + "text": "T ogether, these discoveries will continue to improve our \nunderstanding of the biologic mechanisms that maintain glucose homeostasis, and of still hidden molecular defects leading to \nchronic hyperglycemia, and could also lead to the development of more speci cally targeted antidiabetic drugs or even gene -\n based therapies. Moreover, pharmacogenetic testing might then be used to predict, for each patient, the therapeutic response to different classes of drugs. The identi cation of T2DM genes will", + "title": "2010 - The Genetics of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 258, + "document_id": "a1d211d4-279e-51d7-b2b2-33bc2763d089", + "extraction_id": "c4de4c07-4749-5401-bbf3-16988c132852", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "df21554e-6053-53ae-aae5-e3d1dba1b1f5", + "score": 0.6774097479161723, + "metadata": { + "text": "research will contribute positive ly to the life of people living with T1D . Being able pinpoint \nmutations, and then discover how they contribute to the genetic cause of a condition, can help \nto open up path s for pharmaceutical treatments. Currently, m ost treatment strategies for genetic \ndisorders do not alter the underlying genetic mutation; but are designed to improve particular \nsigns and symptoms associated with the disorder. For instance, T1D is managed by", + "title": "2016 - The Genomics of Type 1 Diabetes.pdf", + "version": "v0", + "chunk_order": 490, + "document_id": "4933cdc2-7d36-5181-87c9-63b58498839f", + "extraction_id": "5e43ab7d-3e2b-551c-9a90-f91e970cb8d7", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "db06230d-31c0-5947-8c1c-f58c48b6f439", + "score": 0.6734455021088717, + "metadata": { + "text": "Epigenomic approaches: applications in diabetic\ncomplications research\nEpigenetic studies in human disease have been greatly accel-\nerated as a result of advances in whole-genome and epige-\nnome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is\nanalysed using bisulfite conversion of genomic DNA, immu-\nnoprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "title": "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "470f1f94-792d-5273-a88f-7e06084951c5", + "extraction_id": "312b1856-e1b1-5ae7-8cba-370becf5f7cb", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "e0b86e8e-4e1a-5f6b-9b41-e9a4f912790c", + "score": 0.6734237269506629, + "metadata": { + "text": "new therapeutic targets and identify potential diabetic neuropathy\nbiomarkers. The genes identied in the current study conrm datagathered from experimental models of diabetes and provide a\ncomprehensive picture of the expression of multiple targets in asingle human tissue sample.\nOur initial analyses of this data set classied the patient samples\nbased on myelinated bre density and found that two large\ngroups emerged; those with a loss of myelinated bre density\n5500 bres/mm", + "title": "2011 - The identification of gene expression.pdf", + "version": "v0", + "chunk_order": 79, + "document_id": "61558082-f092-5a1d-abbb-a5a81e8a959b", + "extraction_id": "e5e4169a-56d8-539f-8ebc-ad44eb75433f", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "cc98a5b9-131e-5b60-919e-82e86b7a37a7", + "score": 0.670618513748823, + "metadata": { + "text": "DNA variation with disease processes in a range of settings, from cell\nlines to human populations, and major advances have been made in\ncoupling these complex datasets with information about extrinsic\nenvironmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions.\nDoing so may teach us about disease etiology and help stratify type 2\ndiabetes (T2D) into subclasses that can be treated more effectively, with", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "c362793d-c70f-5225-afe5-88098042daef", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "score": 0.6671704848307094, + "metadata": { + "text": "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + }, + { + "id": "efd7c210-858d-5125-8da9-46862e19a58a", + "score": 0.6628995343566496, + "metadata": { + "text": "the onset and progression of diabetic neuropathy is of primeimportance.\nThe current study takes an important rst step towards this goal\nby identifying specic sets of genes whose expression accurately\nclassies patient samples with regard to diabetic neuropathy pro-\ngression and by analysing their interactions within known cellularpathways. Identifying common elements in these complex net-works will yield novel insights into disease pathogenesis, provide", + "title": "2011 - The identification of gene expression.pdf", + "version": "v0", + "chunk_order": 78, + "document_id": "61558082-f092-5a1d-abbb-a5a81e8a959b", + "extraction_id": "e5e4169a-56d8-539f-8ebc-ad44eb75433f", + "associatedQuery": "How does studying the DNA of people with diabetes help scientists find better treatments or cures?" + } + } + ], + "kg_search_results": null + } + } + } +}
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The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "d8162fdc-326a-5f90-9fa4-24d86d701184", + "score": 0.7354278564453125, + "metadata": { + "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "91e1f097-b446-5915-9fcb-d38640d8a14a", + "score": 0.7285189032554626, + "metadata": { + "text": "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome \nBrowser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; \nManhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics\n1 Introduction\nGeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started \nin 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "f28836b7-0091-59ff-8d31-2ccad7341718", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "488b9f81-e94f-56ad-9f28-dd71f3acd31f", + "score": 0.7285189032554626, + "metadata": { + "text": "inbred strain; Reverse genetics; dbSNP; GeneWeaver; BioGPS; NCBI; GeneRIF; UCSC Genome \nBrowser; Gemma; GEO; Allen Brain Atlas; GWAS Catalog; GTEx; WebGestalt; PLINK; \nManhattan plot; eQTL analysis; R/qtl; WGCNA; Proteomics; Metabolomics; Metagenomics\n1 Introduction\nGeneNetwork ( www.genenetwork.org , GN) is a web service for systems genetics. It started \nin 2001 as WebQTL an online version of Ken Manlys Map Manager QT program [ 1]", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "f7d5751d-c84d-5332-9dde-f31293ff02e3", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.7264658808708191, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.7258278890950759, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "score": 0.7258278890950759, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. 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As\nappropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "title": "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "extraction_id": "8c423789-3641-5853-9cf3-f4a026ffb446", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + }, + { + "id": "40ecbff2-5039-57f0-a01e-9d412d559dbe", + "score": 0.7141967415809631, + "metadata": { + "text": "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other\ninvestigators can look for correlations between gene expression patterns and phenotypic traits.\nThe GeneNetwork is an open resource and consists of a set of linked resources for systems\ngenetics. It has been designed for integration of networks of genes, transcripts, and traits such\nas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "title": "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf", + "version": "v0", + "chunk_order": 122, + "document_id": "301d6469-2a9c-5960-88ac-8437212d78ab", + "extraction_id": "3ca48658-ca83-5952-8f8d-eb7ae491e6b6", + "associatedQuery": "What is GeneNetwork.org, and how does it help scientists understand genetics?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIGlD8JegvZvagzZ7ZZc2o1BsPAjA", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Researchers use GeneNetwork.org to study diseases by leveraging its capabilities as a bioinformatics tool for systems genetics analysis. This platform allows researchers to explore large phenotype and genome datasets from multiple species, which are essential for understanding complex biological networks and predicting molecular interactions [4], [5]. \n\nGeneNetwork.org supports a systems genetics approach, which examines how diverse sets of genetic and molecular markers contribute to phenotypes and diseases, rather than focusing on single gene mutations [2]. This approach is facilitated by the extensive data available on the platform, including gene expression patterns and drug response data, which can be compared and analyzed statistically [4].\n\nThe platform also enables correlation and network analysis, allowing researchers to compare associations between tissues and across different species, such as rodents and humans [6]. By studying networks of genes, proteins, metabolites, and other biomarkers, researchers can model genuine biological pathways, which helps in uncovering disease genes and understanding complex diseases [9]. \n\nOverall, GeneNetwork.org provides a comprehensive resource for predictive medicine and systems genetics, aiding researchers in constructing biological networks that are predictive of disease outcomes [1], [5].", + "role": "assistant" + } + } + ], + "created": 1728917695, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 223, + "prompt_tokens": 984, + "total_tokens": 1207 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7279516620664811, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "score": 0.7107029729662566, + "metadata": { + "text": "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While\nthe candidate gene approach asks which one gene mutation causes a particular disease, the\nsystems genetics approach explores which phenotypes and diseases result from diverse sets\nof genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in\nGeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "1c8d31d6-bd59-56da-83b8-f603b4a9ec2b", + "score": 0.706132012240172, + "metadata": { + "text": "Based on this, Goh et al. created networks using data from the Online\nMendelian Inheritance in Man (OMIM) [18]database that houses lists of\ndisease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in\nthe same gene, and the disease gene network where gene nodes were", + "title": "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "96657025-7e50-571d-9a6b-1a202cb8a690", + "extraction_id": "82e07232-dd92-52f6-8230-d90a03c71b4f", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.705388682759484, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.7040288624746298, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "score": 0.7006892322672329, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "score": 0.700666957974383, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "d348dfa8-c6c5-5514-8c64-920f254b9571", + "score": 0.6971657068382021, + "metadata": { + "text": "atic way. Users begin by selecting one or more human diseases and\nclicking on Compare. The genes associated with the selected disease\nare tested for enrichment against all sets of known associat ed genes for\nworm phenotypes. The result reveals functionally coherent , evolution-\narily conserved gene networks.\nAlternatively, users can also start by selecting worm pheno types,\nwhich are tested against human diseases. In addition to cross -species", + "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "version": "v0", + "chunk_order": 304, + "document_id": "ef974b09-4ea2-5382-85e5-c2169f440fda", + "extraction_id": "e2190b29-6d30-58fb-978f-d052582698bd", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "score": 0.6926098236310693, + "metadata": { + "text": "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "title": "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "extraction_id": "40850ed1-db52-594e-a9d6-0b661e0bc494", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + }, + { + "id": "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "score": 0.6911102327373582, + "metadata": { + "text": "subnetworks \n GeneNetwork (www.genenetwork.org) is a depository of data-\nsets and tools for use in complex systems biology approaches in \norder to generate or predict higher order gene function ( 23, 24 ).", + "title": "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "extraction_id": "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "associatedQuery": "How do researchers use GeneNetwork.org to study diseases?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIGlKS6puXfNaWUwFF42aUVBShhJ7", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org provides several tools and resources to explore how genes interact with each other. It is a bioinformatics tool that allows users to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [1]. GeneNetwork offers correlation and mapping strategies to assess associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [3]. Additionally, it provides access to a wide variety of data, including genotypes and phenotypes, which can illuminate relationships between different levels of a biological system, such as the genome, transcriptome, and phenome [6]. GeneNetwork also computes gene-gene co-expression correlations, which are essential for understanding gene interactions [9].", + "role": "assistant" + } + } + ], + "created": 1728917702, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 143, + "prompt_tokens": 1177, + "total_tokens": 1320 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.7471606731414795, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "f1d40272-4a35-5b52-b3a8-3a0e7e8626d2", + "score": 0.7247618436813354, + "metadata": { + "text": "Molecular Genetics and Genomics \n1 3\nas overexpression, knockdown, knockout and mutation \n(Online Resource 1).\nGene network construction\nGenegene interaction data were extracted from the STRING database (http://strin g-db.org/) (Christian etal. 2003), a web resource that includes comprehensively predicted and known interaction information. Then, the genegene interaction pairs were imported into Cytoscape software (Version 3.5.1) (http://cytos cape.org/ ) (Smoot etal. 2011 ) to construct a", + "title": "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "extraction_id": "9383f177-92a5-5264-9d81-ff623d0614e3", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "d8162fdc-326a-5f90-9fa4-24d86d701184", + "score": 0.7214549779891968, + "metadata": { + "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7187590599060059, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "01a09a4e-3c30-53b1-8819-6085d4886079", + "score": 0.7137500218302011, + "metadata": { + "text": "is shown in Figure 1A. Associations between transcript\nabundance, phenotypic traits and genotype can be estab-\nlished either using correlation or genetic linkage mapping\nfunctions [29,30]. The main page of GeneNetwork at\nhttp://www.genenetwork.org provides access to subsets of\ndata through pull-down menus that allow specific data\nsets to be queried. The datasets can be further restricted\nusing a single text box for specific database entries to\nquery probe set or trait ID, or annotations associated with", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "score": 0.7134816817354166, + "metadata": { + "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained", + "title": "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "67e804db-8127-5938-8d7f-a5918cdf4f86", + "extraction_id": "2455cf6d-4c9b-5272-8650-da127cc329e8", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "a11bd1db-1c26-54fa-85c8-39bb745d2ebf", + "score": 0.7125128507614136, + "metadata": { + "text": "occurrence; GN, gene neighbor; GT, genetic interaction; LC, literature-curated protein interactions; MS, affinity purification/mass spectrome try; PG, phy-\nlogenetic profiles; PI, fly protein interactions; TS, tertiary structure; and YH, yeast two-hybrid). Detailed descriptions are listed in Suppleme ntal Table S1. ( B)\nEssential genes were highly interconnected in HumanNet, and thus predictable from the network, as shown by ROC analysis. Genes were ranked by their sum", + "title": "2011 - Prioritizing candidate disease genes by network-based boosting of genome-wide association data.pdf", + "version": "v0", + "chunk_order": 36, + "document_id": "db0aa4b3-66ec-5d51-be72-2a1289db944a", + "extraction_id": "4eb8a5f8-5936-523d-971d-302348d6d62f", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "score": 0.7102887630462646, + "metadata": { + "text": "from co-regulation patterns found within tens of thousands of samples\nfor which gene expression was measured. GeneNetwork provid es un-\nprecedented resolution and predictive power across multip le cell types\nand tissues. Analogous to discovering patterns in expressi on data, the\nnetwork of protein-protein interactions can also be comput ationally pre-\ndicted using various methods[381].\nThe combined current knowledge of how cells control functio ns", + "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "version": "v0", + "chunk_order": 100, + "document_id": "ef974b09-4ea2-5382-85e5-c2169f440fda", + "extraction_id": "223e442e-898d-5aea-866a-5cdc0ac915e8", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "c0150694-7ee6-5e4f-a880-302cfd596718", + "score": 0.7086882168836477, + "metadata": { + "text": "(http://string-db.org/ ). STRING creates networks representing the best available knowledge of gene\ninterconnections. Each protein-protein interaction is annotated with scores indicating how likely an\ninteraction should be true. Scores rank from 0 to 1, with one being the highest confidence. A score\nof 0.5 indicates roughly every second interaction might be erroneous. Gene-gene co-expression cor-\nrelations were computed as Pearson product-moment correlations (r) in Genenetwork.org after\nremoving outliers.", + "title": "2019 - Different genetic mechanisms mediate spontaneous versus UVR-induced malignant melanoma.pdf", + "version": "v0", + "chunk_order": 235, + "document_id": "8161c536-c996-5660-b6ae-2d33c5d4aa9a", + "extraction_id": "a771f252-00da-5f52-9c29-d006313c9e7b", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.7086730773047771, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "What can GeneNetwork.org tell us about how genes interact with each other?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIGlO1Tf6FzOyoXrb1Vnt5VYQUM0R", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org aids in finding the genetic causes of common diseases by utilizing a systems genetics approach. This approach allows researchers to explore which phenotypes and diseases result from diverse sets of genetic and molecular markers, rather than focusing on single gene mutations [1]. GeneNetwork provides access to a wide variety of data, including genotypes (e.g., SNPs) and phenotypes, which can be used to study complex traits [7]. The platform integrates data on gene expression, protein interactions, and DNA-protein binding to construct biological networks that are predictive of disease [4]. Additionally, GeneNetwork offers correlation and mapping strategies for assessing associations among multiple genes and quantitative trait loci (QTLs), facilitating the study of complex traits [5]. This makes GeneNetwork a powerful tool for predictive medicine and systems genetics, helping researchers understand the genetic underpinnings of common diseases [6].", + "role": "assistant" + } + } + ], + "created": 1728917706, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 175, + "prompt_tokens": 1085, + "total_tokens": 1260 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "score": 0.7116426229476929, + "metadata": { + "text": "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While\nthe candidate gene approach asks which one gene mutation causes a particular disease, the\nsystems genetics approach explores which phenotypes and diseases result from diverse sets\nof genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in\nGeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "1c8d31d6-bd59-56da-83b8-f603b4a9ec2b", + "score": 0.705133318901062, + "metadata": { + "text": "Based on this, Goh et al. created networks using data from the Online\nMendelian Inheritance in Man (OMIM) [18]database that houses lists of\ndisease gene links. Two networks emerged: the human disease network inwhich disease nodes were connected if they were caused by mutations in\nthe same gene, and the disease gene network where gene nodes were", + "title": "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "96657025-7e50-571d-9a6b-1a202cb8a690", + "extraction_id": "82e07232-dd92-52f6-8230-d90a03c71b4f", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "f8a32960-cfe3-5440-9d5c-b55dfe52ea6d", + "score": 0.7016556681189352, + "metadata": { + "text": "Genetics Home Reference - Genetics Home Reference provides consumer-friendly \ninformation about the effects of genetic variations on human health. \nhttp://ghr.nlm.nih.gov/ \nGene Reviews Features expert-authored, peer-reviewed, current disease descriptions \nthat apply genetic testing to the diagnosis, management, and genetic counseling of \npatients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "title": "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "version": "v0", + "chunk_order": 109, + "document_id": "c37e2ace-171b-5776-8969-86eda9736481", + "extraction_id": "a58546e6-fe89-5d04-8adb-08d1991dc53c", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6997668743133545, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "d8162fdc-326a-5f90-9fa4-24d86d701184", + "score": 0.6987062692642212, + "metadata": { + "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6983277797698975, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "score": 0.6973242940113381, + "metadata": { + "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained", + "title": "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "67e804db-8127-5938-8d7f-a5918cdf4f86", + "extraction_id": "2455cf6d-4c9b-5272-8650-da127cc329e8", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "a9bbd320-eb89-5ae7-a3af-703ca68c8305", + "score": 0.6855560541152954, + "metadata": { + "text": "eron Genetics Center ( https://www.regeneron.com/ge -\nnetics-center ), and aims to identify rare loss-of-function \nmutations in founder populations to delineate further the \ngenetic factors that underpin health and disease. This ini -\ntiative is also addressed at developing countries and those \nin resource-limiting environments, under the coordina -\ntion of the Genomic Medicine Alliance ( http://www.ge -\nnomicmedicinealliance.org ), a founding partner of the", + "title": "2019 - Implementation of Genomic Medicine.pdf", + "version": "v0", + "chunk_order": 81, + "document_id": "a7faf15a-ed90-575b-805c-11f33fb2d6dd", + "extraction_id": "90e220eb-61ba-56bd-b455-ac29a1df5867", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "504b72fb-9a5e-53a4-b6a6-0fc6be18ec4e", + "score": 0.6835275888442993, + "metadata": { + "text": "to understand the genetics of a variety of diseases andbiological systems including aging, the immune system and ironregulation [26,27,28,29,30]. Much of this work has been madeavailable through GeneNetwork (formerly WebQTL ) an on-line", + "title": "2012 - Generating Embryonic Stem Cells from the Inbred Mouse Strain DBA2J, a Model of Glaucoma and Other Complex Diseases.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "a9b08d55-2f85-5d3a-abbf-389eed34009c", + "extraction_id": "ee03f7c5-6eee-5c66-8174-688f06da1587", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.6809336137215142, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "How does GeneNetwork.org help in finding the genetic causes of common diseases?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIGlUj81qIxsp6ZB9jZOhAF3uBNfA", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The context provided does not specifically mention GeneNetwork.org or its capabilities in predicting health conditions based on genetic information. However, the context does discuss the general potential of genetic information to predict disease risk. For example, it mentions the ability to identify individuals at higher genetic risk for common diseases [1], and the potential for genomic profiling to measure susceptibility to diseases [10]. \n\nWhile these references indicate that genetic information can be used to assess disease risk, there is no direct mention of GeneNetwork.org's specific capabilities in this area. Therefore, based on the provided context, it is unclear if GeneNetwork.org itself offers such predictive services.", + "role": "assistant" + } + } + ], + "created": 1728917712, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 126, + "prompt_tokens": 1399, + "total_tokens": 1525 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "d31fc0e8-028a-5879-b262-ec03ca586488", + "score": 0.7035082578659058, + "metadata": { + "text": "Letters NATure GeNeTicsIn our testing dataset, 19.8% of participants were at threefold \nincreased risk for at least 1 of the 5 diseases studied (Table 2).\nThe potential to identify individuals at significantly higher genetic \nrisk, across a wide range of common diseases and at any age, poses a number of opportunities and challenges for clinical medicine.\nWhere effective prevention or early detection strategies are \navailable, key issues will include the allocation of attention and", + "title": "2018 - Genome-wide polygenic scores for common diseases.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "extraction_id": "df306ee1-389f-56bb-bc5e-80ca8ff68fff", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "d57998c0-b045-5f68-a2ad-2173add21137", + "score": 0.6695624589920044, + "metadata": { + "text": "genetic risks of disease on risk-reducing health behaviour: Systematic \nreview with meta-analysis. BMJ. 2016;352:i1102.\n57. Vernarelli JA. Impact of genetic risk assessment on nutrition-related life-\nstyle behaviours. Proc Nutr Soc . 2013;72(1):153159.\n58. Marteau TM, French DP , Griffin SJ, et al. Effects of communicating DNA-\nbased disease risk estimates on risk-reducing behaviours. Cochrane \nDatabase Syst Rev . 2010;(10). \n59. National Human Genome Research Institute. All about The Human", + "title": "2018 - Communication of cancer-related genetic and genomic information A landscape analysis of reviews.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "2a560126-b122-55dc-a213-a16bc00300b7", + "extraction_id": "672e1f6a-25dd-5973-b19e-8d9371ec8973", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "65aa608a-7e60-54bb-a299-ae1e2e66d0cd", + "score": 0.6661686897277832, + "metadata": { + "text": "personalized screening based on age and \npolygenic risk profile.\n12 Pashayan N, Pharoah P. Translating genomics \ninto improved population screening: hype or \nhope? Hum. Genet. 130(1), 1921 (2011).\n13 Pharoah PD, Antoniou A, Bobrow M, \nZimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and \nimplications for prevention. Nat. Genet. 31(1), \n3336 (2002).\nnn\t Examines the potential for prediction of \nrisk based on common genetic variation and \ncompares this with the prediction that", + "title": "2012 - Population-based screening in the era of genomics.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "3a8d8722-9a3a-5062-9548-48e3c3bd6247", + "extraction_id": "706f0647-f63c-5383-9167-724c83faf79c", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "deab786b-11ed-5c75-8ff5-fd2812138917", + "score": 0.6540097594261169, + "metadata": { + "text": "Eur J Hum Genet.\n12. Janssens AC, van Duijn CM (2008) Genome-based prediction of common\ndiseases: advances and prospects. Hum Mol Genet 17: R166173.\n13. Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic\nrisk to disease from genome-wide association studies. Genome Res 17:15201528.\n14. Wray NR, Goddard ME, Visscher PM (2008) Prediction of individual genetic\nrisk of complex disease. Curr Opin Genet Dev 18: 257263.\n15. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE (2009)", + "title": "2009 - From Disease Association to Risk Assessment.pdf", + "version": "v0", + "chunk_order": 154, + "document_id": "a61066d0-0d1a-5f10-96c3-aa96bacdad5e", + "extraction_id": "6f819601-6eea-54a4-ab88-27e1b0602287", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "946c47ae-bbaf-5151-88f0-afa898c28a66", + "score": 0.6536650061607361, + "metadata": { + "text": "within the general population and toutedfor its potential contribution to personal-ized medicine (1315), although the un-derlying clinical utility has yet to bedemonstrated (16,17). Given the poten-tial for individual genetic risk to beempirically quantied and rapidly com-municated, it is of interest to both clini-cians and the general public to discover ifmodiable characteristics like diet canmitigate risk in individuals empiricallydened as high risk on the basis ofgenotype.", + "title": "2010 - Interactions of Dietary Whole-Grain Intake.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "e4d4a19e-18a0-5a08-9ab7-537f31b7cdc1", + "extraction_id": "37a4db8f-72a7-5e4e-b396-94bc0532a29d", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "563b865d-03a4-5607-a6c5-a0ee977010b4", + "score": 0.6529191939282909, + "metadata": { + "text": "Comprehension of Genomic Risk for \nDiabetes Public Health Genomics 2014;17:95104 \nDOI: 10.1159/000358413103 9 Green MJ, Peterson SK, Baker MW, Harper \nGR, Friedman LC, Rubinstein WS, Mauger DT: Effect of a computer-based decision aid on knowledge, perceptions, and intentions about genetic testing for breast cancer suscep-tibility: a randomized controlled trial. JAMA 2004; \n 292: 442452. \n 10 Bernhardt JM, McClain J, Parrott RL: Online", + "title": "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 108, + "document_id": "b2665466-da66-59f0-8581-a68131e924bf", + "extraction_id": "3b79395f-0e1c-564c-9965-b04acf204132", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "8ac717f0-586c-5ee3-b4e3-4334657938b5", + "score": 0.6512569189071655, + "metadata": { + "text": "Comparison of family history and SNPs for predicting risk of complex disease. PLoS Ge-net 2012; \n 8:e1002973. \n Downloaded from http://karger.com/phg/article-pdf/17/2/95/3426597/000358413.pdf by guest on 03 July 2023", + "title": "2014 - Impact of Delivery Models on Understanding Genomic Risk for Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 145, + "document_id": "b2665466-da66-59f0-8581-a68131e924bf", + "extraction_id": "074c3cae-ea97-5e74-8607-74c099df35cd", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "f8a32960-cfe3-5440-9d5c-b55dfe52ea6d", + "score": 0.6511798293101697, + "metadata": { + "text": "Genetics Home Reference - Genetics Home Reference provides consumer-friendly \ninformation about the effects of genetic variations on human health. \nhttp://ghr.nlm.nih.gov/ \nGene Reviews Features expert-authored, peer-reviewed, current disease descriptions \nthat apply genetic testing to the diagnosis, management, and genetic counseling of \npatients and families with specific inherited conditions. www.genetests.org/servlet/access?", + "title": "2009 - Basic Genetics and Genomics A Primer for Nurses.pdf", + "version": "v0", + "chunk_order": 109, + "document_id": "c37e2ace-171b-5776-8969-86eda9736481", + "extraction_id": "a58546e6-fe89-5d04-8adb-08d1991dc53c", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "3c0229cb-f853-5ef6-b45f-5462f62ede91", + "score": 0.6501173973083496, + "metadata": { + "text": "Khoury, M. J. (2006). Family history of type 2 diabetes: apopulation-based screening tool for prevention? Genetics in\nMedicine, 8 (2), 102 108.\nHunter, D. J., Khoury, M. J., & Drazen, J. M. (2008). Letting the\ngenome out of the bottle will we get our wish? The New\nEngland Journal of Medicine, 358 (2), 105 107.\nIoannidis, J. P. A. (2009). Personalized genetic prediction: too limited,\ntoo expensive, or too soon? Annals of Internal Medicine, 150 (2),\n139141.", + "title": "2010 - Considerations for the Impact of Personal Genome Information.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "e4f3c9ce-1cc1-56f1-a290-b874455e53f9", + "extraction_id": "694d74ca-68c2-5874-b143-113c6cc7802a", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + }, + { + "id": "60497a7a-5c86-51a3-bc73-e373ca716270", + "score": 0.6494157314300537, + "metadata": { + "text": "genomic profiling for measuring susceptibility to common diseasesand targeting interventions. Genet Med 2004; 6:3847.\n42Vineis P, Christiani DC. Genetic testing for sale. Epidemiology\n2004; 15:35.\n43Haga SB, Khoury MJ, Burke W. Genomic profiling to promote ahealthy lifestyle: not ready for prime time. Nat Genet 2003; 34:34750.\n44Yang Q, Khoury MJ, Botto L et al. Improving the prediction of\ncomplex diseases by testing for multiple disease-susceptibility genes.Am J Hum Genet 2003; 72:63649.", + "title": "2004 - The emergence of epidemiology.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "53cc9020-d5d9-5c5b-a8df-66b3f1019961", + "extraction_id": "d7c4830a-8d69-531b-855a-eda3fa2ea5e7", + "associatedQuery": "Can GeneNetwork.org predict my risk of developing certain health conditions based on my genes?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIGlYq5ocxHDndnTkp4uj3sxjmcHK", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org utilizes data from different populations around the world by integrating diverse genomic information and phenotypic data from various experimental crosses and reference panels. This allows for comprehensive exploratory and statistical analysis of large published phenotype and genome datasets [3], [4]. The platform includes data from multiple species, which facilitates the comparison of gene expression patterns with drug responses and other phenotypic traits [3]. Additionally, GeneNetwork.org provides analytical tools that enable users to compare traits across datasets from different experimenters, further enhancing the ability to study correlations and perform data mining in genomic regions [5], [9]. This integration of diverse datasets supports the construction of predictive biological networks by interfacing DNA variation data with gene expression, protein interactions, and DNA-protein binding information [6].", + "role": "assistant" + } + } + ], + "created": 1728917716, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 153, + "prompt_tokens": 995, + "total_tokens": 1148 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7506610155105591, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "f5c218f0-1280-55f8-912b-b32b833e93a3", + "score": 0.712983386934795, + "metadata": { + "text": "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "title": "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "version": "v0", + "chunk_order": 263, + "document_id": "73724c99-98df-53b2-a378-29c8b4faa171", + "extraction_id": "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.701063334941864, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "1c26e6f6-680b-5877-9600-fee25a42c943", + "score": 0.6956807971000671, + "metadata": { + "text": "GeneNetwork is an open-access database that collates genomic\ninformation of diverse experimental crosses and reference panels\nas well as phenotypic data from miscellaneous research groups\n[26].\nStatistics\nData generation, statistical analysis and graph creation were\nperformed with SPSS Statistics 21 (IBM, Ehningen, Germany). As\nappropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "title": "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "extraction_id": "8c423789-3641-5853-9cf3-f4a026ffb446", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "21936758-94b1-506f-9229-77e26001ae44", + "score": 0.6955050411519396, + "metadata": { + "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we", + "title": "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "76a715a4-8222-598b-8e65-6d5b6e807989", + "extraction_id": "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6897256970405579, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "7b626f27-5aed-5464-a20d-463954ff057d", + "score": 0.6852350831031799, + "metadata": { + "text": "distributed neuroscience data sharing with ever expanding\nprospects for future breakthroughs.\nGeneNetwork.org : genetic analysis for all\nneuroscientists\nOriginally named webqtl, GeneNetwork.org is the oldest contin-\nuously operating website in biomedical research ( Williams,\n1994). This massive database contains ;40 million datasets.\nGeneNetwork.org also offers a powerful statistical platform for\nonline network analyses and mapping, enabling numerous mo-", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "28892088-5a95-56eb-822d-b12da3a612d0", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "score": 0.6852350831031799, + "metadata": { + "text": "distributed neuroscience data sharing with ever expanding\nprospects for future breakthroughs.\nGeneNetwork.org : genetic analysis for all\nneuroscientists\nOriginally named webqtl, GeneNetwork.org is the oldest contin-\nuously operating website in biomedical research ( Williams,\n1994). This massive database contains ;40 million datasets.\nGeneNetwork.org also offers a powerful statistical platform for\nonline network analyses and mapping, enabling numerous mo-", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6815548132923369, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + }, + { + "id": "251de62d-6e8e-50c7-9616-7fea05a250fb", + "score": 0.68070387840271, + "metadata": { + "text": "abundance data sets directly within GeneNetwork's ana-\nlytical environment we provide simple web access to the\ndata for the research community. In this environment, a\ncombination of correlation analysis and linkage mapping\nprovides the potential to identify and substantiate gene\ntargets for saturation mapping and positional cloning. By\nintegrating datasets from an unsequenced crop plant (bar-\nley) in a database that has been designed for an animal\nmodel species (mouse) with well established genome", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "dc001755-2e77-5b41-8617-263b3ba35af8", + "associatedQuery": "How does GeneNetwork.org make use of data from different populations around the world?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIGlcT3nIUJnhMWAFTwtm7j28RbUR", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org provides a variety of genetic data, including:\n\n1. Genomic information from diverse experimental crosses and reference panels, as well as phenotypic data from various research groups [3].\n2. Genetic variants such as SNPs (single nucleotide polymorphisms), insertions, deletions, and duplications [4].\n3. Extensive phenotype data extracted from the literature and submitted by users, which allows for comparisons of drug responses with gene expression patterns [5].\n4. Microarray data of gene expression in the brain and data of other phenotypes [8].\n5. Genotypes, including SNPs, and phenotypes obtained from various studies [10].\n\nThese datasets are designed to support systems genetics research and include data from multiple species [2], [5].", + "role": "assistant" + } + } + ], + "created": 1728917720, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 154, + "prompt_tokens": 1022, + "total_tokens": 1176 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "bd2eb0ef-24a1-55ff-8597-c21dff0ecf0a", + "score": 0.7519771156338315, + "metadata": { + "text": "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table ,\nand throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID=\nsharinginfo&GN_AccessionId=600 .\nGeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/\ngenenetwork/genenetwork1 .", + "title": "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "version": "v0", + "chunk_order": 187, + "document_id": "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "extraction_id": "d1c32c32-42c8-5065-b7f2-bd2a0baeae62", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.745707169431022, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "1c26e6f6-680b-5877-9600-fee25a42c943", + "score": 0.7299081844146155, + "metadata": { + "text": "GeneNetwork is an open-access database that collates genomic\ninformation of diverse experimental crosses and reference panels\nas well as phenotypic data from miscellaneous research groups\n[26].\nStatistics\nData generation, statistical analysis and graph creation were\nperformed with SPSS Statistics 21 (IBM, Ehningen, Germany). As\nappropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "title": "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "extraction_id": "8c423789-3641-5853-9cf3-f4a026ffb446", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "23de1e96-55b6-5062-a2e1-02bf06fd3565", + "score": 0.7282539172021213, + "metadata": { + "text": "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The\nstrains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic\ncorrelations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15].\nAll data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16].\nGN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be", + "title": "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "d235d186-3d1c-5cde-90d5-9c140cd920f4", + "extraction_id": "98aff04d-a5b2-5cca-bc1a-552055a74262", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.7232027814863468, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "f5c218f0-1280-55f8-912b-b32b833e93a3", + "score": 0.7176529505666553, + "metadata": { + "text": "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "title": "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "version": "v0", + "chunk_order": 263, + "document_id": "73724c99-98df-53b2-a378-29c8b4faa171", + "extraction_id": "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.7170238832206866, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "21936758-94b1-506f-9229-77e26001ae44", + "score": 0.713894878161564, + "metadata": { + "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we", + "title": "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "76a715a4-8222-598b-8e65-6d5b6e807989", + "extraction_id": "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "40ecbff2-5039-57f0-a01e-9d412d559dbe", + "score": 0.7043004808246816, + "metadata": { + "text": "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other\ninvestigators can look for correlations between gene expression patterns and phenotypic traits.\nThe GeneNetwork is an open resource and consists of a set of linked resources for systems\ngenetics. It has been designed for integration of networks of genes, transcripts, and traits such\nas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "title": "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf", + "version": "v0", + "chunk_order": 122, + "document_id": "301d6469-2a9c-5960-88ac-8437212d78ab", + "extraction_id": "3ca48658-ca83-5952-8f8d-eb7ae491e6b6", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + }, + { + "id": "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "score": 0.6975428822585484, + "metadata": { + "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained", + "title": "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "67e804db-8127-5938-8d7f-a5918cdf4f86", + "extraction_id": "2455cf6d-4c9b-5272-8650-da127cc329e8", + "associatedQuery": "What kinds of genetic data are available on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIGljdYmj6PqUgXHWW6b3NFcoOufn", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Scientists use GeneNetwork.org to study differences in gene expression by leveraging a variety of analytical tools and datasets available on the platform. GeneNetwork provides access to large published phenotype and genome datasets from several species, allowing for exploratory and statistical analysis [2]. The platform includes microarray data of gene expression in the brain and other phenotypes, which can be used to compare traits across different datasets [1]. \n\nGeneNetwork also facilitates the comparison of gene expression patterns with drug responses and other phenotypic data, making it practical for identifying candidate genes for complex traits through QTL analyses [2], [4]. The platform supports correlation and network analysis to compare associations between tissues and across rodent or human datasets, which is useful for systems genetics mapping [5]. \n\nAdditionally, bioinformatic analyses on GeneNetwork.org include tools for gene ontology, presence of cis-regulation or polymorphisms, phenotype correlations, and principal component analyses, which help in evaluating differentially expressed genes and understanding distinct biological processes [10].", + "role": "assistant" + } + } + ], + "created": 1728917727, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 201, + "prompt_tokens": 1048, + "total_tokens": 1249 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "21936758-94b1-506f-9229-77e26001ae44", + "score": 0.7291263503356976, + "metadata": { + "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we", + "title": "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "76a715a4-8222-598b-8e65-6d5b6e807989", + "extraction_id": "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.7122059464454651, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "score": 0.7002742290496826, + "metadata": { + "text": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "94f60899-c281-586e-8741-135a4fef2663", + "score": 0.6908105611801147, + "metadata": { + "text": "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites\nfocus to a large extent on correlations of behavioral phenotype with gene expression levels\nin recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means\nto identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to\nthe tools for raw expression data analysis described above, is that the user can not only", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "308bef07-d720-5686-990d-d1e26a48e8a1", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "score": 0.6880026645658824, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "score": 0.687973834517742, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "76ca1a96-ff40-515d-8d8b-5b1cde3c32b5", + "score": 0.6875734534136493, + "metadata": { + "text": "by example in the Supplementary Methods, and in the Users Manual that can be\ndownloaded from the website.\nThere are a number of databases that investigators can use to assist in various aspects of\ngene expression data storage and mining (e.g., (Chesler et al., 2005; Galperin and Cochrane,\n2009; Gentleman et al., 2004; Mailman et al., 2007; Saal et al., 2002; Swertz et al., 2010)).\nOne relatively well-known database is GeneNetwork (www.genenetwork.org) (Chesler et", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 42, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6843081712722778, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "score": 0.6830658316612244, + "metadata": { + "text": "from co-regulation patterns found within tens of thousands of samples\nfor which gene expression was measured. GeneNetwork provid es un-\nprecedented resolution and predictive power across multip le cell types\nand tissues. Analogous to discovering patterns in expressi on data, the\nnetwork of protein-protein interactions can also be comput ationally pre-\ndicted using various methods[381].\nThe combined current knowledge of how cells control functio ns", + "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "version": "v0", + "chunk_order": 100, + "document_id": "ef974b09-4ea2-5382-85e5-c2169f440fda", + "extraction_id": "223e442e-898d-5aea-866a-5cdc0ac915e8", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + }, + { + "id": "90151329-53f0-5d76-b428-da316848daf3", + "score": 0.6777165138040937, + "metadata": { + "text": "differentially expressed were further evaluated. Bioinformatic analyses were predominantly \nperformed using tools available at GeneNetwork. org, and included gene ontology, presence of cis-\nregulation or polymorphisms, phenotype correlations, and principal component analyses. \nComparisons of differential gene expression between groups showed little overlap. Gene Ontology \ndemonstrated distinct biological processes in each group with the combined exposure (RSE) being", + "title": "2017 - Analyses of differentially expressed genes after exposure to acute stress, acute ethanol, or a combination of both in mice.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "433904cc-23b8-50a5-ba84-0ee4d41d23c2", + "extraction_id": "4f3d275e-f521-5ae9-b550-0411d2a1bb33", + "associatedQuery": "How do scientists use GeneNetwork.org to study differences in gene expression?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIGlpL9doFmtJJWVoX5DvTMPsImCv", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, GeneNetwork.org can be used to learn about genetic influences on behavior. It is a comprehensive resource equipped with tools and features for studying genetic correlates to neurobehavioral phenotypes [5]. The platform includes a phenotype database with data on behavioral traits, among others, which can be used for correlation and network analyses to identify relationships with genetic data [4]. Additionally, GeneNetwork focuses on correlations of behavioral phenotypes with gene expression levels in recombinant inbred and inbred panels of mice and rats, which helps in identifying candidate genes for complex traits [6]. The resource is designed for the multivariate genetic analysis of complex traits, including behavior, in genetic reference populations [9].", + "role": "assistant" + } + } + ], + "created": 1728917733, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 137, + "prompt_tokens": 1095, + "total_tokens": 1232 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "score": 0.721941845301281, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.721941845301281, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "d45f4d61-dfd4-57ef-9b52-ae6cbff0e6f4", + "score": 0.7010798454284668, + "metadata": { + "text": "Category 1: Web Resources for Online Analysis\nof the Genetics of Alcoholism and More\nGeneNetwork \n(www.genenetwork.org): This is a comprehensive\nresource for learning about genetics, but users may", + "title": "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "08b12d72-9776-5acb-b1ef-7ee402781897", + "extraction_id": "83ae495f-31a2-5977-a63a-57e704c394e2", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "2b47c0db-8e09-51a2-8689-defa87ee8ac1", + "score": 0.7003071308135986, + "metadata": { + "text": "GeneNetwork also features a phenotype database, a public repository of data from over 700\ntraits previously measured across several laboratories in BXD RI (and other) strains. These\ninclude behavioral, biochemical, and anatomical traits. The data consist of strain means, not\nraw data from individual mice, and so we use the term genetic correlation. Using this\ndatabase, we performed correlation and network analyses to identify relationships with", + "title": "2012 - Systems genetic analysis of the effects of iron deficiency in mouse brain.pdf", + "version": "v0", + "chunk_order": 34, + "document_id": "99fc80f0-f3c3-5766-a604-921552bb3298", + "extraction_id": "22ed818f-78a7-5409-9f6a-1b83284db25d", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "067136a5-b89e-5108-85b0-f638c041e68c", + "score": 0.6970914367328411, + "metadata": { + "text": "biological function of the new gene list. As mentioned previously, GeneNetwork\n(www.genenetwork.org) is a collaborative Web-based resource equipped with tools and\nfeatures for studying gene/gene and exploring genetic correlates to neurobehavioral\nphenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of\ngene expression and phenotypic data from a variety of species and brain regions, with a host", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 139, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "94f60899-c281-586e-8741-135a4fef2663", + "score": 0.6928536891937256, + "metadata": { + "text": "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites\nfocus to a large extent on correlations of behavioral phenotype with gene expression levels\nin recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means\nto identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to\nthe tools for raw expression data analysis described above, is that the user can not only", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "308bef07-d720-5686-990d-d1e26a48e8a1", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "4b91e1d0-f7ce-577c-bad2-b59bd75173b0", + "score": 0.6875191926956177, + "metadata": { + "text": "with another database, GeneNetwork, correlating behavioral phenotypes with geneO'Brien et al. Page 11\nInt Rev Neurobiol . Author manuscript; available in PMC 2014 July 21.\nNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 94, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "be1e859f-c4c7-576d-8a52-9588e15fab44", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "2f453c67-3f97-5d7b-b92d-0530f86e26ee", + "score": 0.6871652603149414, + "metadata": { + "text": "interested in behavioral variation and in ways to exploit bioinformatic resources and \nmethods to dissect and (we hope) reassemble and model behavior. You do not need to be a \nstatistician or geneticist to use these tools.\nIn order to use GeneNetwork, we have to start with some ground rules and assumptions. The \nfirst is that behavioral traits must vary significantly. This is a chapter about behavioral \nvariation with an equal emphasis on both words. If a behavior is a \"fixed action pattern\" that", + "title": "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "extraction_id": "21f8c6e4-ef9b-582b-ac32-2679933c3b59", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "c61e7911-9138-5a2e-8b2f-e035f374e9e3", + "score": 0.6863656640052795, + "metadata": { + "text": "facilitated through the development of GeneNetwork(www.genenetwork.org), an Inte rnet resource for the multi-\nvariate genetic analysis of complex traits in genetic reference\npopulations (Chesler\net al. 2003, 2004; Wang et al. 2003).\nGeneNetwork aids in identication of candidate genesand bio-molecular mechanisms underlying addiction-relatedphenotypes and includes a wealth of data on mRNAexpression proles from various tissues of the centralnervous system (Chesler\net al. 2005; Peirce et al. 2006;", + "title": "2009 - High\u2010throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "423c6929-9d69-5c95-b510-bff6757fed7d", + "extraction_id": "7a89fb44-80f8-5890-b2ae-a4643b587737", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + }, + { + "id": "40ecbff2-5039-57f0-a01e-9d412d559dbe", + "score": 0.6854543685913086, + "metadata": { + "text": "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other\ninvestigators can look for correlations between gene expression patterns and phenotypic traits.\nThe GeneNetwork is an open resource and consists of a set of linked resources for systems\ngenetics. It has been designed for integration of networks of genes, transcripts, and traits such\nas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "title": "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf", + "version": "v0", + "chunk_order": 122, + "document_id": "301d6469-2a9c-5960-88ac-8437212d78ab", + "extraction_id": "3ca48658-ca83-5952-8f8d-eb7ae491e6b6", + "associatedQuery": "Can GeneNetwork.org be used to learn about genetic influences on behavior?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIGluZZhH7wm0mptVn5RRlhFxsJ3L", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org plays a significant role in personalized medicine by serving as an open-access, online data analysis resource for systems biology and systems genetics [1]. It is a tool for systems genetics and predictive medicine, which aims to predict and potentially avoid phenotypic outcomes such as diseases [2]. The platform supports the integration of networks of genes, transcripts, and traits, which is crucial for understanding complex genetic interactions and their implications for personalized medicine [10]. Additionally, GeneNetwork.org facilitates the comparison of data on drug responses with gene expression patterns, which is essential for tailoring therapeutic strategies to individual genetic profiles [9].", + "role": "assistant" + } + } + ], + "created": 1728917738, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 122, + "prompt_tokens": 1197, + "total_tokens": 1319 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "score": 0.720530092716217, + "metadata": { + "text": "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; \nGlade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; \nRubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of \ntumors.\nMethods\nGeneNetwork and WebGestalt\nGeneNetwork is an open access, online data analysis resource for systems biology and \nsystems genetics. It contains a large number of microarray datasets from multiple tissues of", + "title": "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "extraction_id": "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "score": 0.7155238133044516, + "metadata": { + "text": "gathered together into an easily accessible format, not siloed into disparate data pools that \ncannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal \nmodels of so called precision medicine, although perhaps more accurately, we want \npredictive medicine , where a phenotypic outcome (such as disease) can be predicted , and \navoided . \nGeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "aaa9652c-64f3-53e7-ab24-48c5d19136d0", + "score": 0.7074556350708008, + "metadata": { + "text": "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and \nTranslational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 \nGM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, \nand the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of \nMitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "title": "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 135, + "document_id": "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "extraction_id": "2fd15885-4e19-536f-a90a-3650bd23c37e", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "7dd2de60-d539-59e1-9c4c-2927367d650f", + "score": 0.7067998051643372, + "metadata": { + "text": "The GeneNetwork site is supported by the University of Tennessee Center for Integrative and \nTranslational Genomics, NI GMS Systems Genetics and Precision Medicine Project (R01 \nGM123489, 2017 -2021), NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, \nand the Addictome (P30 DA044223, 2017 -2022), NIA Translational Systems Genetics of \nMitochondria, Metabolism, and Aging (R01AG043930, 2013 -2018), NIAAA Integrative", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 135, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "406a0217-5585-5daf-88d0-5904cfb04c3b", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6980934143066406, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "a9bbd320-eb89-5ae7-a3af-703ca68c8305", + "score": 0.6949265003204346, + "metadata": { + "text": "eron Genetics Center ( https://www.regeneron.com/ge -\nnetics-center ), and aims to identify rare loss-of-function \nmutations in founder populations to delineate further the \ngenetic factors that underpin health and disease. This ini -\ntiative is also addressed at developing countries and those \nin resource-limiting environments, under the coordina -\ntion of the Genomic Medicine Alliance ( http://www.ge -\nnomicmedicinealliance.org ), a founding partner of the", + "title": "2019 - Implementation of Genomic Medicine.pdf", + "version": "v0", + "chunk_order": 81, + "document_id": "a7faf15a-ed90-575b-805c-11f33fb2d6dd", + "extraction_id": "90e220eb-61ba-56bd-b455-ac29a1df5867", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "score": 0.6840959787368774, + "metadata": { + "text": "distributed neuroscience data sharing with ever expanding\nprospects for future breakthroughs.\nGeneNetwork.org : genetic analysis for all\nneuroscientists\nOriginally named webqtl, GeneNetwork.org is the oldest contin-\nuously operating website in biomedical research ( Williams,\n1994). This massive database contains ;40 million datasets.\nGeneNetwork.org also offers a powerful statistical platform for\nonline network analyses and mapping, enabling numerous mo-", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "7b626f27-5aed-5464-a20d-463954ff057d", + "score": 0.6840959787368774, + "metadata": { + "text": "distributed neuroscience data sharing with ever expanding\nprospects for future breakthroughs.\nGeneNetwork.org : genetic analysis for all\nneuroscientists\nOriginally named webqtl, GeneNetwork.org is the oldest contin-\nuously operating website in biomedical research ( Williams,\n1994). This massive database contains ;40 million datasets.\nGeneNetwork.org also offers a powerful statistical platform for\nonline network analyses and mapping, enabling numerous mo-", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "28892088-5a95-56eb-822d-b12da3a612d0", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6790583729743958, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + }, + { + "id": "40ecbff2-5039-57f0-a01e-9d412d559dbe", + "score": 0.66768479347229, + "metadata": { + "text": "deposited in the GeneNetwork website (http://www.genenetwork.org) so that other\ninvestigators can look for correlations between gene expression patterns and phenotypic traits.\nThe GeneNetwork is an open resource and consists of a set of linked resources for systems\ngenetics. It has been designed for integration of networks of genes, transcripts, and traits such\nas toxicity, cancer susceptibility, and behavior for several species. Phenotypic QTLs using the", + "title": "2009 - Genetical Toxicogenomics in Drosophila Identifies Master Modulatory Loci that are Regulated by Developmental Exposure to Lead.pdf", + "version": "v0", + "chunk_order": 122, + "document_id": "301d6469-2a9c-5960-88ac-8437212d78ab", + "extraction_id": "3ca48658-ca83-5952-8f8d-eb7ae491e6b6", + "associatedQuery": "What role does GeneNetwork.org play in personalized medicine?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIGlzcVkzqXfNsnzBiVpVbwVplFu6", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The information on GeneNetwork.org aids in developing new treatments for diseases in several ways:\n\n1. **Insight into Gene Function**: GeneNetwork.org provides insights into gene function and how altered gene function can lead to disease. This understanding is crucial for translating genetic discoveries into new therapeutics, as it helps elucidate the mechanisms of action for newly identified disease genes, which is a major bottleneck in drug development [1].\n\n2. **Predictive Medicine and Systems Genetics**: The platform is an exciting resource for predictive medicine and systems genetics. It integrates data from multiple species and omics analyses, which can be used to predict phenotypic outcomes such as disease, potentially allowing for the development of treatments that can prevent these outcomes [2], [4].\n\n3. **Identification of Drug Targets**: Genetic information from GeneNetwork.org can be used to identify new targets for pharmaceutical intervention. This includes providing information about the long-term safety of pathway interventions, which is crucial for developing effective and safe treatments [5].\n\n4. **Exploratory and Statistical Analysis**: GeneNetwork.org is designed for exploratory and statistical analysis of large phenotype and genome datasets. This makes it practical to compare data on drug responses with gene expression patterns, facilitating the identification of potential therapeutic targets [8].\n\n5. **Studying Gene Networks**: By studying networks of genes, proteins, metabolites, and other biomarkers, GeneNetwork.org helps uncover disease genes. This network-based approach combines the effects of multiple genes, producing stronger signals and reducing the complexity of statistical analyses, which can accelerate the discovery of new treatments [10].\n\nOverall, GeneNetwork.org serves as a comprehensive tool for researchers to explore genetic data and develop insights that are critical for the creation of new therapeutic strategies.", + "role": "assistant" + } + } + ], + "created": 1728917743, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 349, + "prompt_tokens": 1146, + "total_tokens": 1495 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "70bf64a4-327c-517d-8ccb-a9012bcfc453", + "score": 0.7130672252654043, + "metadata": { + "text": "mation on gene function and how altered function leads to disease.\nElucidating the mechanisms of action for newly minted disease genes is amajor bottleneck in translating genetic discoveries into new therapeutics.Addressing this limitation, it has been shown that networks can provideinsight on gene function [71,72] . The premise behind this is simple dgenes", + "title": "2016 - A Systems-Level Understanding of Cardiovascular Disease through Networks.pdf", + "version": "v0", + "chunk_order": 94, + "document_id": "96657025-7e50-571d-9a6b-1a202cb8a690", + "extraction_id": "362cb4d9-306b-5bbe-a2b7-3fedfb2cae2a", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7096298087084572, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "score": 0.6876659765641766, + "metadata": { + "text": "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; \nGlade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; \nRubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of \ntumors.\nMethods\nGeneNetwork and WebGestalt\nGeneNetwork is an open access, online data analysis resource for systems biology and \nsystems genetics. It contains a large number of microarray datasets from multiple tissues of", + "title": "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "extraction_id": "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "score": 0.6777848177469211, + "metadata": { + "text": "gathered together into an easily accessible format, not siloed into disparate data pools that \ncannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal \nmodels of so called precision medicine, although perhaps more accurately, we want \npredictive medicine , where a phenotypic outcome (such as disease) can be predicted , and \navoided . \nGeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "b0020ac6-de30-5090-817a-d14d770abd1e", + "score": 0.6731192463827882, + "metadata": { + "text": "vidual patients. For the time being, the contribu -\ntion of genetic information to therapy is most likely to come through the drug-discovery pipe -\nline. Information from genetic studies could be used to identify new targets for pharmaceutical intervention that have validated effects on physi -\nological characteristics, to provide information about new and existing targets (e.g., clues about the long-term safety of pathway intervention),\n32", + "title": "2010 - Genomics, Type 2 Diabetes, and Obesity.pdf", + "version": "v0", + "chunk_order": 92, + "document_id": "3d629777-f1b6-5450-94ef-56736e5a4e10", + "extraction_id": "8dc4ac39-16e2-508e-aaa3-016c51410c79", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "score": 0.664567590135172, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.664567590135172, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6644069356433278, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "067136a5-b89e-5108-85b0-f638c041e68c", + "score": 0.6614331218163677, + "metadata": { + "text": "biological function of the new gene list. As mentioned previously, GeneNetwork\n(www.genenetwork.org) is a collaborative Web-based resource equipped with tools and\nfeatures for studying gene/gene and exploring genetic correlates to neurobehavioral\nphenotypes (Chesler et al., 2003, 2004). The Web site is home to a growing collection of\ngene expression and phenotypic data from a variety of species and brain regions, with a host", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 139, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + }, + { + "id": "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "score": 0.6602752816184864, + "metadata": { + "text": "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "title": "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "extraction_id": "40850ed1-db52-594e-a9d6-0b661e0bc494", + "associatedQuery": "How does the information on GeneNetwork.org help in developing new treatments for diseases?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIGm7DFsh1v2eeUURegyReODMaCec", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "A gene network is a graphical model comprised of nodes and edges, where the nodes typically represent genes, gene products, or other biological entities [1]. These networks illustrate how genes do not function in isolation but operate in complex networks that define the behavior of biological systems [2]. Understanding gene networks is crucial for interpreting the roles of individual genes within the broader context of these networks, which can provide insights into complex system behaviors, including diseases [1], [2]. By considering genes within their networks, researchers can better understand the interrelationships and regulatory mechanisms that contribute to phenotypic traits and disease processes [4].", + "role": "assistant" + } + } + ], + "created": 1728917751, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 122, + "prompt_tokens": 1153, + "total_tokens": 1275 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "509d3815-9994-5afc-9777-52eb80281dc8", + "score": 0.6670463283969861, + "metadata": { + "text": "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play.\nConstructing gene networks can provide a convenient framework for exploring\nthe context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological\nsystems, the nodes in the network typically represent genes, gene products, or other", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 99, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "1d401588-b6dc-532f-8194-4667a7d31153", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "9d6a0871-3235-5fd6-855a-897e6a177db4", + "score": 0.6668222149259515, + "metadata": { + "text": "Genes do not carry out their functions in isolation of other genes, but instead oper-\nate in complex networks that together, in a context-specic way, dene the complex\nbehavior that emerges from biological systems. Therefore, understanding gene net-\nworks in a diversity of contexts will lead to an increased understanding of complex\nsystem behavior, including disease.\nThe reductionist approach to elucidating the complexity of biological systems", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "1d401588-b6dc-532f-8194-4667a7d31153", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "d8162fdc-326a-5f90-9fa4-24d86d701184", + "score": 0.6661463379859924, + "metadata": { + "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "e78c3922-952f-53ea-a1d5-8edd98f9b893", + "score": 0.6643773517383735, + "metadata": { + "text": "genotypes and phenotypes, geneticists hope to discover and interpret the network of causal\ngenotype-phenotype relationships that determine a trait of interest.\nSystems genetics research often follows a workow of nding a gene network, nding\nregulators of that network, and then performing a focused ge ne perturbation experiment\nto determine the role of the associated network on gene expre ssion or function. To be-\ngin, a large gene correlation graph must be sifted through , to nd a highly connected", + "title": "2009 - Visual analytics for relationships in scientific data (1).pdf", + "version": "v0", + "chunk_order": 228, + "document_id": "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "extraction_id": "697332a8-8630-50ff-aa2b-f33478931d24", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "score": 0.6629086337740056, + "metadata": { + "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained", + "title": "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "67e804db-8127-5938-8d7f-a5918cdf4f86", + "extraction_id": "2455cf6d-4c9b-5272-8650-da127cc329e8", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "9c0d7bcf-242c-5ba7-86bb-df799e6e03a6", + "score": 0.6610134243965149, + "metadata": { + "text": "the risk of missing important biological phenomena [43]. \n \n8.4 Defining gene and QTL networks \nIn addition to the genetic dissection of phenotypic variation using QTL mapping \ntechniques, systems geneticists are interested in r econstructing the biological net- \nworks that connect genes, proteins and other traits based on their observed genetic \n(co-)variation. In this context, biological network s are often defined by graphical", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 645, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "a83ca198-3b9d-5355-aa82-30d89ebf018c", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6499451398849487, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "score": 0.64676433801651, + "metadata": { + "text": "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "title": "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "c54da858-9620-588e-8e41-76a960af2ff6", + "extraction_id": "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6466628313064575, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + }, + { + "id": "23de1e96-55b6-5062-a2e1-02bf06fd3565", + "score": 0.6460616588592529, + "metadata": { + "text": "genetic variants (SNPs, insertions, deletions, duplications, etc.) that segregate in the family [ 13]. The\nstrains are appropriate for systems genetics /systems biology analysis [ 14], genetic mapping and genetic\ncorrelations of parameter means, and thus constitute an ideal platform for toxicogenomic research [ 15].\nAll data are available at www.genenetwork.org. GeneNetwork exists in two forms, GN1 and GN2 [ 16].\nGN2 is an expansion and renement of the features of GN1. A tutorial of how to use GN1 may be", + "title": "2020 - Modeling the Genetic Basis of Individual Differences in Susceptibility to Gulf War Illness.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "d235d186-3d1c-5cde-90d5-9c140cd920f4", + "extraction_id": "98aff04d-a5b2-5cca-bc1a-552055a74262", + "associatedQuery": "What is a gene network, and why is it important for understanding genetics?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIGmBVU8OOwhBDyIls65dlks2MJDd", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Researchers identify important genes for certain traits using GeneNetwork.org through a series of steps and tools provided by the platform:\n\n1. **Data Selection and Trait Mining**: Researchers begin by selecting a data set and mining it for traits of interest based on user search queries [1]. This involves using the main search page to query specific data sets and identify traits that are relevant to their study.\n\n2. **Trait Collection and Analysis**: Once traits are identified, they are selected and placed in a collection for further inspection and quantitative analysis [1]. This allows researchers to organize and focus on specific traits for deeper investigation.\n\n3. **Advanced Search Options**: GeneNetwork offers advanced search options that enable researchers to query data sets for specific genomic intervals and locate traits with the highest likelihood ratio statistic (LRS) values, which are indicative of strong genetic associations [4].\n\n4. **Correlation and Genetic Linkage Mapping**: Researchers can establish associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions [5]. This helps in identifying candidate genes linked to specific traits.\n\n5. **QTL Analysis and Network Graphs**: The platform allows for the generation of quantitative trait loci (QTL) analyses, network graphs, and correlation matrices, which are essential for understanding the genetic architecture of complex traits [3].\n\nBy utilizing these tools and processes, researchers can effectively identify and analyze genes that are important for specific traits using GeneNetwork.org.", + "role": "assistant" + } + } + ], + "created": 1728917755, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 293, + "prompt_tokens": 1119, + "total_tokens": 1412 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "1762dc59-0e50-5b7e-bdc2-b754e0e57797", + "score": 0.7366762380801514, + "metadata": { + "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 210, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "6cbea84e-4d8d-5ce0-8e58-45ee75f6f908", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "e030ce79-6970-5300-a1d8-1623d07c2157", + "score": 0.7366175055503845, + "metadata": { + "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 210, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "2bdd2f18-e4d0-53e9-b0fa-a7ed8d710961", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "48cb54db-68ef-50f0-bc7c-83b7db2ec9a5", + "score": 0.7150479724974661, + "metadata": { + "text": "Another powerful feature of GeneNetwork is the ability to create and analyze whole collections \nof data. In Figure 3 there are boxes within the table that can be selected in order to form a trait \ncollection. To do this, select the boxes in the table that su it the interests of the study, and press \nAdd. This function allows groups of traits to be saved for later analysis such as the generation of \na QTL, a network graph, and correlation matrix, some of which will be investigated further in", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 22, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "3033b643-e51e-5467-b7d7-6a5c27061cab", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "bd9e8c5d-405c-5b8b-b731-bf4fdaea1b3a", + "score": 0.7133984565734863, + "metadata": { + "text": "analysis in GeneNetwork, but there is an even more direct way to answer the same question. \nIt is possible to query data sets in GeneNetwork from the Select and Search page using \nadvanced options to locate the highest trait LRS values for any genomic interval, in this case \nthe region within 2 Mb of Comt . (Note: You can explore this and other search options \nfurther by clicking the Advanced Search button and reading the section Advanced", + "title": "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "version": "v0", + "chunk_order": 73, + "document_id": "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "extraction_id": "dbfd3de6-3641-5430-b694-682fed7b32e9", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "01a09a4e-3c30-53b1-8819-6085d4886079", + "score": 0.71033020554005, + "metadata": { + "text": "is shown in Figure 1A. Associations between transcript\nabundance, phenotypic traits and genotype can be estab-\nlished either using correlation or genetic linkage mapping\nfunctions [29,30]. The main page of GeneNetwork at\nhttp://www.genenetwork.org provides access to subsets of\ndata through pull-down menus that allow specific data\nsets to be queried. The datasets can be further restricted\nusing a single text box for specific database entries to\nquery probe set or trait ID, or annotations associated with", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "d261c68c-c253-52c9-8e27-f76fb8d0b4f8", + "score": 0.7099690014142406, + "metadata": { + "text": "genetic mapping, and correlation of quantitative traits such as gene expression data and behavioral parameters (Wang et al, 2003) . GeneNetwork employs \ngenotype data from 3809 markers, selected based on their being informative (i.e., different between progenitor strains). GeneNetwork outputs peak likelihood ratio statistic (LRS) locations for each trait, whic h can be directly converted to", + "title": "2018 - Molecular Brain Adaptations to Ethanol_ Role of Glycogen Synthase (2).pdf", + "version": "v0", + "chunk_order": 233, + "document_id": "cc2690a9-5a87-5f09-87d5-115a6a6b8349", + "extraction_id": "1047bf10-3878-5b70-8bb2-c0249f2a9c53", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "21936758-94b1-506f-9229-77e26001ae44", + "score": 0.7092886740393249, + "metadata": { + "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we", + "title": "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "76a715a4-8222-598b-8e65-6d5b6e807989", + "extraction_id": "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7089078426361084, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "94f60899-c281-586e-8741-135a4fef2663", + "score": 0.7044680118560791, + "metadata": { + "text": "(description of GeneNetwork provided by Dr. Robert W. Williams). Both of these websites\nfocus to a large extent on correlations of behavioral phenotype with gene expression levels\nin recombinant inbred and inbred panels of mice and rats, and on QTL analyses, as a means\nto identify candidate genes for complex traits. What distinguishes PhenoGen, in addition to\nthe tools for raw expression data analysis described above, is that the user can not only", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "308bef07-d720-5686-990d-d1e26a48e8a1", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + }, + { + "id": "d8162fdc-326a-5f90-9fa4-24d86d701184", + "score": 0.7014241814613342, + "metadata": { + "text": "of links to external resources for tracing the interrelationships of a gene among multiple\nWeb-based resources. GeneNetwork also offers a number of correlation and mapping\nstrategies for assessing associations among multiple genes and QTLs. GeneNetwork aims to\nmake the study of complex traits through the use of systems genetics widely available to the\nscientific community. A powerful tool that can be integrated with GeneNetwork or used on", + "title": "2010 - Using expression genetics to study the neurobiology of ethanol and alcoholism.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "64469ae5-5eb6-5e45-ab23-7bafb63d486f", + "extraction_id": "9597c8b3-0d67-5192-9e08-1bccc5e2f75c", + "associatedQuery": "How do researchers identify which genes are important for certain traits using GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIGmJRrNQ5y45QTYEPosOFommIdfp", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org can assist in understanding complex traits like height or intelligence through several key features:\n\n1. **Analytical Tools and Data Sets**: GeneNetwork provides a variety of analytical tools that allow users to compare traits with numerous datasets available from other researchers. This includes microarray data of gene expression in the brain and other phenotypic data, which can be crucial for studying complex traits [1].\n\n2. **Systems Genetics Approach**: The platform offers a systems genetics approach, which helps illuminate the relationships between different biological system levels, such as the genome, transcriptome, and phenome. This comprehensive view can provide insights into the roles of individual genes and developmental pathways involved in complex traits [2].\n\n3. **Correlation and Genetic Linkage Mapping**: GeneNetwork allows for the establishment of associations between transcript abundance, phenotypic traits, and genotype using correlation or genetic linkage mapping functions. This can help identify genetic factors contributing to complex traits like height or intelligence [6].\n\n4. **Data Mining and Trait Correlations**: The platform can be used to study correlations between traits and perform data mining in genomic regions containing candidates for quantitative trait genes. This feature is particularly useful for identifying genetic components of complex traits [4].\n\n5. **Multi-Omics Analysis**: GeneNetwork has been updated to include multi-omics analysis, which integrates various types of biological data. This holistic approach can enhance the understanding of complex traits by considering multiple layers of biological information [7].\n\nOverall, GeneNetwork.org provides a comprehensive suite of tools and data that can facilitate the exploration and understanding of complex traits like height and intelligence through a systems genetics framework.", + "role": "assistant" + } + } + ], + "created": 1728917763, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 329, + "prompt_tokens": 1201, + "total_tokens": 1530 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "21936758-94b1-506f-9229-77e26001ae44", + "score": 0.6708868342109433, + "metadata": { + "text": "GeneNetwork provides users with an array of analyticaltools to compare a given trait with a number of data setsavailable from other experimenters. Microarray data ofgene expression in the brain and data of other phenotypes\nare two such examples of possible tools. For this study, we", + "title": "2008 - Genetic Analysis of Posterior Medial Barrel Subfield Size.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "76a715a4-8222-598b-8e65-6d5b6e807989", + "extraction_id": "66aad1b1-a76d-58a8-aa40-76a6b58c4964", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "18c7c27b-b51f-5ab6-9d09-4235c57811b1", + "score": 0.6695772606386399, + "metadata": { + "text": "genetics approaches can not only provide insights into the roles of \nindividual genes or developmental pathways but also illuminate \nrelationships between different levels of a biologic system, such as \nthe genome, transcriptome, and phenome [ 10]. One such resource \nof systems genetics is the GeneNetwork website and resource \n(www.genenetwork.org ) that provides access to a wide variety of \ndata such as genotypes (e.g., SNPs), phenotypes that are obtained", + "title": "2017 - Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "67e804db-8127-5938-8d7f-a5918cdf4f86", + "extraction_id": "2455cf6d-4c9b-5272-8650-da127cc329e8", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "38f4e070-1a03-566c-b261-c61ed61963c1", + "score": 0.6658043066146568, + "metadata": { + "text": "201 5Nature America, Inc. All rights reserved.\n6 ADVANCE ONLINE PUBLICATION Nature Ge Neticsa n a ly s i s\n11. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human \nheight. Nat. Genet. 42, 565569 (2010).\n12. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide \ncomplex trait analysis. Am. J. Hum. Genet. 88, 7682 (2011).\n13. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of", + "title": "2015 - An atlas of genetic correlations across human diseases.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "7b1f602b-1534-5465-b026-03dedf01352d", + "extraction_id": "70e38f86-69b7-515d-919e-b8d93f5c709f", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6642516455500349, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "ed2def7c-a3bb-5d45-ae88-5100874b0837", + "score": 0.660356879234314, + "metadata": { + "text": "medicine.\nGeneNetwork.org is a tool for quantitative genetics that started in 2001 as WebQTL [38]. It\nevolved from analyses of forward genetics in the BXD mouse family, to phenome-wide\nassociation studies and reverse genetics in a variety of species. Although GeneNetwork contains\ndata for many species and populations, it most prominently contains data for the BXD family.\nOver 10,000 classical phenotypes, measured under a variety of environmental conditions, and", + "title": "2021 - Old data and friends improve with age Advancements with the updated tools.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "55cb2c81-b699-54df-96ab-2bf0b888031e", + "extraction_id": "46f604d3-ba70-5cca-8466-21381131697e", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "01a09a4e-3c30-53b1-8819-6085d4886079", + "score": 0.6540752259448047, + "metadata": { + "text": "is shown in Figure 1A. Associations between transcript\nabundance, phenotypic traits and genotype can be estab-\nlished either using correlation or genetic linkage mapping\nfunctions [29,30]. The main page of GeneNetwork at\nhttp://www.genenetwork.org provides access to subsets of\ndata through pull-down menus that allow specific data\nsets to be queried. The datasets can be further restricted\nusing a single text box for specific database entries to\nquery probe set or trait ID, or annotations associated with", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "a6c480d1-b384-5c6f-b21b-94fe0b3b0f4d", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6447843909263611, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6422666907310486, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.6413672780175651, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + }, + { + "id": "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "score": 0.6413672780175651, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "associatedQuery": "How can GeneNetwork.org help in understanding complex traits like height or intelligence?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIGmRJNSU1IpWwTrk2tDfmXqGWPRd", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Yes, there are known genetic mutations that cause premature aging. Some specific genetic syndromes associated with premature aging include:\n\n1. Hutchinson-Gilford Progeria Syndrome, which is caused by mutations in the LMNA gene [4].\n2. Rothmund-Thomson syndrome and related disorders, which are associated with mutations in the RECQL4 gene [4].\n3. Ataxia-telangiectasia, which is another genetic disorder linked to premature aging [4].\n\nAdditionally, Martin (1978) listed 162 genetic syndromes in humans that exhibit some or many signs of premature aging [1]. These conditions highlight the connection between genetic mutations and premature aging.", + "role": "assistant" + } + } + ], + "created": 1728917771, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 135, + "prompt_tokens": 1643, + "total_tokens": 1778 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a9f7eda5-1b64-507e-95dd-07c81f2d603b", + "score": 0.7192796468734741, + "metadata": { + "text": "logical phenomena is often facilitated by the \nstudy of genetic mutants, and, in the case of \nhumans, genetic disorders. Accordingly, a search \nwas made, over the years, for genetic disorders \ncharacterized by premature aging. If DNA dam- \nage and repair has anything to do with aging it \nshould be evidenced in such individuals. Martin \n(1978) listed 162 genetic syndromes in humans with some or many signs of premature aging. \nAbout 21 feahares are considered as markers for", + "title": "1993 - Genomic Damage and Its Repair.pdf", + "version": "v0", + "chunk_order": 127, + "document_id": "d049f302-a130-5ee4-a1b5-5091605d5173", + "extraction_id": "64063108-0ff2-54e5-9801-bc1c49cbdee4", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "882149e3-8186-5577-a2a7-79f2659ff9b4", + "score": 0.6718193683839645, + "metadata": { + "text": "[315] Szilard, L. On the nature of the aging process. Proc. Natl. Acad. Sci. USA\n45:3545; 1959.\n[316] Vijg, J.; Dolle, M. E. Large genome rearrangements as a primary cause of\naging. Mech. Ageing Dev. 123:907915; 2002.\n[317] Vijg, J. Somatic mutations and aging: a re-evaluation. Mutat. Res.\n447:117135; 2000.\n[318] Martin, G. M. Genetic syndromes in Man with potential relevance to the\npathobiology of aging. Birth Defects Orig. Artic. Ser. 14:539; 1978.", + "title": "2007 - Trends in oxidative aging theories.pdf", + "version": "v0", + "chunk_order": 379, + "document_id": "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "extraction_id": "752c6f1a-0c4d-5419-86cd-687d2aed7817", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "da4e59b7-d5b6-5992-9607-f6697c8f5276", + "score": 0.6711906790733337, + "metadata": { + "text": "19\n 6. Milholland B, Suh Y , Vijg J.Mutation and catastrophe in the aging genome. Exp Gerontol. \n2017;94:3440.\n 7. Maslov AY , Ganapathi S, Westerhof M, Quispe-Tintaya W, White RR, Van Houten B, etal. \nDNA damage in normally and prematurely aged mice. Aging Cell. 2013;12:46777.\n 8. Blokzijl F, de Ligt J, Jager M, Sasselli V , Roerink S, Sasaki N, etal. Tissue-specific mutation \naccumulation in human adult stem cells during life. Nature. 2016;538:2604.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 164, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "ead14808-bfb7-5e32-9830-28efaae71151", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "4841d806-98b4-513e-94a2-714df6c896f5", + "score": 0.6635691523551941, + "metadata": { + "text": "143 Gonzalo S, Kreienkamp R & Askjaer P (2017) Hutchinson -Gilford Progeria \nSyndrome: A premature aging disease caused by LMNA gene mutations. \nAgeing Res. Rev. 33, 1829. \n144 Lu L, Jin W & Wang LL (2017) Aging in Ro thmund -Thomson syndrome and \nrelated RECQL4 genetic disorders. Ageing Res. Rev. 33, 3035. \n145 de Renty C & Ellis NA (2017) Blooms syndrome: Why not premature aging? \nAgeing Res. Rev. 33, 3651. \n146 Shiloh Y & Lederman HM (2017) Ataxia -telangiectasia (A -T): An emerging", + "title": "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "version": "v0", + "chunk_order": 137, + "document_id": "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "extraction_id": "d620ea24-4422-5636-86f5-0943371a4a18", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "fc10c968-3108-5c4b-a49c-cb0feabd18c5", + "score": 0.6526112945444175, + "metadata": { + "text": "genetic disease model of premature aging, In: Harrison,D.E., eds, Genetic Effects on Aging II (Telford Press, Caldwell,NJ), pp. 521542.\n[2] Djawdan, M., Sugiyama, T., Schlaeger, L., Bradley, T.J. and\nRose, M.R. (1996) Metabolic aspects of the trade-off between\nfecundity and longevity in Drosophila melanogaster ,Physiol.\nZool. 69, 11751195.\n[3] Fleming, J.E., Spicer, G.S., Garrison, R.C. and Rose, M.R.", + "title": "2002 - Pharmacology, Genomics, and the Evolutionary Biology.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "1bc636a3-6ce0-5fea-b549-0dae90a78f1b", + "extraction_id": "e501662f-ffca-563b-97a7-b682a5d7f6ba", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "eb8b89de-422a-5e9e-9ac8-60af4cd718c2", + "score": 0.6519961953163147, + "metadata": { + "text": "genes of a whole chromosome ineffective, couldbe a main causal factor in aging (Szilard, 1959).According to Maynard Smith, such types of mu-tations do not seem likely to be common enoughto be the main cause of aging. However, at thetime quantitative information on the possible age-related accumulation of different types of muta-tions in various tissues of mammals wascompletely lacking. The question, therefore,whether somatic mutations are a cause of aging,has not been resolved, more than four decadesafter", + "title": "2002 - Large genome rearrangements as a primary cause of aging.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "8a8926dc-2360-5a54-b586-8acc34e51c32", + "extraction_id": "8f1a0875-8179-5d45-abc0-bbd4c9ac8da5", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "34e6b3c4-63bf-5198-ab09-2a7200a7c19a", + "score": 0.6518602578723288, + "metadata": { + "text": "features of premature aging (16, 17). Subsequent experiments conrmed that mitochondrial DNA\nmutations and deletions were the driving force behind the observed accelerated aging phenotypes(18).\nTHE LINK BETWEEN NUCLEAR GENOME INTEGRITY\nAND PREMATURE AGING\nThe notion that the majority of currently identied progeria syndromes originate from defects\nin genome maintenance highlights the importance of the condition of DNA in the process of", + "title": "2016 - Genome Integrity in Aging.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "extraction_id": "17b26647-4659-5f2d-a9b0-7c122d4b5d1a", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "beed04cc-28c7-5dc7-b334-51226a217439", + "score": 0.6509696453231796, + "metadata": { + "text": "Tryggvason K,ZhouZ.Genomicinstability inlaminopathy based\npremature aging,NatMed. 2005;11:780 785.\n13.MisteliT,ScaffidiP.Genomeinstability inprogeria:when\nrepairgetsold,NatMed. 2005;11:718 719.\n14.PereiraS,Bourgeois P,NavarroC,EstevesVieiraV,CauP,De\nSandreGiovannoli A,LvyN.HGPSandrelatedpremature aging\ndisorders: Fromgenomicidentification tothefirsttherapeutic \napproaches, MechAgeingDev.2008;129:449 459.\n15.SmithED,Kudlow BA,FrockRL,KennedyBK.Atypenuclear", + "title": "2009 - Genomic instability and DNA damage responses in progeria arising.pdf", + "version": "v0", + "chunk_order": 83, + "document_id": "b7d96f9f-8ad4-5f8f-94f9-60404806d478", + "extraction_id": "72beba0d-8c77-5aa9-82ac-ddf6a19355ac", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "badf3a36-1f99-58aa-b80c-725eccf4e8f3", + "score": 0.6501748561859131, + "metadata": { + "text": "Nature Genetics | Volume 55 | February 2023 | 268279 278\nArticle https://doi.org/10.1038/s41588-022-01279-621. Tiwari, V. & Wilson, D. M. 3rd. DNA damage and associated DNA \nrepair defects in disease and premature aging. Am. J. Hum. Genet. \n105, 237257 (2019).\n22. Tamae, D., Lim, P., Wuenschell, G. E. & Termini, J. Mutagenesis and repair induced by the DNA advanced glycation end product N2-1-(carboxyethyl)-2-deoxyguanosine in human cells. Biochemistry \n50, 23212329 (2011).", + "title": "2023 - Genome-wide RNA polymerase stalling.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "78812a12-8d31-5159-8367-b0d38e5bc84b", + "extraction_id": "31088092-778f-59e0-a9de-5ec25c241aab", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + }, + { + "id": "c35d1f43-c3bd-5cac-ae4d-937be35f1121", + "score": 0.6501638889312744, + "metadata": { + "text": "[36] J. de Boer, J.O. Andressoo, J. de Wit, J. Huijmans, R.B. Beems, H. van Steeg, et al.,\nPremature aging in mice decient in DNA repair and transcription, Science\n296 (2002) 12761279.\n[37] S.M. Schuh-Huerta, N.A. Johnson, M.P. Rosen, B. Sternfeld, M.I. Cedars, R.A.\nReijo Pera, Genetic markers of ovarian follicle number and menopause in\nwomen of multiple ethnicities, Hum. Genet. 131 (2012) 17091724.", + "title": "2016 - Menopause Genome stability as new paradigm.pdf", + "version": "v0", + "chunk_order": 145, + "document_id": "564dead1-2737-572f-860c-f00de4d0395e", + "extraction_id": "0855231d-cb95-540c-a3dd-c93729efb34c", + "associatedQuery": "Are there any known genetic mutations that cause premature aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIGmWH13RNXnSJZf14g9zwI9AF1X4", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The research done through GeneNetwork.org has several practical applications:\n\n1. **Predictive Medicine and Systems Genetics**: GeneNetwork is a valuable resource for predictive medicine and systems genetics, providing tools and data for multi-omics analysis across multiple species [1].\n\n2. **Teaching Tool**: It serves as a teaching tool in neuroscience and genetics, allowing educators to use it for dry-lab teaching and helping students explore gene-to-phenotype relationships [2].\n\n3. **Exploration of Systems Genetics Data**: GeneNetwork is used to explore systems genetics data, which is crucial for defining biological networks and predicting molecular interactions [4].\n\n4. **Complex Systems Biology Approaches**: It provides datasets and tools for complex systems biology approaches, aiding in the generation or prediction of higher-order gene functions [5].\n\n5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork can be used as a virtual laboratory to test specific biological hypotheses or to generate new ideas from scratch [8].\n\n6. **Identification of Regulatory Genes**: The platform can identify novel potential master regulatory genes for further investigation, enhancing the understanding of genetic regulation [9].\n\n7. **User-Friendly Systems Genetics Analyses**: It allows researchers without advanced bioinformatics skills to perform systems genetics analyses, making it accessible to a broader range of scientists [10].", + "role": "assistant" + } + } + ], + "created": 1728917776, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 265, + "prompt_tokens": 1063, + "total_tokens": 1328 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7365625343658063, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "score": 0.7107253670692494, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.7107253670692494, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.7105271047667936, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "score": 0.6864207404632041, + "metadata": { + "text": "subnetworks \n GeneNetwork (www.genenetwork.org) is a depository of data-\nsets and tools for use in complex systems biology approaches in \norder to generate or predict higher order gene function ( 23, 24 ).", + "title": "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "extraction_id": "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "f5c218f0-1280-55f8-912b-b32b833e93a3", + "score": 0.680264115333562, + "metadata": { + "text": "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "title": "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "version": "v0", + "chunk_order": 263, + "document_id": "73724c99-98df-53b2-a378-29c8b4faa171", + "extraction_id": "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "a0c173a5-8685-50df-8110-8d7ec02cdbf3", + "score": 0.6789623903609759, + "metadata": { + "text": "resources, gene expression pro les, and gene network\nconstructions, methods for the analysis of gene function\nhave been revolutionised in the past few years. One great\nresource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for\nsystems genetics (Andreux et al., 2012). It has been designed\nfor multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-", + "title": "2015 - Gene network of a phosphoglycerate mutase in muscle wasting in mice.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "912c42f2-1802-5fe9-b9dd-871c5290dd18", + "extraction_id": "b71c7c43-8bd9-5d7e-b337-a8c0396f96b7", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "27eda296-b0b7-5ae4-881e-31987ba63dec", + "score": 0.6785929203033495, + "metadata": { + "text": "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems \ngenetics and systems behavioral approaches. Web services such as GeneNetwork and its \ncompanionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID \n(Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 )\ncan now be used as virtual and free laboratories to test specific biological hypothesis, or they \ncan be used to generate new ideas ab initio .", + "title": "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "extraction_id": "833f822f-9080-5434-b9a1-2473531becfe", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "55461d7c-995b-5f5a-be38-a64acd4a904a", + "score": 0.677646975377023, + "metadata": { + "text": "Its use is centred upon user-specied genes and can identify novel\npotential master regulatory genes for further investigation. We are\nworking to increase the functionality and power of the GeneNet-\nwork and systems genetics further in a number of areas. In partic-\nular, increasing the number of strains studied can increase the\nmapping resolution. By increasing the genetic diversity of the\nfounders of an RI set, the potential for observing regulatory poly-", + "title": "2008 - Systems_genetics_can_provide_new_insight.pdf", + "version": "v0", + "chunk_order": 36, + "document_id": "0bc5b1ee-f7fe-5d12-9ebf-ab7363119424", + "extraction_id": "bfd98d6d-b026-5bbd-896f-b70045a032c8", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + }, + { + "id": "fb6033cf-3df1-5dd6-b3ed-1d32913800b2", + "score": 0.6686759393618082, + "metadata": { + "text": "gration enhances the chance to detect genuine modi ers across \norgans. GeneNetwork is a valuable platform that can be used by \nresearchers without advanced skills of bioinformatics to perform \nsystems genetics analyses. The next step would be to establish soft-\nware tools that allow researchers to combine datasets from multiple \nresources and mapping analyses in different crosses and species (e.g. \nintercross, recombinant inbred lines, and human data). \n References", + "title": "2017 - Systems genetics of liver fibrosis.pdf", + "version": "v0", + "chunk_order": 56, + "document_id": "4efaa694-a204-54e3-8f4b-caa322574f0a", + "extraction_id": "1c595cd2-cdf9-58c7-a344-902eb2facf31", + "associatedQuery": "What are the practical applications of the research done through GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIGmdOlKLAeARCOPtbkwth6fOr9HL", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "To access and use the data available on GeneNetwork.org, you can follow these steps:\n\n1. **Navigating to the Website**: Start by visiting the GeneNetwork website at www.genenetwork.org [8].\n\n2. **Searching for Data**: There are two primary ways to search for data on GeneNetwork:\n - Use the global search bar located at the top of the page. This feature allows you to search for genes, mRNAs, or proteins across all datasets, providing data across various species, groups, and types of data [5].\n - Alternatively, you can follow the main search workflow, which involves selecting a dataset, mining it for traits of interest based on user search queries, selecting traits from the search, and placing them in a collection for further inspection and quantitative analysis [3].\n\n3. **Analyzing Data**: Once you have selected the data, GeneNetwork provides an analytical environment where you can perform correlation analysis and linkage mapping. This environment helps identify and substantiate gene targets for further research [7].\n\n4. **Accessing Genotype Files**: If you need genotype files, they can be accessed directly via a specific URL: http://www.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=600 [2].\n\n5. **Using Additional Resources**: The banner menu on the main search page contains additional search options and helpful resources to assist with your analyses [3].\n\nBy following these steps, you can effectively access and utilize the data available on GeneNetwork.org for your research needs.", + "role": "assistant" + } + } + ], + "created": 1728917783, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 319, + "prompt_tokens": 1150, + "total_tokens": 1469 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7203701734542847, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "bd2eb0ef-24a1-55ff-8597-c21dff0ecf0a", + "score": 0.6956073823209189, + "metadata": { + "text": "This paper analyzes existing, publicly available data. These data sets accession numbers are provided in the Key Resource Table ,\nand throughout the manuscript. Genotype les can be found at http://www.genenetwork.org/webqtl/main.py?FormID=\nsharinginfo&GN_AccessionId=600 .\nGeneNetwork.org original code is publicly available at https://github.com/genenetwork/genenetwork2 and https://github.com/\ngenenetwork/genenetwork1 .", + "title": "2020 - A platform for experimental precision medicine The extended BXD mouse family.pdf", + "version": "v0", + "chunk_order": 187, + "document_id": "dd4994b9-9546-59c0-bc71-60e2617b6bcd", + "extraction_id": "d1c32c32-42c8-5065-b7f2-bd2a0baeae62", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "e030ce79-6970-5300-a1d8-1623d07c2157", + "score": 0.6918747425079346, + "metadata": { + "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 210, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "2bdd2f18-e4d0-53e9-b0fa-a7ed8d710961", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "1762dc59-0e50-5b7e-bdc2-b754e0e57797", + "score": 0.6916453448428319, + "metadata": { + "text": "Fig. 2. \nGeneNetwork main search page and organization. Most analyses in GeneNetwork will \nfollow the steps shown in panels A through D. In this workfl ow, a data set is selected ( A) \nand mined for traits of interest based on user search queries ( B). Traits are then selected \nfrom the search ( C) and placed in a collection for further inspection and quantitative analysis \n(D). The banner menu contains additional search options and helpful resources under the", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 210, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "6cbea84e-4d8d-5ce0-8e58-45ee75f6f908", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "f574ef17-062c-5bc8-be3e-81184e141970", + "score": 0.6894698348483397, + "metadata": { + "text": "1. Data \nOnce you have navigated to genenetwork.org, t here are two ways to search for data in GN. The \nfirst is to use the global search bar located at the top of the page (Figure 1 ). This is a new \nfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of the \ndatasets. This will give the user data for that search term across many different species, groups, \nand types of data. Because of this, the global search bar is a good area to start ones searches if", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "779b4029-6cc7-535e-a8b7-0ee31fa97162", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "score": 0.684724748134613, + "metadata": { + "text": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "251de62d-6e8e-50c7-9616-7fea05a250fb", + "score": 0.6788209676742554, + "metadata": { + "text": "abundance data sets directly within GeneNetwork's ana-\nlytical environment we provide simple web access to the\ndata for the research community. In this environment, a\ncombination of correlation analysis and linkage mapping\nprovides the potential to identify and substantiate gene\ntargets for saturation mapping and positional cloning. By\nintegrating datasets from an unsequenced crop plant (bar-\nley) in a database that has been designed for an animal\nmodel species (mouse) with well established genome", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "dc001755-2e77-5b41-8617-263b3ba35af8", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "f5c218f0-1280-55f8-912b-b32b833e93a3", + "score": 0.6750081375236441, + "metadata": { + "text": "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "title": "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "version": "v0", + "chunk_order": 263, + "document_id": "73724c99-98df-53b2-a378-29c8b4faa171", + "extraction_id": "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "db6bfa4e-9612-5f7e-8b7f-162f60b91c9d", + "score": 0.6725579500198364, + "metadata": { + "text": "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "title": "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "08b12d72-9776-5acb-b1ef-7ee402781897", + "extraction_id": "83ae495f-31a2-5977-a63a-57e704c394e2", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6707387566566467, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How can I access and use the data available on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIGmkanSOkp2jhcX3KUkfSSqCSJEs", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The provided context does not explicitly mention any specific recent discoveries made using GeneNetwork.org. However, it highlights the platform's capabilities and improvements, such as the addition of data from 10 species, multi-omics analysis, updated code, and new tools, which make it a valuable resource for predictive medicine and systems genetics [1]. Additionally, GeneNetwork.org is described as a powerful statistical platform for online network analyses and mapping, which could enable numerous breakthroughs in neuroscience and other fields [7]. While these improvements and capabilities suggest potential for discoveries, specific recent discoveries are not detailed in the context provided.", + "role": "assistant" + } + } + ], + "created": 1728917790, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 119, + "prompt_tokens": 1017, + "total_tokens": 1136 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7628134489059448, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "105b8e0a-a88c-59a6-9d39-7d894d059b73", + "score": 0.6967442631721497, + "metadata": { + "text": "18 GeneNetwork Time Machine : Full versions from 2009 to 2016 (mm9); UTHSC Genome \nBrowser Classic and Newest ; UTHSC Galaxy Servic e; UTHSC Bayesian Network \nWeb Server ; GeneNetwork Classic on Amazon Cloud ; GeneNetwork Classic Code on \nGitHub ; GeneNetwork 2.0 Development Code on GitHub ; and GeneNetwork 2.0 \nDevelopment. \n \nTechnologies or techniques: \n None \n \nInventions, patent applications, and/or licenses: \nNone \n \nOther products: \nNone", + "title": "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "extraction_id": "0c76461f-34ff-5604-aa4c-12eb9d2877aa", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6875818967819214, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6855115481906219, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "f5c218f0-1280-55f8-912b-b32b833e93a3", + "score": 0.6850242206439853, + "metadata": { + "text": "GeneNetwork (www.genenetwork.org). The web -based software further allows extraction of sets of", + "title": "2013 - Pathogenesis and reversal of liver fibrosis Effects of genes and environment.pdf", + "version": "v0", + "chunk_order": 263, + "document_id": "73724c99-98df-53b2-a378-29c8b4faa171", + "extraction_id": "71f1aefb-6e32-5add-804e-6fbaa39ca720", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "8b8a24da-a175-5cb8-91bd-8966fca5d344", + "score": 0.6837974990545961, + "metadata": { + "text": "subnetworks \n GeneNetwork (www.genenetwork.org) is a depository of data-\nsets and tools for use in complex systems biology approaches in \norder to generate or predict higher order gene function ( 23, 24 ).", + "title": "2011 - Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "ac61753e-bcb2-55c3-804b-e821e3d1a4ad", + "extraction_id": "a8b40857-7ae8-512a-9817-bea1ae3345ba", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "aaf19f3a-353a-595b-b7de-da9f01a7b0f1", + "score": 0.6797840595245361, + "metadata": { + "text": "distributed neuroscience data sharing with ever expanding\nprospects for future breakthroughs.\nGeneNetwork.org : genetic analysis for all\nneuroscientists\nOriginally named webqtl, GeneNetwork.org is the oldest contin-\nuously operating website in biomedical research ( Williams,\n1994). This massive database contains ;40 million datasets.\nGeneNetwork.org also offers a powerful statistical platform for\nonline network analyses and mapping, enabling numerous mo-", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "62c12bdc-ae2b-5cc0-88f5-a3c1a264326b", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "7b626f27-5aed-5464-a20d-463954ff057d", + "score": 0.6797840595245361, + "metadata": { + "text": "distributed neuroscience data sharing with ever expanding\nprospects for future breakthroughs.\nGeneNetwork.org : genetic analysis for all\nneuroscientists\nOriginally named webqtl, GeneNetwork.org is the oldest contin-\nuously operating website in biomedical research ( Williams,\n1994). This massive database contains ;40 million datasets.\nGeneNetwork.org also offers a powerful statistical platform for\nonline network analyses and mapping, enabling numerous mo-", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "28892088-5a95-56eb-822d-b12da3a612d0", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "score": 0.6733382344245911, + "metadata": { + "text": "1 \n GeneNetwork: a continuously updated tool for systems genetics analyses \n \nPamela M. Watson1, David G. Ashbrook1 \n \n1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science \nCenter, Memphis, TN 38163, USA \n \nAbstract \n \nGeneNetwork and its earlier iteration , WebQTL, have now been an important database and \ntoolkit for quantitative trait genetics research for two decades. Recent improvements to", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + }, + { + "id": "a0c173a5-8685-50df-8110-8d7ec02cdbf3", + "score": 0.6725320219993591, + "metadata": { + "text": "resources, gene expression pro les, and gene network\nconstructions, methods for the analysis of gene function\nhave been revolutionised in the past few years. One great\nresource for the analysis of gene networks is the databaseGeneNetwork, which consists of a set of linked resources for\nsystems genetics (Andreux et al., 2012). It has been designed\nfor multiple scale integration of networks of genes,transcripts in multiple tissues. GeneNetwork is an interac-", + "title": "2015 - Gene network of a phosphoglycerate mutase in muscle wasting in mice.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "912c42f2-1802-5fe9-b9dd-871c5290dd18", + "extraction_id": "b71c7c43-8bd9-5d7e-b337-a8c0396f96b7", + "associatedQuery": "What are some recent discoveries made using GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIGmr7v0rrhLH7kaV38yDCwjdEEpc", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Scientists ensure the accuracy of the data on GeneNetwork.org through several methods:\n\n1. **Quality Control and Normalization**: Data are entered into GeneNetwork after being processed through systems like PhenoGen, which have extensive capabilities for normalization and quality control [3].\n\n2. **Quality Checking and Preprocessing**: Phenotypic data undergo quality checks and preprocessing before being uploaded to GeneNetwork. This includes normalization, removal of outliers, and transformation of data to achieve a normal distribution [8].\n\n3. **Data Curation and Informatics Support**: The GeneNetwork.org team provides excellent data curation and informatics support to maintain data accuracy [4].\n\nThese steps help ensure that the data on GeneNetwork.org is accurate and reliable for scientific research.", + "role": "assistant" + } + } + ], + "created": 1728917797, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 152, + "prompt_tokens": 1281, + "total_tokens": 1433 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6618846852148978, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "a2875189-1592-59ad-ad10-f3c4911411e2", + "score": 0.6463315890332608, + "metadata": { + "text": "files), and GeneNetwork (a free scientific web resource, http://www.genenetwork.org/). Statistical analysis was performed using GraphPad Prism (GraphPad Software, Inc., CA, USA).", + "title": "2014 - Sirt1 induction confers resistance to etoposide-induced genotoxic apoptosis in thyroid cancers.pdf", + "version": "v0", + "chunk_order": 31, + "document_id": "18e62e2f-643c-5c42-b80a-bab5432a8894", + "extraction_id": "e3d1b792-6241-5ba3-b06f-ee29eb0106fc", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "fa07b1bf-94e6-515b-8400-cf3afa8b8741", + "score": 0.6378567408876643, + "metadata": { + "text": "data are entered into GeneNetwork after they have been shepherded through a system like\nPhenoGen that has extensive capabilities for normalization and quality control. A\ncomparison of the brain gene expression datasets and some of the tools for data analysis\navailable on PhenoGen and GeneNetwork is shown in Table 3, and more detailed\ninformation on features provided by each site is outlined in the Supplementary DiscussionHoffman et al. Page 5\nAddict Biol . Author manuscript; available in PMC 2012 July 1.", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "8f734e2a-cd29-5021-84be-a9e08bc21a99", + "score": 0.6347903202411631, + "metadata": { + "text": "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and\ninformatics support.\nConicts of Interest: The authors declare no conict of interest.\nReferences\n1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos,\nL.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018.\n[CrossRef]", + "title": "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GN.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "6f5d0c5b-0bbb-5eca-9e3e-73c3b0675472", + "extraction_id": "50d920fa-3482-52ca-899f-15b182fdb4fd", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "219cfeab-8877-5c92-92d0-87b17c0d4206", + "score": 0.6347903202411631, + "metadata": { + "text": "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and\ninformatics support.\nConicts of Interest: The authors declare no conict of interest.\nReferences\n1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos,\nL.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018.\n[CrossRef]", + "title": "2022 - New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "d71efa0d-5de8-549c-964d-489ef6b73a1f", + "extraction_id": "ee874620-8c4e-55df-8274-2dcd4eba2ca9", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "8a3abc37-292a-5bd3-9527-bcf17dc9eafc", + "score": 0.6347903202411631, + "metadata": { + "text": "thank the members of the GeneNetwork.org team for their assistance, excellent data curation, and\ninformatics support.\nConicts of Interest: The authors declare no conict of interest.\nReferences\n1. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos,\nL.B.; Bourne, P .E.; et al. The FAIR Guiding Principles for scientic data management and stewardship. Sci. Data 2016 ,3, 160018.\n[CrossRef]", + "title": "2022 -Chunduri- Drugs Animal Models.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45", + "extraction_id": "4cafc4e9-69df-5a08-921c-de6c66267056", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "29c406c6-34e1-5f8a-8a6f-1b239dd633ae", + "score": 0.6286235230340673, + "metadata": { + "text": "9 Scientific Data | (2019) 6:258 | https://doi.org/10.1038/s41597-019-0171-x\nwww.nature.com/scientificdata www.nature.com/scientificdata/with more than 10% missing information, low quality ( <5000), and redundant information were removed. \nGeneNetwork genotypes, which were discrepant with our RNA-seq experiment, were tagged as unknown \n(mean of 1% of the GeneNetwork genotypes/strain [0.05% n 8%]). Finally, GeneNetwork and our RNA-seq", + "title": "2019 - A multi-omics digital research object for the genetics of sleep regulation.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "af97f766-ca4d-56c0-9eb8-ba6c5e7db1da", + "extraction_id": "a002e2e0-b978-540d-b435-5701c30496b6", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "45ce962b-f534-59a7-ab21-c5f858d4ec20", + "score": 0.6113969269811194, + "metadata": { + "text": "1. Phenotypic data should be quality checked and preprocessed \nbefore being uploaded to GeneNetwork. This includes nor-\nmalization of data, removal of outliers or windsorization, even-\ntually transformation of data to obtain normal distribution. \n 2. When uploading data to GeneNetwork for permanent and \npublic storage, make sure to follow the GeneNetwork naming \nguide for phenotypes. \n 3. When uploading your own data make sure that for any pheno-", + "title": "2016 - Systems Genetics of Obesity.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "c38d1bad-8690-5d4d-a60a-dcbb4ac4aa93", + "extraction_id": "d214b44c-c033-59f7-b120-fa4d6bf35bb4", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "19ba23ee-9d24-55cc-85cb-bee95894f710", + "score": 0.6113969269811194, + "metadata": { + "text": "1. Phenotypic data should be quality checked and preprocessed \nbefore being uploaded to GeneNetwork. This includes nor-\nmalization of data, removal of outliers or windsorization, even-\ntually transformation of data to obtain normal distribution. \n 2. When uploading data to GeneNetwork for permanent and \npublic storage, make sure to follow the GeneNetwork naming \nguide for phenotypes. \n 3. When uploading your own data make sure that for any pheno-", + "title": "2017 - Systems genetics of obesity.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "f10cf311-0397-5c0a-81e0-3b84090e434b", + "extraction_id": "674a8666-6310-5df3-8539-e274cd629e9c", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + }, + { + "id": "4188099c-aba1-5f0d-b2ec-a7c8f5bb1bc5", + "score": 0.6061892690118932, + "metadata": { + "text": "analysis of behavior and for neurologic diseases are provided in\nthe study by\nMulligan et al. (2017) .\nGeneNetwork.org is committed to data and code workflows\nthat are FAIR compliant, ensuring that those who generate data\nand key ideas get the deserved credit. To further ensure effective\nand secure dissemination of data and ideas, as well as improved\nreproducibility, the GeneNetwork.org infrastructure is currently\nbeing redesigned using more modular structures and APIs that", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 86, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "How do scientists ensure the accuracy of the data on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIGmxl54ZXUf76PPUFFgbX3aFiiFS", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The difference between looking at one gene and studying a whole gene network lies in the scope and context of the analysis. When examining a single gene, the focus is on understanding the role and function of that specific gene, often in isolation. This approach can be limited because it does not consider the interactions and relationships that gene may have with others.\n\nIn contrast, studying a whole gene network involves analyzing a system of interconnected genes, which provides a broader context. A gene network is a graphical model where nodes represent genes or gene products, and edges represent interactions between them [1]. This approach allows researchers to explore how multiple genes interact within biological pathways, potentially uncovering complex relationships and combined effects that a single-gene analysis might miss [2]. By studying gene networks, researchers can gain insights into the collective behavior of genes, which can be crucial for understanding complex diseases and biological functions [2]. Additionally, gene networks can help identify highly connected subgraphs that correspond to biologically relevant networks, aiding in the identification of causative genes and their regulatory roles [5].", + "role": "assistant" + } + } + ], + "created": 1728917803, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 211, + "prompt_tokens": 1148, + "total_tokens": 1359 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "509d3815-9994-5afc-9777-52eb80281dc8", + "score": 0.6699192324304836, + "metadata": { + "text": "considering single genes in the context of a whole gene network may provide thenecessary context within which to interpr et the disease role a given gene may play.\nConstructing gene networks can provide a convenient framework for exploring\nthe context within which single genes operate. A network is simply a graphicalmodel comprised of nodes and edges. For gene networks associated with biological\nsystems, the nodes in the network typically represent genes, gene products, or other", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 99, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "1d401588-b6dc-532f-8194-4667a7d31153", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "1cf9d2ee-62b7-5dc8-8f58-23cecab650dc", + "score": 0.6577027848121184, + "metadata": { + "text": "is tackling this immense challenge bystudying networks of genes, proteins,metabolites, and other biomarkers thatrepresent models of genuine biologicalpathways. Studying complex diseasesin terms of gene networks rather thanindividual genes or genomic loci shouldaid in uncovering disease genes. Withthis approach, the effects of multiplegenes in the network are combined,producing a stronger signal and reducingthe number of statistical tests of associ-ation that must be performed.", + "title": "2012 - Identifying Gene Networks Underlying the Neurobiology of Ethanol and Alcoholism.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "c02542c0-eff8-5ec7-8f73-78f5d28d4226", + "extraction_id": "40850ed1-db52-594e-a9d6-0b661e0bc494", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "85a8d5cf-0da0-5273-b1f2-f10e440ea24e", + "score": 0.6500645682196474, + "metadata": { + "text": "traditional genetical genomics approaches. It should also be noted that our approach is\ndifferent from studying gene-gene regulation within a pathway, which focuses on the\ninteractive activities of individual gene pairs genes within a pathway.\nA biological pathway is defined as a series of molecular interactions and reactions. If there\nare subtle changes in the expression level of a few genes located in the upper cascade of a", + "title": "2007 - Identifying genomic regulators of set-wise co-expression.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "34b8aa80-7150-5c53-8cef-9f1d614ae886", + "extraction_id": "51cb3178-b604-5869-98bd-cd32def3bd54", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "fc76d75c-37d3-5a29-9093-d25ff746b465", + "score": 0.6405381392791105, + "metadata": { + "text": "genes rapidly that may be in the same genetic network as the gene you are interested in. Then you need to validate the role of that gene and to identify its function in that network. The point is this is a powerful methodology that can provide data in half an hour that allows you to form hypotheses that you can then spend years investigating.\nReference\nLee PD, Ge B, Greenwood CM et al 2006 Mapping cis-acting regulatory variation in recombi-\nnant congenic strains. Physiol Genomics 25:294302", + "title": "2007 - Systems genetics the next generation.pdf", + "version": "v0", + "chunk_order": 1404, + "document_id": "a6202d00-514d-5b48-89cd-5fc9649c0ee4", + "extraction_id": "eff279b1-0d36-5dd4-9230-72adfe2ed79a", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "a06d0485-b264-53b2-8a13-0c13c600c026", + "score": 0.6393366671003909, + "metadata": { + "text": "ment to determine the role of the associated network ongene expression or function. To begin, a large genecorrelation graph must be sifted through, to find a highlyconnected subgraph that corresponds biologically to a genenetwork in which genes are expressed together, presumablyto regulate or subserve a common function. They must thenfind a small set of causative genes, highly correlated withthe subgraph and likely to regulate coexpression, to be usedas targets of focused investigation. By manipulating the", + "title": "2008 - Dynamic Visualization of Coexpression in Systems Genetics Data.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "ff6bf912-b590-582e-a841-6499cea56508", + "extraction_id": "84072d11-c436-5405-a356-7dd9886db6e8", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "8358a79b-ac66-510f-9ee2-9763a0f9d95e", + "score": 0.6357495243997243, + "metadata": { + "text": "Confronted with this daunting complexity, the field often \nprogresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "title": "2005 -Lovinger- Lab models of alcoholism.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "91621f34-9602-5cdc-91d8-c608c4e0b02c", + "extraction_id": "ee39bb1c-a55c-5aad-8e43-77eb8f38ff85", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "343c3cb8-779b-571c-9633-cbd37a941f25", + "score": 0.6357410388301564, + "metadata": { + "text": "Confronted with this daunting complexity, the field often \nprogresses in small steps. A study may identify one or two relevant genes and assess their interactions with other factors. Gradually, genetic knowledge from many studies then can be assembled into a larger system of interactants that enables us to understand a set of related behaviors. We term this perspective behavioral genomics ( Fig. 2b ).2005 Nature Publishing Group http://www.nature.com/natureneuroscience", + "title": "2005 - Laboratory models of alcoholism treatment target identification and insight into mechanisms.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "148a4120-6ab8-554a-ab30-3394f61f98e1", + "extraction_id": "440c9c21-a03a-576f-8206-2a354508bb82", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "1b8201cc-bdc4-5cb7-ad3b-da9d6ae59fc8", + "score": 0.6323862294920557, + "metadata": { + "text": "From the network, modules of coexpressed genes can be obtained, i.e. com- \nmunities of highly interconnected nodes within the graph. Such coexpressed \nmodules can then be studied as putative functional units, thereby considerably \nreducing the dimensionality of the data. Different approaches have been proposed, \nmany of which are inspired by social network resear ch. Chesler et al. choose to \nfocus on sets of genes in which all nodes are inter connected; such sets are termed", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 649, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "6ee23564-711a-5bc9-bb04-14a4b611c8bf", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "c6c3f636-c6bb-539d-861f-6cf1145f4f50", + "score": 0.6294281107528155, + "metadata": { + "text": "large-scale human and experimental populations, focusing on how a single protein\nor RNA impacts disease will ultimately give way to how a network of gene interac-\ntions impacts disease. The integration of genetic, molecular proling, and clinical\ndata has the potential to paint a more detailed picture of the particular network statesthat drive disease, and this in turn has the potential to lead to more progressive treat-\nments of disease that may ultimately invol ve targeting of whole networks as opposed", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 138, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "fad81c4c-0da6-54c0-898a-755ffb8870b6", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + }, + { + "id": "72cac585-5de7-56ca-8ea5-c133d3ff7acf", + "score": 0.6286278588397598, + "metadata": { + "text": "from co-regulation patterns found within tens of thousands of samples\nfor which gene expression was measured. GeneNetwork provid es un-\nprecedented resolution and predictive power across multip le cell types\nand tissues. Analogous to discovering patterns in expressi on data, the\nnetwork of protein-protein interactions can also be comput ationally pre-\ndicted using various methods[381].\nThe combined current knowledge of how cells control functio ns", + "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "version": "v0", + "chunk_order": 100, + "document_id": "ef974b09-4ea2-5382-85e5-c2169f440fda", + "extraction_id": "223e442e-898d-5aea-866a-5cdc0ac915e8", + "associatedQuery": "What\u2019s the difference between looking at one gene and studying a whole gene network?" + } + } + ], + "kg_search_results": null + } + } + }, + "21": { + "results": { + "completion": { + "id": "chatcmpl-AIGn3AbKULjVBzRjcrJYXxi0av0dH", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org can contribute to advancements in genetic engineering in several ways:\n\n1. **Predictive Medicine and Systems Genetics**: GeneNetwork.org is an exciting resource for predictive medicine and systems genetics, providing a platform for multi-omics analysis and data from multiple species [1]. This can help in understanding complex genetic interactions and predicting phenotypic outcomes, which are crucial for genetic engineering.\n\n2. **Identification of Master Regulatory Genes**: The platform allows users to identify novel potential master regulatory genes for further investigation [4]. This capability is essential for genetic engineering as it can help in pinpointing key genes that control important traits or pathways.\n\n3. **Increased Mapping Resolution**: By increasing the number of strains studied and the genetic diversity of the founders of a recombinant inbred (RI) set, GeneNetwork.org enhances the mapping resolution [5]. This improvement can lead to more precise genetic modifications and better understanding of genetic variations.\n\n4. **Construction of Biological Networks**: GeneNetwork.org facilitates the construction of biological networks by integrating data on DNA variation, gene expression, protein interactions, and DNA-protein binding [6]. These networks are predictive of molecular interactions, which can guide genetic engineering efforts.\n\n5. **Virtual Laboratory for Hypothesis Testing**: GeneNetwork.org, along with its companion tools, can be used as a virtual and free laboratory to test specific biological hypotheses or generate new ideas [7]. This feature supports experimental design and innovation in genetic engineering.\n\nOverall, GeneNetwork.org provides a comprehensive analytical environment and a wealth of data that can significantly aid in the advancement of genetic engineering by enabling detailed genetic analysis, hypothesis testing, and the discovery of key genetic regulators.", + "role": "assistant" + } + } + ], + "created": 1728917809, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 335, + "prompt_tokens": 1268, + "total_tokens": 1603 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7368593811988831, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "7beda13b-1ea5-53c0-9380-72eee2df79fe", + "score": 0.6863816567431905, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2020 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "f2f0dc2f-eaf5-57f6-9ffa-816d3fab6640", + "extraction_id": "14530ed7-e49e-5a1a-9df6-820c7495a8ce", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "a1124460-ae34-57fb-846b-e033f4bbf49c", + "score": 0.6863816567431905, + "metadata": { + "text": "GeneNetwork.org is also a valuable\nteaching tool. While mainly designed for\nresearchers interested in testing gene-to-\nphenotype relationships, GeneNetwork.\norghas been adapted for dry-lab teaching\nin neuroscience and genetics ( Grisham et\nal., 2017 ). A useful approach is to assign\nsets of vetted questions, such as the exam-\nples discussed above, and to help students\nwork toward answers, solutions, or novelquestions. Several examples relating to the", + "title": "2021 -Highlights from the Era of Open Source Web-Based Tools.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "0a6f3d2e-70c3-5db7-bfc0-93ad04806104", + "extraction_id": "ffafdd06-808c-58be-bcb5-bd74d7ffa89a", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "55461d7c-995b-5f5a-be38-a64acd4a904a", + "score": 0.6829421520233154, + "metadata": { + "text": "Its use is centred upon user-specied genes and can identify novel\npotential master regulatory genes for further investigation. We are\nworking to increase the functionality and power of the GeneNet-\nwork and systems genetics further in a number of areas. In partic-\nular, increasing the number of strains studied can increase the\nmapping resolution. By increasing the genetic diversity of the\nfounders of an RI set, the potential for observing regulatory poly-", + "title": "2008 - Systems_genetics_can_provide_new_insight.pdf", + "version": "v0", + "chunk_order": 36, + "document_id": "0bc5b1ee-f7fe-5d12-9ebf-ab7363119424", + "extraction_id": "bfd98d6d-b026-5bbd-896f-b70045a032c8", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "f3821133-e965-535b-88d0-f43b14d311b6", + "score": 0.6748145222663879, + "metadata": { + "text": "and can identify novel potential master regulatory genes for further investigation. We are working to increase the functionality and power of GeneNetwork and systems genetics in a number of areas. In particular, the mapping resolution can be increased by increasing the number of strains studied. By increasing the genetic diversity of the founders of an RI set, the potential for observing regulatory poly-morphisms increases dramatically. In this context, the availability of 1000 RI strains from The Gene", + "title": "2007 - Systems genetics the next generation.pdf", + "version": "v0", + "chunk_order": 1375, + "document_id": "a6202d00-514d-5b48-89cd-5fc9649c0ee4", + "extraction_id": "d7d6d0a8-db35-5be6-a3fc-82f347e5d37b", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6684175729751587, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "27eda296-b0b7-5ae4-881e-31987ba63dec", + "score": 0.6644343933800236, + "metadata": { + "text": "files on GeneNetwork) will also reduce the energy barrier of adopting powerful systems \ngenetics and systems behavioral approaches. Web services such as GeneNetwork and its \ncompanionsGeneWeaver ( Baker et al., 2012 ), WebGestalt ( Zhang et al., 2005 ), DAVID \n(Huang et al., 2009a ; Huang et al., 2009b ), and the Allen Brain Atlas ( Lein et al., 2007 )\ncan now be used as virtual and free laboratories to test specific biological hypothesis, or they \ncan be used to generate new ideas ab initio .", + "title": "2012 - Genetic and Molecular Network Analysis of Behavior.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "4b6759f8-fdaf-59a1-94bd-5a7cf184e1f9", + "extraction_id": "833f822f-9080-5434-b9a1-2473531becfe", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "b82c0ee6-9e1b-595d-95b3-1cc9d7aff44d", + "score": 0.6633566222574352, + "metadata": { + "text": "within GeneNetwork's analytical en vironment we provide simple we b access to the data for the\nresearch community. In this envi ronment, a combination of correl ation analysis and linkage mapping\nprovides the potential to identify and substant iate gene targets for saturation mapping and\npositional cloning. By integratin g datasets from an unsequenced cr op plant (barley) in a database\nthat has been designed for an animal model species (mouse) with a well established genome", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "b3c1f52a-f5be-586f-a3c9-04bed83fb12e", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "db6bfa4e-9612-5f7e-8b7f-162f60b91c9d", + "score": 0.6617767810821533, + "metadata": { + "text": "need to read the help files, FAQs, or one of the references(Chesler et al., 2003; Grisham et al., 2010, www.lifescied.org/content/9/2/98.full.pdf). GeneNetwork is one ofan interlinked trio of sites built up by NIAAA (GeneWeaverand WebGestalt are the other two) to house extensivedata for human, monkey, rat, mouse, and fruit fly. Itincludes hundreds of data sets on responsesto alcohol,particularly in a family of mice called the BXDs. Dataare linked with powerful gene analysis and mappingtools. Think of it as", + "title": "2012 - Genetic and Genomic Web Resources for Research on Alcohol Use and Abuse.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "08b12d72-9776-5acb-b1ef-7ee402781897", + "extraction_id": "83ae495f-31a2-5977-a63a-57e704c394e2", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + }, + { + "id": "dc06eebe-4d67-5bd0-9ed8-3dd9d5a588bd", + "score": 0.6605179309844971, + "metadata": { + "text": "will be convincing both the community and its leaders that it is \ndoable today.\nacKnoWledgmenTs\nThanks to Glenn D. Rosen and Kathryn Graehl for comments and editorial improvements. Thanks also to the NIH for support of many \ndata sets for GRPs now being incorporated into GeneNetwork, a \npilot project in systems genetics.\nreferences\nCarneiro, A. M., Airey, D. C., Thompson, B., \nZhu, C. B., Lu, L., Chesler, E. J., \nErikson, K. M., and Blakely, R. D. \n(2009). Functional coding variation", + "title": "2009 - Herding cats the sociology of data integration.pdf", + "version": "v0", + "chunk_order": 37, + "document_id": "15e14cfc-dbeb-5998-b42a-9fbfb7464bf2", + "extraction_id": "c5e5623c-2373-535c-978d-3af1cec77f1a", + "associatedQuery": "How can GeneNetwork.org contribute to advancements in genetic engineering?" + } + } + ], + "kg_search_results": null + } + } + } +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/gpt4o/de_aging_responses.json b/gnqa/paper2_eval/data/responses/gpt4o/de_aging_responses.json new file mode 100644 index 0000000..de2fc92 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/gpt4o/de_aging_responses.json @@ -0,0 +1,3222 @@ +{ + "1": { + "results": { + "completion": { + "id": "chatcmpl-AIHWB476q1MhqSkzMBJiel5B4S1fK", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent single-cell transcriptomics studies have significantly enhanced our understanding of cellular heterogeneity in aging tissues by providing detailed insights into the molecular and cellular changes that occur with age. These studies allow for the examination of small populations of cells, enabling the detection of low-represented mutations and transcriptional events that may alter cellular identity, supporting hypotheses about the cellular and genetic origins of age-related dysfunctions [1]. \n\nSingle-cell transcriptomics has also been used to gain a more nuanced view of cell type-specific molecular changes during aging, which is not possible with bulk tissue analysis that neglects variations in cell compositions and cell-to-cell aging heterogeneity [2]. This approach has revealed that different cell identities express unique genes with aging, indicating cell-identity-specific aging phenotypes [5]. Furthermore, similar cell types across different tissues have shown broadly similar aging trajectories, suggesting inherent characteristics of cell types play a significant role in shaping aging patterns [5], [8].\n\nAdditionally, single-cell RNA-seq has been applied to study transcriptional heterogeneity within specific cell types, such as hematopoietic stem cells (HSCs), and how this heterogeneity changes with age [4]. This level of detail helps to understand the diversity of cell types and states in young and old tissues, providing insights into the intrinsic complexity of aging at the cellular level [9]. Overall, these studies are revolutionizing our understanding of cellular heterogeneity in aging tissues, opening new avenues for early disease detection and personalized treatments [6].", + "role": "assistant" + } + } + ], + "created": 1728920607, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 299, + "prompt_tokens": 1126, + "total_tokens": 1425 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "91b0d8d4-949d-528f-b56d-8e5d99655fbc", + "score": 0.7524119314656392, + "metadata": { + "text": "Single-cell sequencing has helped to support several hypotheses about the cel-\nlular and genetic origin of age-related dysfunctions. Since single-cell sequencing \nallows us to study small populations of cells, it has been possible to find low repre-\nsented mutations as well as transcriptional events that alter cellular identity. This \nnewly generated data suggests that aging could be the result of mutational accumu-\nlation, epigenetic errors, and transcriptional noise that occurs in cells altering the", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 705, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "7fcf2db7-a5cc-5ffe-aee1-98f447d9b8e5", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "27854d68-63aa-5b56-9580-46c83cd121db", + "score": 0.7382032303325581, + "metadata": { + "text": "structed using data from bulk tissues, which neglect the variationsin cell compositions and cell-to-cell aging heterogeneity. To gain amore detailed and nuanced view of cell type specific molecular\nchanges during aging, several studies have applied machine-learn-\ning models to single-cell transcriptomics and DNA methylation", + "title": "2023 - A transcriptome-based single-cell biological age model.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "extraction_id": "f277b0c9-43b7-542a-828f-1bebcf0cddf9", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "f74570ce-e096-50b2-8061-b9a538299b3a", + "score": 0.7330317378583436, + "metadata": { + "text": "within whole tissues or individual cell types in aging (Rodwellet al. 2004; Jonker et al. 2013; Cosgrove et al. 2014; O Brown\net al. 2015; Su et al. 2015; White et al. 2015; Keyes et al. 2016;\nBenayoun et al. 2019). However, it remains unclear to what degree\nage-related transcriptional changes are shared or unique across cellidentities. To address this outstanding question, we performed dif-ferential expression analysis within each cell identity betweenyoung and old mice.", + "title": "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "version": "v0", + "chunk_order": 48, + "document_id": "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "extraction_id": "6411b0be-b683-56dd-a3a5-e76618ede4e4", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "04c9d851-f9d9-53e6-b1ba-1bf3398395f5", + "score": 0.7270391475488293, + "metadata": { + "text": "populations. Furthermore, single cell analysis should allow us to relate prospective profiles of HSCs that \nhave just been isolated with known heterogeneity in their retrospective functional capacity in transplantation assays. \n \nHere, we leveraged single cell RNA-seq to directly assess transcriptional heterogeneity within the HSCs \nand how it may change with age in the steady-state unperturbed hematopoiesis. Given that HSCs are", + "title": "2015 - Single cell RNA-seq reveals changes in cell cycle and differentiation programs.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "42f2b0ac-0991-58fb-bb24-128b368cc1d7", + "extraction_id": "e6e9d12b-1784-5f0e-924e-442be1636afb", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "f037fb19-8998-5f24-ab7c-b1ecc0e1b7ef", + "score": 0.7255837052364752, + "metadata": { + "text": "cells. Here, we used single-cell RNA-seq to investigate aging across\na diverse set of murine cell identities in three tissues.\nWe found that cell identities differentially express unique\ngenes with aging, consistent with previous reports of cell-identi-\nty-specific aging phenotypes (Angelidis et al. 2019). Similar celltypes (e.g., kidney capillary endothelial cells and lung endothelial\ncells) showed broadly similar aging trajectories across tissues, and", + "title": "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "version": "v0", + "chunk_order": 116, + "document_id": "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "extraction_id": "a0a95eb8-8214-5918-9b54-7f69eec9df53", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "cdc7970a-4c9a-55c8-accb-8f99d88b0f6c", + "score": 0.7213000225246055, + "metadata": { + "text": "Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent dis-coveries", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "60355441-16f5-53a2-9b24-9616624f8d00", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "9c580ff4-e422-56e4-b774-44cbc2e5d87c", + "score": 0.7190852118356417, + "metadata": { + "text": "Genomics 114 (2022) 110379\n2have been observed in multiple species and tissues [7,8]. Transcriptome \nanalysis using aged oocyte samples have confirmed the impact of aging \non transcriptome landscapes [9,10]. Advances in single-cell sequencing \ntechnology promote our understanding of intrinsic complexity to \nanother level [11]. Recently, we have successfully applied single-cell \ntranscriptome technique to reveal cellular and molecular transitions in", + "title": "2022 - Parallel bimodal single-cell sequencing of transcriptome and methylome.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e112f7dc-0ca4-51df-82ed-2f9748f9a3b5", + "extraction_id": "1e4f0bd9-2e45-536b-ae84-33c3ed01ba34", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "225b6504-a754-5aa3-ae95-b4019dbcaa8b", + "score": 0.7190478779020506, + "metadata": { + "text": "present in multiple tissues, such as endothelial cells andepithelial cells, also tended to belong to the same category acrosstissues ( Supplemental Fig. S23). These findings indicate that\ninherent characteristics of cell types play an important role in\nshaping cell aging patterns, even when situated in different tissue\nenvironments.\nDiscussion\nHere we show that tissue-specific aging programs can be learnedfrom scRNA-seq data and applied to describe aging heterogeneity", + "title": "2023 - A transcriptome-based single-cell biological age model.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "extraction_id": "548ece22-253e-512f-8dcd-7ffc6c95d482", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "4a1ef9aa-4fbc-5093-9c53-73937397c715", + "score": 0.7175643780750829, + "metadata": { + "text": "creased in old lung stromal cells. Using matrix factorization andoptimal transport methods, we computed trajectories of agingfor each cell identity and assessed the influence of identity and en-vironment on these trajectories.\nResults\nSingle-cell RNA-sequencing identifies a diversity of cell types and\nstates in young and old mouse tissue\nWe collected transcriptional profiles of young and old cells of\nmany identities by isolating single cells from the kidney, lung,", + "title": "2019 - Murine single-cell RNA-seq reveals cell-identity.pdf", + "version": "v0", + "chunk_order": 21, + "document_id": "0d36d6a5-5c98-5c67-af47-4e00d32ce9c8", + "extraction_id": "4104881f-c452-54ba-b7ab-5c13f9dc8bad", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + }, + { + "id": "1a51a565-e5bf-5659-84a2-39e06def18fd", + "score": 0.7174133995278649, + "metadata": { + "text": "during the last decades. However, different types of cells in the cardiovascular system may be highly heterogeneous dur -\ning aging and disease progression. Single-cell genomics, such \nas massively parallel single-cell RNA-seq, facilitate detailed \ntranscriptome analysis to identify variants of key epigen-etic enzymes/pathways in specific diseased cohorts or cell types.\n54,57,58,146 Altogether, new sequencing technologies have", + "title": "2018 - Epigenetic Modifications in Cardiovascular Aging and Diseases.pdf", + "version": "v0", + "chunk_order": 121, + "document_id": "6559d6a2-bd88-5538-a611-da6288439bed", + "extraction_id": "7109021e-4b15-502a-a9d1-ef6055acdf9d", + "associatedQuery": "How do recent single-cell transcriptomics studies enhance our understanding of cellular heterogeneity in aging tissues?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIHWHar7CqMtwymwPwmCVtJ5SKWUm", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction highlight several key aspects:\n\n1. SASP factors, which include cytokines, chemokines, proteases, and other inflammatory molecules, are secreted by senescent cells and disrupt tissue homeostasis through paracrine mechanisms [1]. These factors contribute to a deleterious microenvironment that promotes aging and age-related diseases [2].\n\n2. SASP is associated with chronic inflammation and exacerbates age-associated degeneration and hyperplasia in many tissues [4]. This chronic inflammation is a significant contributor to tissue dysfunction as organisms age.\n\n3. The accumulation of senescent cells and their SASP factors is closely linked to aging-related diseases. These factors can induce chronic inflammation and cell proliferation, leading to cell dysfunction and potentially cancer [8].\n\n4. SASP factors exert their effects in both autocrine and paracrine manners, influencing not only the senescent cells themselves but also the surrounding tissue environment [8].\n\nOverall, SASP factors play a critical role in driving the chronic inflammation and tissue dysfunction associated with aging, highlighting their importance in the study of age-related diseases and potential therapeutic targets.", + "role": "assistant" + } + } + ], + "created": 1728920613, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 245, + "prompt_tokens": 1321, + "total_tokens": 1566 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "4d256f76-7065-5eeb-a961-db7e7cbe75ff", + "score": 0.7416803394746592, + "metadata": { + "text": "SASP (senescence-associated secretoryphenotype):cytokines, chemokines,proteases, and otherfactors secreted bysenescent cells, whichare inammatory anddisrupt tissuehomeostasis viaparacrine mechanisms\nATM\n(ataxia-telangiectasiamutated):serine/threoninekinase and centralregulator of the DDR;activated by DNAdamage and transducesthat signal througheffectorphosphorylationphenotype (SASP) (84). SASP proteins include interleukin-6 (IL-6), transforming growth factor-", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "4b00515d-e599-5ce1-84e3-012d7efe1a30", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "92618cf9-f512-5011-9d76-17f313ad850e", + "score": 0.7378990430265979, + "metadata": { + "text": "SASP is one of the most representative features of senescent cells and may explain \nthe organismal expression of aging and age-related diseases. Senescent cells pro-\nduce a deleterious microenvironment through the production and secretion of pro-\nliferative and proinflammatory molecules such as IL-1 and -1, IL-6, IL-8, the \nchemotactic cytokine GRO, IGBP-7, growth factors, VEGF, TGF-, serine prote-\nases, and matrix remodeling enzymes [146]. It has been determined that the activa-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "eebc478a-d4b4-5547-a7e0-9c305d8bbd0f", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "77589e08-f16b-5bb2-9f89-833f1833d5be", + "score": 0.7260628505907757, + "metadata": { + "text": "context. For example, SASP likely contributes to early tumorigenesis (84), chemoresistance (94),and potentially neurodegenerative diseases (95). However, SASP is also important for mammalian\ndevelopment (96), tissue repair (97), and wound healing (98). SASP plays an important role in\nstimulating clearance of damaged, senescent cells by the innate immune system (99). However,inefcient immune clearance of senescent cells in aged organisms is thought to contribute to\nchronic inammation of aging.", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 132, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "fe836e95-1d70-51e5-b3fe-2f3005517606", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "e1dde75e-c4f2-51f6-b601-abe56c2109c3", + "score": 0.7254395052528689, + "metadata": { + "text": "many tissues, where theSASP promotes chronic inflammation and exacerbates age-associated\ndegeneration and hyperplasia. Recent evidence suggests that neurological aging and neurode-\ngeneration areaccompanied byanaccumulation ofsecretory cells inbrain, suggesting that cel-\nlular senescence may contribute tobrain aging [2]through ashared mechanism. Overlapping\nmechanisms canbedetected using functional genomics studies ofboth thebiology ofcellular\nsenescence and cognitive aging.", + "title": "2019 - Integration of heterogeneous functional.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "cf134202-50af-5700-9b1b-962501d9470d", + "extraction_id": "c21052ac-b3d9-59bc-8164-3d2df613929f", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "8c9d1720-5a2d-5559-831d-419208813d61", + "score": 0.7103631073020049, + "metadata": { + "text": "senescence-associated with the secretory phenotype (SASP) are other markers of \ncellular senescence.\n Inflammation andIntercellular Communication\nWhile senescent cells no longer replicate, they are still metabolically active and \nsecrete proteins in a recognizable pattern known as SASP.This is a widely heteroge-\nneous group of proteins with autocrine and paracrine effects [47], including soluble \nsignaling factors, such as interleukins, chemokines, and growth factors, as well as", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1748, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "b1eabac8-e6d1-50ba-9c42-60c107b56a65", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "955cffc5-cb1d-5638-bb3e-bbf5b0fe5dd4", + "score": 0.6998117923960934, + "metadata": { + "text": "matory mediators. This particular phenotype is termed the senescence- associated \nsecretory phenotype (SASP). Replicative cellular aging includes biochemical, mor -\nphological, and functional modifications that lead to the irreversible impairment of \ncell proliferation associated with DNA damage, shortening of the telomeres, and \nchanges in chromatin architecture, as previously described [135, 136].\nThe molecular mechanisms that drive cellular senescence in proliferative and", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 142, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "97753738-7225-59cc-b573-72cdf4ba569d", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "28976d8b-7996-51e7-b35b-213476f6ed7b", + "score": 0.6997167647053752, + "metadata": { + "text": "secretion of a range of proinammatory cyto- and chemokines, a state that has been dened asthe senescence-associated secretory phenotype (SASP) (103). Major SASP factors include IL1,\nIL6, IL8, and various matrix metalloproteases (MMPs), all of which individually are thought to\ndrive aging and age-related diseases. Thus, DNA damage is a major determinant in controllingcell death, stem cell exhaustion, and cellular senescence, which are considered important events", + "title": "2016 - Genome Integrity in Aging.pdf", + "version": "v0", + "chunk_order": 87, + "document_id": "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "extraction_id": "3625a61e-f376-5bea-b2c9-582b6ef16957", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "6d4a1a0b-2af3-5cc4-b7c0-a7223ce3edfa", + "score": 0.6976129800792669, + "metadata": { + "text": "senescent cells [150]. SASP factors exert their functions in either an autocrine or a \nparacrine manner and are responsible for the induction of the chronic inflammation \nand cell proliferation that contributes to cell dysfunction and cancer. Thus, the accu-\nmulation of senescent cells in tissue is closely associated with aging-related dis-\neases. Recently, it was determined that senescent fibroblasts significantly increase \nthe expression of HLA-E, which inhibits the receptor NKG2A in killer cells, and", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 152, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "6a2a94de-cfc0-50eb-b50e-bf3a0f813c78", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "a7675e04-876d-5026-88b5-842cd4ca237a", + "score": 0.6952213826643608, + "metadata": { + "text": "Role of L1 and Alu in cellular senescence and age-related inflammation\nA key feature of cellular senescence is the senescence-associatedsecretory phenotype (SASP), whereby senescent cells secretenumerous proinflammatory cytokines, chemokines, growth\nfactors, and proteases (Campisi, 2013). This altered secretome", + "title": "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "extraction_id": "ea17d9f1-0991-5a69-930d-3212a3fabe1f", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + }, + { + "id": "da4ca7c3-653d-584f-8956-7f3f710fd45e", + "score": 0.6779017832396289, + "metadata": { + "text": "8. Coppe JP, Patil CK, Rodier F, et al. Senescence-associated\nsecretory phenotypes reveal cell-nonautonomous func-\ntions of oncogenic RAS and the p53 tumor suppressor. PLoS\nBiol2008; 6:285368.\n9. Wiley CD, Liu S, Limbad C, et al. SILAC analysis reveals\nincreased secretion of hemostasis-related factors by senes-\ncent cells. Cell Rep 2019; 28:33293337 e3325.\n10. Basisty N, Kale A, Jeon OH, et al. A proteomic atlas of\nsenescence-associated secretomes for aging biomarker", + "title": "2022 - Functional genomics of inflamm-aging.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "435dc081-e3d1-52c5-93a1-caa11206422f", + "extraction_id": "72b29fff-be72-5ede-85c9-7dc81894c956", + "associatedQuery": "What are the latest findings on the role of senescence-associated secretory phenotype (SASP) factors in age-related tissue dysfunction?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIHWNXCXElapoM0J1wCt0Uh4pwpDs", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Age-related changes in chromatin architecture contribute to the decline in cellular function through several mechanisms:\n\n1. **Loss of Chromatin Homeostasis**: Sustained alterations in the chromatin landscape, such as changes in DNA methylation and histone modifications, can mediate the propagation of age-associated functional decline [1]. These changes are relatively stable and can persist through cell division, affecting cellular function over time.\n\n2. **Changes in Chromatin Distribution**: During aging, there is an extensive change in the global distribution of euchromatin and heterochromatin. Specifically, there is an overall closing of chromatin in euchromatic gene-rich regions, which contributes to tissue dysfunction and the eventual decline of cellular function [2].\n\n3. **Increased DNA Damage**: Aging-associated defects in chromatin structure lead to increased DNA damage and persistent DNA breaks. This is possibly due to structural changes that increase the genome's susceptibility to damage, further contributing to the decline in cellular function [5].\n\n4. **Histone Loss and Chromatin Remodeling**: There is a general loss of histones and chromatin remodeling, leading to an imbalance of activating and repressive histone modifications. This results in transcriptional changes that are observed in all aging models, contributing to the decline in cellular function [9].\n\n5. **Epigenetic Changes and Gene Expression**: Age-related chromatin dysregulation and epigenetic changes drive the loss of cellular function by altering gene expression patterns. These changes can lead to increased transcriptional activity in certain chromosomal regions, ultimately driving the aging process [10].\n\nThese changes in chromatin architecture collectively contribute to the decline in cellular function observed with aging.", + "role": "assistant" + } + } + ], + "created": 1728920619, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 337, + "prompt_tokens": 1244, + "total_tokens": 1581 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "1290eb6d-c454-5177-b55c-2e0f17265ab8", + "score": 0.8041154146194458, + "metadata": { + "text": "loss of chromatin homeostasis drives aspects of aging. As chroma-tin marks are relatively stable and can even persist through cell divi-sion (Kouskouti and Talianidis 2005), sustained alterations to thechromatin landscape may mediate the propagation of age-associat-\ned functional decline.\nAge-dependent changes in chromatin marks (e.g., DNA meth-\nylation, histone modifications) have been observed in multiple\nspecies and tissues (Benayoun et al. 2015; Booth and Brunet", + "title": "2019 - Remodeling of epigenome and transcriptome.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "87ffccee-fc33-5373-948d-67736aa0f069", + "extraction_id": "ab26a306-0581-5bdc-a6d1-689622689e90", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "f51d2566-aef3-51af-ac47-cfba546bd293", + "score": 0.7541544437408447, + "metadata": { + "text": "contributes to the onset of tissue dysfunction and the eventual demise of organisms as they age. During replicative senescence of human fibroblasts chromatin \nis subject to extensive changes in the global distribution \nof euchromatin and heterochromatin [25,35]. We found that the fundamental architecture of the genome undergoes profound alterations: an overall closing of \nchromatin in euchromatic gene-rich regions, which is", + "title": "2013 - Transposable elements become active and mobile in the genomes.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "c6901c06-c8ed-5220-a989-807bacdc9d0d", + "extraction_id": "dab38594-466b-50bc-8213-150f3862ff03", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "212e1fcc-f0f0-5bd0-81af-aea694179b9e", + "score": 0.7349187731742859, + "metadata": { + "text": "impaired function of histone modifying activ-ities, which in turn lead to structural chroma-\ntin changes. The number of known diseasesOrganismal\nagingAging-associated\ngene expression programsCellular\nstress\nDNA\ndamageChromatin\nremodelingEpigenetic\nstatus \nSusceptibilityHistone modifier\nredistribution\nNon-specific\ngene expression events\nFigure 3. Chromatin effects in aging. A complex network of interactions links chromatin structure to aging.", + "title": "2010 - Higher-order Genome Organization.pdf", + "version": "v0", + "chunk_order": 110, + "document_id": "91339298-860e-57d0-b58d-5a4571b4fc2b", + "extraction_id": "c4a47fc1-b528-5e29-9d13-e64be4e04938", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "12a416a1-9833-5e88-b86d-7ce6c54850b7", + "score": 0.7320639491081238, + "metadata": { + "text": "by Pelicci and colleagues in this issue). However, it could \nalso be argued that chromatin structure is directly affected \nby the ageing process through an as-yet-unknown mecha -\nnism that leads to increased DNA damage and a perma -\nnent damage response that alters gene-expression patterns \nin a similar way to the model proposed in this review.\no\nver the coming years, as researchers use mammalian \nmodels to map the global pattern of chromatin modifi -", + "title": "2007 - The role of nuclear architecture.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "extraction_id": "c5185d6d-b244-57d7-886c-2ebb364a3ac7", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "bada4b21-3c6d-55a4-b857-091a3a86f65d", + "score": 0.7274716658907149, + "metadata": { + "text": "and peripheral heterochromatin blocks are lost\nduring aging (Haithcock et al. 2005).\nThe aging-associated defects in chromatin\nstructure have various functional consequences.T o start with, aged genomes are characterized by\nincreased DNA damage and high levels of per-sistent DNA breaks, possibly brought about by\nstructural changes, which increase the suscepti-\nbility of the genome to damage. Furthermore,probably as a consequence of loss of pericentro-\nmeric heterochromatin structure, physiologi-", + "title": "2010 - Higher-order Genome Organization.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "91339298-860e-57d0-b58d-5a4571b4fc2b", + "extraction_id": "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "ebd7a483-80a4-5f16-959d-e021635c88db", + "score": 0.7208220362663269, + "metadata": { + "text": "related changes in gene expression and the ageing \nprocess4,5. Changes in gene expression were already \nknown to contribute to cellular senescence6, a possible \ncause of ageing7, and may provide an explanation for \nthe age-related decline in organ and tissue function in \ncomplex organisms.Although chromatin reorganization was linked to \nageing in budding yeast over 10 years ago8,9, these ideas \nhave remained untested. Recently, a growing appre -\nciation for the importance of chromatin in regulating", + "title": "2007 - The role of nuclear architecture.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "extraction_id": "b36b1865-2949-50be-ad95-bdc9d05b82eb", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "b2d6de59-f3d4-5f74-9bcb-96f00f885ba2", + "score": 0.7171167135238647, + "metadata": { + "text": "tone loss in the ageing process has been attributed to alterations in heterochromatin, \nwhich are characterized by a decrease in its distribution in the genome and the \ncontent of characteristic heterochromatin histone marks (such as H3K9me3 and \nH3K27me3) as evidenced in fibroblasts cells from a HGS patient and healthy aged \nindividuals [59, 60]. Interestingly, it has been suggested that the increase in chroma-\ntin opening in T cells from aged people could be related to histone loss, which in", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1027, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "04e838ad-d90d-5e9d-af94-8e975af339a0", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "fa95b6a0-b4ef-5343-95aa-93d38aa291be", + "score": 0.7140518639699184, + "metadata": { + "text": "long lifespan (Dang et al. 2009). Given theseextensive changes in histone modications,\nnot surprisingly, aged cells show dramatic and\nglobal misregulation of gene expression. Al-though some of these changes are likely part\nof specic aging-related gene expression pro-\ngrams including inammation and cellularstress responses, others likely occur largely sto-\nchastically because of random changes in epi-\ngenetic modications and chromatin structure.\nThe mechanisms that drive chromatin and", + "title": "2010 - Higher-order Genome Organization.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "91339298-860e-57d0-b58d-5a4571b4fc2b", + "extraction_id": "1a3a302a-4009-5ccf-aafa-f5f5a258ffde", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "a681ba09-0707-5611-9a91-36f9967f91c8", + "score": 0.7083029958100022, + "metadata": { + "text": "general loss of histones coupled with local and global chromatinremodeling, an imbalance of activating and repressive histone\nmodications, and transcriptional change in all aging models.\nAdditionally, particularly in mammalian systems, there is globaland local change in DNA methylation, site-specic loss and\ngain in heterochromatin, and signicant nuclear reorganization\n(Figure 1 ).\nIt is as yet unclear whether changes in the activity of epigenetic", + "title": "2016 - Epigenetic Mechanisms of Longevity and Aging.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "588185a0-e157-552f-a304-4beefb85d398", + "extraction_id": "718d36c5-299d-596e-90be-416d12f7b5d1", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + }, + { + "id": "14898b2f-4643-5362-be34-31d5ee5a4be6", + "score": 0.7080286741256714, + "metadata": { + "text": "Amarcb1) as well as histone deacetylases (Hdac1, -5, and -6) and a DNA methyltransferace (Dnmt3b) were downregulated in aged cells. They also showed that several chromosomal regions changed with age in a coordinated manner resulting in an overall increase in transcriptional activity. They propos e that chromatin dysregulation and epigenetic changes drive the loss of cellular function and ultimately drive the aging process in HSCs. Consistent with these data, Polycomb proteins (transcriptional", + "title": "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "version": "v0", + "chunk_order": 66, + "document_id": "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "extraction_id": "6efb8add-cedc-5089-9374-2466867e388a", + "associatedQuery": "How do age-related changes in chromatin architecture contribute to the decline in cellular function?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIHWU7LIWS22cXcNTfkSGgjRTVQIK", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Studying the epigenetic reprogramming of aged cells to a more youthful state has provided several insights:\n\n1. **Reversal of Aging-Associated Epigenetic Features**: Experiments have shown that epigenetic features associated with aging can be reversed. For instance, in successfully reprogrammed induced pluripotent stem cells (iPSCs), the chromatin state of the CDKN2A locus, which is associated with aging, is erased and restored to that of youthful cells [1].\n\n2. **Potential for Longevity**: Proper epigenetic gene silencing is required for longevity, as observed in multiple model organisms. This suggests that the process of epigenetic reprogramming might be evolutionarily conserved and could play a role in extending lifespan [1].\n\n3. **Rewinding the Aging Clock**: There is an apparent ability to rewind the aging clock without losing cellular differentiation. However, this requires clear epigenetic signatures of young and old cells and evidence that aged cells have regained a youthful signature [2].\n\n4. **Risks and Uncertainties**: While reprogramming the epigenome to a youthful state holds promise, it also carries inherent risks and uncertainties, highlighting the need for further research to understand the full implications and safety of such interventions [2].\n\n5. **Mechanisms of Rejuvenation**: The study of epigenetic reprogramming provides a framework for understanding the mechanisms of rejuvenation, suggesting that aging is at least partly a manifestation of epigenetic changes. This offers opportunities to alter the trajectory of age-related diseases [8], [10].\n\n6. **Prolonging Healthy Life Expectancy**: There are at least two ways to reverse or inhibit senescence through epigenetic mechanisms, which could prolong healthy life expectancy. One involves rejuvenation through effective epigenetic reprogramming in cells undergoing senescence or derived from very aged patients [7].\n\nThese insights collectively suggest that epigenetic reprogramming holds significant potential for reversing aging processes and extending healthy lifespan, although further research is needed to fully understand and safely harness these capabilities.", + "role": "assistant" + } + } + ], + "created": 1728920626, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 427, + "prompt_tokens": 1214, + "total_tokens": 1641 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b4eebcc5-781b-505b-a340-305b29285c66", + "score": 0.7631409905468841, + "metadata": { + "text": "experiments suggest that epigenetic features associated withaging can be reversed. In successfully reprogrammed iPSCs,\nthe chromatin state of CDKN2A locus associated with aging is\nerased and restored to that of youthful cells ( Meissner, 2010 ).\nThe requirement for proper epigenetic gene silencing for\nlongevity has been observed in multiple model organisms, sug-\ngesting an evolutionarily conserved process ( Lin et al., 2000;\nChen et al., 2005; Greer et al., 2010 ). The function of Polycomb", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 89, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "f244a68b-5127-5507-94a2-d2b8ca84f0ee", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "78059a6b-4809-5d36-b961-6fcddbb06f2b", + "score": 0.7370838396995367, + "metadata": { + "text": "apparent rewinding of the aging clock without loss of differenti-ation. Formal demonstration will require clear epigenetic signa-\ntures of young and old cells and evidence that the aged cells\nhave regained a youthful signature. It should be noted thatreprogramming of the epigenome to a youthful state in an\naged cell has inherent risks and uncertainties. For example, the", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "0e274732-b0df-53b8-999b-30b798af92e2", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "6baf63a6-fa5a-54e2-8290-af586a51243f", + "score": 0.7322699587184228, + "metadata": { + "text": "et al., 2010 ). Clearly, inhibiting single signaling pathways\n(NF-k B and mTOR) is sufcient to restore some features of\nyouthful cells, but the number of transcriptional regulatorsthat need to be modulated to result in full rejuvenation is\nunknown. Third, is the youthful state or the aged state domi-\nnant? It would be interesting to determine which epigeneticand transcriptional prole is more robust in experiments of\nfusion of young and old cells.\nConcluding Remarks", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 113, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "915ca931-d49d-5837-97fd-f06c145764d0", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "ef0f46ad-2e78-5666-b83d-36d2920b64ea", + "score": 0.7291497370854128, + "metadata": { + "text": "Rejuvenation: Is It Epigenetic Reprogramming?By analogy to the attainment of a pluripotent state by epigenetic\nreprogramming of a differentiated cell, is cellular rejuvenation byheterochronic parabiosis, NF- kB inhibition, or inhibition of\nmTOR signaling ( Figure 1 ) a form of epigenetic reprogramming\nfrom an aged state to a youthful state? If so, then these would\nbe examples of an uncoupling of the differentiation program\nfrom the aging clock, with cells in each case manifesting an", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "0e274732-b0df-53b8-999b-30b798af92e2", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "02361135-a01e-55f2-9efa-b7c465f2498b", + "score": 0.7267110727217503, + "metadata": { + "text": "with a healthy lifestyle may preserve a more intact epigenome and hence experi-ence longevity. Reprogramming of aged cells into iPSCs and regeneration of dif-ferentiated cells may provide a mechanism for epigenetic rejuvenation. In\naddition to epigenetic drift, telomere shortening has been associated with", + "title": "2013 - Age-associated epigenetic drift implications.pdf", + "version": "v0", + "chunk_order": 56, + "document_id": "8513121f-71f3-5bb0-9433-feece9fd9fbc", + "extraction_id": "42343f61-f147-520b-bd14-0c2bf7b63262", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "82815a35-f43e-56fc-a254-92b03a278ab5", + "score": 0.7258869193230026, + "metadata": { + "text": "tion through the lens of epigenetic reprogramming. By dening youthfulness and senescence as\nepigenetic states, a framework for asking new questions about the aging process emerges.\nIntroduction\nThe inexorable tolls of aging are evident in almost all living\nbeings. From the onset of reproductive maturity, organismalaging is generally characterized by a decline in fecundity, an\nincreased susceptibility to disease and tissue dysfunction, and\nincreased risk of mortality ( Kirkwood, 2005; Hayick, 2007; Kirk-", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "617f523f-b892-5bfc-b99c-2e67a4cc185f", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "b5f6d630-dc24-50d7-af74-b3034cbb1055", + "score": 0.7174960374832204, + "metadata": { + "text": "others (i.e. DNA methylation influences chromatin structures, histones PTMs).\nSeveral important conclusions emerge from the presented findings: there are at \nleast two ways to reverse or inhibit senescence by epigenetic mechanisms, whereby \na healthy life expectancy could be prolonged. The first way involves rejuvenation \nthrough effective epigenetic reprogramming in cells undergoing senescence or cells \nderived from very aged patients or patients with progeroid syndromes, by which the", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1091, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "704a88b4-f49e-57cb-b572-1fa948b6065b", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "8822b363-e906-5f83-a494-caad665c7af2", + "score": 0.7124422363971343, + "metadata": { + "text": "aging is at least in part, if not largely, a manifestation of epigeneticchanges, including those that may be secondary to genomicmutations, offers a theoretical construct for understanding the\nmechanisms of rejuvenation. If so, it should be possible to char-\nacterize young and old cells by specic transcriptional andepigenetic proles and states. Furthermore, the processes that\nunderlie aging and rejuvenation should be identiable in terms", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "f244a68b-5127-5507-94a2-d2b8ca84f0ee", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "0e8901a7-c123-5e96-97fe-4d5cd85eb0c9", + "score": 0.7098463350626043, + "metadata": { + "text": "determinants of the aged state by genetically manipulatingspecic biochemical pathways. A recent example demonstratesthe power of transcriptional proling and bioinformatic analysis\nto reveal an aging signature that can be genetically engineered\nto reect a more youthful state ( Adler et al., 2007 ). In a compar-\nison of old and young tissues from mice and humans, old tissues\nwere found to express at signicantly higher levels a set of genes\nthat contained sequences in their 5\n0regulatory regions, indica-", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "7f8f4ca0-9b27-55e3-a889-030af08dc84b", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + }, + { + "id": "0aede05b-f0dd-595a-a11d-acac0970d25d", + "score": 0.6993312835693409, + "metadata": { + "text": "Recently, studying the direct relationship between epigeneticmechanisms and the aging process itself is gaining increasing\nattention. The potential reversibility of these epigenetic\nchanges that occur as a hallmark of aging offers excitingopportunities to alter the trajectory of age-related diseases.\n8\nThis is especially important given the remarkable plasticityof aging.\n9,10In the literature, age-associated epigenetic alter-\nations have been identified by epigenome-wide association", + "title": "2016 - Epigenetic drift in the aging genome a ten-year.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "52f09ef3-4e4c-538f-909c-d28eb72d91f3", + "extraction_id": "2f6d20f0-addc-51e8-979d-1aac7ac26694", + "associatedQuery": "What insights have been gained from studying the epigenetic reprogramming of aged cells to a more youthful state?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIHWdEvFttNJ6ZbP6sReC3nxIXsfz", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Alterations in the mitochondrial genome and bioenergetics significantly influence the aging process in humans through several mechanisms:\n\n1. **Mitochondrial DNA Mutations**: As humans age, there is an increase in mitochondrial DNA (mtDNA) mutations. These mutations can lead to a decline in mitochondrial function, which is a fundamental mechanism in the physiological declines associated with aging [3]. Specifically, the aged heart shows a significant increase in mtDNA mutations compared to younger hearts, which may contribute to the failure in mitochondrial metabolism observed in aging [2].\n\n2. **Respiratory Function Decline**: Aging is associated with a decline in respiratory function and increased oxidative stress, which can lead to further DNA mutations and altered gene expression in mitochondria [6]. This decline in mitochondrial respiratory function is linked to the production of reactive oxygen species (ROS), which can damage mtDNA and exacerbate mitochondrial dysfunction [7].\n\n3. **Mitochondrial Dynamics**: Changes in mitochondrial dynamics, such as increased fragmentation and decreased fusion, are observed in aging tissues like skeletal muscle, heart, and brain. These alterations can impair mitochondrial biogenesis and mitophagy, leading to reduced energy production and increased cellular stress [5].\n\n4. **Bioenergetic Shifts**: The aging process involves shifts in mitochondrial metabolism, particularly in high-energy-demand tissues. For example, the brain experiences a decline in energy production due to mitochondrial dysfunction, which can affect cognitive function and overall brain health [9].\n\nOverall, the accumulation of mtDNA mutations, decline in mitochondrial respiratory function, and alterations in mitochondrial dynamics and bioenergetics contribute to the aging process by impairing cellular energy production and increasing oxidative stress, leading to cellular and tissue dysfunction.", + "role": "assistant" + } + } + ], + "created": 1728920635, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 349, + "prompt_tokens": 1517, + "total_tokens": 1866 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "4206977e-23df-5307-8d8a-cb2ed7b33595", + "score": 0.7622008607148099, + "metadata": { + "text": "abolic regulation through mitochondrial signaling. Am J Physiol Endocrinol Metab. \n2014;306:E58191.\n 74. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial DNA \nquantity and quality in humans. BMC Genomics. 2017;18:890.\n 75. Hebert SL, Lanza IR, Nair KS.Mitochondrial DNA alterations and reduced mitochondrial \nfunction in aging. Mech Ageing Dev. 2010;131:45162.\n 76. Liu D, Li H, Lu J, Bai Y .Tissue-specific implications of mitochondrial alterations in aging.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 607, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "ef9463cd-cf21-527f-ae4a-3df211c78435", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "7853fd79-e251-5e3f-8b6f-7d1ebf8182bc", + "score": 0.7536232022271645, + "metadata": { + "text": "mechanisms that lead to mitochondrial metabolism shifts in human aging are not \ncompletely understood, the literature reports that the failure in the mitochondrial \nmetabolism of aged heart might be associated with mutations in the mtDNA.In this \nsense, the aged heart shows an increase over 15-fold on mtDNA mutations in com-\nparison to hearts from young people [101]. Mutations in genes that encode Polg-a, \nresponsible for mtDNA repair machinery, cytochrome b, and several subunits of", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 563, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "391985ac-70b7-57c9-97b2-940d8ebd2366", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "1436639f-3759-5172-9b13-b1dd9105420e", + "score": 0.741439655933402, + "metadata": { + "text": "22. Fleming JE, Miquel J, Cottrell SF, Yengoyan LS, Economos AC: Is cell aging\ncaused by respiration-dependent injury to the mitochondrial genome?Gerontology 1982, 28:, 44-53.\n23. Pak JW, Herbst A, Bua E, Gokey N, McKenzie D, Aiken JM: Mitochondrial\nDNA mutations as a fundamental mechanism in physiological declinesassociated with aging. Aging Cell 2003, 2:1-7.\n24. Jacobs HT: The mitochondrial theory of aging: dead or alive. Aging Cell\n2003, 2:11-17.", + "title": "2011 - Mitochondrial genome deletions and minicircles.pdf", + "version": "v0", + "chunk_order": 157, + "document_id": "c28cecbc-be20-54e2-afdd-afb8d25b1ab1", + "extraction_id": "8a8e649d-6689-5d6d-91b6-157abfd8f990", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "7095cdbb-852e-541e-884b-a9e67c2c790c", + "score": 0.7352016129969602, + "metadata": { + "text": "Sun., N, Youle, R. J. and Finkel, T. (2016). The mitochondrial basis of aging.\nMol. Cell 61, 654-666. doi:10.1016/j.molcel.2016.01.028\nSymer, D. E., Connelly, C., Szak, S. T., Caputo, E. M., Cost, G. J., Parmigiani, G.\nand Boeke, J. D. (2002). Human L1 retrotransposition is associated with genetic\ninstability in vivo. Cell110, 327-338. doi:10.1016/S0092-8674(02)00839-5\nSzabo, L., Morey, R., Palpant, N. J., Wang, P. L., Afari, N., Jiang, C., Parast,", + "title": "2020 - Transposable elements, circular RNAs and mitochondrial.pdf", + "version": "v0", + "chunk_order": 300, + "document_id": "7bebb41c-ac73-5917-91d3-4f59fbb3266a", + "extraction_id": "5cbace8d-e538-5531-9311-ea9726ad2f15", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "a1ea550b-8017-58c5-a80f-f22f4869f792", + "score": 0.7337075034554313, + "metadata": { + "text": "limitations to study mitochondrial metabolism in human samples, in this section \nwe briefly described the implications of mitochondrial metabolism for aging in \nthe most studied and high energy demand human tissues, such as skeletal muscle, \nheart, and brain.Table 4.1 Main mitochondrial dynamics proteins that are altered in human tissues during the \naging process\nTissue/\norgan Fission Fusion Biogenesis Mitophagy Refs\nSkeletal \nmuscleIncreased \nfragmentation\nDecreased Drp1 \nproteinIncreased \ninterconnected", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 532, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "385c192b-a416-5208-9615-20111ce782aa", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "8ec531e8-2692-5995-8f1e-246406b9de04", + "score": 0.732002485036407, + "metadata": { + "text": "96. Wei Y-H, Wu S-B, Ma Y-S, Lee H-C.Respiratory function decline and DNA mutation in \nmitochondria, oxidative stress and altered gene expression during aging. Chang Gung Med J. \n2009;32:11332.\n 97. Kates AM, Herrero P, Dence C, Soto P, Srinivasan M, Delano DG, Ehsani A, Gropler \nRJ. Impact of aging on substrate metabolism by the human heart. J Am Coll Cardiol. \n2003;41:2939.\n 98. Gmez LA, Monette JS, Chavez JD, Maier CS, Hagen TM.Supercomplexes of the mito-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 616, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "7cf75da1-3c2a-5155-84dd-0dfe77d3fe41", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "f41af83b-dd40-5128-b051-2b0f26942786", + "score": 0.729981871845499, + "metadata": { + "text": "phenotype, such as the Mitochondrial Free Radical Theory of Aging (MFRTA), and \nalthough these theories have been recently confronted, the role of mitochondria in \nthe aging process is undeniable because of their versatile roles and implications \nfor cellular function. MFRTA suggests that the oxidative damage of mtDNA is the \nkey event disturbing the respiratory chain proteins to induce its dysfunction and \nincrease ROS production in a vicious cycle [123]. However, alterations in mito-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 134, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "c7041bbd-983f-5532-8b0e-cbd5f114a75f", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "1a9d5c26-f606-5cb5-98ee-4120de3fbd1a", + "score": 0.7225154207637668, + "metadata": { + "text": "102. Zhang R, Wang Y , Ye K, Picard M, Gu Z.Independent impacts of aging on mitochondrial \nDNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/\ns12864-017-4287-0.\n 103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hema-\ntopoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. \n2011;8:499510. https://doi.org/10.1016/j.stem.2011.03.009.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 783, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "c8db1d28-f6c2-5896-95ec-bb01159ba483", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "e183f824-0ca8-58aa-a06e-110a3a94c2e9", + "score": 0.7214284109217889, + "metadata": { + "text": "78\np53, which regulate the catalytic subunits of ETC complexes [103]. Unfortunately, \nthese data have only been observed in murine models of aging and require further \nverification in human samples.\n Mitochondrial Metabolism intheAged Brain\nIn normal conditions, the brain consumes around 25% of the total body glucose via \nglycolysis and mitochondrial OxPhos [104]. So besides the mitochondrial dynam-\nics dysfunctions described above, during aging there is also a decline in energy", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 566, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "d226a80b-8a07-52ea-82b8-30adce468571", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + }, + { + "id": "39019881-9b6d-5111-87ea-71c413bdf4ff", + "score": 0.720432417366958, + "metadata": { + "text": "mitochondrial DNA mutations can reduce lifespan. Sci Rep. 2014;4:6569.\n20. Ross JM, Stewart JB, Hagstrm E, Bren S, Mourier A, Coppotelli G,\nFreyer C, Lagouge M, Hoffer BJ, Olson L. Germline mitochondrial DNA\nmutations aggravate ageing and can impair brain development. Nature.\n2013;501(7467):412 5.\n21. Sondheimer N, Glatz CE, Tirone JE, Deardorff MA, Krieger AM, Hakonarson H.\nNeutral mitochondrial heteroplasmy and the influence of aging. Hum Mol\nGenet. 2011;20(8):1653 9.", + "title": "2017 - Independent impacts of aging.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "d1d0b9ce-f827-5dfb-8e39-d87a9ca52f6d", + "extraction_id": "1f0b6363-a045-53aa-a124-4cf89e61fc26", + "associatedQuery": "How do alterations in the mitochondrial genome and bioenergetics influence the aging process in humans?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIHWn49FE1NOTaexKIcZmCPOm6e2F", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The insulin/IGF-1 signaling pathway has been identified as a significant target for extending healthspan and lifespan due to its role as a nutrient sensor and its control over the transcription of stress response genes [1]. Here are the therapeutic potentials and challenges associated with targeting this pathway:\n\n### Therapeutic Potentials:\n1. **Treatment of Age-Related Diseases**: Lowering IGF signaling, such as by targeting IGF receptors, has been proposed as a treatment for age-related diseases including cancer, Alzheimer's disease, and autoimmune diseases [2]. This suggests that modulating this pathway could have broad therapeutic applications in managing diseases associated with aging.\n \n2. **Lifespan Extension**: Genetic interference in the insulin-signaling pathway has been shown to prolong life in various organisms, including C. elegans, D. melanogaster, and certain mouse models [8]. This indicates a potential for extending lifespan through targeted interventions in this pathway.\n\n3. **Improved Cellular Maintenance**: The insulin/IGF-1 signaling pathway is involved in processes such as cellular senescence, protein refolding, and autophagy, which are crucial for cellular maintenance and protection against aging-related diseases [3]. Enhancing these processes could lead to slowed aging and improved healthspan.\n\n### Challenges:\n1. **Complexity of the Pathway**: The role of IGF-1 in lifespan regulation is complex, and it is not fully understood how alterations in this pathway contribute to aging phenotypes [9]. This complexity poses a challenge in developing targeted therapies without unintended consequences.\n\n2. **Balancing Growth and Longevity**: The insulin/IGF-1 pathway is also involved in regulating growth and development. Therefore, interventions that reduce IGF signaling must carefully balance the trade-offs between promoting longevity and maintaining necessary growth functions [2].\n\n3. **Species-Specific Responses**: While interventions in the insulin/IGF-1 pathway have shown promising results in model organisms, translating these findings to humans is challenging due to species-specific differences in the pathway's role and regulation [8].\n\nOverall, while targeting the insulin/IGF-1 signaling pathway holds significant promise for extending healthspan and lifespan, it requires careful consideration of the pathway's complexity and the potential trade-offs involved.", + "role": "assistant" + } + } + ], + "created": 1728920645, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 456, + "prompt_tokens": 1439, + "total_tokens": 1895 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "bc91a693-0eff-5911-ae9a-b192f1088119", + "score": 0.698900580406189, + "metadata": { + "text": "the attention of researchers as a therapeutic target for age-related diseases [109]. \nResveratrol, a phytochemical enriched in the skin of red grapes and wine, has been \nactively investigated to determine whether it promotesSIRTs activity with conse-\nquent beneficial effects on aging [110].\n IGF\nBecause insulin/IGF-1 function through signaling as a nutrient sensor and controls \nthe transcription of stress response genes, the insulin/IGF-1 pathway provides a", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "a933e419-b369-5de5-8236-a1944a486e51", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "8ac8b243-f23c-596d-add2-441df4e980a9", + "score": 0.6942377868749042, + "metadata": { + "text": "the use of lowered IGF signaling (e.g., by target-ing IGF receptors) to treat certain age-related diseasessuch as cancer (Pollak et al., 2004), Alzheimers disease(Cohen et al., 2009), and autoimmune diseases (Smith,2010). Moreover, a number of genes and pathways associ-ated with longevity and CR are part of nutrient-sensingpathways that also regulate growth and development, in-cluding the insulin/IGF1/GH pathway (Narasimhan et", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 66, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "759ea147-5ac2-5d48-80f2-3693f56d4afc", + "score": 0.6894049829599284, + "metadata": { + "text": "as insulinIGF-1 signalling [6], cellular senescence [4], protein refolding [4345] ,\nautophagy [41] and phase 1 and 2 detoxication [36,37,52] . These represent major\npoints of intervention against ageing-related disease. As shown here, lifespan\npathways control improved cellular maintenance, which leads to slowed ageing(e.g. slowed normal cognitive ageing) and protection against diseases of ageing\n(e.g. neurodegenerative diseases of ageing, such as Alzheimers and Parkinsons", + "title": "2006 - Beyond the evolutionary theory.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "a8da3f57-a8dc-55c3-9dc9-eb778105e680", + "extraction_id": "373c0bb8-f6b2-5c6b-b768-226b12ba6385", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "fc227aaf-85c1-553f-aa59-d9bcdd803aaf", + "score": 0.6818851232528687, + "metadata": { + "text": "ent-sensing pathways such as insulin/insulin-likegrowth factor (IGF-1) signalling (IIS) and target of\nrapamycin (TOR) signalling mediated lifespan exten-\nsion, and also the extension of lifespan by DR [ 2].\nAn interesting observation from the perspective ofhuman ageing is that, in rodents and monkeys, dietsrestricted in glucose, fat or protein uptake reduced ordelayed the risk of cancer and metabolic disease,thus extending the healthspan of the animals [ 2]. Fol-", + "title": "2011 - Genomics of human longevity.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "2e038219-fdaa-506f-9cd3-51379054130e", + "extraction_id": "89586b79-902d-5e2b-9b8a-b7a8c4971783", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "a0198ed1-1303-5652-aafc-1a1287914ac4", + "score": 0.6767085194587708, + "metadata": { + "text": "43. Svensson, J. et al. Liver-derived IGF-I regulates mean life span in mice. PLoS ONE 6, e22640 (2011).\n44. Junnila, R. K., List, E. O., Berryman, D. E., Murrey, J. W. & Kopchick, J. J. The GH/IGF-1 axis in ageing and longevity. Nat. Rev. \nEndocrinol. 9, 366376 (2013).\n45. Yuan, R. et al. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. \nAging Cell 8, 277287 (2009).\n46. Zhu, H. et al. Reference ranges for serum insulin-like growth", + "title": "2023 - Genome-wide RNA polymerase stalling.pdf", + "version": "v0", + "chunk_order": 128, + "document_id": "78812a12-8d31-5159-8367-b0d38e5bc84b", + "extraction_id": "31088092-778f-59e0-a9de-5ec25c241aab", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "e3a78ec1-7f79-55db-a13d-196f718f8a1d", + "score": 0.6761923625354562, + "metadata": { + "text": "5. Piper MD, Selman C, McElwee JJ, Partridge L: Separating cause\nfrom effect: how does insulin/I GF signalling control lifespan\nin worms, flies and mice? J Intern Med 2008, 263:179-191.\n6. Holzenberger M, Kappeler L, De Magalhaes Filho C: IGF-1 signaling\nand aging. Exp Gerontol 2004, 39:1761-1764.\n7. Zahn JM, Kim SK: Systems biology of aging in four species. Curr\nOpin Biotechnol 2007, 18:355-359.\n8. McElwee JJ, Schuster E, Blanc E, Piper MD, Thomas JH, Patel DS,", + "title": "2009 - High tandem repeat content in the genome of the short-lived.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "bcc64bfb-9b7f-5f6f-83f3-861ab8f8a8e3", + "extraction_id": "fcb05f39-0821-56e1-a627-92911d4d46bc", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "bdebc11c-26ca-5ac0-bab3-503bd7d25f50", + "score": 0.6721978788552799, + "metadata": { + "text": "humans enriched for familial longevity. Aging Cell. 2016;15(6):112631.\n 44. Lee WS, Kim J.Insulin-like growth factor-1 signaling in cardiac aging. Biochim Biophys Acta \nMol basis Dis. 2018;1864(5 Pt B):19318.\n 45. Balasubramanian P, Longo VD. Growth factors, aging and age-related diseases. Growth \nHormon IGF Res. 2016;28:668.\n 46. Suzuki K, etal. Serum insulin-like growth factor-1 levels in neurodegenerative diseases. Acta \nNeurol Scand. 2019;139(6):5637.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 867, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "8f165f13-b4a5-5553-a992-f4a70b079898", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "9868d78e-6151-5383-9d52-542a8b43c50f", + "score": 0.6709864139556885, + "metadata": { + "text": "paradigms for lifespan extension (C. elegans, D. melanogaster),\ngenetic interference in the insulin-signaling pathway can\nprolong life multi-fold [47,48]. In mammals, IGF1-decient,\nAmes and Snell dwarf mice (characterized by defects in the\ndevelopment of the anterior pituitary due to mutations in the\nProp-1 and Pit1 loci and diminished levels of GH, thyroid\nstimulating hormone, and prolactin hormone) combine", + "title": "2007 - Impaired Genome Maintenance Suppresses.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "4ed9d527-4f92-51a3-a5d7-6caab655b1be", + "extraction_id": "74482eef-9eb3-5915-838e-5f1f0439c410", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "58d61a19-d5b0-501c-90a9-2eeb66866c07", + "score": 0.6700917682079011, + "metadata": { + "text": "the role of IGF-1 in life span regulation is complex. In theory,SIRT6 might play a role in insulin signaling, similar to Sir2 fac-\ntors in other lower organisms. However, as in the prematureaging mouse models described above, it remains unclear\nwhether the altered serum IGF-1/insulin levels of SIRT-6-\ndecient mice directly contribute to aging-like phenotypesor, alternatively, reect compensatory alterations. In this re-\ngard, it will be of interest to determine whether SIRT6 is", + "title": "2006 - Genomic Instability.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "c9c9a8d6-2daf-5ff2-86bd-84e087ba1a47", + "extraction_id": "634526cb-daa7-5769-a3f2-741931964ccd", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + }, + { + "id": "e51c4436-0895-5adb-8a80-a3e1ee6956dd", + "score": 0.6700788736343384, + "metadata": { + "text": "lin-like growth factors (IGFs), and receptors in theinsulin-signaling pathway has been shown to confergreater longevity in yeast (12, 16), nematodes (21, 44),fruit ies (10, 43), mutant long-lived mice (4, 11), and\ncaloric-restricted mice (40). Therefore, the as-yet un-identi ed mechanism of insulin signaling on lifespan", + "title": "2003 - Lifelong voluntary exercise in the mouse prevents.pdf", + "version": "v0", + "chunk_order": 126, + "document_id": "24d4f270-f45b-5830-84f9-b1e5bcd3c070", + "extraction_id": "b6422281-0ef4-58f3-9d43-4c8c7534e057", + "associatedQuery": "What are the therapeutic potentials and challenges of targeting the insulin/IGF-1 signaling pathway for extending healthspan and lifespan?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIHX1EytrrBFzyZb7piMsWydaKzhq", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The integration of proteomics and metabolomics data can provide a comprehensive understanding of age-associated metabolic shifts by revealing changes in protein expression and metabolite profiles that occur with aging. This multi-omics approach allows for the identification of specific pathways and molecular mechanisms that are altered as organisms age.\n\n1. **Proteomics Insights**: Proteomics data can identify plasma proteins that predict age and are predominantly associated with immunity [1]. This suggests that changes in protein expression related to immune function are significant in the aging process.\n\n2. **Metabolomics Insights**: Metabolomics approaches enable the study of age-related changes in metabolite profiles, providing new insights into the physiological mechanisms of aging [1]. For example, metabolomics has identified significant alterations in glutathione metabolism, a key antioxidant pathway, which is indicative of oxidative stress associated with aging [10].\n\n3. **Integrated Analysis**: By integrating transcriptome and metabolome data, researchers have identified transcriptionally-driven alterations in metabolism during aging, such as changes in glycolysis and glycerolipid biosynthesis, and reductions in protein and polyamine biosynthesis [4], [8]. These changes can affect cellular signaling, epidermal barrier function, and skin structure and morphology, highlighting the interconnected nature of metabolic pathways and their impact on aging.\n\n4. **Functional Changes**: The integration of these datasets can also reveal age-dependent changes in the activity of metabolic enzymes, which are driven by altered gene expression [6]. This helps in understanding how mild adaptations in metabolite and transcript levels contribute to maintaining functions like epidermal homeostasis during aging.\n\nOverall, the integration of proteomics and metabolomics data provides a holistic view of the molecular changes that occur with aging, allowing for the identification of biomarkers and pathways that could be targeted to mitigate age-related decline.", + "role": "assistant" + } + } + ], + "created": 1728920659, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 359, + "prompt_tokens": 1181, + "total_tokens": 1540 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a8194abc-51ab-5c29-a6be-f34bb24e0b47", + "score": 0.7574900388717651, + "metadata": { + "text": "learning to show that plasma proteins that predict age are predominantly associated with immunity\n[91]. State-of-the-art metabolomics approaches are also now allowing age-related changes in me-\ntabolite pro les to be studied, which provide new insights into the physiological mechanisms of age-\ning [ 92,93]. The integration of multiple datasets generated from genomes, epigenomes,\ntranscriptomes, proteomes, and metabolomes, an approach termed multi-omics , offers great", + "title": "2022 - Immunity and lifespan answering.pdf", + "version": "v0", + "chunk_order": 71, + "document_id": "a834e7ee-7bab-5c4d-a236-b570d1ae635f", + "extraction_id": "d4db0b82-40d3-5341-ad30-c70a91fdc785", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "1d8fd475-f7a7-55c6-881e-6985826c1e23", + "score": 0.7088847371603474, + "metadata": { + "text": "13. Menni C, Kastenmuller G, Petersen AK, et al. Metabolomic markers reveal novel pathways of ageing \nand early development in human populations. Int J Epidemiol 2013;42:1111- 9. \n14. Evans AM BB, Liu Q, Mitchell MW, Robinson RJ, et al. . High Resolution Mass Spectrometry Improves \nData Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High- Throughput \nProfiling Metabolomics. Metabolomics 2014;4:132.", + "title": "2018 - Metabolomic pathways to osteoporosis in middle-aged women A genome-metabolome-wide.pdf", + "version": "v0", + "chunk_order": 67, + "document_id": "f9aa8a09-5148-5399-b6be-c3350f12c0f3", + "extraction_id": "e92950f9-a8d6-5aa5-bf83-ab1cef74627d", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "4547b6ad-efaf-509e-8e0b-5587542905fd", + "score": 0.6993709206581116, + "metadata": { + "text": "Due to the mild adaptions, the identification of func-\ntionally altered metabolic activity in aged skin interpret-\nation of significant metabolite and transcript changes of\nsmall magnitude is especially challenging. Therefore, we\nemployed the previously presented locality scoring ap-\nproach [60] to identify age-dependent transcriptional al-\nterations of enzymes that functionally effect proximal\nmetabolic activity and thus metabolite levels. This inte-\ngrated analysis revealed age-dependent, concerted me-", + "title": "2017 - An integrative metabolomics.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "cb0831f4-540a-5620-b69e-03d6127f84e5", + "extraction_id": "09a73df7-f690-5984-a498-69a8077fe327", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "3dba594a-b79b-5bc6-95f6-6e0a36193818", + "score": 0.695720094697471, + "metadata": { + "text": "matched transcriptome and metabolome data highlighted transcriptionally-driven alterations of metabolism during\naging such as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine\nbiosynthesis. Together, we identified several age-dependent metabolic alterations that might affect cellular\nsignaling, epidermal barrier function, and skin structure and morphology.", + "title": "2017 - An integrative metabolomics.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "cb0831f4-540a-5620-b69e-03d6127f84e5", + "extraction_id": "af201c05-daed-5cba-abc8-e714483e602f", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "ce9d4d88-2586-5071-bf9e-45b7172b0e8e", + "score": 0.6835841317672621, + "metadata": { + "text": "used to assess biological responses provides new oppor -\ntunities to understand the impact of the environment on \nthe risk of age-related diseases. For example, the multi -\nomics analysis and integration method produces a pri -\nority list of multiple sets of biomarkers, which together \nreflect the molecular responses of the exposome. Each of \nthese data warrants integration into a biomarker panel to \naid physicians in developing age-related disease diagno -\nses and prognoses [78].", + "title": "2022 - A review on the application of the exposome.pdf", + "version": "v0", + "chunk_order": 78, + "document_id": "803a14cc-d8ab-54ca-80d6-78f1677457f9", + "extraction_id": "cac0d599-4e0a-5826-b47f-e71b52203956", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "beea72ed-e213-5877-8144-d0ef000a2912", + "score": 0.6832429359410456, + "metadata": { + "text": "summary, we identified age-dependent changes in gene\nexpression in different metabolic pathways that have\nbeen associated with epidermal homeostasis and there-\nfore might be important to sustain epidermal function.\nIntegrated analysis of transcriptome and metabolome data\nSince the age-dependent adaptations of metabolite and\ntranscript levels are only mild, we set out to identify\nmetabolic enzymes that featured an age-dependent and\nfunctional change in activity driven by altered gene ex-", + "title": "2017 - An integrative metabolomics.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "cb0831f4-540a-5620-b69e-03d6127f84e5", + "extraction_id": "f9c942d2-a191-52d4-8018-1030e414649d", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "6ad38ef0-c6bd-5b6a-9fb6-53c04f18a76d", + "score": 0.6780407428741455, + "metadata": { + "text": "These high throughput prof iling experiments have gener-\nated large amounts of data for meta-analysis [24], which can \ncompare molecular functions and expression patterns that change during aging in different systems. However, such studies are far from exhaustive, as they only describe the molecular changes during aging, which could in fact be the consequence of aging, rather than the cause of aging. Thus to \nexplore the causal factors for aging, studies are increasingly", + "title": "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "version": "v0", + "chunk_order": 31, + "document_id": "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "extraction_id": "6794bfa0-86ff-506f-ac40-35a9b1e33bcf", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "554f2525-a8cb-5003-be3d-137da97ea97f", + "score": 0.6730986627222749, + "metadata": { + "text": "over, the integration of trans criptome and metabolome data\nrevealed a transcriptionally re gulated reduction in protein\nas well as polyamine biosynthesis and adaptation in upper\nglycolysis and glycerolipid biosynthesis in aged skin.\nResults\nDifferences in the epidermal skin metabolome of young\nand old human volunteers\nTo chart metabolic adaptations in human skin during\naging in vivo , we performed non-targeted metabolomicsanalysis of epidermal skin tissue samples obtained from", + "title": "2017 - An integrative metabolomics.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "cb0831f4-540a-5620-b69e-03d6127f84e5", + "extraction_id": "500f52f7-9205-5859-a156-6d30575a3d62", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "d0b9df07-f6aa-52a5-9696-81f9034d9548", + "score": 0.6728132562152602, + "metadata": { + "text": "proteomes overlap significantly with the waves of aging proteins (Supplementary Table 15). Accounting for heterogeneous and com\n-\nplex changes to the plasma proteome during life will likely improve the sensitivity and specificity of prognostic and diagnostic tests. Moreover, these results are pertinent when considering the use of \nblood or blood products to treat aging and age-related diseases\n39. \nSpecifically, identifying plasma proteins that promote or antagonize", + "title": "2019 - Undulating changes in human plasma proteome.pdf", + "version": "v0", + "chunk_order": 193, + "document_id": "53c3130f-7029-50de-8dba-8714dfa36420", + "extraction_id": "24e63f26-0bac-59d4-b325-9c8ead69a4de", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + }, + { + "id": "07a5111b-b38b-5e1a-bd76-9372499a4dd9", + "score": 0.6681016285358901, + "metadata": { + "text": "rmed using authentic standards. One of the key nodes identi ed by\nmetabolomics as signi cantly altered with accelerated and normal\naging was glutathione metabolism ( Fig. 4A), a key antioxidant and\nindex of oxidative stress [71].\nDierential MS was used for proteomics analysis to identify redox-\nrelated proteins signi cantly altered in the livers of 3 4 month-old\nprogeroid Ercc1/mice and old WT mice (> 2 years-old) vs. adult WT\nmice. Expression of catalase, SOD1 (CuZnSOD) and SOD2 (MnSOD)", + "title": "2018 - Spontaneous DNA damage to the nuclear genome promotes senescence.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "08be7274-78a3-5e93-9e8c-3d4f6dbeacf9", + "extraction_id": "40e2d528-9297-575f-82a9-178aae0bab81", + "associatedQuery": "How can the integration of proteomics and metabolomics data shed light on age-associated metabolic shifts?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIHX9ExmiM3mDYaf83XTHzQDSE0IN", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Long non-coding RNAs (lncRNAs) play significant roles in the regulation of aging and age-related diseases through various mechanisms:\n\n1. **Regulation of Age-Associated Cardiovascular Diseases**: LncRNAs are involved in the regulation of age-associated cardiovascular diseases by acting as non-canonical precursors for specific microRNAs, such as hsa-miR-4485 and hsa-miR-1973, which participate in tissue age-related changes [1].\n\n2. **Senescence-Associated lncRNAs**: Certain lncRNAs are associated with cellular senescence, a key process in aging. These senescence-associated lncRNAs are implicated in the regulation of aging mechanisms [2].\n\n3. **Telomere Length Regulation**: LncRNAs are involved in the regulation of telomere length by modulating TERT activity and the synthesis of telomeric repeats, which is crucial for cellular aging and longevity [3].\n\n4. **Gene Expression Regulation**: LncRNAs interact with proteins and nucleic acids to regulate gene expression through epigenetic mechanisms, acting as antisense transcripts or transcriptional coactivators. They also influence the nuclear location of transcription factors and stabilize ribonucleoprotein complexes, which are important in aging-associated mechanisms [4].\n\n5. **Disease Progression**: LncRNAs play roles in the progression of various age-related diseases, such as atherosclerosis, diabetic nephropathy, glomerular disease, and renal fibrosis. For example, lncRNA H19 is involved in the activation of signaling pathways that induce atherosclerosis [5].\n\n6. **Neurodegeneration**: LncRNAs are implicated in neurodegenerative diseases, such as Huntington's disease, by regulating transcriptional networks and chromatin states [6].\n\n7. **Impaired Learning and Senescence**: Specific lncRNAs, like Gas5, are associated with impaired learning in aged brains, and others, like H19, interact with methyl-CpG binding domains, influencing senescence and aging pathways [7].\n\n8. **Angiogenic Capacity**: The expression of lncRNA Meg3 is linked to age-related impairment of the angiogenic capacity of endothelial cells, indicating a role in vascular aging processes [9].\n\nOverall, lncRNAs are crucial regulators of aging and age-related diseases through their diverse roles in gene expression, cellular senescence, disease progression, and other aging-related mechanisms.", + "role": "assistant" + } + } + ], + "created": 1728920667, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 494, + "prompt_tokens": 1337, + "total_tokens": 1831 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "41e5a2ca-1c83-5394-8fbf-c9dcc75e6a51", + "score": 0.7691522974281148, + "metadata": { + "text": "lncRNA which overexpression participates in the regulation of age-associated car -\ndiovascular diseases as it is a non-canonical precursor for hsa-miR-4485 and hsa- \nmiR- 1973 microRNAs [62]. These studies demonstrate that not only coding genes \n(which represent only 2% of the genome sequence) are implicated in aging regula-\ntion, but also lncRNAs and microRNAs participate in tissue age-related changes.\ncircRNAs are non-coding covalently closed single-stranded transcripts produced", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 685, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "e2fc0d00-8552-5b39-830f-6df7ec7c32e4", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "cb309a6a-4566-5de2-9687-cffa2f7737d2", + "score": 0.728266260870286, + "metadata": { + "text": "(2008).\n192. K. Abdelmohsen, A. Panda, M.-J. Kang, J. Xu, R. Selimyan, J.-H. Yoon, J. L. Martindale, S. De,\nW. H. Wood III, K. G. Becker, M. Gorospe, Senescence-associated lncRNAs: Senescence-\nassociated long noncoding RNAs. Aging Cell 12, 890 900 (2013).\n193. S. Kour, P. C. Rath, Long noncoding RNAs in aging and age-related diseases. Ageing Res. Rev.\n26,1 21 (2015).\n194. R. Johnson, Long non-coding RNAs in Huntington s disease neurodegeneration. Neurobiol. Dis.\n46,2 4 5 254 (2012).", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 318, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "91434549-bda3-5154-b089-28efed9c1089", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "8fa044d2-c807-5207-8361-ea22659d8b63", + "score": 0.7174522280693054, + "metadata": { + "text": "155\nPremature ageing has been associated with altered expression of lncRNAs that \nparticipate in the regulation of the telomere length by modulating the TERT activity \nand synthesis of telomeric repeats [155, 161]. Furthermore, it has been reported that \nchanges in the expression levels of some lncRNAs are associated with the develop-\nment of AD [162].\n Circular RNAs andAgeing\nCircular RNAs (circRNAs) are highly conserved covalently closed non-coding", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1075, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "b45152cc-6626-5989-a1b0-148c137ea1f3", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "e4d9a99d-4d28-5432-8e91-09388ea4b613", + "score": 0.7167888452536568, + "metadata": { + "text": "interacting with proteins and nucleic acids in order to regulate gene expression (by \nindirect epigenetic mechanisms or by direct mechanisms acting as antisense tran-\nscripts or transcriptional coactivators), nuclear location of transcription factors and \nstabilization of ribonucleoprotein complexes [155]. It has been reported that lncRNAs \nare important in the regulation of ageing-associated mechanisms in humans and ani-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1072, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "3932ada5-6fc4-5354-b52a-60fddbd47d3e", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "85a38fea-bd20-5170-bba0-963b12633c55", + "score": 0.7063658237457275, + "metadata": { + "text": "progression. LncRNA H19 was recently reported to play \na crucial role in the activation of MAPK and the NF-kB \nsignaling pathway and the induction of atherosclero -\nsis [3]. lncRNAs play crucial roles in the progression of \ndiabetic nephropathy [12], glomerular disease [13] and \nrenal fibrosis [14]. The lncRNA Arid-IR promotes NF-\nkB-mediated kidney inflammation by targeting NLRC5 \ntranscription [15]. The cell cycle changes during aging. \nPrevious studies have shown that lncRNAs are related to", + "title": "2022 - Predict the role of lncRNA in kidney aging.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "0d3b0558-289c-5af0-843a-f288d5da3d8c", + "extraction_id": "67e3cb94-cc30-58fe-8ff7-a9790c74325f", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "36a2ed56-a0b9-589d-b178-f1515337f1ae", + "score": 0.705976503731222, + "metadata": { + "text": "expression of SIRT1 and are decreased in lymphoblastic cell lines generated from \ncentenarians compared with those of AD patients, suggesting a protective effect of \nthese miRNAs against neurodegeneration [66].\nLong noncoding RNAs are important regulators of transcriptional networks and \nthe closed or opened chromatin state [2]. One interesting example of an lncRNA \nis that associated with aging, H19. This lncRNA interacts with MBD1 (a methyl-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "cb24a361-e7b4-5d35-a507-12ef65603d1f", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "577459d5-e2fc-599f-9806-3d18ab6837e6", + "score": 0.7041338295976471, + "metadata": { + "text": "associated factors, modulating aging and senescence directly or in-directly. One such example includes a specific lncRNA, Gas5 ,w h i c h\nis highly expressed in aged mice brain and has been associated with im-paired learning ( 189). Another bona fide example is H19lncRNA, a dif-\nferentially spliced product from the H19gene located at the IGF2/H19\nimprinted locus, which interacts with methyl-CpG binding domain", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 161, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "5221f1f8-1c53-590f-86c2-23ab47ac0cbc", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "ab28b2fc-4144-5b86-92af-d6054794a0b1", + "score": 0.7032895441902277, + "metadata": { + "text": "tempting to speculate that these lncRNAs may exert some\nregulatory control of this locus, possibly contributing to\nsenescent phenotypes. Together, these findings point to-\nwards a host of age-related ncRNAs as regulators of aging\npathways and networks.\nInteraction network analysis\nThe increased accuracy and breadth of our RNA-seq\ndata sets allowed us to generate networks of gene func-\ntional change in aging liver, above and beyond what was\nobserved using DAVID or GOrilla. Using Ingenuity", + "title": "2015 - Comprehensive transcriptional landscape.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "6f223b7b-d0ed-55d3-be91-a9e704149a94", + "extraction_id": "45ff5315-7500-5641-9c1a-e03b8aafc2d5", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "90c36562-0443-5100-b710-d750bd365b46", + "score": 0.7004715204238892, + "metadata": { + "text": "RNAs interact with proinflammatory signaling pathways and regulate senescence; however, their role on regulation of vas-cular aging processes is virtually unknown.\n151 Interestingly, \nthere is initial evidence linking the expression of the long \nnoncoding RNA Meg3 (maternally expressed 3) to age-related \nimpairment of angiogenic capacity of endothelial cells.152\nFurther studies are definitely needed to understand the", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 106, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "e308005e-d6ef-5492-a9a9-95256bb9ccee", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + }, + { + "id": "c2978dcd-0bab-5ca9-8130-0cdca1cc9330", + "score": 0.6980729103088379, + "metadata": { + "text": "Page 2 of 11 Lietal. BMC Genomics (2022) 23:254 \nmechanism of kidney aging will be of great significance \nfor delaying the occurrence and development of renal \naging. Although a small number of studies have been \nconducted on renal aging, it is still meaningful to com -\nprehend the mechanism of renal aging.\nLong chain noncoding RNAs (lncRNAs) are more than \n200 nucleotides in length. LncRNAs regulate transcrip -\ntional and posttranscriptional RNA processing, transla -", + "title": "2022 - Predict the role of lncRNA in kidney aging.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "0d3b0558-289c-5af0-843a-f288d5da3d8c", + "extraction_id": "67e3cb94-cc30-58fe-8ff7-a9790c74325f", + "associatedQuery": "What role do long non-coding RNAs (lncRNAs) play in the regulation of aging and age-related diseases?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIHXK8F2Ohi1RX10guI90pglYXyhM", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent advancements in CRISPR/Cas9 technology have opened new avenues for studying and potentially reversing aging in several ways:\n\n1. **Development of New Research Models**: CRISPR/Cas9 is significantly impacting research by enabling the creation of new models for studying age-related diseases. This includes manipulating disease-associated gene pathways, which can lead to a better understanding of the cellular and molecular origins of these diseases [1], [2].\n\n2. **Understanding Aging Processes Across Species**: The technology is beneficial in clarifying aging processes across different species. This improved understanding, particularly of epigenetic mechanisms affecting longevity, is crucial for identifying new potential therapeutic targets [3], [9].\n\n3. **Targeting Non-Proliferating Cells**: One notable contribution of CRISPR/Cas9 to aging research is its ability to target non-proliferating cells. This capability is important for studying cellular senescence, which is a key factor in both physiological aging and age-associated diseases [4].\n\n4. **Molecular Pathogenesis of Neurodegenerative Diseases**: CRISPR/Cas technologies have significantly contributed to studies on the molecular pathogenesis of age-related neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. This includes developing new tools to study the molecular mechanisms underlying these diseases using patient-derived cell lines with pathogenic mutations [10].\n\nThese advancements suggest that CRISPR/Cas9 technology not only aids in understanding the mechanisms of aging but also holds potential for developing interventions that could reverse or mitigate age-related conditions.", + "role": "assistant" + } + } + ], + "created": 1728920678, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 306, + "prompt_tokens": 930, + "total_tokens": 1236 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9e4d48fb-e942-52a6-8e7e-57313d567a72", + "score": 0.7790865302085876, + "metadata": { + "text": "models of ageing, but it will also drastically accelerate the generation of refined ver -\nsions of those models or even allow the development of new research approaches in \nnon-model organisms. Moreover, CRISPR-based genome editing is already having \na significant impact in research aiming to understand the cellular and molecular \norigins of age-related diseases, as well as developing potential treatments against 11 Applications ofCRISPR-Cas inAgeing Research", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1551, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "fcc88af4-1949-59fe-8111-200ec0dcb7d6", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "d7a12958-6d0b-546f-b0aa-152b6812e2fd", + "score": 0.7558349370956421, + "metadata": { + "text": "of ageing. Finally, we will review how CRISPR-Cas has been used for creating new \nmodels for the study of age-related diseases, as well as for manipulating disease- \nassociated gene pathways.\nS. Haston et al.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1470, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "c072d600-8450-5842-ade1-aefd03854312", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "093e7604-5108-5fda-850e-007817090a9a", + "score": 0.7537974265934584, + "metadata": { + "text": "ularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9) will be beneficial in clari-\nfying aging-processes across species. \nAn improved understanding of epigenetic mechanisms affecting longevity will be \ndeciding crucial step towards the identification of new potential therapeutic targets. In \nfact, epigenetic drugs are of particular interest to the clinic due to their reversible and \ntransient effect. \nA limitation of manifold epigenetic studies, however, are the variations among sin-", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "8db25d5e-25bd-5873-a53d-3815badbfd32", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "9a06df0b-a5b6-52d8-82c1-9dda446f9132", + "score": 0.7460278424008, + "metadata": { + "text": "224\nhigh-throughput assays able to further delineate important molecular pathways \ninvolved in inducing and maintaining cellular senescence in both physiological \nageing and age-associated diseases.\n Applications ofCRISPR-Cas intheStudy ofAgeing-Related \nDisease\n Cardiovascular Disease\nOne of the most notable contributions of CRISPR-Cas to ageing research is its \nability to target non-proliferating cells (contrary to HDR-directed gene targeting),", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1538, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "267468ed-0f9f-5a55-9334-9630792f300d", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "49c65d89-ec44-5412-a5bf-d94649e4afc3", + "score": 0.7414271235466003, + "metadata": { + "text": "219\n Applications ofCRISPR-Cas inBasic Research oftheMolecular \nCauses ofAgeing\n Investigating theMechanisms ofLongevity\nCurrently there have been no studies exploring the utility of the CRISPR-Cas sys-\ntem on experimentally extending the lifespan of physiologically aged laboratory \nanimals. A main issue in this regard is that established vertebrate models already \npossess relatively long lifespans that make longevity extension studies economi-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1500, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "625c559f-9ef6-5bef-8b4c-c57a72d421ed", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "a5ffc379-24d5-5c73-8435-41ca43af6347", + "score": 0.7320237159729004, + "metadata": { + "text": "CRISPR-Cas genome- editing tools will provide feasible implementation of 11 Applications ofCRISPR-Cas inAgeing Research", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1537, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "7d0ed573-4d0a-5de2-8be2-1ec0fb3a5800", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "7387d1f6-323a-52ea-90d4-6821fea31bf9", + "score": 0.729721188545227, + "metadata": { + "text": "the basis for future investigations into the spatio-temporal dynamics of the telom-\nerase protein invivo.11 Applications ofCRISPR-Cas inAgeing Research", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1521, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "1caf6ac0-0409-5b28-8fcf-bdffff2738a8", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "a02244c8-44da-595f-8a61-42bae541d784", + "score": 0.7277640104293823, + "metadata": { + "text": "induced by telomere erosion. Protein Cell. 2019;10:3705.11 Applications ofCRISPR-Cas inAgeing Research", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1583, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "5f85264a-a5cd-5ef6-a4c9-900dcb7b07ad", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "4eb34c07-921b-55bb-98eb-ff013bb2ace0", + "score": 0.7236873672194871, + "metadata": { + "text": "using bulk mRNA or even analyzing single cells (scRNA-seq). In addition, advances in\nmolecular biology and cell culture approaches (for instance Clustered Regularly Interspaced\nShort Palindromic Repeats (CRISPR)/Cas9) will be benecial in clarifying aging-processes\nacross species.\nAn improved understanding of epigenetic mechanisms affecting longevity will be\ndeciding crucial step towards the identication of new potential therapeutic targets. In", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 146, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "e2bc9b8e-2349-509b-a148-fbd86f0455f4", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + }, + { + "id": "c6c119e6-362e-5ae7-a1f1-a5e75eb456ba", + "score": 0.7211248874664307, + "metadata": { + "text": "In recent years, CRISPR-Cas technologies have significantly contributed to studies \naddressing the molecular pathogenesis of age-related neurodegenerative conditions \nsuch as Alzheimers disease (AD) and Parkinsons disease (PD). Currently, it has \nmostly been utilised for developing new or improved tools in which to study the \nmolecular mechanisms underlying these diseases, such as in patient-derived cell \nlines carrying pathogenic mutations.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1542, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "267468ed-0f9f-5a55-9334-9630792f300d", + "associatedQuery": "How do recent advancements in CRISPR/Cas9 technology open new avenues for studying and potentially reversing aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIHXSI5xx8VWq2TPqps22AUgX04Pq", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The DNA damage response (DDR) plays a crucial role in both replicative and chronological aging by maintaining genomic stability and influencing cell fate in response to DNA damage. Here are the key points regarding its significance:\n\n1. **Premature Aging and DDR Impairment**: Impaired DDR is directly correlated with premature aging phenotypes, as evidenced by studies on certain genetic models like Ercc1 [1]. This suggests that a functional DDR is essential for normal aging processes.\n\n2. **Cellular Senescence and DDR**: Persistent DDR signaling is a shared mechanism that triggers cellular senescence, which is a hallmark of aging [4]. This indicates that DDR not only repairs damage but also influences aging by promoting senescence when damage is irreparable.\n\n3. **Replicative Senescence**: DDR activation at telomeres, especially when they are critically short or damaged, triggers replicative cellular senescence or apoptosis [5]. This highlights the role of DDR in controlling the replicative lifespan of cells.\n\n4. **Age-related DNA Damage Accumulation**: As organisms age, DNA damage accumulates, and the DDR pathway becomes increasingly important in managing this damage to prevent mutations and maintain cellular function [6].\n\n5. **Tumor Suppression and Aging**: While DDR mechanisms like apoptosis and senescence are potent tumor suppressors, they also contribute to aging by removing or halting the proliferation of damaged cells [7].\n\nOverall, the DDR is significant in aging as it balances repair and cell fate decisions, influencing both the replicative capacity of cells and the overall aging process by managing DNA damage and maintaining genomic integrity.", + "role": "assistant" + } + } + ], + "created": 1728920686, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 325, + "prompt_tokens": 1225, + "total_tokens": 1550 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "42a07dfa-c5ac-535f-9a65-8c53b8f10aec", + "score": 0.7411069869995117, + "metadata": { + "text": "Chromatin Remodeling, DNA Damage Repair and Aging Current Genomics, 2012 , Vol. 13, No. 7 539 \nErcc1 also show premature aging phenotypes, providing evi-\ndence of a direct correlation between impaired DDR and premature aging [137, 138]. \n The relationship between DNA damage accumulation \nand aging has gained maximum credibility through studies", + "title": "2012 - Chromatin Remodeling, DNA Damage Repair and Aging.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "594e5dbe-b92a-5b0c-9f65-2a10670f9517", + "extraction_id": "d9a12bd9-c65e-547a-89aa-4e0231558ddc", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "86bd9226-94dc-5186-984e-3dd140de9af3", + "score": 0.728931725025177, + "metadata": { + "text": "genome is being transcribed or replicated, the threshold of damage needed to activate DDRs,\nand the choice of cell fate in response to genotoxic stress. It is important to point out that cross-sectional studies, which are largely all we have to date, yield information about the burden of DNA\ndamage and cannot inform as to whether lesions accumulate over time. Longitudinal studies on\ntissues that can be serially accessed are desperately needed.\nDNA Repair Capacity Decreases with Aging", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 74, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "e3e52327-4a23-5003-b418-dafdcdcae82c", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "79535f3c-51b2-5696-9081-3fdf146e8e61", + "score": 0.7221173048019409, + "metadata": { + "text": "INTRODUCTION\nDamage to DNA occurs with surprising frequency. DNA lesions can cause mutations, blocktranscription and replication, and trigger the DNA damage response (DDR). The DDR arrests\ncell cycle progression and activates signaling pathways that impact cell fate: repair, apoptosis, or\ncellular senescence. DNA damage is widely recognized as a cause of cancer, and strong evidencenow links DNA damage to aging and diseases associated with aging.", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "3da50120-f7fd-5256-ae05-4ffd57876a5c", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "6b4d2b61-4c6b-5b9e-a175-7a3c53a923a5", + "score": 0.7206555175390544, + "metadata": { + "text": "DNA damage and persistent DDR signalling as a shared causative mechanism of cellular senescence andageing. Curr. Opin. Genet. Dev. 26:8995\n103. Rodier F, Coppe JP, Patil CK, Hoeijmakers WA, Munoz DP, et al. 2009. Persistent DNA damage\nsignalling triggers senescence-associated inammatory cytokine secretion. Nat. Cell Biol. 11:97379\n104. Garinis GA, Uittenboogaard LM, Stachelscheid H, Fousteri M, van Ijcken W, et al. 2009. Persistent", + "title": "2016 - Genome Integrity in Aging.pdf", + "version": "v0", + "chunk_order": 154, + "document_id": "85d5fcbb-5385-5a01-8139-d11fc8b1fe3a", + "extraction_id": "fdd9c5d5-2cca-5fe1-baed-c672f464dab0", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "609e97e2-babd-5a49-9451-1a6162eb01e4", + "score": 0.7086535692214966, + "metadata": { + "text": "persistent DNA damage response (DDR) at telomeres and that even long telomeres may be a target for the accu-mulation of irreparable DNA damage. Therefore, DDR activation either at critically short telomeres or caused by persistent telomeric DNA damage represents the trigger of replicative cellular senescence or apoptosis\n48, 50. The \nanalysis of apoptosis by TUNEL assay showed that leukocytes from untrained T2D subjects were more sensitive to H", + "title": "2017 - Regular exercise participation improves genomic stability in diabetic patients an exploratory study to analyse telomere length and DNA damage.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "dcaf7b09-2d54-5cbf-b061-e3c4e6c6c518", + "extraction_id": "a5caef7f-f1c1-55af-8807-3c9db425df7b", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "9fac0777-2bcb-528c-9c16-cbcd85e28522", + "score": 0.7073437397088862, + "metadata": { + "text": "E) (2931) and have alleviated the dependency on invitro and \ninvivo models by using direct human samples.\nAGe-ReLATeD DNA DAMAGe AND DNA \nDAMAGe ReSPONSe (DDR) ACTiviTY\nAge-related accumulation of DNA damage has been studied \nthoroughly, showing correlation between age and damage levels \nor mutation frequency (32, 33). In the presence of DNA lesions \nor abnormalities, the DDR, a complex multigenic pathway, is", + "title": "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "extraction_id": "4cd1741b-c96f-592f-af69-95f3a10a157b", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "b9de772a-53c5-5128-a595-9baf9420e534", + "score": 0.7070512771606445, + "metadata": { + "text": "Spontaneous damage is stochastic. But the response to DNA damage is highly conserved, geneti-cally controlled, and with evolution exceedingly more complex. DNA damage triggers activation\nof signaling pathways termed the DDR, which facilitates repair and arrests cell cycle progression\nuntil repair is complete. If DNA damage is extensive or irreparable, DDR effectors trigger celldeath (apoptosis) or cell senescence. These are potent tumor suppressor mechanisms. However,", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 115, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "1d1662ae-28d6-514d-a600-8860b061a504", + "score": 0.7031606049287502, + "metadata": { + "text": "to senescence. Genetic attenuation of the DDR enables reversal of cellular senescence (81). Incontrast, introduction of DSBs in mouse liver, using a tetracycline-inducible SacI restriction\nendonuclease system, increases the burden of senescent cells in vivo and triggers hallmarks of liver\naging (82), illustrating a clear path for how DNA damage can play a causal role in aging.\nMarkers of senescence are detected at higher levels in tissues of older mice, humans, and other", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 118, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "2b1396d1-ea5d-5708-a6b1-2adf1712c7b4", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "43c4d87f-c0ce-5148-b601-77e6fd8956b2", + "score": 0.7019306421279907, + "metadata": { + "text": "mechanisms. In general, it appears that DDR signaling enhances DNA repair and autophagy tocontrol the level of damage in the cell.\nInterestingly, evidence, albeit early evidence, has been found that DNA damage is linked to\nproteostasis. Expression of proteins containing polyglutamine tracts that drive protein aggrega-\ntion linked to neurodegeneration activates the DDR and H2AX foci (148). Interestingly, DNA\nbreaks in cells and H2AX foci in brain of a murine model of Huntington disease are detected", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 172, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "0a7a0a01-a262-51bf-bfaf-4f301a0a467b", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + }, + { + "id": "0acc43f6-5d5b-53f5-af2f-53077b26591a", + "score": 0.6958593726158142, + "metadata": { + "text": "its relevance to age -related functional decline at the molecular and cellular level. \nThe importance of oxidative stress and key DNA damage response (DDR) pathways \nin cellular aging is discussed, with a special focus on poly (ADP -ribose) polymerase \n1, whose persistent activation depletes cellular energy reserves, leading to \nmitochondrial dysfunction, loss of energy homeostasis , and altered cellular \nmetabolism. Elucidation of the relationship between genomic instability ,", + "title": "2019 - Towards Understanding Genomic Instability, Mitochondrial.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "9b34514d-3d0e-52b5-8e5e-2f3c0708fd82", + "extraction_id": "93dbd5fc-d568-5b19-a9cd-fa192ed94ca7", + "associatedQuery": "What is the significance of the DNA damage response (DDR) in the context of both replicative and chronological aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIHXbfIiqBOfJAG67WB3RBf5qTOVk", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases through several mechanisms:\n\n1. **Functional Decline of the Adaptive Immune System**: Immunosenescence is characterized by a decline in the adaptive immune system's function, which leads to reduced protection against infections and decreased effectiveness of vaccinations [1]. This decline is primarily due to changes in T and B lymphocytes, which are crucial for adaptive immunity [2].\n\n2. **Loss of Diversity in Immune Receptors**: There is a loss of diversity in the T-cell receptor (TCR) and B-cell receptor repertoire as people age. This is due to the accumulation of dysfunctional cells and decreased output from the thymus and bone marrow, which are essential for generating new immune cells [9]. This loss of diversity impairs the immune system's ability to recognize and respond to new pathogens effectively.\n\n3. **Chronic Inflammation (Inflammaging)**: Aging is also associated with a state of low-grade chronic inflammation, known as inflammaging. This chronic inflammation can further compromise immune function and contribute to the development of age-related diseases [1], [4].\n\n4. **Overall Immune System Alterations**: All components of the immune system are affected by aging, not just the adaptive immune system. This widespread alteration can lead to a compromised defense against pathogens, making the elderly more susceptible to infectious diseases and less responsive to vaccinations [2], [9].\n\nThese changes collectively lead to an increased susceptibility to diseases in the elderly, highlighting the importance of understanding and potentially intervening in these age-related immune alterations to improve health outcomes in older populations.", + "role": "assistant" + } + } + ], + "created": 1728920695, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 331, + "prompt_tokens": 1134, + "total_tokens": 1465 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "65fe4bdc-890e-53bf-ad11-2d9c67adac7f", + "score": 0.74183189868927, + "metadata": { + "text": "immune system are one of the hallmarks of the aging\nbody. Immunosenescence is the functional decline of the\nadaptive immune system brought on by natural agingwhereby protection against infection by pathogens and\nthe effectiveness of vaccination decline [45,46]. The sec-\nond aging-induced change in the immune system iscalled inflammaging which is characterized by a low-\ngrade chronic inflammation process that contributes to", + "title": "2012 - Structural, functional and molecular analysis.pdf", + "version": "v0", + "chunk_order": 100, + "document_id": "0e803003-d6e5-570e-a810-1aea89d7ea63", + "extraction_id": "d9ef944b-b9a5-5b45-aaa6-c48f6fe54893", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "0c2a9ad8-054d-5a03-af43-704d2b7722d0", + "score": 0.7318152189254761, + "metadata": { + "text": "the increased susceptibility of the elderly to infectious disease and tothe poor outcome of vaccination. Defence against pathogens is com-promised mainly because of changes in adaptive immunity mediatedby T and B lymphocytes; however, all components of the immunesystem are affected (Fig 1). Dissecting the crucial alterations responsi-ble for dysfunctional immunity in old age will facilitate the develop-ment of rational interventions to reconstitute appropriate immunefunction. Given the increasing", + "title": "2007 - Immunosenescence comes of age.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "22313267-b0be-572f-8170-dcb814fe6140", + "extraction_id": "1ec3aae0-b171-511c-8250-fc0731aa3ec8", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "a8f4f7d2-85f9-5097-b588-614c7973c3b5", + "score": 0.7281038761138916, + "metadata": { + "text": "[39] C. Castelo-Branco, I. Soveral, The immune system and aging: a review, Gynecol. \nEndocrinol. 30 (2014) 1622. \n[40] S.A. Johnson, S.J. Rozzo, J.C. Cambier, Aging-dependent exclusion of antigen-in\n-\nexperienced cells from the peripheral B cell repertoire, J. Immunol. 168 (2002) \n50145023\n. \n[41] D.P. Shanley, D. Aw, N.R. Manley, D.B. Palmer, An evolutionary perspective on the \nmechanisms of immunosenescence, Trends Immunol. 30 (2009) 374381.", + "title": "2020 - Age-related gene expression and DNA methylation changes in rhesus.pdf", + "version": "v0", + "chunk_order": 108, + "document_id": "0f1fe2f6-b9c8-514d-ac1c-4e7c07a19ff0", + "extraction_id": "245e6d14-fa43-5af6-92d3-c5d7bf0235c2", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "6822e1b6-b9bc-5e26-b6d5-d0d141854dd4", + "score": 0.7085440158843994, + "metadata": { + "text": "immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems. Age-relatedimmune decline also links to the concept of inflamm-aging, whereby aging is accompanied by sterile chronic\ninflammation. Along with a decline in immune function, aging is accompanied by a widespread of omics remodeling.", + "title": "2022 - Functional genomics of inflamm-aging.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "435dc081-e3d1-52c5-93a1-caa11206422f", + "extraction_id": "1635dbe1-1dcb-5213-9446-74129d50c5f8", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "c0eedfc9-fd74-51f8-ace9-dfd79ad16b71", + "score": 0.703385966814402, + "metadata": { + "text": "ence the development of inflamm-aging and immunosenes-\ncence phenotypes.\nFinally, although discussed studies have reported age-related\nchanges in innate immune cell processes, there is still little\nknown about how these changes are influenced by biologicalsex. Indeed, both the adult mammalian immune system [ 80,125]\nand the aging process [ 126] are sex-dimorphic, suggesting that", + "title": "2022 - Functional genomics of inflamm-aging.pdf", + "version": "v0", + "chunk_order": 87, + "document_id": "435dc081-e3d1-52c5-93a1-caa11206422f", + "extraction_id": "72b29fff-be72-5ede-85c9-7dc81894c956", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "c4f7a0e2-0d13-5928-aaf2-8fc70dc9face", + "score": 0.6911636775038447, + "metadata": { + "text": "tion has also been implicated in ageing across a range of non-model organisms, including mice,nematode worms ( Caenorhabditis elegans ), and primates [ 4042].\nThe damage caused by the ageing adaptive and innate immune systems gives us insights into\nhow these different arms of the immune system may in uence longevity. In general, adaptive im-\nmune function diminishes with age, whereas innate immune function is maintained [ 34,4346].", + "title": "2022 - Immunity and lifespan answering.pdf", + "version": "v0", + "chunk_order": 31, + "document_id": "a834e7ee-7bab-5c4d-a236-b570d1ae635f", + "extraction_id": "b7467732-698f-5ca4-be08-08b011b0d343", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "1683b89a-86bd-5439-9a6f-df120b67d0e8", + "score": 0.6838449443532513, + "metadata": { + "text": "development to senescence, innate immunity to adaptive immunity,and genes to environments, in organisms ranging from mice to monkeys and humans. Understanding and eventually modulatingimmune dysfunction in the elderly now beckons.\nLymphocyte development and ageing", + "title": "2007 - Immunosenescence comes of age.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "22313267-b0be-572f-8170-dcb814fe6140", + "extraction_id": "1ec3aae0-b171-511c-8250-fc0731aa3ec8", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "fb4173c8-cf14-59d2-804c-3c2824a3fdc5", + "score": 0.6819573234345137, + "metadata": { + "text": "an age-related decline in the capacity of adaptive immunity,consisting of more specic responses carried out by B andT cells [ 7]. Thus, with advanced age, the immune system\nundergoes a gradual remodeling in the attempt to reestablisha new balance that assures survival, however, favoring thedevelopment of chronic inammatory conditions [ 5,6,8,9].\nDNA damage and inammation are inevitably linked by", + "title": "2012 - Pleiotropic Cellular Functions of PARP1 in Longevity.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "e67324c0-474b-5280-8cbc-3778c6c0e5f0", + "extraction_id": "f12b7e5c-29bc-5f56-9303-ab9286f22d88", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "f16127b0-68dc-50bc-b39e-8ead81d723ee", + "score": 0.6773538589477539, + "metadata": { + "text": "All components of the immune system are altered as ageing pro-ceeds (Fig 1); however, the T-cell and B-cell compartments seem tobe particularly susceptible. The most severe clinical impact is proba-bly a result of the loss of diversity in the TCR and B-cell-receptorrepertoire, owing to the accumulation of dysfunctional cells, anddecreased thymic and bone-marrow output. Several interventionsdiscussed at the meeting could conceivably contribute to therestoration of appropriate immune function in the near", + "title": "2007 - Immunosenescence comes of age.pdf", + "version": "v0", + "chunk_order": 62, + "document_id": "22313267-b0be-572f-8170-dcb814fe6140", + "extraction_id": "170e6d89-2624-5b49-a6d1-95d4f35f73f3", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + }, + { + "id": "ba9fdb3c-b9c2-57a2-9bb7-df5472d20e73", + "score": 0.6744434043433323, + "metadata": { + "text": "more susceptible to DNA damage. One of the major rea-sons are the impaired DNA repair mechanisms which havebeen described in several studies and have been associated\nwith the initiation of age-associated diseases and progeroidsyndromes ( Hasty et al., 2003; Lieber and Karanjawala,\n2004). Furthermore, dysregulated immune and inamma-\ntory responses have been already documented both inhumans and mouse with increasing age ( Badawi et al.,\n2004; Kovaiou et al., 2007 ).", + "title": "2007 - The skin as a mirror of the aging process in the human organism.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "c429b80b-ad40-5fd3-b189-3982e5a8ab23", + "extraction_id": "daf4bb0f-4be5-5c47-baa5-686cd61adc1a", + "associatedQuery": "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIHXkz3iFRslvxy1Jaw30l5EF9v8O", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Advancements in machine learning and artificial intelligence significantly aid in the identification of biomarkers for biological aging by enabling the development of predictive models and personalized medical treatments. These technologies allow for the integration and analysis of complex biological data, which can be used to forecast an individual's lifespan and potential age-related diseases, thereby facilitating personalized medical interventions [2]. Machine learning algorithms, such as linear regression and its variants, are employed to select aging-related biomarkers and construct aging clocks, which are predictors of chronological and biological age based on various omics datasets [3]. Additionally, computational methods have been developed to predict biological age from gene expression data, which can help in evaluating lifestyle changes and therapeutic strategies aimed at promoting healthy aging [8].", + "role": "assistant" + } + } + ], + "created": 1728920704, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 141, + "prompt_tokens": 1171, + "total_tokens": 1312 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "8139ed83-471f-5aa8-a6e1-2294b106ffd7", + "score": 0.6753724410907971, + "metadata": { + "text": "tifications of biological aging: do they measure the same thing? Am J Epidemiol. \n2018;187(6):122030.\n 74. Putin E, etal. Deep biomarkers of human aging: application of deep neural networks to bio-\nmarker development. Aging (Albany NY). 2016;8(5):102133.\n 75. Rehkopf DH, etal. Leukocyte telomere length in relation to 17 biomarkers of cardiovascular \ndisease risk: a cross-sectional study of US adults. PLoS Med. 2016;13(11):e1002188.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1795, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "660d608e-8333-590f-8183-31b51779cec3", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "eeed3c27-9717-5592-8d69-937eca35bfff", + "score": 0.6621368334130294, + "metadata": { + "text": "studied (Table 13.1). Thus, due to the generation of these data and technological \nadvances, possibly in the future, artificial intelligence programs will be able to \nreliably forecast the life of an individual, as well as the possible diseases that he \nmay suffer in ageing; so these advances and discoveries will allow us to achieve \na personalized medical treatment as a result of to the integration of biomarkers \nof ageing.\n Ageing Is aTreatable Condition", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1763, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "1af20df8-561f-59cb-9996-106a3be3f82f", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "b545cd47-00c7-5bd8-bd25-8d2bf59be62e", + "score": 0.6620581347363634, + "metadata": { + "text": "the data. However, construction of such models is often highlydegenerate, yielding little overlap of identified biomarkers be-tween studies and thus making results difficult to interpret(Thompson et al. 2018; Galkin et al. 2020).\nAmong the many computational algorithms, linear regres-\nsion and its variants have been widely used to select aging-relatedbiomarkers and build aging clocks, namely, predictors of chro-\nnological age and biological age, in various omics data sets and ag-", + "title": "2023 - A transcriptome-based single-cell biological age model.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "extraction_id": "f9312bd9-9f67-5e36-9986-f01d66d4b7ac", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "4b418218-07f6-5103-a9f4-4a28be7247c8", + "score": 0.6509723275320346, + "metadata": { + "text": "states, which can be monitored using various biomarkers (Belskyet al. 2015). These markers are usually measurable indicators of aparticular outcome or source of aging, such as phenotypical mea-sures like frailty and molecular measures like DNA methylation dy-\nnamics (Schumacher et al. 2021; Lpez-Otn et al. 2023). Although\ninformative, they are not always quantitatively predictive of anindividual s true biological age, nor are they easy to obtain. The ad-", + "title": "2023 - A transcriptome-based single-cell biological age model.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "extraction_id": "f9312bd9-9f67-5e36-9986-f01d66d4b7ac", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "11d9e838-e4a1-50d4-92e8-658d4ff57b68", + "score": 0.6488766670227051, + "metadata": { + "text": "biomarkers of the aging process.", + "title": "2007 - Biological Aging Is No Longer.pdf", + "version": "v0", + "chunk_order": 58, + "document_id": "efef1c11-52f9-5b95-878a-07980080f0f8", + "extraction_id": "5362f054-bb14-53fd-8d6d-9fb7aa41b3f3", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "71a04373-81b9-5219-bbde-6f9cd1935491", + "score": 0.6426215980874551, + "metadata": { + "text": "supervisedmachinelearningappliedtoageingresearch.\nBiogerontology\n,18,171188.\n47. Kriete,A.,Lechner,M.,Clearfield,D.andBohmann,D.(2011)\nComputationalsystemsbiologyofaging. WileyInterdiscip.Rev.Syst.\nBiol.Med. ,3,414428.Downloaded from https://academic.oup.com/nar/article/46/D1/D1083/4599180 by guest on 14 October 2023", + "title": "2018 - Human Ageing Genomic Resources new and updated.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "82726cea-f77c-5a92-9f2e-ecccc369953a", + "extraction_id": "62ff5c38-25a5-5729-a160-ce89e2ceb1c8", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "ed814cb1-4fd3-5586-bd75-131d2a3ae96b", + "score": 0.637718677520752, + "metadata": { + "text": "associated with age, such as mouth width, nose width, and eye corner droop. This \ntype of bioimage analysis has rendered relatively accurate calculations of the actual \nage, although this accuracy tended to fall with increasing age after 40years [71].\n Integration ofBiomarkers ofAgeing\nBiomarkers of ageing allow estimating the biological age of an organism (Table 13.1) \nwhile providing information on their health status. Different studies are looking for", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1752, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "5a07784a-755c-598d-9d2d-3eb2ab8285cc", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "bb3a61fd-7137-5735-b65c-8aabab7eb971", + "score": 0.637002310931546, + "metadata": { + "text": "Background\nThere is a marked heterogeneity in human lifespan and\nhealth outcomes for people of the same chronological\nage. Thus, one fundamental challenge is to identify mo-\nlecular and cellular biomarkers of aging that could pre-\ndict lifespan and be useful in evaluating lifestyle changes\nand therapeutic strategies in the pursuit of healthy aging.\nHere, we developed a computational method to predict\nbiological age from gene expression data in skin fibro-", + "title": "2018 - Predicting age from the transcriptome.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "73128c69-30e0-5b7a-9504-1502e3f062c7", + "extraction_id": "be79444e-743f-5289-9607-db6bc3b35493", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "c2ea0dae-b466-5c5b-babb-bfa74243bd34", + "score": 0.6301597952842712, + "metadata": { + "text": "Background\nAgeing is a major risk for diseases and mortality [ 1,2].\nChronological age has been widely used as a marker of\nageing due to ease and accuracy of measurement [ 1].\nHowever, it is not necessarily a good predictor of\nbiological ageing since individuals with the same\nchronological age can vary in health, especially in later\nlife [ 3]. Therefore, researchers have attempted to search\nfor biomarkers of ageing that can predict functional cap-\nability at a later age [ 4,5]. In 2013, Hannum et al. and", + "title": "2019 - Improved precision of epigenetic clock.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "556d0179-023f-581f-9c2d-febe4e75722f", + "extraction_id": "6e048749-b423-54c0-9505-439db5595254", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + }, + { + "id": "96135704-e84c-53fc-9b57-b1e7b8dcd81f", + "score": 0.6267950161221625, + "metadata": { + "text": "discriminate between adverse aging-related events,\nsuch as frailty (Mitnitski et al. 2002 ), immobility\n(Simonsick et al. 2001 ), and propensity to fall (Lord et\nal.1994 ).\nThere are additional considerations when choosing\nbiomarkers to characterize aging. First, biomarkers\nmeasured at a given age are merely snapshots of\nimportant regulatory systems (Seeman et al. 2004 );\nthere is no information on system dynamics if each\nbiomarker is measured only once. Having longitudinal", + "title": "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "ed31486c-a651-5894-bd96-21fbd78f2646", + "extraction_id": "1b0806b9-729c-581f-9e3f-a98a5e0ce7eb", + "associatedQuery": "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIHXpT1Oa9sduYt2d6yF1iu8bJvoN", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent discoveries regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes include the following:\n\n1. The importance of progeronic (aging-promoting) and antigeronic (aging-delaying) circulating factors in the development of vascular aging phenotypes has been discussed. This highlights the role of systemic factors in contributing to age-related vascular pathologies and suggests potential interventions to prevent or delay these conditions by targeting fundamental cellular and molecular aging processes [1].\n\n2. Studies using heterochronic parabiosis, which involves connecting the circulatory systems of young and aged mice, have demonstrated the impact of circulating factors on aging phenotypes. This research provides initial evidence that circulating factors can influence cerebromicrovascular density, which typically declines with advanced age [3].\n\nThese findings underscore the significant role that systemic factors, including circulating microvesicles, play in influencing aging phenotypes, particularly in the context of vascular aging and potential rejuvenation strategies.", + "role": "assistant" + } + } + ], + "created": 1728920709, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 193, + "prompt_tokens": 1367, + "total_tokens": 1560 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c4c7b861-6d13-5814-818d-a79ddabd742c", + "score": 0.6523131728172302, + "metadata": { + "text": "in the vascular system are considered in terms of their contribution to the pathogenesis of both microvascular and macrovascular diseases associated with old age. The importance of progeronic and antigeronic circulating factors in relation to development of vascular aging phenotypes are discussed. Finally, future directions and opportunities to develop novel interventions to prevent/delay age-related vascular pathologies by targeting fundamental cellular and molecular aging processes are presented. (Circ", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "bfeb5c38-4fa6-5df5-90ce-63204deba3a8", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "9d96fdeb-3b94-57d2-8025-db47be7c52ad", + "score": 0.6351242065429688, + "metadata": { + "text": "pression of numerous mRNAs, some of which directly influence aging and age-related diseases. Jung and Suh describe what we know about the importance of microRNAs in aging and how this exciting new field is just starting to become explored. \n The last review in this special issue by Hou et al. brings things together nicely with a systems biology perspective of aging. \nIn order to model the immense complexity of aging, we require systems-level approaches. This review describes how several", + "title": "2012 - Genomics and Genetics of Aging.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "3c2efc4d-b5a8-5843-be7e-44c3b52f3d9b", + "extraction_id": "726bbaa2-97e8-5f62-a731-a1ba3cf1778f", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "e9ddeedc-70ba-516f-ad9b-77e2b45cd01f", + "score": 0.6344840148398878, + "metadata": { + "text": "autoregulation of blood flow,218 vascular structural remodel-\ning, atherogenesis,219 and angiogenic processes.220\nThe impact of circulating factors on aging phenotypes \nwas also demonstrated by studies using mice with heter -\nochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse.\n221 \nCerebromicrovascular density typically declines with ad-vanced age,\n222 and there is initial evidence that circulating an-", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 143, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "415a6dd6-0e64-5aef-8561-289d728ad721", + "score": 0.6310828704287343, + "metadata": { + "text": "components, particularly chemokines and cytokines, in theblood and tissues ( Villeda et al., 2011 ). In addition to illuminating\nthe inuence of the systemic environment on cellular function,such heterochronic studies emphasize the potential role of envi-ronmental factors in rejuvenating aged cells.\nMolecular signatures of aging have been directly tested as", + "title": "2012 - Aging, Rejuvenation, and Epigenetic.pdf", + "version": "v0", + "chunk_order": 46, + "document_id": "bde26feb-f423-51b0-89ec-6f079bfc8b17", + "extraction_id": "7f8f4ca0-9b27-55e3-a889-030af08dc84b", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "729ae0a3-95f3-50c7-8c00-d1ce0673ea08", + "score": 0.6283429861068726, + "metadata": { + "text": "related diseases. Ageing Res Rev. 2018;47:21477.\n 115. Kumar S, Vijayan M, Bhatti JS, Reddy PH.MicroRNAs as peripheral biomarkers in aging \nand age-related diseases. Prog Mol Biol Transl Sci. 2017;146:4794.\n 116. Smith-Vikos T, Liu Z, Parsons C, Gorospe M, Ferrucci L, Gill TM, etal. A serum miRNA \nprofile of human longevity: findings from the Baltimore Longitudinal Study of Aging \n(BLSA). Aging (Albany NY). 2016;8(11):297187.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1140, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "575a9f30-8504-5526-90e0-e558bfc29c02", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "571e50a8-c009-59a5-b01c-0f01c4b5e163", + "score": 0.627212643623352, + "metadata": { + "text": "in the endothelium and the VSMCs and specific disease pro-cesses. There is evidence that the senescence-associated se-cretory phenotype can also induce paracrine senescence and \nalter the function of neighboring cells, and the role of this \nmechanism in vascular aging should be further evaluated. \nThe possibility of paracrine transmission of senescence from \nmicrovascular endothelial cells to parenchymal cells also requires further investigations. It should be noted that many", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 89, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "fe270a46-7f2f-5a25-b98f-a782511801fb", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "ab32705e-4e02-59ab-986d-4552a4a522b9", + "score": 0.6239165663719177, + "metadata": { + "text": "protein VSIG4 as a biomarker of aging in murine adiposetissue. Aging Cell 2020; 19:e13219.\n128. Angelidis I, Simon LM, Fernandez IE, et al. An atlas of the\naging lung mapped by single cell transcriptomics and deeptissue proteomics. Nat Commun 2019; 10:963.\n129. Clark D, Brazina S, Yang F, et al. Age-related changes to\nmacrophages are detrimental to fracture healing in mice.\nAging Cell 2020; 19:e13112.\n130. Tabula Muris Consortium. A single-cell transcriptomic", + "title": "2022 - Functional genomics of inflamm-aging.pdf", + "version": "v0", + "chunk_order": 137, + "document_id": "435dc081-e3d1-52c5-93a1-caa11206422f", + "extraction_id": "14dbffca-9dc8-5d8c-bb23-98bc80b77e86", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "55a6fe97-29cd-5969-8ea8-3b350b8e0554", + "score": 0.6122685074806213, + "metadata": { + "text": "Ungvari et al Mechanisms of Vascular Aging 861\nmechanisms of vascular aging and identify translationally \nrelevant treatments for the promotion of vascular health in older adults.\nThe same cellular and molecular aging processes that af-\nfect arterial vessels and capillaries also affect veins and the lymphatic/glymphatic system, likely contributing to various \ndisease pathologies. Examples include the potential role of \ncerebral venules in neuroinflammation, Alzheimer disease, and cerebral microhemorrhages", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 166, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "2836777b-037b-52e4-a160-9cb02dd98b92", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "3914af93-b251-54ae-b7bf-9c8243a24f74", + "score": 0.6107358336448669, + "metadata": { + "text": "et al., Plasma proteomic signature of age in healthy humans, Aging Cell 17 (2018). \n[17] D. Mari, P.M. Mannucci, R. Coppola, B. Bottasso, K.A. Bauer, R.D. Rosenberg, \nHypercoagulability in centenarians - the paradox of successful aging, Blood 85 \n(1995) 31443149. \n[18] S.A. Phillips, The vasculature in cardiovascular diseases: will the vasculature tell us what the future holds? Prog. Cardiovasc. Dis. 57 (2015) 407408. \n[19] R.A. Gibbs, J. Rogers, M.G. Katze, R. Bumgarner, G.M. Weinstock, E.R. Mardis,", + "title": "2020 - Age-related gene expression and DNA methylation changes in rhesus.pdf", + "version": "v0", + "chunk_order": 99, + "document_id": "0f1fe2f6-b9c8-514d-ac1c-4e7c07a19ff0", + "extraction_id": "245e6d14-fa43-5af6-92d3-c5d7bf0235c2", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + }, + { + "id": "d2ce22fd-6c12-56cf-948d-fc6604cf0f23", + "score": 0.6083986163139343, + "metadata": { + "text": "16Lidzbarsky et al.\nGenomic Instabilities, Cellular Senescence, and Aging\nFrontiers in Medicine | www.frontiersin.org April 2018 | Volume 5 | Article 104\n177. Smith-Vikos T, Slack FJ. MicroRNAs and their roles in aging. J Cell Sci (2012) \n125:717. doi:10.1242/jcs.099200 \n178. Lanceta J, Prough RA, Liang R, Wang E. MicroRNA group disorganiza-\ntion in aging. Exp Gerontol (2010) 45:26978. doi:10.1016/j.exger.2009. \n12.009", + "title": "2018 - Genomic Instabilities, Cellular Senescence, and Aging In Vitro, In Vivo and Aging-Like Human Syndromes.pdf", + "version": "v0", + "chunk_order": 228, + "document_id": "7de8d462-8a3c-5625-8cbb-374f3bb46425", + "extraction_id": "d3686eba-0aa4-5c56-b60d-bf76c3ab433b", + "associatedQuery": "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIHXx0hXjoPni1lj2qiHnS6BLuSSU", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Changes in the gut microbiome composition are closely linked to aging and longevity. As individuals age, the composition and function of the gut microbiome undergo significant modifications. These changes are thought to contribute to various age-related processes, including immunosenescence and inflammaging, which are associated with the aging immune system [6]. \n\nResearch has shown that a healthy microbiota can promote survival and is linked to longevity. Specifically, certain bacterial families such as Christensenellaceae, Akkermansia, and Bifidobacterium have been associated with immunological and metabolic regulation, which may contribute to increased lifespan [1]. \n\nAdditionally, the gut microbiota of older adults differs in type and number of microorganisms compared to younger adults, with Bacteroidetes and Firmicutes being the most prevalent species in older individuals [4]. These changes in microbial composition can be influenced by both intrinsic and extrinsic factors, which play a significant role in the health and function of the microbiome as people age [8]. \n\nOverall, maintaining a healthy gut microbiome is crucial for promoting longevity and mitigating some of the negative effects associated with aging.", + "role": "assistant" + } + } + ], + "created": 1728920717, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 225, + "prompt_tokens": 1456, + "total_tokens": 1681 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "1bcfcf33-d9b4-55b7-a384-bc8e08893a22", + "score": 0.7504067420959473, + "metadata": { + "text": "the adaptation of the microbiota to the physiological changes of the long aging \nprocess. It has been demonstrated that the microbiota on this population maintains \nthe health and promotes the survival. Additionally, a relationship between a healthy \nmicrobiota and longevity had been proposed [44]. A possible pathway is an immu-\nnological and metabolic regulation linked to the increase of bacterial compounds \nlike Christensenellaceae, Akkermansia, and Bifidobacterium [44, 45].", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1207, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "07a2b9a1-d683-568d-b2e6-c2cc1fcffba5", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "f4ec4435-00f7-5477-984a-68d1eff9e7a0", + "score": 0.7143480777740479, + "metadata": { + "text": "Marchesi JR, Falush D, Dinan T, Fitzgerald G, et al:Composition, variability,\nand temporal stability of the intestinal microbiota of the elderly. Proc\nNatl Acad Sci USA 2011, 108(Suppl 1):4586 4591.\n21. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S,\nDonehower LA, Issa JP: Widespread and tissue specific age-related DNA\nmethylation changes in mice. Genome Res 2010, 20(3):332 340.\n22. Englander EW: Gene expression changes reveal patterns of aging in the", + "title": "2012 - Structural, functional and molecular analysis.pdf", + "version": "v0", + "chunk_order": 124, + "document_id": "0e803003-d6e5-570e-a810-1aea89d7ea63", + "extraction_id": "faae2e40-6de8-5285-8410-ac1ef5dac6ad", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "393bd8fc-14c6-5fc3-be3b-3ddf1c218531", + "score": 0.7123257264702697, + "metadata": { + "text": "microbiota present in infants, adults, and the elderly. Appl. Environ. Microbiol. 73, \n77677770 (2007).\n40. Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832R833 \n(2016).\n41. Tremaroli, V. et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce \nlong-term changes on the human gut microbiome contributing to fat mass regulation. \nCell Metab. 22, 228238 (2015).\n42. Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved", + "title": "2017 - Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.pdf", + "version": "v0", + "chunk_order": 120, + "document_id": "448d68d1-19a8-5f4c-a48b-8d33597bd03b", + "extraction_id": "b2654364-b3e8-5e26-9664-d19ca8f5605e", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "0856bafc-06ce-5716-af52-f65dc3abfafe", + "score": 0.7113192677497864, + "metadata": { + "text": "Therefore, research in the field has demonstrated that aging is a potential modi-\nfier of the composition and function of the human microbiome. Figure 9.3 shows the \nlocal composition of the microbiome in an average older adult. It can be seen that \nBacteroidetes and Firmicutes species are the most prevalent in this age.\nRecent data has shown that older people hide a microbiota that differs in the \ntype and number of microorganisms from that of younger adults [38]. Young people", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1202, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "c50b343b-3eef-548c-88cd-d5bda6605619", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "3742fdda-bdba-5c09-bf7c-732b2554c5fe", + "score": 0.7079554200172424, + "metadata": { + "text": "related malnutrition. Furthermore, it has been shownthat aging can cause bacterial overgrowth in the smallintestine [16,17] and promote changes in microbial com-\nposition in the colon [18-20]. In addition, reported age-\nrelated changes in DNA methylation of the mouseintestine [21] might play a role in the altered gene\nexpression levels observed in the duodenum and colon\nof aging mice [22]. Together these observations demon-strate that although certain aspects of the aging intestine", + "title": "2012 - Structural, functional and molecular analysis.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "0e803003-d6e5-570e-a810-1aea89d7ea63", + "extraction_id": "66edc533-58a4-5ad1-96c4-7e0c05462de5", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "bb367137-9186-53aa-8765-af837b7b4242", + "score": 0.6961455345153809, + "metadata": { + "text": "detectable.\nChanges in the gut microbiota in terms of compos-\nition and functionality during the process of aging\nhave previously been reported [19,20,51] and it hasbeen postulated that these changes might contribute\nto the development of immunosenescence and inflam-\nmaging [18,52]. To establish whether the enhanced\nexpression of genes playing a role in the immune sys-\ntem are due to modifications in the microbiota wemeasured the total number of all bacteria and of the", + "title": "2012 - Structural, functional and molecular analysis.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "0e803003-d6e5-570e-a810-1aea89d7ea63", + "extraction_id": "d9ef944b-b9a5-5b45-aaa6-c48f6fe54893", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "a6a78000-8744-5f89-bcbb-d26781ece651", + "score": 0.6960152387619019, + "metadata": { + "text": "37. Li H, Qi Y , Jasper H.Preventing age-related decline of gut compartmentalization limits micro-\nbiota Dysbiosis and extends lifespan. Cell Host Microbe. 2016;19(2):24053.\n 38. Mihajlovski A, Dor J, Levenez F, Alric M, Brugre J.Molecular evaluation of the human gut \nmethanogenic archaeal microbiota reveals an age-associated increase of the diversity. Environ \nMicrobiol Rep. 2010;2(2):27280.\n 39. Quercia S, Candela M, Giuliani C, Turroni S, Luiselli D, Rampelli S, etal. From lifetime to", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1276, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "307ac6d0-46d2-50e8-a618-d640136d4131", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "39564137-871b-5464-b364-ba63cbf9cc31", + "score": 0.6929019689559937, + "metadata": { + "text": "[26], but at advanced ages, dramatic changes in its composition are associated with \nvarious diseases and frailty [27, 28].\nRegarding pathological processes, it is known that cancer, obesity, diabetes, and \ninflammatory bowel disease (IBD) are associated with specific microbial alterations \n[29, 30]. In older ages, a burden of intrinsic and extrinsic factors affects the compo-\nsition of the microbiome and plays a determining role in every tract and tissue. Such \nmentioned factors can be seen in Fig.9.2.", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1197, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "a0bb2ab8-44b4-5409-814c-22005b259479", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "7a775400-f8f2-5758-af40-b461adc83aa3", + "score": 0.6868119239807129, + "metadata": { + "text": "Osawa R. Age-related changes in gut microbiota composition from\nnewborn to centenarian: a cross-sectional study. BMC Microbiol. 2016;16:90.\n14. Dugue PA, Bassett JK, Joo JE, Jung CH, Ming Wong E, Moreno-Betancur M,\nSchmidt D, Makalic E, Li S, Severi G, et al. DNA methylation-based biological\naging and cancer risk and survival: pooled analysis of seven prospective\nstudies. Int J Cancer. 2018;142(8):1611 9.\n15. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA", + "title": "2020 - Blood-based epigenetic estimators.pdf", + "version": "v0", + "chunk_order": 92, + "document_id": "2673299f-21e5-5746-9c33-84b99b373355", + "extraction_id": "062e4ac3-ef28-5bfa-be8c-770757083cfb", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + }, + { + "id": "35f973f6-2ca0-5d89-98b2-8e28a67323c5", + "score": 0.6831619739532471, + "metadata": { + "text": "survival advantage that is age- and site-specific: Results from a large multi-site study. Aging Cell \n18, e12905 (2019). [PubMed: 30801953] \n51. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep. 1, 134 (2011). [PubMed: \n22355651] \n52. Morrison KE, Jaarevi E, Howard CD & Bale TL Its the fiber, not the fat: significant effects of \ndietary challenge on the gut microbiome. Microbiome 8, 15 (2020). [PubMed: 32046785]", + "title": "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "version": "v0", + "chunk_order": 148, + "document_id": "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "extraction_id": "bca61863-81b3-5ef7-850d-10cc9577a9e1", + "associatedQuery": "How do changes in the gut microbiome composition correlate with aging and longevity?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIHY3hgOmiQgttq4BdrpX79X5LkzF", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Caloric restriction extends lifespan across various species through several key molecular mechanisms:\n\n1. **Sirtuin Activation**: Caloric restriction may exert some of its effects through the sirtuin family of genes, particularly SIR2, which is known to prolong lifespan in organisms like yeast, worms, and flies [3], [4]. Sirtuins are involved in chromatin regulation and promoting DNA stability, which are crucial for maintaining cellular health and longevity [4].\n\n2. **Insulin-like Signaling Pathways**: In mammals, caloric restriction is thought to modulate aging through the insulin-like signaling pathways. This mechanism is also observed in organisms like C. elegans and Drosophila, where it plays a role in regulating lifespan [6].\n\n3. **Oxidative Stress Reduction**: Caloric restriction is associated with reduced oxidative damage, which is a significant factor in aging. This reduction in oxidative stress is a common mechanism observed across different species [9].\n\n4. **AMPK Activation**: In mammals, caloric restriction has been linked to the activation of AMP-activated protein kinase (AMPK), which plays a role in energy homeostasis and has protective effects on the aged myocardium [10].\n\nThese mechanisms highlight the complex interplay of genetic and metabolic pathways through which caloric restriction can extend lifespan across diverse species.", + "role": "assistant" + } + } + ], + "created": 1728920723, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 270, + "prompt_tokens": 1293, + "total_tokens": 1563 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b516b1a9-d0f2-5d1e-9015-4799c902770b", + "score": 0.7854373689126187, + "metadata": { + "text": "Metabolism\nStudies show that calorie restriction is the most consistent means to prolong life \nexpectancy and health across several experimental models [55], ranging from yeasts \nto primates. It not only increases life expectancy, but it also delays the onset of many \nfeatures and hallmarks of ageing, including age-related diseases. Transcriptional \nprofiles are currently being applied and investigated. One of them is a caloric restric-", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1732, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "5d1905a8-536e-5efd-a92b-21053093d956", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "6870f741-be38-5d34-aafd-25da39e1ff68", + "score": 0.757936381040108, + "metadata": { + "text": "Keywords: caloric restriction; hepatic expression\nprofiling; lifespan prolongation; metabolic signaling;microarray analysis; nutrition response.\nIntroduction", + "title": "2004 - Linking nutrition to genomics.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "99891ef7-0589-5c41-a61f-1ab1fe1c8939", + "extraction_id": "b193c536-dc9e-5ea6-9a01-064243a6cbf3", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "c5b37b9a-1ffa-516b-9681-22fecc5aee5b", + "score": 0.7539928330781241, + "metadata": { + "text": "(154, 155).\nCaloric restriction has been shown to sig-\nnicantly increase life span and promote resis-tance to a broad range of age-related pathol-ogy in worms, ies, and mice. Some of theeffects of caloric restriction may be mediatedthrough the sirtuin family of genes, as exem-plied by SIR2, which prolongs life span in", + "title": "2008 - The Aging Brain.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "874f5d02-35c9-5233-8ded-6e06c7570ca9", + "extraction_id": "f63ffca5-2418-5683-9958-558c46b48def", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "e01c4c58-342d-5369-89e6-98344af55000", + "score": 0.7516640052695279, + "metadata": { + "text": "Calorie restriction, a dietary regimen that extends \nthe lifespan of numerous organisms, also delays the \nmajority of age-related gene-expression changes in \nmice and, to a certain extent, in flies45,50. It is currently \nunclear whether the effect of calorie restriction on gene \nexpression underlies its beneficial effect on lifespan or is merely a consequence thereof. Findings in yeast suggest \nthat there may be a causal link: Sir2 not only facilitates \nheterochromatin and promotes DNA stability, but is", + "title": "2007 - The role of nuclear architecture.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "578e2f7d-ddd4-56c8-a5b0-670969f8ff1e", + "extraction_id": "9655c555-838e-5cdf-85cf-13736c3cf028", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "b990eb0a-709a-500c-836e-83e202e0d6a6", + "score": 0.7479555829750179, + "metadata": { + "text": "life-span extension by calorie restriction in Saccharomyces cerevisiae. Science\n289:21262128.\nMair W, Goymer P, Pletcher SD, and Partridge L (2003) Demography of dietary\nrestriction and death in Drosophila. Science 301:17311733.\nMasoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev\n126:913922.\nMathers JC (2006) Nutritional modulation of ageing: genomic and epigenetic ap-\nproaches. Mech Ageing Dev 127:584589.\nMeric-Bernstam F and Gonzalez-Angulo AM (2009) Targeting the mTOR signaling", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 214, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "5745c701-a549-51c3-adcc-b19c47436740", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "ffe5fc40-f6d4-5066-9e07-424f7b8e3dc9", + "score": 0.7476480229988108, + "metadata": { + "text": "that caloric restriction also regulates mammalian aging, perhaps via the modulationof insulin-like signaling pathways. The nervous system has been implicated as a keytissue where insulin-like signaling and free radical protective pathways regulate lifespan inC. elegans andDrosophila . Genes that determine the life span could act in", + "title": "2001 - The genetics of aging.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "aa9a9193-b6f3-5ef8-aefd-e01ec44abb46", + "extraction_id": "713d3122-d856-5dbc-a3bf-d8cd836830cb", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "2b081115-d36e-57ec-aedc-2fd9691bc5e9", + "score": 0.7473168595825693, + "metadata": { + "text": "extension by dietary restriction. Annu Rev Biochem 2008,\n77:727-54.\n8. Harper JM, Leathers CW, Austad SN: Does caloric restriction\nextend life iin wild mice? Aging Cell 2006, 5:441-9.\n9. Forster MJ, Morris P, Sohal RS: Genotype and age influence the\neffect of caloric intake on mortality in mice. FASEB J 2003,\n17:690-2.\n10. Spindler SR, Mote PL: Screening candidate longevity therapeu-\ntics using gene-e xpression arrays. Gerontology 2007, 53:306-21.", + "title": "2009 - Genes and gene expression modules associated with caloric.pdf", + "version": "v0", + "chunk_order": 276, + "document_id": "893ba204-2e69-563f-9046-7246ca61494f", + "extraction_id": "0b45ae60-562c-5e48-a1c1-9eb29614a63c", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "03196bec-4ae2-5408-b90c-12dcb38e5831", + "score": 0.742712237158056, + "metadata": { + "text": "Corton JC, Apte U, Anderson SP, Limaye P, Yoon L. Mimetics of caloric restriction include agonists\nof lipid-activated nuclear receptors. J Biol Chem 2004;279:4620446212. [PubMed: 15302862]\nFerguson M, Sohal BH, Forster MJ, Sohal RS. Effect of long-term caloric restriction on oxygen\nconsumption and body temperature in two different strains of mice. Mech Ageing Dev\n2007;128:539545. [PubMed: 17822741]\nForster MJ, Morris P, Sohal RS. Genotype and age influence the effect of caloric intake on mortality in", + "title": "2010 - Genetic Dissection of Dietary Restriction in Mice Supports the Metabolic Efficiency Model of Life Extension.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "92419d8a-27ed-5142-8a87-189c1ba5459b", + "extraction_id": "da7abebd-f7c0-5b9c-b0f2-e29871326855", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "2cf68c41-aa60-5dca-8aa1-04bc0d7a4db3", + "score": 0.7404902798083511, + "metadata": { + "text": "A key question still unresolved is to what extent the\nmechanisms of aging are conserved between species with\nvastly different lifespans. Some studies suggest that similar\nmechanisms are involved in aging in many species. Forexample, caloric restriction extends lifespan in yeast, worms,ies, mice, and primates (Weindruch 2003). Additionally,signaling through the insulin-like growth factor pathway,chromatin regulation by sir2,and oxidative damage have each", + "title": "2004 - A Transcriptional Profile of Aging.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "4ab656a7-9656-526b-94e1-422875409b44", + "extraction_id": "b382fe8a-0267-5515-ac4b-07be55420040", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + }, + { + "id": "51a448cf-6015-53f7-a949-f247b71efcef", + "score": 0.7371145706508929, + "metadata": { + "text": "10.1111/acel.12103\n 241. Edwards AG, Donato AJ, Lesniewski LA, Gioscia RA, Seals DR, Moore \nRL. Life-long caloric restriction elicits pronounced protection of the \naged myocardium: a role for AMPK. Mech Ageing Dev. 2010;131:739\n742. doi: 10.1016/j.mad.2010.09.007\n 242. Colman RJ, Beasley TM, Kemnitz JW, Johnson SC, Weindruch \nR, Anderson RM. Caloric restriction reduces age-related and all-\ncause mortality in rhesus monkeys. Nat Commun. 2014;5:3557. doi: \n10.1038/ncomms4557", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 303, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "fddca610-97a6-5f2c-88b4-dc6e96c60cf3", + "associatedQuery": "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIHY9RBdJPzHPCH0uE5dG6bbj0z6D", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Oxidative stress contributes to cellular aging through the accumulation of oxidative damage in various macromolecules, which leads to a decline in cellular function. This process occurs due to an imbalance between prooxidants and antioxidants, resulting in a steady-state accumulation of oxidative damage that increases with age [1]. The oxidative stress theory of aging posits that damage caused by reactive oxygen species (ROS) plays a critical role in determining lifespan, as it leads to the deterioration of biochemical and physiological processes [4]. \n\nOxidative damage affects all cellular macromolecules, including lipids, proteins, and DNA, and this damage increases with age [3]. The accumulation of such damage is a key hallmark of aging physiology [5]. Specifically, oxidative damage to mitochondrial DNA (mtDNA) and the generation of ROS from the mitochondrial electron transport chain are significant contributors to this process [6]. \n\nOverall, the accumulation of oxidative damage is causally linked to aging and death, as it impairs cellular processes and bioenergetics, leading to the progressive loss of functional efficiency in cells [2], [8].", + "role": "assistant" + } + } + ], + "created": 1728920729, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_e5e4913e83", + "usage": { + "completion_tokens": 220, + "prompt_tokens": 1147, + "total_tokens": 1367 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b39d86ef-3c6a-561f-b8eb-f90ac124c12c", + "score": 0.7463477405102421, + "metadata": { + "text": "under normal physiological conditions because of an imbal-ance between prooxidants and antioxidants. The imbalanceleads to a steady-state accumulation of oxidative damage in avariety of macromolecules t hat increases during aging,\nresulting in a progressive loss in the functional efficiency ofvarious cellular processes. In a recent review, Beckman andAmes made a useful addition to this debate by dividing the", + "title": "2007 - Trends in oxidative aging theories.pdf", + "version": "v0", + "chunk_order": 22, + "document_id": "0d752c1a-706a-5b9e-88ef-ba7c51735c3c", + "extraction_id": "9994d4e6-e53d-5381-af9c-e811afe7a802", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "091ca29b-5c85-5d0d-8fbb-e829bb71bd0c", + "score": 0.7439146194508912, + "metadata": { + "text": "tributing to impaired bioenergetics in aged cells include oxida-tion/nitration of mitochondrial proteins, destabilization of the macromolecular organization of electron transport chain com-plexes, and impaired mitophagy (a mitochondria-specific form of autophagy). The combination of increased mitochondrial \nFigure 2. Proposed scheme for mechanisms and pathological consequences of age-related oxidative stress in vascular endothelial cells. The", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "6dcd5550-7f8d-5668-bb82-b6040cbf1e61", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "69365543-2760-5376-8e90-9a922a9759a7", + "score": 0.7431315932791489, + "metadata": { + "text": "over the years to become the oxidative stress theory of aging, but the principle is the same, inthat the accumulation of oxidative damage drives aging. In support of this theory, a large body\nof literature indicates that oxidative damage to all cellular macromolecules increases with age.\nFurthermore, overexpression of antioxidant enzymes that detoxify ROS, such as copper- andzinc-containing superoxide dismutase (SOD), manganese-containing SOD, or catalase, increase", + "title": "2018 - Nuclear Genomic Instability.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "54d28a91-8db6-56b1-baaa-b67274c93a36", + "extraction_id": "b934a2a9-a672-5d65-9d0d-bbc36652a148", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "9713b3c5-cd67-57d1-8c17-b3a4db7f911f", + "score": 0.7227561804346824, + "metadata": { + "text": "predicted from the oxidative stress theory of aging. Thistheory,whichisbasedonthetenetthatdamagecausedbyROSplays a critical role in determining life span, has been one ofthe most popular theories to explain the deterioration in bio-chemical and physiological processes that occur during theaging process. A large number of studies have producedcorrelative data in support of this theory, e.g., an increase inoxidativedamagetolipid,protein,andDNAwithagehasbeendemonstrated in a variety of tissues and organisms", + "title": "2003 - Life-long reduction in MnSOD activity results.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "0cef9dec-dbbe-5b5d-bb43-1a21a601fde2", + "extraction_id": "f0a1875a-9969-598b-a670-e6f61bf11898", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "4bab1bd2-05a4-5c8e-897d-e456be8c8998", + "score": 0.7122546434402517, + "metadata": { + "text": "during\tthe\taging\tprocess\t(Yi,\tChang,\t&\tShong,\t2018).\tOxidative\tdam -\nage to cellular macromolecules, or stress arising from mitochondrial DNA\t(mtDNA)\tmutation\tand\tincreased\treactive\toxygen\tspecies\t\n(ROS),\tis\ta\tkey\thallmark\tof\taging\tphysiology\t(Yi\tet\tal.,\t2018).\tAlthough", + "title": "2020 - Growth differentiation factor 15 protects against the aging\u2010mediated systemic inflammatory response in humans and mice.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "0ceff9cf-2b2b-5fe8-b844-f3f8ee7704ad", + "extraction_id": "cebd8a1c-01ea-5c43-a2f1-96ea3c304259", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "d99e64c1-2fe1-50c5-8a75-a2390ed0eac0", + "score": 0.7102594721007977, + "metadata": { + "text": "radical theory of aging, which argues that oxidative damageplays a key role in senescence. Among the numerousmechanisms known to generate oxidants, leakage of super-oxide anion and hydrogen peroxide from the mitochondrialelectron transport chain are the chief candidates. Increased\ndamage to mtDNA could exacerbate this leakage of reactive\noxygen species (ROS) (4).\nIt is not known how mtDNA deletions accumulate during", + "title": "2002 - Human mitochondrial DNA with large deletions.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "35de1e32-95eb-5b1d-acf9-2c37ea1cc3c4", + "extraction_id": "14f137b3-20cf-5b34-a3dd-4b550a3dec92", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "0f1d7692-a2c0-5def-9545-c2c16019536e", + "score": 0.7093930417661058, + "metadata": { + "text": "most plausible explanation for aging. But, as we have discussed, not all types of damage contribute equally to aging. From this point of view, it seems that ROS generated by complex I (at sulfur iron clusters or flavin sites) may damage specific targets that can alter homeosta -\nsis in a significant enough way to influ -\nence aging. The most obvious target for this damage is mtDNA. The generation of ROS specifically by complex I corre -\nlates with levels of oxidative damage in mtDNA.", + "title": "2011 - Mitochondrial complex I.pdf", + "version": "v0", + "chunk_order": 37, + "document_id": "6943c112-611d-5108-9d0f-d52c1138871b", + "extraction_id": "c195a6a2-d6a9-53f3-a0dd-abe76ae29588", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "fec5b83b-cd2c-51ea-83c9-45efdcbff83d", + "score": 0.7084904486840709, + "metadata": { + "text": "increase lifespan also confer resistance to oxidative stress (1).This finding supports the free-radical hypothesis of aging, whichsuggests that reactive oxygen species that accumulate withincreasing age cause oxidative damage to macromolecules (in-cluding nucleic acids, proteins, and lipids) and are causally linkedto aging and death (8, 9). Free radicals have been found toregulate the expression of a number of genes that includeantioxidant defense genes involved in repairing oxidative dam-age, as well as", + "title": "2000 - Genome-wide study of aging and oxidative stress.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "3fc2266c-d677-54f9-b3a2-5129eedf214a", + "extraction_id": "ac5d00c0-f445-5c6a-b248-12c82c985d9a", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "cbfc2dc4-99ae-5177-955f-4bc243689419", + "score": 0.7079625899814028, + "metadata": { + "text": "Molecular Biomarkers forOxidative Stress\nThere are many theories that try to explain the nature of aging; however, none of \nthem can explain every aspect of the biology of aging. One of the most accepted and \nstudied is the one proposed by Denham Harman in 1956. This theory proposed that \nduring lifespan organisms accumulate oxidative damage in their biomolecules. \nOxidative damage is generated by reactive oxygen species (ROS), which are the", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 811, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "7f1594a3-120c-5982-aa4d-babd6ab70265", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + }, + { + "id": "6d58996a-1250-5eaa-bc6f-bd1057ccca88", + "score": 0.7078255589112319, + "metadata": { + "text": "production by mitochondria and increased 8-oxo-dG con-tent in the mtDNA are frequently detected in aged tissues\n[40,4750], suggesting that progressive accumulation of\noxidative DNA damage is a contributory factor to the agingprocess. Consistently, many studies have found that increasedoxidative damage in cells is associated with aging [ 5153].\nFurthermore, genetic studies in worm, y, and mouse havelinked enhanced stress resistance or reduced free radical", + "title": "2012 - Oxidative Stress, Mitochondrial Dysfunction, and Aging.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "24277eba-69dd-5e12-9aa4-bbb6f0a88f52", + "extraction_id": "32c4c0b2-d44c-5121-8975-196040fb2a1d", + "associatedQuery": "How do oxidative stress and the subsequent accumulation of damaged macromolecules contribute to cellular aging?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIHYGBcI0VJ8rQxINM8Z5Fqy6gz6y", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Extracellular matrix (ECM) remodeling and tissue stiffness are significant factors in the aging process. As we age, several changes occur in the ECM that contribute to increased tissue stiffness. These changes include decreased elastin synthesis, elastin degradation and fragmentation, and alterations in the cross-linking of ECM components, such as increased presence of advanced glycation end products (AGEs) [1]. AGEs can interfere with collagenolysis by forming cross-links that confer resistance to enzymatic degradation, thereby contributing to increased arterial stiffness [2].\n\nAdditionally, the activity of transforming growth factor-beta (TGF-\u03b2) increases with age, stimulating the synthesis of interstitial collagen by vascular smooth muscle cells (VSMCs), which further augments arterial stiffness [2]. The renin-angiotensin-aldosterone system (RAAS) also plays a role in this process by augmenting collagen synthesis and promoting elastolysis [2].\n\nThe ECM is crucial for providing mechanical scaffolding and mediating biomechanical and biochemical signals necessary for tissue homeostasis and cell differentiation [4]. However, with aging, ECM stiffness increases, affecting various organs, including the larynx [6]. This increased stiffness is associated with a decline in tissue health, as seen with the accumulation of damage in long-lived proteins like collagens, which become resistant to proteolysis and affect their turnover [8].\n\nOverall, these changes in ECM remodeling and tissue stiffness contribute to the aging process by affecting vascular and tissue elasticity, leading to conditions such as arterial stiffening and vascular remodeling [1], [3], [9].", + "role": "assistant" + } + } + ], + "created": 1728920736, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 320, + "prompt_tokens": 1293, + "total_tokens": 1613 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9f768c0d-8518-5ac9-9d66-9ffdba704a84", + "score": 0.7314896045466908, + "metadata": { + "text": "208 Additional features that contribute to increased ar -\nterial stiffness include decreased elastin synthesis, elastin degradation and fragmentation, elastin calcification, al-terations in cross-linking of extracellular matrix compo-nents (eg, by increased presence of advanced glycation end products).\n208,210,211\nThe pathophysiological consequences of age-related \nECM remodeling and arterial stiffening have been the sub-ject of a recent comprehensive review by AlGhatrif and Lakatta.", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 139, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "e7f8f5f2-9102-56bf-b579-43ad3c8d6b84", + "score": 0.7306935408918128, + "metadata": { + "text": "collagen. AGE-mediated cross-links can confer resis-tance to enzymatic degradation, and thus interferewith collagenolysis (56). In addition, increased ac-\ntivity of TGF-\nbwith aging stimulates the synthesis of\ninterstitial collagen by vascular smooth muscle cells(VSMCs), and thereby augments arterial stiffness (57).\nLikewise, increased activity of the RAAS may\naugment collagen synthesis and heighten elastolysis\n(58).\nEndothelial dysfunction and arterial stiffness are", + "title": "2017 - The Aging Cardiovascular System.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "d3ff8471-986b-5fa0-b9c4-96eaaa8fce7c", + "extraction_id": "d60f1e7d-cde2-5c66-8863-507065ed5c7f", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "b7cd7044-b2fe-5dd2-b7b4-6388b9f4765d", + "score": 0.708830931519543, + "metadata": { + "text": "that many of these age-related ECM alterations are governed by circulating factors and factors produced in the vascular wall, including the extended renin-angiotensin-aldosterone system (see above) and an age-related decline in circulating IGF-1.\n209\nCollagen synthesis is also dysregulated with age in the \nvascular wall likely because of the effects of increased para-crine action of TGF- (transforming growth factor- ),\n123 \nwhich contributes to vascular fibrosis and arterial stiffen-ing.", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 138, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "ab8d8d0e-f91a-538a-bd84-beafa1fe8ce8", + "score": 0.7016867190096788, + "metadata": { + "text": "Ungvari et al Mechanisms of Vascular Aging 859\nRole of Extracellular Matrix Remodeling in \nVascular Aging\nThe extracellular matrix (ECM) is an important contribu-\ntor to health and longevity. This noncellular compartment, ubiquitous to all tissues and organs does not only provide es-sential mechanical scaffolding but mediates highly dynamic \nbiomechanical and biochemical signals required for tissue \nhomeostasis, morphogenesis, and cell differentiation. Studies", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 133, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "4b0673e0-fb5e-5212-ba68-417de0e867b7", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "e7121d85-7538-5cdd-8b2d-6d3d536439b9", + "score": 0.688574077657528, + "metadata": { + "text": "1996;25(3):20915.\n 79. Bonnans C, Chou J, Werb Z. Remodelling the extracellular matrix in \ndevelopment and disease. Nat Rev Mol Cell Biol. 2014;15(12):786801.\n 80. Swift J, Ivanovska IL, Buxboim A, Harada T, Dingal PCDP , Pinter J, et al. \nNuclear Lamin-A scales with tissue stiffness and enhances matrix-\ndirected differentiation. Science. 2013;341(6149):1240104.\n 81. Vogel C, Marcotte EM. Insights into the regulation of protein abun-\ndance from proteomic and transcriptomic analyses. Nat Rev Genet.", + "title": "2022 - Proteomic analysis reveals that aging rabbit.pdf", + "version": "v0", + "chunk_order": 189, + "document_id": "f6c524a5-acf9-5a07-8bbf-31091443cab3", + "extraction_id": "a099ce3c-cdff-5971-b3d5-f31e03aace96", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "cf5f0034-c806-52d6-bd26-137fb9d8a418", + "score": 0.686365228078677, + "metadata": { + "text": "result in extracellular matrix stiffness in aging larynx and \nother organs [59, 79]. Finally, Lamin A was upregulated \nby dehydration, by a smaller magnitude, especially when \nobserving the mean difference within the young groups. \nPrevious data has identified that Lamin proteins A and \nC are important for imparting the nucleus with its stiff -\nness, and their expression has been reported to scale with", + "title": "2022 - Proteomic analysis reveals that aging rabbit.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "f6c524a5-acf9-5a07-8bbf-31091443cab3", + "extraction_id": "c738a4b2-0aea-5157-bed4-fecdac9863b9", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "58e94400-b0f0-5757-b964-83a6b2b6f98f", + "score": 0.6824693287154835, + "metadata": { + "text": "aging. Annu Rev Biomed Eng. 2015;17:113141. doi: 10.1146/ \nannurev-bioeng-071114-040829\n 208. Jacob MP. Extracellular matrix remodeling and matrix metalloprotein-\nases in the vascular wall during aging and in pathological conditions. \nBiomed Pharmacother. 2003;57:195202.\n 209. Tarantini S, Valcarcel-Ares NM, Yabluchanskiy A, Springo Z, Fulop \nGA, Ashpole N, Gautam T, Giles CB, Wren JD, Sonntag WE, Csiszar A, \nUngvari Z. Insulin-like growth factor 1 deficiency exacerbates hyperten-", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 284, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "e91c9a2a-a797-59d5-8565-91b45b0113a1", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "4dfd7818-9111-5bf9-bbcf-e917b1c9b9fc", + "score": 0.6723205452251925, + "metadata": { + "text": "able human diseases such as osteoporosis and musculo-\nskeletal diseases [53]. Collagens are long-lived proteins\nknown to accumulate damage during aging, leading to a\ndecline in tissue health [54]. Also, type I collagens be-\ncome resistant to proteolysis upon age [55, 56], affecting\ntheir turnover. Interestingly, mice expressing cleavage-\nresistant type I collagen go through an accelerated aging\nprocess [57]. Thus, cellular aging can be affected by the\nstate of the extracellular matrix in mammals.", + "title": "2016 - The genome-wide role of HSF-1.pdf", + "version": "v0", + "chunk_order": 122, + "document_id": "e3c48474-21da-51d2-b378-200138fda0d3", + "extraction_id": "b2c1c466-d4b3-5c01-a8a4-2f49e9f246a2", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "d5cd4d54-b051-5638-ba76-39c385f3e423", + "score": 0.6651890875230129, + "metadata": { + "text": "the characteristics of endothelial dysfunction and pheno-\ntypic transition of smooth muscle cells, resulting in in-\ncreased vascular stiffness and increased thickness of\nvascular walls. It has been reported that the age-\nassociated phenotypic transition of VSMCs is a crucial\ncontributor to vascular remodeling [ 17,25]. However,\nthe mechanism that drives phenotypic transition ofVSMCs with aging remains unclarified. In this study,\nusing RNAs extracted from the in vitro cultured VSMCs,", + "title": "2019 - Downregulation of miR-542-3p promotes.pdf", + "version": "v0", + "chunk_order": 45, + "document_id": "527e562f-f7c3-5a01-b70b-5737d63e2457", + "extraction_id": "32322971-f8f4-53d3-8104-ac44cf03ebef", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + }, + { + "id": "479ae037-3dd5-57f7-9bf7-78a3a45ac47f", + "score": 0.6571096377703443, + "metadata": { + "text": "downregulation with aging of genes involved in the synthesisof the ECM and in particular of different forms of collagen(Table 2). In addition, aging males but not females showed adecrease in collagen type III. Interestingly, collagen type IIIdecreases the size of collagen bundles and thereby increasesvascular elasticity (11). Therefore, a decreased expression ofcollagen type III can participate in the increased stiffness thatcharacterizes the aging aorta (23). An interesting observationfrom our study that", + "title": "2007 - Sex-specific regulation of gene expression in the aging monkey aorta.pdf", + "version": "v0", + "chunk_order": 100, + "document_id": "6c2a7135-31ed-57e3-89fa-42856979ea1a", + "extraction_id": "1d889462-37d6-5cb5-b0df-8ae9c50560b7", + "associatedQuery": "How are extracellular matrix remodeling and tissue stiffness implicated in the aging process?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIHYO1XLJbUnaqsOWyFh9a97rwIzB", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent developments in autophagy research have significantly enhanced our understanding of its role in aging and longevity. Here are some key contributions:\n\n1. **Energy Metabolism and Lifespan Extension**: Research has shown that the depletion of acetyl-coenzyme A, an energy metabolite, can stimulate autophagy and prolong lifespan. This suggests that autophagy is closely linked to energy metabolism and can be a mechanism for extending lifespan [1].\n\n2. **Autophagy as an Anti-Aging Mechanism**: Autophagy is increasingly recognized as an emerging anti-aging mechanism. It plays a crucial role in maintaining cellular homeostasis by degrading and recycling damaged cellular components, which is essential for longevity [1].\n\n3. **Genetic Regulation and Dietary Restriction**: Studies have identified autophagy genes as important for lifespan extension, particularly in the context of dietary restriction. This indicates that genetic regulation of autophagy is a key factor in promoting longevity [2].\n\n4. **Pharmacological Activation**: There is evidence that pharmacological activation of autophagy can increase lifespan in animal models, including mice. This highlights the potential for therapeutic interventions targeting autophagy to promote healthy aging [3].\n\n5. **Impaired Autophagy and Cellular Aging**: Impaired autophagy is considered a principal determinant of cellular aging. The decline in autophagy with age is linked to various age-related diseases, emphasizing the importance of maintaining autophagic activity for longevity [4].\n\n6. **Spermidine and Longevity**: The induction of autophagy by compounds like spermidine has been shown to promote longevity, further supporting the role of autophagy in extending lifespan [7].\n\nThese findings collectively underscore the critical role of autophagy in aging and longevity, suggesting that enhancing autophagic processes could be a viable strategy for promoting healthy aging and extending lifespan.", + "role": "assistant" + } + } + ], + "created": 1728920744, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 381, + "prompt_tokens": 1775, + "total_tokens": 2156 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "603183d9-d22c-5008-bfa5-147ee5df4198", + "score": 0.7353694438934378, + "metadata": { + "text": "D. Carmona-Gutierrez, C. Ruckenstuhl, J. Ring, W. Reichelt, K. Schimmel, T. Leeb,C. Moser, S. Schatz, L.-P. Kamolz, C. Magnes, F. Sinner, S. Sedej, K.-U. Frhlich,G. Juhasz, T. R. Pieber, J. Dengjel, S. J. Sigrist, G. Kroemer, F. Madeo, Nucleocytosolic de-pletion of the energy metabolite acetyl-coenzyme a stimulates autophagy and prolongs\nlifespan. Cell Metab. 19, 431 444 (2014).\n225. S. Gelino, M. Hansen, Autophagy An emerging anti-aging mechanism. J. Clin. Exp. Pathol.\n(Suppl. 4), pii: 006 (2012).", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 339, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "9b7b806c-cac3-549e-9ae9-424cc3e5f869", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "a6d18c4e-632c-52a2-b3f9-6296025e0ce7", + "score": 0.7156161835422419, + "metadata": { + "text": "[73] Vellai, T. Autophagy genes and ageing . Cell Death Differ. , 2009 , \n16(1), 94-102. \n[74] Kaeberlein, M.; Kapahi, P. Cell signaling. Aging is RSKy business . \nScience , 2009 , 326(5949), 55-6. \n[75] Hansen, M.; Chandra, A.; Mitic, L.L.; Onken, B.; Driscoll, M.; \nKenyon, C. A role for autophagy genes in the extension of lifespan \nby dietary restriction in C. elegans. PLoS Genet. , 2008 . \n[76] Hansen, M.; Taubert, S.; Crawford, D.; Libina, N.; Lee, S.J.;", + "title": "2012 - Genome-Wide RNAi Longevity Screens in Caenorhabditis elegans.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "7589fec2-e893-5a4d-9f0c-09abb35858ab", + "extraction_id": "f160f818-03bf-5b4e-b1f4-bfbd3b0bfb99", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "d43449f1-2d90-5e0e-8ba8-8afdc306f32d", + "score": 0.7129322632631941, + "metadata": { + "text": "chinery and upstream regulators provide evidence for\na transcriptional decline in autophagy gene expression\nwith age in human monocytes. The identification of\nkey genes contributing to a decline in autophagy are\nof great interest, as pharmacologic activation of au-\ntophagy has been linked with increasing lifespan in\nanimal models, including mice [45]. Further, dysfunc-\ntional autophagy is now widely implicated in patho-\nphysiological processes of many age-related diseases", + "title": "2015 - Transcriptomic profiles of aging in purified.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "extraction_id": "a972e2fb-b73f-51bf-980a-85c9db1482be", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "ca8ae9e1-f598-56b9-952e-bb5bea62d8fe", + "score": 0.7100938346954065, + "metadata": { + "text": "invasive pathogens, and to transport these cargos to the\nlysosomes for degradation [25]. In the aging field, im-\npaired autophagy is considered one of the principal de-\nterminants of cellular aging, which is supported by\nin vitro and animal study findings that autophagy de-\nclines with age [26]. However, studies of autophagy and\nage in humans are sparse.\nOne of the most significant age-gene expression asso-\nciations we observed in monocytes from 1,264 individ-", + "title": "2015 - Transcriptomic profiles of aging in purified.pdf", + "version": "v0", + "chunk_order": 37, + "document_id": "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "extraction_id": "a972e2fb-b73f-51bf-980a-85c9db1482be", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "581ca468-d3f3-5846-9fba-7f1f860df956", + "score": 0.7099761539711873, + "metadata": { + "text": "226. F. Madeo, N. Tavernarakis, G. Kroemer, Can autophagy promote longevity? Nat. Cell Biol.\n12, 842 846 (2010).\n227. J. Fllgrabe, M. A. Lynch-Day, N. Heldring, W. Li, R. B. Struijk, Q. Ma, O. Hermanson,\nM. G. Rosenfeld, D. J. Klionsky, B. Joseph, The histone H4 lysine 16 acetyltransferase\nhMOF regulates the outcome of autophagy. Nature 500, 468 471 (2013).\n228. F. Ng, B. L. Tang, Sirtuins modulation of autophagy. J. Cell. Physiol. 228, 2262 2270 (2013).", + "title": "2016 - Epigenetics and aging.pdf", + "version": "v0", + "chunk_order": 340, + "document_id": "71b206ec-81bd-5194-8b21-ae522f8cbc2d", + "extraction_id": "9b7b806c-cac3-549e-9ae9-424cc3e5f869", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "a2effd64-3d9d-5bdf-8fc6-0cd72762763d", + "score": 0.7057594411659907, + "metadata": { + "text": "(2013) The hallmarks of aging. Cell 153(6):11941217. doi: 10.\n1016/j.cell.2013.05.039\n3. Vellai T, Takacs-Vellai K, Sass M, Klionsky DJ (2009) The\nregulation of aging: does autophagy underlie longevity? TrendsCell Biol 19(10):487494. doi: 10.1016/j.tcb.2009.07.007\n4. Kirkwood TB (2008) A systematic look at an old problem. Nature\n451(7179):644647. doi: 10.1038/451644a\n5. Koubova J, Guarente L (2003) How does calorie restriction\nwork? Genes Dev 17(3):313321. doi: 10.1101/gad.1052903", + "title": "2015 - The mechanism of ageing primary role of transposable elements.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "de558db9-dc04-5bbd-83bf-3e3368ff906b", + "extraction_id": "20245b79-fa8f-52fc-832e-1478a080d6e1", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "f82ef429-c823-5173-a93b-5c476df110f5", + "score": 0.6964636625967239, + "metadata": { + "text": "Eisenberg, T., Knauer, H., Schauer, A., Bu ttner, S., Ruckenstuhl, C., Carmona-\nGutierrez, D., Ring, J., Schroeder, S., Magnes, C., Antonacci, L., et al. (2009).Induction of autophagy by spermidine promotes longevity. Nat. Cell Biol. 11,\n13051314.\nEnns, L.C., Morton, J.F., Treuting, P.R., Emond, M.J., Wolf, N.S., Dai, D.F.,\nMcKnight, G.S., Rabinovitch, P.S., and Ladiges, W.C. (2009). Disruption of\nprotein kinase A in mice enhances healthy aging. PLoS ONE 4, e5963.", + "title": "2012 - Replicative and Chronological Aging.pdf", + "version": "v0", + "chunk_order": 168, + "document_id": "496e387e-4278-5f74-8ecc-4edc1cee7dfe", + "extraction_id": "8bc194af-6e9d-51c5-8116-6d4186a885dd", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "949f7420-bfb6-564d-8537-18c47e40bbc6", + "score": 0.6928066794391478, + "metadata": { + "text": "its essential part in the anti-aging mechanism of caloric restriction.\nAnn N Y Acad Sci. 2007;1114:69 78.\n41. Cuervo AM, Bergamini E, Brunk UT, Droge W, Ffrench M, Terman A.\nAutophagy and aging: the importance of maintaining clean cells.\nAutophagy. 2005;1:131 40.\n42. Terman A. The effect of age on formation and elimination of autophagic\nvacuoles in mouse hepatocytes. Gerontology. 1995;41 Suppl 2:319 26.\n43. Donati A, Recchia G, Cavallini G, Bergamini E. Effect of aging and anti-aging", + "title": "2015 - Transcriptomic profiles of aging in purified.pdf", + "version": "v0", + "chunk_order": 169, + "document_id": "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "extraction_id": "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "8ede28e5-ed8e-5c68-bd03-18c3c96bb31b", + "score": 0.6868521757810023, + "metadata": { + "text": "103 Experimental findings showing increased oxidative \nstress, impaired bioavailability of NO, and upregulation of in-flammatory mediators in autophagy-deficient endothelial cells \nsupport this view.\n104 Further, pharmacological interventions \nthat stimulate autophagy (eg, trehalose or spermidine treat-ment) were reported to reverse aspects of arterial aging.\n105,106 \nProteasomes degrade unneeded or damaged proteins by pro-teolysis. There is evidence that proteasome activity declines in \nadvanced aging", + "title": "2018 - Mechanisms of Vascular Aging.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "659b84b6-63dd-5bb1-80ee-7478ed3c47e3", + "extraction_id": "0d4cd402-e032-5edd-98eb-170357a3092e", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + }, + { + "id": "82060e66-87b7-5ac2-9877-fc7b26325b73", + "score": 0.6832387040115188, + "metadata": { + "text": "Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects\nenergy sensing to mitophagy. Science. 2011;331:456 61.\n38. Xiao B, Sanders MJ, Underwood E, Heath R, Mayer FV, Carmena D, et al.\nStructure of mammalian AMPK and its regulation by ADP. Nature.\n2011;472:230 3.\n39. Tang D, Kang R, Livesey KM, Cheh CW, Farkas A, Loughran P, et al.\nEndogenous HMGB1 regulates autophagy. J Cell Biol. 2010;190:881 92.\n40. Bergamini E, Cavallini G, Donati A, Gori Z. The role of autophagy in aging:", + "title": "2015 - Transcriptomic profiles of aging in purified.pdf", + "version": "v0", + "chunk_order": 168, + "document_id": "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "extraction_id": "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", + "associatedQuery": "How do recent developments in autophagy research contribute to our understanding of its role in aging and longevity?" + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIHYWWczI6kl71Lbbg4Wx4xLfOmE6", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Age-related shifts in stem cell niche composition and function have significant implications for tissue regeneration capacity. As individuals age, the stem cell niche, which is crucial for maintaining stem cell function, undergoes changes that can adversely affect the regenerative potential of stem cells. Here are some key implications based on the provided context:\n\n1. **Deterioration of the Stem Cell Niche**: The aging process leads to the deterioration of the stem cell niche, which can result in a failure to support the balance between stem cell self-renewal and differentiation. This is evident in the spermatogonial stem cell niche, which deteriorates with age, affecting its ability to maintain stem cell function [1].\n\n2. **Functional Impairments of Stem Cells**: The regenerative potential diminishes with age due to functional impairments in adult stem cells. This is linked to the phenomenon of replicative senescence, where cells lose their ability to proliferate after a certain number of divisions [3].\n\n3. **Changes in Gene Expression**: Age-related changes in gene expression have been observed in stem cells, such as mesenchymal stem cells (MSCs) and hematopoietic progenitor cells (HPCs). These changes can lead to declines in stem cell function and, consequently, a reduction in tissue regeneration capacity [6].\n\n4. **Loss of Stem Cell Pool Division Potential**: Aging is associated with a loss of stem cell pool division potential, which directly impacts the regenerative capacity of tissues. This loss can also indirectly affect adult stem and progenitor cells by altering the tissue microenvironment essential for stem cell support [8].\n\n5. **Reduction in Stem Cell Numbers**: There is evidence of a decline in the number of MSCs in the bone marrow with age, which can further hinder the ability of these cells to participate in tissue regeneration processes such as osteogenesis and chondrogenesis [10].\n\nOverall, these age-related shifts in stem cell niche composition and function contribute to a decline in the body's ability to repair and regenerate tissues, which is a hallmark of aging and is linked to various degenerative conditions [9].", + "role": "assistant" + } + } + ], + "created": 1728920752, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 426, + "prompt_tokens": 1208, + "total_tokens": 1634 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "cade861a-f60d-51fd-bfac-edce8860b395", + "score": 0.7382577061653137, + "metadata": { + "text": "into old versus young recipients (Liang et al., 2005 ). Further experiments demonstrated that the muscle stem cell niche adversely effects stem cell function as evidenced by the restoration of old stem cell regenerative potential upon expos ure to a young systemic microenvironment (Conboy et al., 2005; Conboy and Rando, 2005). It has also been reported that the spermatogoni al stem cell niche deteriorates with age, causing the failure to suppor t an appropriate balance between stem cell self-renewal and", + "title": "2008 - GENETIC REGULATION OF HEMATOPOIETIC STEM CELL AGING (3).pdf", + "version": "v0", + "chunk_order": 71, + "document_id": "7412a162-ee3b-5f09-9886-8e9172dd3ee8", + "extraction_id": "fca849bb-6e08-5200-8c66-5250e902dca3", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "7fcd630b-0f09-5947-8a28-f72d4418d8f8", + "score": 0.723791198504497, + "metadata": { + "text": "matopoietic stem cells is regulated by the stemcell niche. Exp Gerontol. 2008;43(11):974-980.\n18. Geiger H, Rudolph KL. Aging in the lympho-\nhematopoietic stem cell compartment. Trends\nImmunol. 2009;30(7):360-365.\n19. Muller-Sieburg C, Sieburg HB. Stem cell aging:\nsurvival of the laziest? Cell Cycle. 2008;7(24):\n3798-3804.\n20. Beerman I, Maloney WJ, Weissmann IL,\nRossi DJ. Stem cells and the aging hematopoieticsystem. Curr Opin Immunol. 2010;22(4):500-506.\n21. Teschendorff AE, Menon U, Gentry-Maharaj A,", + "title": "2011 - Genome-wide promoter DNA methylation dynamics of human hematopoietic.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "30081f4e-7189-5c9f-abf2-895250c0173e", + "extraction_id": "3be2a7fa-1d97-5280-ba37-cc3d311cfb75", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "8f53ce05-7527-52f2-8a25-9c3ee9a38861", + "score": 0.7134526968002319, + "metadata": { + "text": "Abstract\nThe regenerative potential diminishes with age and this has been ascribed to functional impairments of adult stem cells.\nCells in culture undergo senescence after a certain number of cell divisions whereby the cells enlarge and finally stop\nproliferation. This observation of replicative senescence has been extrapolated to somatic stem cells in vivo and might", + "title": "2009 - Aging and Replicative Senescence Have Related Effects.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "0703ba80-b7a5-5873-9ab0-5d66d57f4750", + "extraction_id": "f5b29cc7-fe8b-5230-adb1-0531fb1c3187", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "ccf7dace-b7d8-576f-bb59-c6707e5180f5", + "score": 0.6983357251682458, + "metadata": { + "text": "Because of their plasticity and accessibility these cells are also prime candidates for regenerative medicine. The \ncontribution of stem cell aging to organismal aging is un der debate and one theory is that reparative processes \ndeteriorate as a consequence of stem cell aging and/or de crease in number. Age has been linked with changes in \nosteogenic and adipogen ic potential of MSCs.\nResults: Here we report on changes in global gene expression of cultured MSCs isolated from the bone marrow of", + "title": "2010 - Age-related molecular genetic changes of murine.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "a69ce6db-4a5e-58a5-9dc5-d529768edcb1", + "extraction_id": "d39327b0-59b1-5e24-813d-099a48a8de85", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "f8e0e878-451b-519d-b6e5-e9834d5d3b77", + "score": 0.6977025270462036, + "metadata": { + "text": "suggesting that stem cells are not likely to be a factor limiting\nhematopoietic regeneration with age. However, their func-tional decits do show that HSCs are impacted by the forces\nof aging in a manner similar to that of differentiated cells\n[3134].\nIn our molecular analysis, we identied global age-related\nchanges in gene expression in murine HSCs, with a view to\nidentifying mechanisms that could be responsible for these\nage-associated declines in HSC function. Genes involved in", + "title": "2007 - Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation.pdf", + "version": "v0", + "chunk_order": 64, + "document_id": "a6fabf0c-e4a5-59f6-82c5-ebabce24fd0a", + "extraction_id": "188bdad0-f63b-5e4c-8eed-73cd01b8d66f", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "de67cf90-712a-5c28-9f6b-404d84a06d22", + "score": 0.6905411297405452, + "metadata": { + "text": "Discussion\nThe deterioration of the regenerative potential upon aging\nmight be due to functional changes in adult stem cells. To test this\nhypothesis we have investigated differential gene expression in\nprimary, human MSC and HPC derived from different agegroups. In this study, we demonstrate for the first time age-related\ngene expression changes in human MSC and HPC and that there", + "title": "2009 - Aging and Replicative Senescence Have Related Effects.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "0703ba80-b7a5-5873-9ab0-5d66d57f4750", + "extraction_id": "23921b67-8911-5086-a2e4-a909394a6df4", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "e6bb4c40-7fe8-5ff7-af36-1c2b749ed1fb", + "score": 0.6867024507135886, + "metadata": { + "text": "cells, which may explain the observed decline of stem cell function with age. Age-associated increases inDNAm target developmental genes, overlapping those associated with environmental disease risk factors\nand with disease itself, notably cancer. In particular, cancers and precursor cancer lesions exhibit aggravated", + "title": "2013 - Age-associated epigenetic drift implications.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "8513121f-71f3-5bb0-9433-feece9fd9fbc", + "extraction_id": "24500f0a-0e60-574e-9039-e9dd3b5be569", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "01740a78-e141-56f0-8f34-7c02c5602344", + "score": 0.6859941482543945, + "metadata": { + "text": "tion associated with age: loss of stem cell pool division\npotential (loss of regenerative capacity) and loss ofdierentiated somatic cell function, which directly leads\nto loss of organ function. Loss of dierentiated somatic\ncell function can additionally indirectly aect adult stem\nand progenitor cells by altering the tissue microenviron-\nment that is essential for stem cell support (the stem cellniche). In general, loss of stem cell pool division potential", + "title": "2007 - Two faces of p53 aging and tumor suppression.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "b1ef905a-c145-5270-9110-ae6954ea3d72", + "extraction_id": "270c5516-f5b2-54d3-8865-b84d8a9506c1", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "ae2ad88f-6e02-5541-b6be-966fef7712f1", + "score": 0.6849208661961051, + "metadata": { + "text": "1. Introduction\nStem cell aging is regarded as one of the contributors to several\ndegenerative conditions af icting the elderly because it underlies\nthe physiological decline in tissue maintenance and regenerative\ncapacity of many organs ( Rossi et al., 2008 ). The brain is one such\norgan that contains discrete populations of stem cells and their\nprecursors (collectively referred to as neural progenitor cells\n[NPCs]) that continue to generate new neurons throughout life", + "title": "2013 - Effects_of_age_and_strain_on_cell_prolif.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "d7e861e7-cdee-5145-9403-ef05e2d532c0", + "extraction_id": "b0fb2185-a2ee-5174-94d0-877ad2d87158", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + }, + { + "id": "1dffbbdb-f76d-581b-8384-751ce5f41e90", + "score": 0.6800036835672679, + "metadata": { + "text": "spective of tissue regeneration and repair because there isevidence that these beneficial functions may becomehandicapped with age. Age-related decline in the numberof MSCs in the bone marrows of rodents, monkeys, andhumans have been reported [26-33]. Most studies to datefocused on the effects of aging on the ability of MSCs toenter osteogenic, chondrogenic and adipogenic pro-grams. Some, but not all studies suggest that agingreduces osteogenesis and chondrogenesis while enhanc-", + "title": "2010 - Age-related molecular genetic changes of murine.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "a69ce6db-4a5e-58a5-9dc5-d529768edcb1", + "extraction_id": "d39327b0-59b1-5e24-813d-099a48a8de85", + "associatedQuery": "What are the implications of age-related shifts in stem cell niche composition and function for tissue regeneration capacity?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIHYkQV1s8mGJ0u0OlIT1WoCFkj8X", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The accumulation of cross-links and advanced glycation end-products (AGEs) significantly impacts the structural integrity and function of aging tissues in several ways:\n\n1. **Inflammation and Oxidative Stress**: AGEs accumulation leads to inflammation and oxidative stress, which can cause long-term vascular and end-organ damage [1], [4]. This is partly due to the interaction of AGEs with specific receptors such as RAGE, which perpetuates these adverse processes.\n\n2. **Vascular Changes**: AGEs contribute to vascular hypertrophy, stiffening of collagen, and reduced arterial compliance, which are associated with aging and are accelerated by hyperglycemia [2]. This stiffening of collagen and reduction in arterial compliance can lead to decreased vascular function and increased risk of vascular complications.\n\n3. **Cross-linking of Proteins**: AGEs cause cross-linking of proteins, which affects the structural integrity of tissues. For example, the cross-linking of collagen is associated with increased susceptibility to atherosclerosis, osteoporosis, decreased joint elasticity, and the formation of cataracts [10].\n\n4. **Endothelial Dysfunction**: AGEs impair endothelial function and vascular reactivity, which can lead to complications such as atherosclerosis and diabetic complications [5]. This impairment is due to the modification of lipoproteins and the release of cytokines and growth factors upon AGE interaction with receptors.\n\n5. **Pathological Changes in Tissues**: AGEs induce various pathological changes, including increased basement membrane thickening, arterial stiffness, and glomerular sclerosis [7]. These changes contribute to the decline in tissue function and structure as they age.\n\nOverall, the accumulation of AGEs and the resulting cross-links compromise the structural integrity and function of tissues, contributing to the aging process and the development of age-related diseases.", + "role": "assistant" + } + } + ], + "created": 1728920766, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 366, + "prompt_tokens": 1188, + "total_tokens": 1554 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "388d90ef-1bfc-572d-b783-af945ab9519b", + "score": 0.765750527381897, + "metadata": { + "text": "vascular and kidney diseases [47].\nAdvanced glycation end-products (AGE) are the result of nonenzymatic glyca-\ntion, which produces heterogeneous bioactive molecules, such as lipids, proteins, \nand nucleic acids [59]. The accumulation of AGEs in aged tissues leads to several \nprocesses, such as inflammation, obesity, apoptosis, and other adverse processes \nrelated to ageing [47]. These AGEs are detected by various techniques, such as", + "title": "2020 - Clinical Genetics and Genomics of Aging.pdf", + "version": "v0", + "chunk_order": 1739, + "document_id": "62b635c3-040e-512a-b016-6ef295308a1e", + "extraction_id": "a3427d8a-366e-5edc-9a9d-fa1da5d9e800", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "aad43b5f-c345-53c4-a37e-4b59e54082bb", + "score": 0.7629441618919373, + "metadata": { + "text": "and leading to vascular hypertrophy and stiffening of collagen with \nsubsequent reduction of arterial compliance. These are processes that are \nassociated with aging but seem to be accelerated by hyperglycemia. These \ncross-linked macromolecules, called advanced glycosylation end products \n(AGEs), are implicated in the pathogenesis of vascular complications. Once", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1668, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "60ec7e90-7c38-5bda-a94e-ef15369c710c", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "edfb3091-1629-53bc-9f0b-88d552862fd9", + "score": 0.7581884120815059, + "metadata": { + "text": "proposed mechanisms are the development of advanced glycosylation end \nproducts and sorbitol accumulation. \nAdvanced glycosylation end products (AGEs) comprise a \nheterogeneous group of molecules that accumulate in plasma and tissues \nwith advancing age, diabetes and renal failure. They are characterized by \nbrowning, fluorescence, cross-linking and biological response through \nspecific AGE receptors and were first described in 1912 by French chemist \nL.C. Maillard (Fig. 5).", + "title": "2004 - Diabetes Genes b.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "extraction_id": "272b3625-6f21-51f5-a83b-cfdbf4ddc841", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "3d613e0f-9ab0-575f-88cc-2b35f51f9d9d", + "score": 0.7527935729263301, + "metadata": { + "text": "the accumulation of AGEs which can further perp etuate and amplify local inflammation and 197 \noxidant stress through irreversible glycation of the various protei ns and lipids to promote long 198 \nterm vascular and end-organ damage. Thus AGEs, acting through receptors such as RAGE, 199 \ncould also contribute to hyperglycemic memo ry (18, 96, 147). These studies have begun to 200", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "cc350a5a-f474-597d-93c8-4359b9ddcc38", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "34533770-24ba-57b7-95f9-06b201c92aa5", + "score": 0.7425909638404846, + "metadata": { + "text": "AGEs are taken up by specific AGE receptors (RAGE), cytokines, growth \nfactors, and adhesion factors are released, leading to further cellular changes. \nAGEs also can impair endothelial function and vascular reactivity, such as \nin response to nitric oxide. Modification of LDL as a result of glycation may \ncontribute to foam cell formation.4 Thus, AGEs appear to be main players \nnot only in the development of diabetic complications and atherosclerosis,", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1669, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "f5f2abef-9ccd-5147-a433-489c7225017c", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "e1c2f05b-b04a-5c74-98ad-69af532d2ae9", + "score": 0.7370566443137407, + "metadata": { + "text": "geneous group of macromolecules that are formed by the nonenzymatic glycation of proteins, lipids, and nucleic acids. Overproduction of AGEs is considered the most important pathophysiological mechanism that induces diabetic complications (Semba etal. 2010). On one hand, AGEs mediate intracellular glycation of mitochondrial respiratory chain proteins and increase ROS levels, thus triggering oxidative stress (Coughlan etal. 2009) and endoplasmic reticulum stress (Piperi etal. 2012). On the", + "title": "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "version": "v0", + "chunk_order": 87, + "document_id": "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "extraction_id": "98c7d4f6-45b7-53d4-979d-5503e91b1415", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "50a3dd44-9747-5456-91e3-ebeb2b6a9248", + "score": 0.7351436614990234, + "metadata": { + "text": "Introduction\nIn individuals with diabetes, nonenzymatic\nglycation of proteins leads to the formation of\nadvanced glycation end products (AGE) and\nthis process occurs at an accelerated rate in\nchronic hyperglycaemia1, and also the levels are\nfound to be increased in complications of\ndiabetes, such as diabetic retinopathy (DR).2\nAGE induces a variety of pathological changes,\nsuch as increased basement membrane\nthickening, arterial stiffness, and glomerular\nsclerosis.3,4AGEs bind to a specic receptor", + "title": "2007 - Rage gene promoter polymorphisms and diabetic retinopathy in a clinic-based population from South India.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "de5a5a08-3a63-587c-b835-41c74b37f570", + "extraction_id": "2903bc47-30d8-5e1c-acd9-5db4908f5ee9", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "a8fe389d-7249-50d5-8c4a-2f9d62fa73f6", + "score": 0.7340881982989206, + "metadata": { + "text": "AGEs accelerate atherosclerosis through cross-linking of proteins, \nplatelet aggregation, defective vascular relaxation, and abnormal lipoprotein \nmetabolism. 30 \nAGEs have a vital role in pathogenesis of diabetic nephropathy and \nprogression of renal failure. Renal failure, in turn, results in decreased \nexcretion and increased generation of AGEs (Figure 6). \n629", + "title": "2004 - Diabetes Genes b.pdf", + "version": "v0", + "chunk_order": 141, + "document_id": "da2f2624-e3e6-5e2d-b406-941db2fe7671", + "extraction_id": "26eeaac7-6846-51ee-a69b-51a75402a1bf", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "94f15877-0b3a-5dee-8d1f-d0a034f14220", + "score": 0.7306285500526428, + "metadata": { + "text": "vessels show enhanced subintimal protein and lipoprotein deposition; \nincreased vascular permeability, e.g. to albumin; \ninactivation of nitric oxide; \nactivation of endothelial receptors, leading to vasoconstriction and \nthrombosis; \naltered proteoglycan milieu; \naltered basement membrane cellular structure; \nproliferation of matrix. \nStrategies directed at the prevention of formation or the disruption of \nAGE cross-links may be promising. \nREFERENCES:", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1715, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "f6de03c3-cbbd-5963-ab23-e934f6ff1d56", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + }, + { + "id": "0b6eb47a-1fd1-58d2-81db-3a17b967f2d6", + "score": 0.7291850286236435, + "metadata": { + "text": "proteins and nucleic acids, leads to modification and then decline in structure \nand function of these molecules, as the cross-links accumulate both \nextracellularly and intracellularly over time. A prime example would be the \ncrosslinking of collagen, which is thought to lead to typical phenomena \nobserved in aging, such as increased susceptibility to atherosclerosis, \nosteoporosis, decreased joint elasticity, the formation of cataracts, and", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 1664, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "60ec7e90-7c38-5bda-a94e-ef15369c710c", + "associatedQuery": "How do cross-links and advanced glycation end-products (AGEs) accumulation affect the structural integrity and function of aging tissues?" + } + } + ], + "kg_search_results": null + } + } + } +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/gpt4o/de_diabetes_responses.json b/gnqa/paper2_eval/data/responses/gpt4o/de_diabetes_responses.json new file mode 100644 index 0000000..61ee90f --- /dev/null +++ b/gnqa/paper2_eval/data/responses/gpt4o/de_diabetes_responses.json @@ -0,0 +1,3222 @@ +{ + "1": { + "results": { + "completion": { + "id": "chatcmpl-AIHIPLyXp5Go74Qys43ojpQ0czAzb", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent advancements in multi-omics approaches, including proteomics and metabolomics, have significantly enhanced our understanding of the pathogenesis of Type 2 diabetes (T2D). These approaches allow for a comprehensive analysis of the molecular components involved in T2D, providing insights into the disease's complexity and heterogeneity.\n\n1. **Integration of Omics Data**: The integration of data from proteomics, genomics, and transcriptomics helps in understanding the pathophysiology of T2D and the varied responses to different glucose-lowering therapies. This holistic view is crucial for identifying the molecular underpinnings of T2D and tailoring personalized treatment strategies [1].\n\n2. **Metabolomics Insights**: Metabolomics studies have identified specific metabolites, such as branched-chain and aromatic amino acids, that are associated with obesity and T2D. These findings suggest that metabolic alterations play a significant role in the development of T2D, offering potential targets for therapeutic intervention [1].\n\n3. **Complex Interactions**: T2D is caused by complex interactions between genetic and environmental factors. Metabolomics has helped elucidate these interactions by identifying metabolic footprints associated with T2D, thus moving from mere information gathering to actionable knowledge [2], [3].\n\n4. **Biomarker Discovery**: Advances in omics technologies have led to the discovery of numerous candidate biomarkers for T2D. Although only a small proportion of these biomarkers have been thoroughly investigated, they hold promise for improving the prediction and management of T2D [9].\n\n5. **Beyond Traditional Models**: The use of metabolomics has expanded our understanding of T2D beyond traditional models focused on the pancreas, liver, and muscle. This broader perspective is essential for developing more effective prevention and treatment strategies [10].\n\nOverall, these advancements in multi-omics approaches provide a more detailed and nuanced understanding of T2D pathogenesis, paving the way for improved diagnostic and therapeutic options.", + "role": "assistant" + } + } + ], + "created": 1728919753, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 394, + "prompt_tokens": 1432, + "total_tokens": 1826 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "012b6e5f-ab45-53aa-a392-45a46916e752", + "score": 0.7536256166473412, + "metadata": { + "text": "proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way. The ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to di erent glucose-lowering therapies. Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes.", + "title": "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "extraction_id": "8b15673a-deaf-5e34-945c-ea2a1365552d", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "aaf89eb0-09a8-517d-b8ae-4e76a8211be6", + "score": 0.7278114557266235, + "metadata": { + "text": "Metabolomics Applied to Diabetes Research\nMoving From Information to Knowledge\nJames R. Bain, Robert D. Stevens, Brett R. Wenner, Olga Ilkayeva, Deborah M. Muoio, and\nChristopher B. Newgard\nType 2 diabetes is caused by a complex set of\ninteractions between genetic and environmentalfactors. Recent work has shown that human type2 diabetes is a constellation of disorders associ-\nated with polymorphisms in a wide array of genes, witheach individual gene accounting for /H110211% of disease risk", + "title": "2009 - Metabolomics Applied to Diabetes Research.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "a6ae2fb6-88ae-588f-a98d-b6092f886ed9", + "extraction_id": "380e9a2e-8f9f-5f9e-ba20-3695b1c60fda", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "6919bc75-2637-5359-9c05-96d192be8c4e", + "score": 0.7128690481185913, + "metadata": { + "text": "between protein signals and type 2 diabetes incidence.\nActa Diabetol. doi: 10.1007/s00592-012-0376-3\n82. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM,\nNewgard CB (2009) Metabolomics applied to diabetes re-search: moving from information to knowledge. Diabetes 58:\n2429 244383. Suhre K, Meisinger C, Dring A et al (2011) Metabolic footprint of\ndiabetes: a multiplatform metabolomics study in an epidemiological\nsetting. PLoS One 5:e13953", + "title": "2014 - The potential of novel biomarkers to improve risk prediction of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 148, + "document_id": "2bc2f4be-378f-5ced-8288-e2a132a94540", + "extraction_id": "75485c9d-6c66-52fe-8fb1-e6d2440a7f49", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "93455356-fe0b-58f4-9ae7-58f932d33560", + "score": 0.6947442293167114, + "metadata": { + "text": "The future: genetics, epigenetics, and omics\nAlthough understanding of the genetics of type 2 diabetes has advanced rapidly, much remains unknown. How genes interact with the environment to cause progressive loss of -cell function is unclear. Environmental factors and hyperglycaemia could contribute to epigenetic changes in DNA and histones, thereby modifying gene expression in organs implicated in the pathogenesis and progression of type 2 diabetes, including in cells.\n82,83", + "title": "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 49, + "document_id": "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "extraction_id": "8b15673a-deaf-5e34-945c-ea2a1365552d", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "cfc35db4-346c-55fd-b0bc-fa3cac307731", + "score": 0.6932104229927063, + "metadata": { + "text": "potential to make far-reaching contributions to our understanding of molecular\nbasis of T2D and the development of novel strategies for patient care.\n2.1 Introduction\nType 2 diabetes (T2D) is a common, chronic disorder whose prevalence is increas-ing rapidly across the globe. Like other complex diseases, T2D represents achallenge for genetic studies aiming to uncover the underlying pathophysiological\nmechanisms. It is predicted that T2D will affect 592 million individuals by 2035", + "title": "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "extraction_id": "7cec13b8-d349-5ea4-b866-17fc760d364c", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "3b5c1a49-cb11-57ef-9046-e3c8f7af589e", + "score": 0.692622322283846, + "metadata": { + "text": "inthepathogenesisoftype2diabetesandmetabolism, Current\nOpinion in Clinical Nutrition and Metabolic Care ,vol.10,no .4,\npp .420426,2007 .\n[110] M.C.Cornelis,E.J.T.Tchetgen,L.Liangetal.,Gene-environ-\nment interactions in genome-wide association studies: a com-\nparative study of tests applied to empirical studies of type 2\ndiabetes, American Journal of Epidemiology ,v o l.17 5,no .3,p p .\n191202,2012.\n[111] M.L.Metzker,Sequencingtechnologiesthenextgeneration,\nNature Reviews Genetics ,vol.11,no.1,pp.3146,2010.", + "title": "2013 - Variants of Insulin-Signaling Inhibitor Genes.pdf", + "version": "v0", + "chunk_order": 164, + "document_id": "d43a59e8-fe3b-503a-863b-235af8790f2a", + "extraction_id": "f258a3c5-02d6-5f8f-a989-27f6c795145c", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "b74d0bb9-eb0d-59bb-8a37-d3425d5591a2", + "score": 0.6839994192123413, + "metadata": { + "text": "meta-ana lysis provides insight intothegenetic architecture oftype2diabetes susceptibility. NatGenet.\n2014; 46:234 244. https://doi.or g/10.103 8/ng.2897 PMID: 24509480\n26. Morris AP,Voight BF,Teslovich TM,Ferreira T,Segr A-V, Steinthorsdot tirV,etal.Large-sc aleassoci-\nation analysis provide sinsights intothegenetic architecture andpathophysi ology oftype2diabetes.\nNatGenet. 2012; 44:981 990. https://doi.or g/10.103 8/ng.2383 PMID: 228859 22", + "title": "2021 - A genome-wide association study identifies 5 loci associated with frozen shoulder and implicates diabetes as a causal risk factor.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "8276e137-4591-51bd-9351-f4d27d3b35da", + "extraction_id": "2052d37d-f778-53e2-a2f9-9e4311e8a953", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "ead10261-182f-5ab1-9af0-ce8a17677d4a", + "score": 0.6823235750198364, + "metadata": { + "text": "monitoring and preventing progression to costly co-morbidities.\nThe principal concept of metabolomics being able to find some\nmetabolites differing in a control and a type 2 diabetic group is\nestablished. It is not our goal here to show this once again. The\nquestions we ask are rather How well are different approaches\nsuited to attain this goal? and What are optimal settings under\nwhich such studies can be successful?. Others have already\ninvestigated these questions before [16,17,18]. However, we", + "title": "2010 - Metabolic Footprint of Diabetes A Multiplatform.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "b199607e-293e-56e8-88c8-e0716d1ee9eb", + "extraction_id": "97b6d492-9139-50ec-9685-53a803f5c995", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "4971b4de-b190-56b5-b7b6-64b2c8e2a565", + "score": 0.6784495115280151, + "metadata": { + "text": "Owing to current advances in -omics technologies, such as\ngenomics, transcriptomics, proteomics and metabolomics, the\nnumber of candidate biomarkers keeps growing; however,\nonly a small proportion of these has been investigated withreference to their potential to improve the prediction of type 2\ndiabetes.\nGenetic variants\nThe heritability of glycaemic traits and type 2 diabetes is high\n[40], and the large genome-wide association studies published\nto date since the first in 2007, based on up to >10\n5study", + "title": "2014 - The potential of novel biomarkers to improve risk prediction of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "2bc2f4be-378f-5ced-8288-e2a132a94540", + "extraction_id": "496d9615-7530-530c-bea1-62fe63ea54ca", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + }, + { + "id": "01a2230a-b91d-57b6-b138-7aae805f4383", + "score": 0.6783756017684937, + "metadata": { + "text": "have improved our understanding of the complexity of \nT2DM pathophysiology, beyond the classic triumvirate of \n-cell, skeletal muscle and liver87. However, the ability of \nthese biomarkers to predict future risk of T2DM beyond \nanthropometric measures, lifestyle factors and fasting \nlevels of glucose and lipids is still debatable87.\nWithin the past 7years, a complementary, novel set of \nT2DM biomarkers has largely been generated by metabo-\nlomic studies, which systematically analyse metabolites", + "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "version": "v0", + "chunk_order": 67, + "document_id": "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "extraction_id": "751ccb98-2846-5ca7-8ab8-2684100c28fa", + "associatedQuery": "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIHIcyJRqSPUlYLtzZ5hVN5aLL9iw", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified several novel loci associated with diabetes. Specifically, the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, using genotype data from almost 150,000 individuals predominantly of European descent, identified 10 novel type 2 diabetes (T2D) susceptibility loci that reached genome-wide significance [8].", + "role": "assistant" + } + } + ], + "created": 1728919766, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 82, + "prompt_tokens": 1620, + "total_tokens": 1702 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "0c0634ba-c437-52d3-b3a9-caa5eda120c6", + "score": 0.7532689571380615, + "metadata": { + "text": "wide association study identi es novel risk loci for type 2 diabetes. Nature\n(2007) 445:881 5. doi: 10.1038/nature05616\n27. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A\ngenome-wide association study of type 2 diabetes in Finns detects multiple\nsusceptibility variants. Science (2007) 316:1341 5. doi: 10.1126/science.1142382\n28. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton\nKJ, et al. The genetic architecture of type 2 diabetes. Nature (2016) 536:41 7.", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 162, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "5f148509-8a55-5e9c-8c68-e327f519c1c9", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "1ab64c6e-e930-597e-bc12-ed540eabcf46", + "score": 0.7524292020541918, + "metadata": { + "text": "novel loci for type 1 diabetes. Diabetes 58:290295. DOI: https://doi.org/10.2337/db08-1022, PMID: \n18840781\nHuang J, Ellinghaus D, Franke A, Howie B, Li Y . 2012. 1000 Genomes- based imputation identifies novel and \nrefined associations for the Wellcome Trust Case Control Consortium phase 1 Data. European Journal of \nHuman Genetics 20:801805. DOI: https://doi.org/10.1038/ejhg.2012.3, PMID: 22293688\nHundhausen C, Roth A, Whalen E, Chen J, Schneider A, Long SA, Wei S, Rawlings R, Kinsman M, Evanko SP ,", + "title": "2022 - A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.pdf", + "version": "v0", + "chunk_order": 167, + "document_id": "368e0215-393e-5bec-a87c-e976adaa3ca5", + "extraction_id": "692b342f-5d48-5046-84f9-37f1cf4275b5", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "46ac5572-ac56-5f29-b7bf-49a1e29d3936", + "score": 0.7521799802780151, + "metadata": { + "text": "general population, these loci show limited effect in DKD,\nespecially in individuals with type 1 diabetes [ 6]. Genome-\nwide association studies (GWAS) have previously identified ahandful of genetic loci for DKD at the genome-wide signifi-\ncance level ( p<510\n8)[711]. Recently, a meta-analysis of\nGWAS, including up to 19,406 individuals with type 1 diabetes\nfrom the Diabetic Nephropathy Collaborative Research", + "title": "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "b9194555-5fdb-549e-9edb-d108132a7dd1", + "extraction_id": "d7e0e5ad-bad5-5b14-896e-45702d6605f9", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "6d5d4c24-5bc8-539a-9faa-8b2370f8c87a", + "score": 0.7515840530395508, + "metadata": { + "text": "Table 2.1 Major published T2D GWAS and meta-analyses\nStudyEthnicity/\norigin NcasesaN\ncontrolsaNovel loci identiedGWAS or\nmeta-analysis\ndiscoveryapproach GWAS arrayReference\npanel forimputationT2D phenotype\ndenition/otherspecs\nDiabetes Gene\nDiscovery Group\n(Sladek et al. 2007 ),\nNatureEuropean 694 645 SLC30A8 ,HHEX /IDE GWA Illumina 300k + Family history of\nT2D, AAO <45\nyears, BMI <30\nkg/m\n2\nFinlandUS Investi-gation of NIDDMGenetics (FUSION)(Scott et al. 2007a ),\nScienceEuropean 1161 1174 CDKN2A/2B ,", + "title": "2016 - Genome-Wide Association Studies of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 27, + "document_id": "185aad8a-6a5b-5b18-81c4-ef251edef5e7", + "extraction_id": "a620eedf-5d5b-506f-97f5-c25dbe0493c0", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "54da57b3-e577-5c00-a7d5-6f569a41d28b", + "score": 0.7501821517944336, + "metadata": { + "text": "scale gene-centric meta-analysis across 39 studies identifies type 2diabetes loci. Am J Hum Genet. 2012;90(3):410 25.\n13. Haiman C, Fesinmeyer M, Spencer K, Buzkova P, V oruganti V ,\nWan P, et al. Consistent directions ofeffect for established type 2\ndiabetes risk variants across populations: the Population Architectureusing Genomics and Epidemiology (PAGE) Consortium. Diabetes.\n2012;61(6):1642 7.In the most complete trans-ethnic T2D GWAS", + "title": "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 62, + "document_id": "7d051350-d939-5183-be22-742727573a75", + "extraction_id": "1213249d-8ed3-5d13-9137-f11b87a7a78b", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "0cf52952-0d83-58ed-b402-05dd2f085841", + "score": 0.7471976280212402, + "metadata": { + "text": "9. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, et al. (2007) A genome-wide\nassociation study identifies novel risk loci for type 2 diabetes. Nature 445:881885.\n10. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta-\nanalysis of genome-wide association data and large-scale replication identifies\nadditional susceptibility loci for type 2 diabetes. Nat Genet 40: 638645.11. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease.\nScience 322: 881888.", + "title": "2010 - Liver and Adipose Expression Associated SNPs.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "ebeef1bf-341d-5aa1-807b-1f23186cf2bc", + "extraction_id": "35ce49d5-7af3-5f24-927c-f800e8ae024d", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "2a91a466-c271-5368-b0a1-cf15e6478bb1", + "score": 0.7458800077438354, + "metadata": { + "text": "scale ongoing efforts to localize and characterize T2D susceptibility\ngenes using genome-wide association study (GWAS) approaches. To\ndate, the GWAS method has achieved substantial success in localizing\nnovel T2D susceptibility loci and loci for T2D-related glycemic traits\n(about 90 loci), obesity loci (~90), and loci for metabolic syndrome or\nits components (~50 loci), e.g. reviews: [4,20,28,29,41,47,51,64,65,67] .\nHowever, common variants identi ed by GWAS explain only about", + "title": "2016 - Transcriptomics in type 2 diabetes Bridging the gap between genotype and phenotype.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "98e49a13-9887-5b27-879b-0816a3da1c1d", + "extraction_id": "71934c29-338d-57a2-8f45-e3e795e0ec9b", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "de3b49f1-9dcc-5056-8232-b76e5f985736", + "score": 0.7458185705054672, + "metadata": { + "text": "T2D GWA meta-analysis performed by the DIAbetes Genet-ics Replication and Meta-analysis (DIAGRAM) Consortium\n[6]. Using genotype data from almost 150,000 individuals,\npredominantly of European descent, the consortium was ableto define 10 novel T2D-susceptibility loci to genome-wide\nsignificance, and to highlight several hundreds more that,\nwhilst failing to reach the stringent criteria typically regardedas proof, are nonetheless highly likely to reflect genuine", + "title": "2012 - Recent Developments in the Genetic and Genomic Basis of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "7d051350-d939-5183-be22-742727573a75", + "extraction_id": "924d35c5-0ee8-53a7-9fdf-9309a27ce9ae", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "72622bca-2fce-5732-9c8b-2909d231d09d", + "score": 0.745085597038269, + "metadata": { + "text": "18. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. \nA genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-885.\n19. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren \nWL, et al. A genome-wide association study of type 2 diabetes \nin Finns detects multiple susceptibility variants. Science 2007; \n316:1341-1345.\n20. Diabetes Genetics Initiative of Broad Institute of Harvard and \nMIT , Lund University, and Novartis Institutes of BioMedical", + "title": "2012 - Finding Genetic Risk Factors of Gestational Diabetes.pdf", + "version": "v0", + "chunk_order": 42, + "document_id": "81d6ccba-6203-5879-b206-b8711d1ff35c", + "extraction_id": "e7bf3f2d-8180-5a84-965c-8289f107a718", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + }, + { + "id": "5af0c2b9-9957-5c8f-b8ae-c115e365576f", + "score": 0.7448982818394391, + "metadata": { + "text": "additive, dominant, and recessive) and did not adjust for mul -\ntiple comparisons. The third study is the largest GWAS con -\nducted to date and is a meta-analysis of two GWASs, Genetics \nof Kidneys in Diabetes (GoKinD) and Epidemiology of Dia -\nbetes Interventions and Complications (EDIC) studies [24]. \nThis study by Grassi et al. [24] involved 2,829 European sub -\njects with T1DM. The most significant variant was rs476141 \nlocated in a long non-coding RNA ( LOC339529 ) in chromo -", + "title": "2015 - Genetic Studies on Diabetic Microvascular Complications.pdf", + "version": "v0", + "chunk_order": 28, + "document_id": "1df9d9a8-0fb0-5a03-9749-9471b4b2b2f3", + "extraction_id": "d3335459-5fec-5104-932f-f4fd7566edf7", + "associatedQuery": "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIHIljZhr1AUuC7qfsdHZaKkRKz2A", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Epigenetic modifications, such as DNA methylation and histone modification, play significant roles in influencing the expression of diabetes-related genes. \n\n1. **DNA Methylation**: This modification can lead to the silencing of key genes involved in diabetes. For example, increased DNA methylation of the promoter of the PPARGC1A gene, which is crucial for regulating mitochondrial genes, has been observed in diabetic islets. This methylation can modulate diabetes by altering gene expression [1]. Additionally, altered DNA methylation patterns can change the expression of inflammatory and pathological genes, contributing to the development of diabetic complications [2].\n\n2. **Histone Modifications**: These modifications can affect chromatin structure and gene expression. In the context of diabetes, histone post-translational modifications (PTMs) have been associated with the regulation of genes susceptible to type 1 diabetes [3]. Changes in chromatin histone modifications can regulate pathological gene expression in diabetes, influencing the disease's progression [2].\n\nOverall, both DNA methylation and histone modifications can lead to the repression or activation of genes that are critical in the pathogenesis and progression of diabetes, affecting insulin production, inflammation, and other metabolic processes.", + "role": "assistant" + } + } + ], + "created": 1728919775, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 247, + "prompt_tokens": 1468, + "total_tokens": 1715 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "eb133825-7500-5160-b39a-298961323f9c", + "score": 0.7901671764304395, + "metadata": { + "text": "diabetes due to epigenetic silencing of Pdx1, a key transcription factor that regulates insulin gene 301 \nexpression and beta cell differentiation. Both hi stone modifications a nd DNA methylation were 302 \nimplicated (111). In another study, it was shown th at, in diabetic islets , there was increased DNA 303 \nmethylation of the promoter of PPAR-gamma co-activator 1 gene ( PPARGC1A ), a factor that 304 \nplays a key role in regulating mitochondrial ge nes and in the modulation of diabetes (87). 305", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 63, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "77eb6a3d-2e3b-5304-873f-4fe14ec290d1", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "a97f140f-63b1-5963-9c38-d90f59f58ced", + "score": 0.786042356075119, + "metadata": { + "text": "altered DNA methylation (DNA-me) at various genes in target cells all of which over time can 1009 \nresult in changes to the expr ession patterns of inflammatory, sclerotic and other pathological 1010 \ngenes and the ultimate developm ent of diabetic complications. 1011 \n 1012 \nFigure 2: Model for epigenetic regulation of pa thological gene expressi on in diabetes via 1013 \nchanges in chromatin histone modifications. Post translational modifications on the N- 1014", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 206, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "21de4c95-4171-52bb-a867-2df5336c3c71", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "41899c3d-64db-556a-882a-4e39b964c6d5", + "score": 0.7817012564807093, + "metadata": { + "text": "Dependent Demethylation of Regulatory Elements Correlates with Chromatin State and Improved Cell Function. Cell Metab.\n2015 ,22, 619632. [CrossRef]\n228. Zhang, H.; Pollin, T.I. Epigenetics Variation and Pathogenesis in Diabetes. Curr. Diab. Rep. 2018 ,18, 121. [CrossRef]\n229. Miao, F.; Chen, Z.; Zhang, L.; Liu, Z.; Wu, X.; Yuan, Y.-C.; Natarajan, R. Proles of epigenetic histone post-translational\nmodications at type 1 diabetes susceptible genes. J. Biol. Chem. 2012 ,287, 1633516345. [CrossRef]", + "title": "2021 - Epigenetics of Aging and Aging-Associated Diseases.pdf", + "version": "v0", + "chunk_order": 271, + "document_id": "70945353-4808-539a-80f9-5632c27913e5", + "extraction_id": "3d7cb780-5f0a-5500-8176-4c2055cac9dc", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "6f647f65-0c70-5abf-8944-e2b1ade8ee1d", + "score": 0.7764175765638824, + "metadata": { + "text": "Epigenetic Mechanisms in Diabetic Complications 14 \nDNA methylation at prom oter CpG islands has been associ ated with gene repression and 292 \nis a well studied epigenetic mark in the c ontext of tumor suppressor genes and cancer (129). 293 \nHowever, much less is known a bout DNA methylation in diabetes . A recent report has shown 294 \nthat the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes 295", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 61, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "77eb6a3d-2e3b-5304-873f-4fe14ec290d1", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "883de652-2a30-5587-89bb-474facc861fe", + "score": 0.7744899026897524, + "metadata": { + "text": "Epigenetics: deciphering its role in diabetes and \nits chronic complications. Clin. Exp. Pharmacol. \nPhysiol. 38, 401409 (2011).\n61. Cooper, M.E. & El-Osta, A. Epigenetics: \nmechanisms and implications for diabetic complications. Circ. Res. 107, 14031413 \n(2010).\n62. Miao, F. etal. Profiles of epigenetic histone post-\ntranslational modifications at type1 diabetes \nsusceptible genes. J.Biol. Chem. 287, \n1633516345 (2012).\n63. Sapienza, C. etal. DNA methylation profiling", + "title": "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "version": "v0", + "chunk_order": 160, + "document_id": "be05127e-1be8-5573-b571-51a11c3b2be2", + "extraction_id": "cbbe696b-8541-537a-ac5f-77b82cdb8201", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "796ed77e-4539-543b-a392-5736392f93ba", + "score": 0.7697410812896816, + "metadata": { + "text": "Emerging evidence shows that epigenetic mecha-nisms in chromatin including histone PTMs,\nDNAme, and miRNAs also might play key roles in\nthe etiology of diabetes and DN. The persistence ofepigenetic modi cations triggered by diabetic stim-\nuli could be one of the key mechanisms underlying\nmetabolic memory. A role for several HMTs and thecorresponding histone PTMs has been shown in the\nexpression of brotic and in ammatory genes asso-", + "title": "2013 - Epigenetic Modifications in the Pathogenesis of Diabetic Nephropathy.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "9cffb997-a205-5f72-89a6-945df5b9af28", + "extraction_id": "7680731d-0b98-5f45-85f9-d06883504dd1", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "3f3fb648-0a87-5d2b-82c8-da1f3caf91b0", + "score": 0.7688729992173019, + "metadata": { + "text": "inflammation-related epigenetic modifications: focus on DNA\nmethylation. Exerc Immunol Rev. 2015;21:26 41.\n17. Milagro FI, Mansego ML, De Miguel C, Martinez JA. Dietary\nfactors, epigenetic modifications and obesity outcomes: progresses\nand perspectives. Mol Aspects Med. 2013;34(4):782 812.\n18. Caramori ML, Kim Y , Goldfine AB, et al. Differential gene expres-\nsion in diabetic nephropathy in individuals with type 1 diabetes. J\nClin Endocrinol Metab. 2015;100(6):E876 82.", + "title": "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "version": "v0", + "chunk_order": 71, + "document_id": "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "extraction_id": "767d65c7-b99d-5427-8f5a-4afa10669e11", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "aaeb4ad0-7848-554e-8ec1-2b5a094d3112", + "score": 0.7677191724693532, + "metadata": { + "text": "elevated glucose level is not the only factor that leads to mal-\nadaptive epigenetic modifications in diabetes. DNA methyla-\ntion can also be influenced by reactive oxygen species, both\ndirectly through oxidative m odification DNA preventing\nmethylation and indirectly through its effects on methylation\nwriting/erasing enzymes [ 15]. Many other factors including\nhypoxia, inflammation, cytokines and growth factors, drugs,\nnutrition and even physical activity can modify epigenetic", + "title": "2016 - Epigenetic Mechanisms in Diabetic Kidney Disease.pdf", + "version": "v0", + "chunk_order": 31, + "document_id": "6f773bda-0b8f-5da2-a9b5-e6c013d75050", + "extraction_id": "7a924f08-78ef-528a-8f9e-7bc12b004ff2", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "c51c94d1-c182-5e77-8a14-6af868d66ee1", + "score": 0.7632846691182058, + "metadata": { + "text": "1306 1313.\n31. Miao F, et al.; DCCT/EDIC Research Group (2014) Evaluating the role of epigenetic\nhistone modifications in the metabolic memory of type 1 diabetes. Diabetes 63(5):\n1748 1762.\n32. Reddy MA, Tak Park J, Natarajan R (2013) Epigenetic modifications in the patho-\ngenesis of diabetic nephropathy. Semin Nephrol 33(4):341 353.\n33. Bell CG, et al. (2010) Genome-wide DNA methylation analysis for diabetic nephrop-\nathy in type 1 diabetes mellitus. BMC Med Genomics 3:33.", + "title": "2016 - Epigenomic profiling reveals an association betweenpersistence of DNA methylation and metabolicmemory in the DCCTEDIC type 1 diabetes cohor.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "4b44425c-00c2-504f-be3c-34c002951cc2", + "extraction_id": "745c11f0-789f-5f0a-9f19-69af42a19c75", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + }, + { + "id": "1c0b9205-340f-568f-990f-785c66154732", + "score": 0.7595745551459362, + "metadata": { + "text": "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber-\nrant DNAme has also been reported in the reduced expression\nof genes involved in diabetes and metabolism, and DNAme\nvariations have also been noted near diabetes susceptibility\ngenes and enhancers [ 15,47].\nGenomic DNA from diabetic patients with nephropa-\nthy relative to those without displayed differential meth-\nylation at several genes, including UNC13B , which had", + "title": "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "470f1f94-792d-5273-a88f-7e06084951c5", + "extraction_id": "44d96546-84c3-51f1-85f9-22790a91d105", + "associatedQuery": "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIHItZX0vwpceBtjbHWMD13xwSdHl", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes through various mechanisms. The interactions between host genetics, metabolism, and the immune system are crucial in shaping the microbiome and influencing the predisposition to diseases like diabetes [1]. \n\nResearch suggests that the gut microbiota affects numerous biological functions throughout the body, and its characterization has become a major research area in biomedicine. Studies have shown that gut bacteria play a fundamental role in diseases such as obesity and diabetes [6]. Dysbiosis, or an imbalance in the gut microbiota, is associated with obesity and type 2 diabetes (T2D), indicating that the composition of the gut microbiome can influence metabolic diseases [6].\n\nMoreover, genetic control of the gut microbiota composition has been observed, suggesting that host genetics can influence the microbiome, which in turn can modulate metabolic traits and potentially affect the risk of developing diabetes [10]. This implies a bidirectional relationship where genetics can shape the microbiome, and the microbiome can influence the expression of genetic predispositions to diabetes.\n\nIn summary, the gut microbiome interacts with host genetics and metabolism, potentially modulating the risk of diabetes by influencing metabolic processes and immune responses [1], [6], [10].", + "role": "assistant" + } + } + ], + "created": 1728919783, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 254, + "prompt_tokens": 1527, + "total_tokens": 1781 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "d79a5c86-df6a-5b3d-93b4-a26f47b47e83", + "score": 0.7613752768784431, + "metadata": { + "text": "diabetes?\n Is altered gut epithelial function and integrity important in the pathoge nesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to\ndysbiosis and how do we demonstrate impaired function in humans?\n How important are the interactions between host genetics, metab olism and the immune system in shaping the microbiome and predilection to\ndisease?", + "title": "2014 - The intestinal microbiome in type 1 diabetes.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "138189d1-a16e-5c76-9b19-bd6877e7ee6d", + "extraction_id": "b5ecc19b-eae0-51de-8e87-e5d01060e5be", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "6cef232c-d7c6-5968-ad74-2903b688793a", + "score": 0.7209897373926952, + "metadata": { + "text": "the gut, which might trigger an inflammatory response and play arole in the development of diabetes.\nIn conclusion, our data suggest that the levels of glucose tolerance or\nseverity of diabetes should be considered while linking microbiota with\nobesity and other metabolic diseases in humans. It is especially\nimportant for developing the strategies to modify the gut microbiota inorder to control metabolic diseases, since obesity and diabetes mightbe associated with different bacterial populations.\nMethods", + "title": "2010 - Gut Microbiota in Human Adults with Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 51, + "document_id": "27aaf82e-944d-55b3-8b6d-cc43bcdb3eab", + "extraction_id": "0eb4bb40-b16c-5203-8c83-dac0695d43a2", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "89360f80-d048-5c02-a61d-6d56a99eedcd", + "score": 0.6944796625089859, + "metadata": { + "text": "2011;342:d35.\n[68] Hara N, Alkanani AK, Ir D, Robertson CE, Wagner BD,\nFrank DN, et al. The role of the intestinal microbiota in\ntype 1 diabetes. Clin Immunol 2013;146:1129.\n[69] Beyan H, Wen L, Leslie RD. Guts, germs, and meals: the\norigin of type 1 diabetes. Curr Diab Rep 2012;12:45662.\n[70] Atkinson MA, Chervonsky A. Does the gut microbiota have\na role in type 1 diabetes? Early evidence from humans and", + "title": "2014 - Diabetes in Europe An update.pdf", + "version": "v0", + "chunk_order": 124, + "document_id": "81e1fc53-6768-590f-9b47-9a5105b6ddb5", + "extraction_id": "5c27f434-3a7c-5ec9-80fc-6399dd3570c3", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "e7e8ef7b-bad0-54bc-814d-d947ea04756b", + "score": 0.6925423924599664, + "metadata": { + "text": "diabetes. ISME J. 5,8291 (2011).\n30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a\nfunctional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011).\n31. Endesfelder, D. et al. Compromised gut microbiota networks in children with\nanti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014).\n32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in\ndevelopment and in progression toward type 1 diabetes. Cell Host Microbe 17,\n260273 (2015).", + "title": "2016 - Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes.pdf", + "version": "v0", + "chunk_order": 161, + "document_id": "f0405966-38bf-5a04-aa2c-1474b11362bb", + "extraction_id": "092a9b75-9985-5876-a650-59bc3f0d10fb", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "da881999-9d70-560f-91b3-eda465b7a639", + "score": 0.6895030325947397, + "metadata": { + "text": "661678 (2007).\n4. Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns\ndetects multiple susceptibility variants. Science 316, 13411345 (2007).\n5. Musso, G., Gambino, R. & Cassader, M. Interactions between gut microbiota and\nhost metabolism predisposing to obesity and diabetes. Annu. Rev. Med. 62,\n361380 (2011).\n6. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308,\n16351638 (2005).", + "title": "2012 - A metagenome-wide association study of gut microbiota in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 91, + "document_id": "0c088ef3-83a7-5a5e-8308-011cf4b25924", + "extraction_id": "0a4d545f-0682-5ce1-b38c-88b5fdb4add3", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "2589b0db-190e-5847-aef0-0bc3b415fb94", + "score": 0.6874836683273364, + "metadata": { + "text": "The gut microbiota affects numerous biological functionsthroughout the body and its characterisation has becomea major research area in biomedicine. Recent studieshave suggested that gut bacteria play a fundamental rolein diseases such as obesity, diabetes and cardiovasculardisease. Data are accumulating in animal models andhumans suggesting that obesity and type 2 diabetes(T2D) are associated with a profound dysbiosis. Firsthuman metagenome-wide association studiesdemonstrated highly signi cant", + "title": "2014 - Microbiota and diabetes an evolving relationship.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "4bbbe579-1d9e-50b8-9403-b50bc3282c8f", + "extraction_id": "44b12386-be75-5141-a5a0-77ab97136863", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "a5d5d05b-a824-5b8f-a774-b0b9ec5d0182", + "score": 0.6868209429490016, + "metadata": { + "text": "18 Burcelin R. Regulation of metabolism: a cross talk between gut microbiota and its\nhuman host. Physiology (Bethesda) 2012;27:300 7.\n19 Breen DM, Rasmussen BA, Cote CD, et al . Nutrient-sensing mechanisms in the gut\nas therapeutic targets for diabetes. Diabetes 2013;62:3005 13.\n20 Karlsson F, Tremaroli V, Nielsen J, et al . Assessing the human gut microbiota in\nmetabolic diseases. Diabetes 2013;62:3341 9.\n21 Backhed F, Ding H, Wang T, et al . The gut microbiota as an environmental factor", + "title": "2014 - Microbiota and diabetes an evolving relationship.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "4bbbe579-1d9e-50b8-9403-b50bc3282c8f", + "extraction_id": "223f3f31-fb62-5f0d-ac8a-5a6deb1191d2", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "63e887b3-0db0-547d-a81c-716909ead0b6", + "score": 0.6848769971801653, + "metadata": { + "text": "interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev 2011; 12: 27281.\n47 Kootte RS, Vrieze A, Holleman F, et al. The therapeutic potential of \nmanipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab 2012; 14: 11220.\n48 Qin J, Li Y , Cai Z, et al. A metagenome-wide association study of \ngut microbiota in type 2 diabetes. Nature 2012; 490: 5560.\n49 Karlsson FH, Tremaroli V, Nookaew I, et al. Gut metagenome in", + "title": "2014 - Pathophysiology and treatment of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 172, + "document_id": "ab9288ab-e3ad-58f1-b5ba-183ee17ce4bd", + "extraction_id": "3754ce7f-9671-5636-a4e6-849fb672366a", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "d9bc6a49-c40e-520f-9e2d-afa05829416f", + "score": 0.6843650717300092, + "metadata": { + "text": "Other factors\nInterest in the role of the gut microbiome in the devel -\nopment of T2DM has exploded in the past few years, \nand variation in the diversity and composition of the gut \nmicrobiota has been tied to T2DM100. For example, levels \nof butyrate-producing bacteria are decreased in the gut \nmicrobiota of patients with T2DM compared with that \nof healthy individuals101. In addition, evidence suggests \nthat ambient air pollution is an emerging risk factor for", + "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "extraction_id": "736476e2-62be-52c5-b4a2-ee7cd7666a6f", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + }, + { + "id": "b0aa9c89-a8f4-5388-97ed-5d6556c565e7", + "score": 0.6784937573377051, + "metadata": { + "text": "52. Parks, B.W., et al., Genetic control of obesity and gut microbiota composition in response to high -fat, high -sucrose \ndiet in mice. Cell Metab, 2013. 17(1): p. 141 -52. \n53. Org, E., et al., Genetic and environmental c ontrol of host -gut microbiota interactions. Genome Res, 2015. 25(10): \np. 1558 -69. \n54. McKnite, A.M., et al., Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. \nPLoS One, 2012. 7(6): p. e39191.", + "title": "2019 - Systems genetics approaches to probe gene function.pdf", + "version": "v0", + "chunk_order": 516, + "document_id": "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "extraction_id": "5ab39f63-c4e0-56b8-b6ed-26df7bee89af", + "associatedQuery": "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIHJ0Y0u83bg8S6UfGkVEYfllY6XI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Machine learning algorithms show promise in integrating genomic data to predict individual risk and progression of diabetes, but there are challenges and limitations to consider.\n\n1. Genomic data is considered to yield better patient-centric outcomes than traditional tabular data for predicting diabetic illnesses [1]. This suggests that machine learning models that incorporate genomic data may provide more accurate predictions.\n\n2. Machine learning has been applied to integrate various types of data, including genomic and epigenomic biomarkers, to determine type 2 diabetic status. This approach has revealed connections between diabetic classification and other biological functions, indicating the potential of machine learning in this area [5].\n\n3. The integration of physiological, biochemical, genetic, and epigenetic features with machine learning algorithms has shown potential for more informative diagnostics and personalized treatment approaches for diabetes [8].\n\n4. However, there are limitations, such as the need for larger sample sizes and extensive training to achieve considerable accuracy when using polygenic scores-based approaches with genomic data [4].\n\nOverall, while machine learning algorithms have demonstrated potential in integrating genomic data for diabetes prediction, further research and development are needed to overcome current limitations and improve accuracy and applicability in clinical settings.", + "role": "assistant" + } + } + ], + "created": 1728919790, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 231, + "prompt_tokens": 1226, + "total_tokens": 1457 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a1e1110f-551a-5481-bbd6-0495d8effd38", + "score": 0.755035593805577, + "metadata": { + "text": "All the mentioned models rely on tabular datasets such as PIMA and ECG signals [ 47]\nin classifying the records with possible diabetic illnesses. The current study considers that\ngenomic data yields a better patient-centric outcome than tabular data.\n2.3. Genomics for Type 2 Diabetes\nMany research studies have been carried out on genetic-based illness prediction.\nIncorporating machine learning approaches with genetic-based illness prediction could", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "6b4157fa-dcf0-5b70-b508-38ffb5fcda8d", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "73df0579-3c65-590b-9293-e0dc8fa32863", + "score": 0.723041968164328, + "metadata": { + "text": "-\nchondrially rich, provides a direct connection between physiological dysfunction observed in the heart and the impact of altered genomic profiles in the mitochondrion and nucleus. Machine-learning, which at current has been applied to very few genetic applications, may play a significant role in defining the epigenome of those with diabetes mellitus, likely unveiling genes and molecular pathways first impacted by the pathology.\nThe challenges ofmachine learning intheclinical setting", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 146, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "aff84b9e-3855-5960-accd-dcac6b362346", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "d0503557-1135-531d-9316-d3be3f620f3f", + "score": 0.7097123438291357, + "metadata": { + "text": "15. Ali, M.M.; Paul, B.K.; Ahmed, K.; Bui, F.M.; Quinn, J.M.W.; Moni, M.A. Heart disease prediction using supervised machine\nlearning algorithms: Performance analysis and comparison. Comput. Biol. Med. 2021 ,136, 104672. [CrossRef]\n16. Bell, C.G.; Teschendorff, A.E.; Rakyan, V .K.; Maxwell, A.P .; Beck, S.; Savage, D.A. Genome-wide DNA methylation analysis for\ndiabetic nephropathy in type 1 diabetes mellitus. BMC Med. Genom. 2010 ,3, 33. [CrossRef]", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 245, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "a500eb31-13d8-5a0f-adfc-d260189a7555", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "4a73b79f-f3bb-5341-865d-c6162c2f4b98", + "score": 0.7002751409843493, + "metadata": { + "text": "Diagnostics 2022 ,12, 3067 6 of 30\nTable 1. Various existing models for diabetes prediction.\nApproach Type of Data Applicability Limitations\npolygenic scores-based\napproach\n[12]Genomic DataUsed in the evaluation of clinical\ntrials and illness screening\nmechanismsThe polygenic score approach needs\nlarger samples and tremendous\ntraining for considerable Accuracy.\nSingular Value\nDecomposition\n[13]Genomic Data\nTabular Data\nThe image they are\nusedThey are used in ranking the feature", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 50, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "a0ebb8e0-1414-52f4-aa8d-9bde3a9f26c2", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "9fc6ebb2-02a9-5bc3-9623-6cd353ada65e", + "score": 0.700267034753143, + "metadata": { + "text": "In the current study, machine-learning was used as a \npredictive tool to integrate cardiac physiological, bio\n-\nchemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status. In 50 patients, machine-learning algorithms revealed the interconnectedness between dia\n-\nbetic classification, mitochondrial function, and methyla -", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 22, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "8d323598-fdf7-56cf-8290-be85929f0eaf", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "c15a7933-675c-5790-9165-9fef8c091920", + "score": 0.6963416699955857, + "metadata": { + "text": "Diabetes mellitus is a multifaceted disease, consisting \nof systemic comorbidities which necessitate a variety of treatment modalities and stratify those affected with the disease [5]. Before the implementation of machine-learning algorithms in medicine, linear statistical models have highlighted measures, such as HbA1c, as diagnos\n-\ntic staples for the evaluation of diabetes mellitus onset and progression [6]. By exploring these previously pub\n-", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "8d323598-fdf7-56cf-8290-be85929f0eaf", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "d3d65022-c072-5880-8d27-a95b285e77cd", + "score": 0.6921416108523851, + "metadata": { + "text": "tool that combines both genetic and clinical featur es in order to identify diabetic \nnephropathy in patients with T2D [81]. Leung et al . compared several machine \nlearning methods that include partial least square regression, classification and \nregression tree, the C5.0 Decision Tree, Random For est, naive Bayes, neural networks \nand support vector machines [82]. The dataset used consists of both genetic (Single \nNucleotide Polymorphisms - SNPs) and clinical data. Age, age of diagnosis, systolic", + "title": "2017 - Machine Learning and Data Mining Methods in Diabetes Research.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "e2dcbb80-5ad7-5441-b170-9b46607445b0", + "extraction_id": "20ba070b-900d-5213-9b38-d53492e48532", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "d8b7be7f-3f83-5f1d-897b-da01d2a7baaf", + "score": 0.6870492287248845, + "metadata": { + "text": "-\nylation status and total nuclear methylation provided the best predictive measures for assessing type 2 diabetes mellitus. The incorporation of physiological, biochemical, genetic, and epigenetic features with machine-learning algorithms exemplifies the potential for more informa\n-\ntive diagnostics in the future, as well as personalized approaches to generalized treatment modalities (Fig.6). \nDiscussion\nMachine-learning can be applied as a systems biol -", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "7079e9da-e08b-5e9f-ad3d-4709915aa9e0", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "83b5d15e-5c0e-5abd-aa88-1affe9148052", + "score": 0.6858820710813409, + "metadata": { + "text": "-\ntures is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient. Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled popu\n-\nlation grows.\nConclusions\nOur work highlights the importance of identifying bio -", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 156, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "493e5840-f65b-5245-8f07-126e1d9eedc3", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + }, + { + "id": "065cb845-8ff9-5919-8e1f-7d2604a52e34", + "score": 0.681487638861679, + "metadata": { + "text": "10 Meigs JB, Shrader P, Sullivan LM et al. Genotype score in addition to common risk factors for prediction of Type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008).\n11 Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA 299, 13201334 (2008).\n\t Systematic\treview\tof\tearly\tresearch\tinto\tgenomic\tmedicine \t\nadoption\tin\tthe\tclinical\tcare\tof\tcommon\tchronic\tdiseases. \t\nOutlines\tboth\tphysician\tand\tpatient\tperspectives\ttowards", + "title": "2014 - Do physicians think genomic medicine will be useful for patient care.pdf", + "version": "v0", + "chunk_order": 95, + "document_id": "5418b59c-465c-5b1e-aee1-52ca7a1ead52", + "extraction_id": "5feb39eb-3945-5a31-9d03-7b83766df1e1", + "associatedQuery": "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIHJ7kKFoNZYhA6ZvKYEgyC7wipHg", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent findings highlight the significant role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity, with several implications for understanding and potentially treating diabetes. \n\n1. **Regulation of Islet Function**: LncRNAs have been shown to regulate the development and function of pancreatic islets, which are crucial for insulin secretion. For instance, the lncRNA H19 is involved in this regulatory process [1]. This suggests that lncRNAs could be critical in maintaining normal insulin secretion and could be targets for therapeutic intervention in diabetes.\n\n2. **Impact on Insulin Synthesis and Secretion**: Specific lncRNAs, such as Meg3, have been found to affect insulin synthesis and secretion in pancreatic beta cells [4]. This indicates that lncRNAs play a direct role in the cellular mechanisms that control insulin production, which is essential for maintaining glucose homeostasis.\n\n3. **Therapeutic Potential**: Due to their specific functions in regulating cellular pathways, lncRNAs are considered promising therapeutic targets. Their expression patterns in tissues often correlate with the progression of diabetes, making them potential biomarkers for diagnosis and prognosis [3].\n\n4. **Association with Insulin Resistance and Diabetes**: Altered levels of lncRNAs are closely associated with the onset and progression of insulin resistance and diabetes [5]. This association underscores the potential of lncRNAs as targets for interventions aimed at improving insulin sensitivity and managing diabetes.\n\n5. **Research and Clinical Implications**: The diverse roles of lncRNAs in insulin resistance and diabetes suggest their importance in future research for diagnosis, prognosis, and therapy of the disease [2]. This calls for further investigations and collaborations among researchers, clinicians, and patients to fully understand and harness the potential of lncRNAs in diabetes management.\n\nOverall, these findings imply that lncRNAs are integral to the regulation of insulin secretion and sensitivity, offering new avenues for research and treatment strategies in diabetes.", + "role": "assistant" + } + } + ], + "created": 1728919797, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 393, + "prompt_tokens": 1190, + "total_tokens": 1583 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "66b05301-179b-597c-bb68-e6fd0e0d1d5a", + "score": 0.7479288578033447, + "metadata": { + "text": "NAs to be mapped to diabetic susceptible loci [49 52], \nall suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications.\nLncRNAs asregulators ofislet function\nThe pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 42, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "e85449e5-801e-5431-80e1-521699d18780", + "score": 0.7348612108909375, + "metadata": { + "text": "this would require further investiga-tions, both invivo and invitro and critical networking among researchers, clinicians, and patients. Nevertheless, the implications of lncRNAs in diverse facets of insulin resistance and diabetes are indicative of their roles in the diagnosis, prognosis, and therapy of this disease in future.", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "2d9e043b-a3fa-52dc-9a4e-71ed49f9ec1d", + "score": 0.7105234861373901, + "metadata": { + "text": "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 88, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "a0146183-d255-5eae-85eb-adaf007d1b32", + "score": 0.7006916164527447, + "metadata": { + "text": "58. You L, Wang N, Yin D etal (2016) Downregulation of long noncoding RNA Meg3 affects insulin synthesis and secretion in mouse pancreatic beta cells. J Cell Physiol 231:852862\n 59. Arnes L, Akerman I, Balderes DA, Ferrer J, Sussel L (2016) betalinc1 encodes a long noncoding RNA that regulates islet beta-cell formation and function. Genes Dev 30:502507\n 60. Akerman I, Tu Z, Beucher A etal (2017) Human pancreatic beta cell lncRNAs control cell-specific regulatory networks. Cell Metab 25:400411", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 114, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "8bbfb009-87b7-54ae-8465-8796db8c271a", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "b3c5f734-aa0d-5da9-bdb9-e330e6c02e00", + "score": 0.6984752236887959, + "metadata": { + "text": "of lncRNAs in the development and function of metabolic tissues, and therefore, their altered levels are closely asso-ciated with the onset and progression of insulin resistance and diabetes.\nRoles oflncRNAs indiabetic complications\nApart from being involved in major metabolic tissues dur -", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 66, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "d7b2d258-d566-5552-8308-4ac35953884d", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "b774bf7b-4546-56d2-ae7b-7bc2c9f2fb08", + "score": 0.6904380527269263, + "metadata": { + "text": "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes,\nlncRNAs and their associated orchestrated networks are implicated in mediating complex pathological\nmechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and\n172 iScience 19, 162176, September 27, 2019", + "title": "2019 - Development and Genome Sequencing.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "18820c9e-f7ae-57ae-897d-0d9c3f616b6a", + "extraction_id": "d971dced-935c-566b-a4a2-11bcf99b9c84", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "c8d55dea-0656-527e-93bd-9624cec8f3c9", + "score": 0.6839436481032594, + "metadata": { + "text": "in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and func-tion [ 54]. To understand the roles of lncRNAs in regu-\nlating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancre-atic islets [55, 56]. Transcriptome analysis in pancreatic \n-cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR -", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "e5669569-f9ba-5797-b468-3a1980addc0a", + "score": 0.6827709674835205, + "metadata": { + "text": "1831 Lnc-ing non- coding RNAs withmetabolism anddiabetes: roles oflncRNAs \n1 3\nendocrine hormones, insulin and glucagon, where insulin \nis the anabolic master regulator which controls periph -", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "9ca17d26-cc06-5afe-a7dd-3f80b1b99da0", + "score": 0.6780256628990173, + "metadata": { + "text": "Vol.:(0123456789)1 3Cellular and Molecular Life Sciences (2018) 75:18271837 \nhttps://doi.org/10.1007/s00018-018-2760-9\nREVIEW\nLncing noncoding RNAs withmetabolism anddiabetes: roles \noflncRNAs\nNehaGoyal1,2 DeveshKesharwani1,2 MalabikaDatta1,2 \nReceived: 18 September 2017 / Revised: 29 December 2017 / Accepted: 24 January 2018 / Published online: 31 January 2018 \n Springer International Publishing AG, part of Springer Nature 2018\nAbstract", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "efc73cf6-99c6-5272-9bb0-7bd6a34633f0", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + }, + { + "id": "45d35985-9183-55f0-8b51-41df27cd7677", + "score": 0.6768291198262313, + "metadata": { + "text": "(2013). A novel mechanism regulating insulin secretion involving Herpud1 inmice. Diabetologia 56, 15691576 .\nZhao, X.Y., and Lin, J.D. (2015). Long noncoding RNAs: a new regulatory code\nin metabolic control. Trends Biochem. Sci. 40, 586596 .\n1806 Cell Reports 17, 17951806, November 8, 2016", + "title": "2016 - A genetic screen identifies hypothalamic Fgf15 as a regulator of glucagon secretion.pdf", + "version": "v0", + "chunk_order": 137, + "document_id": "288adb9b-a547-5e61-8593-1b2ab36271d3", + "extraction_id": "a847f5f4-0c56-5678-9e1e-93b9b5b294f2", + "associatedQuery": "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIHJKDULZO7wnGlvKQDCFnpw8Cumo", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Post-translational modifications (PTMs) of proteins play a significant role in regulating key signaling pathways involved in glucose homeostasis. Here are some ways PTMs affect these pathways:\n\n1. **Protein Kinases and Glucose Homeostasis**: The PI3K/AKT, MAPK, and AMPK signaling pathways are crucial for glucose homeostasis, and these pathways are regulated by protein kinases, which can be modulated by PTMs such as phosphorylation [1].\n\n2. **Histone Modifications**: Histone post-translational modifications are involved in the regulation of genes associated with diabetes pathogenesis, including those related to insulin and islet-specific transcription factors. These modifications can influence gene expression and thereby affect glucose metabolism [3].\n\n3. **N-glycosylation**: The glycosylation of glucose transporter 2 is an example of a PTM that promotes insulin secretion, which is vital for maintaining glucose levels and suppressing diabetes [6].\n\n4. **Sirtuins and Deacetylation**: The sirtuin family, particularly SIRT1, is involved in regulating factors related to metabolism and insulin secretion. Sirtuins are deacetylases, and their activity represents a form of PTM that can influence glucose homeostasis [10].\n\nThese examples illustrate how PTMs can modulate signaling pathways and protein functions, ultimately impacting glucose homeostasis and related metabolic processes.", + "role": "assistant" + } + } + ], + "created": 1728919810, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 286, + "prompt_tokens": 1455, + "total_tokens": 1741 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "64482aec-5688-5431-adda-b8f1de92a183", + "score": 0.7271759510040283, + "metadata": { + "text": "regulates glucose-induced biological responses in pancreatic \nbeta-cells. Diabetes. 2008;57:2708-17.\n29. Schultze SM, Hemmings BA, Niessen M, Tschopp O. \nPI3K/AKT, MAPK and AMPK signalling: protein kinases \nin glucose homeostasis. Expert Rev Mol Med. 2012;14:e1.\n30. White MF. IRS proteins and the common path to diabetes. \nAm J Physiol Endocrinol Metab. 2002;283:E413-22.\n31. Erener S, Marwaha A, Tan R, Panagiotopoulos C, Kieffer \nTJ. Profiling of circulating microRNAs in children with", + "title": "2018 - MicroRNA profiling and their pathways in South African.pdf", + "version": "v0", + "chunk_order": 97, + "document_id": "b6bb090d-7176-59db-af04-582aa1d5cf10", + "extraction_id": "14577d73-d320-54dd-93f2-c55f986bc8bc", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "b604dabf-3dc2-5d01-9cc4-6e9f916c464a", + "score": 0.7032889723777771, + "metadata": { + "text": "pathological processes involved in glucose metabolism \nby post transcriptional regulation of gene expression. \nParticular microRNAs can regulate cell function271, \nexposing key regulatory signalling pathways involved in \nrestoration of cell mass, and provide a promising strat \negy for improving insulin secretion and cell health in \nT2DM. Identification of novel insulin secretagogues \nthat act directly on cells and enteroendocrine Kcells \nand Lcells in the intestine are under investigation, and", + "title": "2015 - Type 2 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 163, + "document_id": "415516ba-5365-501b-84ce-0789045862f8", + "extraction_id": "617b6fab-a5e5-59b7-a593-a0477e6bf9fe", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "e1f984ac-aa42-5eb4-92cb-303886f6f1db", + "score": 0.7016167640686035, + "metadata": { + "text": "can result in diabetes and its complications including DN.\nSeveral studies show that key histone post- translational \nmodifications are involved in the regulation of genes \nassociated with the pathogenesis of diabetes, such as \ninsulin and islet-specific transcription factors.48,60 Inaddi -\ntion, several groups are examining the role of histone \npost-translational modifications in adipocytes related to \ntype2 diabetes, obesity and the metabolic syndrome.48,60", + "title": "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "be05127e-1be8-5573-b571-51a11c3b2be2", + "extraction_id": "bf537fe8-5508-5355-a656-b4053febe0e5", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "7b6e89ec-b690-5ff1-b24d-3ed6744f3486", + "score": 0.6925344054380426, + "metadata": { + "text": "cascade of protein kinases and regulatory proteins of which IRS-1 and IRS-2 \nare most important. This causes suppression of glucose release from liver \nand kidney/ translocation of glucose transporters in muscle and adipose \ntissue to increase their glucose uptake, and inhibition of release of FF A into \nthe circulation due to suppression of the activity of hormone-sensitive lipase \nand a simultaneous increase in their clearance from the circulation. Although", + "title": "2004 - Diabetes Genes a.pdf", + "version": "v0", + "chunk_order": 178, + "document_id": "805ad0d5-6a3d-5e5f-b1ba-121e10c6daaa", + "extraction_id": "06912a59-fdd6-5731-af8f-6c98ff1ace5c", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "9a8edd2d-c06a-559e-8397-beaaa84705b7", + "score": 0.6861554384231567, + "metadata": { + "text": "Magnan C, Postic C, Prip-Buus C, Vasseur-Cognet M (2008) The\ntranscription factor COUP-TFII is negatively regulated by insulin and\nglucose via Foxo1- and ChREBP-controlled pathways. Mol Cell Biol 28:\n65686579Rodgers JT, Lerin C, Haas W, Gygi SP, Spiegelman BM, Puigserver P (2005)\nNutrient control of glucose homeostasis through a complex ofPGC-1alpha and SIRT1. Nature 434: 113118\nSchwer B, Verdin E (2008) Conserved metabolic regulatory functions of\nsirtuins. Cell Metab 7:104112", + "title": "2011 - CREB and ChREBP oppositely regulate SIRT1 expression in response to energy availability.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "32538f01-9eaf-5f9b-8615-ec47cc4ca8e2", + "extraction_id": "4217906f-87c5-54b0-95a5-7c26dc08afce", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "7d522337-e875-55eb-9b67-4718e5db8ffd", + "score": 0.6820673942565918, + "metadata": { + "text": "of glucose transporter 2 glycosylation promotes insulin secretion in suppressing diabetes. Cell 123:1307 1321. PMID: 16377570\n47. Whitaker GM, Lynn FC, McIntosh CH, Accili EA (2012) Regulation of GIP and GLP1 receptor cell sur-\nface expression by N-glycosylation and receptor heteromerization. PLoS One 7: e32675. doi: 10.1371/\njournal.pone.0032675 PMID: 22412906\n48. Johswich A, Longuet C, Pawling J, Abdel Rahman A, Ryczko M, et al. (2014) N-glycan remodeling on", + "title": "2015 - Transcript Expression Data from Human.pdf", + "version": "v0", + "chunk_order": 175, + "document_id": "2b30d4f3-9ec3-574f-9a36-709b0e09c3f2", + "extraction_id": "867d0b1b-16a1-53ea-b014-3c204b9001a5", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "1edee360-5de0-51c9-bf8d-7c2e2f23a682", + "score": 0.6818087697029114, + "metadata": { + "text": "strate 1), Pde3b (phosphodiesterase 3B), Hk2 (hexokinase 2), Foxo1\n(forkhead box O1), Socs6 (suppressor of cytokine signaling 6), and Ogt\n(O-linked N-acetylglucosamine (GlcNAc) transferase). Impaired insulinsignaling is well known to negatively in uence glucose and lipid\nmetabolism [62]. In adipose tissue, insulin stimulates glucose uptake\nby inducing translocation of GLUT4 to the cell surface, it increasesglycolysis rate by stimulating hexokinases ( Hk2) and suppresses\nlipolysis ( Acaca and Prkaa1 )[63].", + "title": "2018 - A computational biology approach of a genome-wide screen.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "ae727c80-a0a3-52f4-9e2f-b93a539558ad", + "extraction_id": "ab4f6ea7-767f-5783-9e1a-8570eaabe96c", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "43a104b3-f34b-5f52-86ff-fd7d45827f32", + "score": 0.6812280416488647, + "metadata": { + "text": "signalling pathways by reducing insulin induced tyro \nsine phosphorylation of IRS1 and IRS2 (REF. 161) and by \nincreasing degradation of IRS1 (REF. 162). Recent studies \nhave demonstrated that the p85 regulatory subunit of \nPI3K interacts with XBP1s (the spliced, transcription \nally active isoform of XBP1) and promotes the trans \nlocation of XBP1s into the nucleus to initiate the ER \nstress response163.Diabetic complications\nDiabetic microvascular complications are closely related", + "title": "2015 - Type 2 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 88, + "document_id": "415516ba-5365-501b-84ce-0789045862f8", + "extraction_id": "e4e89eba-6032-5781-83f4-8d47ab5b3825", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "3e08ef82-888b-58a0-9a80-3547ab4bd516", + "score": 0.6800271677987092, + "metadata": { + "text": "activated protein kinase. J Biol Chem. 2007;282:9777 -88. \n[44] Chakrabarti S, Davidge ST. High glucose -induced oxidative stress alters estrogen effects on ERalpha and \nERbeta in human endothelial cells: reversal by AMPK activator. J Steroid Biochem Mol Biol. 2009;117:99 -106. \n[45] Mortuza R, Chen S, Feng B, Sen S, Chakrabarti S. High glucose induced alteration of SIRTs in endothelial \ncells causes ra pid aging in a p300 and FOXO regulated pathway. PLoS One. 2013;8:e54514.", + "title": "2015 - Cellular and Molecular Biology of Aging Endothelial Cells.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "815d7f3e-e219-502f-aba0-57a68ae787d3", + "extraction_id": "283e34bb-6e2b-5aa9-85c5-2584b669f122", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + }, + { + "id": "cf4f3239-dd62-5eef-b5fc-85f4780e3f48", + "score": 0.6797555685043335, + "metadata": { + "text": "Epigenetic Mechanisms in Diabetic Complications 17 \nInterestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 \nregulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 \nand HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "41ac576d-b850-5ee8-9753-ba9b060ba798", + "associatedQuery": "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIHJRHE43F2P7lugdT3xuuPfMvKGY", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome, has provided several insights:\n\n1. **Genotype-Phenotype Correlation**: Research has highlighted the importance of understanding genotype-phenotype correlations in Wolfram Syndrome. This involves identifying specific genetic mutations and understanding how they manifest in clinical symptoms, which can aid in more accurate diagnosis and management of the syndrome [1], [6].\n\n2. **Genetic Mutations**: Wolfram Syndrome is primarily caused by mutations in the WFS1 gene, which encodes the wolframin ER transmembrane glycoprotein. These mutations lead to a range of symptoms including diabetes mellitus, optic atrophy, and deafness. Some severe cases are associated with dominant heterozygous variants [4].\n\n3. **Early Onset and Symptoms**: The syndrome is characterized by early onset of diabetes mellitus, typically around the age of 6, followed by other symptoms such as optic atrophy and sensorineural deafness in later years [5].\n\n4. **Potential for Broader Implications**: Insights from studying Wolfram Syndrome have implications for basic science and clinical practice. They emphasize the need for accurate clinical descriptions and early recognition of symptoms, which can improve patient outcomes and inform treatment strategies [6].\n\n5. **Risk Assessment**: There is ongoing research to determine if heterozygotes for Wolfram Syndrome are at risk for maturity-onset diabetes, which could have implications for understanding genetic risk factors in broader populations [2].\n\nThese insights not only enhance our understanding of Wolfram Syndrome but also contribute to the broader field of genetic research in diabetes, potentially informing personalized medicine approaches for more common forms of the disease.", + "role": "assistant" + } + } + ], + "created": 1728919817, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 346, + "prompt_tokens": 1377, + "total_tokens": 1723 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "d0465c0a-e45a-5fff-a9b3-40ad191963a3", + "score": 0.7042259187765696, + "metadata": { + "text": "WFS1 and genotype-phenotype correlation in Wolfram syndrome. Am J\nMed Genet A. 2007;143A(14):1605 12.\n61. McCarthy MI. Painting a new picture of personalised medicine for diabetes.\nDiabetologia. 2017;60(5):793 9.\n62. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of\ntype 2 diabetes. Nature. 2016;536(7614):41 7.\n63. Patch AM, Flanagan SE, Boustred C, Hattersley AT, Ellard S. Mutations in the\nABCC8 gene encoding the SUR1 subunit of the KATP channel cause", + "title": "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "version": "v0", + "chunk_order": 146, + "document_id": "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "extraction_id": "0f16b510-caa9-521f-8d87-e225f52de9f5", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "f4b3a7bd-4a5c-5c95-aa11-936340eecb8b", + "score": 0.6707017225445343, + "metadata": { + "text": "enable physicians to ameliorate some of the complications\nthat so devastate the lives of these patients.\nThree questions need answers from further studies: is\nthere really a lack of diabetic complications in Wolfram\nsyndrome patients compared with other diabetics? What\nis the nature of the neurodegeneration and its relation to\ndiabetes mellitus? Are heterozygotes for Wolfram\nsyndrome at risk of maturity-onset diabetes?\nThis paper is dedicated to the memory of Robin Smith, a Wolfram", + "title": "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "version": "v0", + "chunk_order": 54, + "document_id": "1fa2280b-868e-571f-830e-bebdd874fc9d", + "extraction_id": "744f3821-fc61-58d1-8107-17d5674fe1d8", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "e4240b4f-b19f-5f8c-b9ce-e0b60cf4aceb", + "score": 0.6695254048563664, + "metadata": { + "text": "Monogenic and syndromic forms account for only a small,though highly informative, proportion of cases of nonau-toimmune diabetes. The challenge for medical science liesin bringing equivalent mechanistic insights and transla-tional benets to the hundreds of millions of peoplealready affected by, or at risk of, more common, typicalforms of diabetes. For type 2 diabetes, there is abundantevidence that individual susceptibility is inuenced byboth the combination of genetic variation at multiple sitesand a", + "title": "2008 - Learning From Molecular Genetics.pdf", + "version": "v0", + "chunk_order": 48, + "document_id": "3aa0aea1-fe53-519b-b367-79fa1c79aa01", + "extraction_id": "979b0578-b02a-526e-b3b4-aa7fec3eeb91", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "3efa9423-0651-5096-8ccd-50a0f906e30e", + "score": 0.6586581664877587, + "metadata": { + "text": "responding to two causative genes have been identified to date. \nWolfram syndrome 1 (WS1), characterized by diabetes insipidus, \nDM, optic atrophy, and deafness, is a rare autosomal recessive \ndisease caused by variants in wolframin ER transmembrane gly-\ncoprotein (WFS1). Severe cases with dominant heterozygous vari-\nants are also reported (92). Often, patients first manifestation \nis DM at an average age of 6 years. Though most WS1 patients", + "title": "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "version": "v0", + "chunk_order": 68, + "document_id": "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "extraction_id": "f01be500-1e96-57ca-b164-1b97017ec44d", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "1ae4ea48-5dbd-5502-aa74-a1ef1527a493", + "score": 0.6534469517139764, + "metadata": { + "text": "finding study to describe the natural history, complications,\nprevalence, and inheritance of the syndrome.\nWe identified 45 patients with Wolfram syndrome—a\nprevalence of one per 770000. Non-autoimmune, insulin-\ndeficient diabetes mellitus presented at a median age of 6\nyears, followed by optic atrophy (11 years). Cranial\ndiabetes insipidus occurred in 33 patients (73%) with\nsensorineural deafness (28, 62%) in the second decade;\nrenal-tract abnormalities (26, 58%) presented in the third", + "title": "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "1fa2280b-868e-571f-830e-bebdd874fc9d", + "extraction_id": "782db3c2-ab52-59df-b798-c5b2492c8946", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "e0234ab8-850f-585c-9de2-edaa3ac615fd", + "score": 0.6526280836054115, + "metadata": { + "text": "Wolfram patients have a mitochondrial genome\nabnormality, but this has not yet been shown. The\ndifferential diagnosis indicates the importance of accurate\nclinical descriptions when presenting cases of the\nsyndrome.\nOur study has implications for basic science and\npractice: more accurate characterisation of the syndrome\nwill allow assessment of genotype/phenotype correlations;\nand earlier recognition of diabetes insipidus,\ngastrointestinal dysfunction, and central apnoeas should", + "title": "1995 - Neurodegeneration and diabetes UK nationwide study of Wolfram syndrome.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "1fa2280b-868e-571f-830e-bebdd874fc9d", + "extraction_id": "744f3821-fc61-58d1-8107-17d5674fe1d8", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "85de5eb6-8034-5ebb-8526-1de0a173b2ae", + "score": 0.6522817806089458, + "metadata": { + "text": "onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and \nlipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges \nfacing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.Monogenic diabetes: a gateway to precision medicine \nin diabetes\nHaichen Zhang,1 Kevin Colclough,2 Anna L. Gloyn,3,4 and Toni I. Pollin1", + "title": "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "extraction_id": "5a39ee4b-ba00-56d6-ba6c-0edeac3b4f2e", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "0b2437f9-1aff-5e6a-bca6-cc17d96c5d56", + "score": 0.6496991917580633, + "metadata": { + "text": "WFS1 mutations underlie a genetic syndrome \nof neonatal/infancy-onset diabetes, congenital \nsensorineural deafness, and congenital cataracts. \nDiabetes . 2017;66(7):20442053.\n 93. Rigoli L, Di Bella C. Wolfram syndrome 1 \nand Wolfram syndrome 2. Curr Opin Pediatr. \n2012;24(4):512517 .\n 94. Bansal V, et al. Identification of a missense vari-\nant in the WFS1 gene that causes a mild form of \nWolfram syndrome and is associated with risk for \ntype 2 diabetes in Ashkenazi Jewish individuals.", + "title": "2021 - Monogenic diabetes a gateway to precision medicine.pdf", + "version": "v0", + "chunk_order": 156, + "document_id": "0b6ff786-6a7b-5d24-ba5e-7a61fee7757f", + "extraction_id": "31b3b484-d769-5b88-9633-45ca3508be8f", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "8fbdd64f-d761-5e8d-989c-7e4bf376b1ec", + "score": 0.6475472257272261, + "metadata": { + "text": "established. It has been corroborated by a series of obser-vations that include ethnic differences, familial aggrega-tion, twin studies, admixture studies, linkage studies, monogenic cases (e.g., MODY), mitochondrial cases of diabetes, and a constantly growing number of molecular markers \n[5] . On the other hand, the genetics of the meta-\nbolic syndrome remains complex [6] . It is highly unlikely \nthat a single gene will account for a substantial portion", + "title": "2010 - Family History of Diabetes and Prevalence.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "f16c4c6e-bb5f-5d4a-9945-8af4d0df19f4", + "extraction_id": "59b6bf24-b9f7-53bc-b809-5ce97d5e9898", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + }, + { + "id": "bd8d61dd-e027-5a43-8b00-1e7e9e748ea3", + "score": 0.6464164449696677, + "metadata": { + "text": "diabetes (0.5% carrier frequency) compared to controls (0.035%). One individual with early onset diabetes was\nhomozygous for a rare pathogenic missense variant in the WFS1 gene but did not have the additional phenotypes\nassociated with Wolfram syndrome.\nConclusion: Targeted sequencing of genes linked with monogenic diabetes can identify disease-relevant mutations in\nindividuals diagnosed with type 2 diabetes not suspected of having monogenic forms of the disease. Our data suggests", + "title": "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "extraction_id": "36a362bd-1d4a-5e47-a00f-5ae6d3a0626f", + "associatedQuery": "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIHJckmqoH46qp1BDk6zYdLg1Akw5", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The study of monozygotic twins discordant for diabetes provides valuable insights into the etiology of the disease by highlighting the roles of both genetic and epigenetic factors. Since monozygotic twins share identical genomes, any differences in disease manifestation between them can often be attributed to non-genetic factors, such as epigenetic modifications or environmental influences.\n\n1. **Genetic Component**: The high concordance rates for diabetes in monozygotic twins, compared to dizygotic twins, underscore a significant genetic component to the disease [1], [6]. This suggests that genetic predispositions, particularly those affecting insulin sensitivity and secretion, play a crucial role in the development of diabetes [1].\n\n2. **Epigenetic Influences**: Despite identical genetic makeup, monozygotic twins can exhibit differences in disease susceptibility due to epigenetic variations. These variations can arise from environmental factors and gene-environment interactions, which are crucial in understanding the etiology of diabetes [4]. Epigenetic differences, such as DNA methylation patterns, have been observed in monozygotic twins and may contribute to discordance in disease states [9].\n\n3. **Environmental and Lifestyle Factors**: The discordance in diabetes among monozygotic twins also points to the influence of environmental factors and lifestyle choices, such as diet and physical activity, which can modify epigenetic marks and affect disease outcomes [6].\n\nIn summary, the study of monozygotic twins discordant for diabetes highlights that while genetic predispositions are significant, epigenetic modifications and environmental factors also play critical roles in the disease's etiology. This understanding can help in developing more targeted prevention and treatment strategies that consider both genetic and non-genetic factors.", + "role": "assistant" + } + } + ], + "created": 1728919828, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 351, + "prompt_tokens": 1453, + "total_tokens": 1804 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "6623716f-8275-5f17-be27-a8241560a645", + "score": 0.7489541472251293, + "metadata": { + "text": "Studies of twins also provide compelling evidence for a genetic component to \nT2D. Estimates for concordance rates range from 0.29 to 1.00 in monozygotic (MZ) twins, while in dizygotic (DZ) twins the range is 0.100.43 [57, 58, 6164]. \nThe high levels of heritability observed for insulin sensitivity and insulin secretion [6567] further reinforce the role of genetics in diabetes and indicate the primary genetic lesions for diabetes are likely to localize to genes in beta-cell-centric pathways.", + "title": "2011 - Interaction Between Exercise and Genetics.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "c36db75e-4b76-540d-9efb-d0e156e61541", + "extraction_id": "861346c7-0fcf-5cae-ace6-a012a370d297", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "15e73a17-cae0-5dc1-8cad-5805809ae872", + "score": 0.7349158245425486, + "metadata": { + "text": "It is therefore intriguing that A1C levels are signicantly\ncorrelated in monozygotic twins whether they are concor-\ndant for type 1 diabetes or not (4): in a discordant twin pairone twin is treated with insulin, whereas the other oneisnt, and thus this degree of correlation suggests thatgenetic contributors to A1C may be detectable despite thesuperimposition of a strong environmental modier. Rig-orous estimates of heritability of treated A1C, however,\nare not available.", + "title": "2010 - A Genome-Wide Association Study of Treated A1C.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "4de9f054-4a02-5b6a-905d-420744075755", + "extraction_id": "cce780d7-60c0-5cb3-976f-15e9808cab59", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "c57caee6-9b8f-5cb5-ab53-337dda1d92a7", + "score": 0.7254222958776649, + "metadata": { + "text": "Concordance rate for type II diabetes mellitus in monozy-gotic twins: actuarial analysis. Diabetologia 42:146150\n3. Lehtovirta M, Kaprio J, Forsblom C, Eriksson J, Tuomilehto\nJ, Groop L (2000) Insulin sensitivity and insulin secretionin monozygotic and dizygotic twins. Diabetologia43:285293\n4. Florez JC, Hirschhorn J, Altshuler D (2003) The inherited\nbasis of diabetes mellitus: implications for the genetic anal-ysis of complex traits. Annu Rev Genomics Hum Genet4:257291", + "title": "2004 - Common polymorphisms of the PPAR-\u03b32 (Pro12Ala) and PGC-1\u03b1 (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial.pdf", + "version": "v0", + "chunk_order": 73, + "document_id": "07b6ab4b-1aff-5d0e-aa98-d49a66b7b5d1", + "extraction_id": "feb52f56-db94-5e03-90a8-af3bf38d087e", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "bcfe4c03-f0bb-540d-b529-adda644998a6", + "score": 0.7199152016791354, + "metadata": { + "text": "disease susceptibility is not explained by genetics alone; environ-\nmental factors, gene by environment interactions, and epigenetic\ninuences are likely to play important roles in the etiology of T1D\n[5,6] . Monozygotic (MZ) twin pairs, discordant for T1D, represent\nan ideal system to test susceptibility factors not attributable to\ngenetic variation, especially epigenetic variation, since the ge-\nnomes of the twins are identical. The ascertainment of disease-", + "title": "2016 - Hypomethylation within gene promoter regions and type 1 diabetes.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "3d0f1bab-738c-5ba3-8fd9-206b3455704d", + "extraction_id": "bc569d05-fc39-5487-95e7-63b0d7bf9b7e", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "4e393756-1b48-56f7-a3e7-33d15a55dc73", + "score": 0.7184282876934713, + "metadata": { + "text": "epigenetic differences among monozygotic twins. A critical\nquestion is whether epigenetic marks are transmitted intactfrom parent to offspring and whether DNAm is allele-\nspecific and covaries with allele-specific gene expression.\nFor example, can we develop an epigenetic transmissiontest comparable to the transmission disequilibrium test used\nin genetic epidemiology? Finally, and most excitingly, we", + "title": "2010 - Genome-scale approaches to the epigenetics of common.pdf", + "version": "v0", + "chunk_order": 80, + "document_id": "3f21702c-3fdf-50b5-8060-04a9a1ce09ae", + "extraction_id": "8881623e-fe7a-53bd-b703-2e8bf6a5c240", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "a4c3abc2-635c-5ace-ad39-c678a505fa48", + "score": 0.7131724699530092, + "metadata": { + "text": "their dietary and physical activity habits (Maes\net al, 1997 ).\nThere is also ample evidence that diabetes\nhas a substantial genetic component. The con-\ncordance of type 2 diabetes in monozygotictwins ranges between 50 and 70% compared to\n2037% in dizygotic twins (Kaprio et al, 1992 ;\nNewman et al, 1987 ; Poulsen et al 1999). Further\nevidence comes from studies that compare therisk in offspring with a family history of type\n2 diabetes with offspring without such a fam-", + "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "version": "v0", + "chunk_order": 4221, + "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c", + "extraction_id": "2778ece8-df84-58d2-9002-e036f0d007dc", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "f295f1d3-43d5-5eef-a39f-a8b91c47500f", + "score": 0.7123925432690977, + "metadata": { + "text": "monozygotic and dizygotic Danish twin pairs withinsulin dependent diabetes mellitus. Bmj 1997: 314:1575 1579.\n30. R\nEDONDO MJ, R EWERS M, Y UL et al. Genetic deter-\nmination of islet cell autoimmunity in monozygotictwin, dizygotic twin, and non-twin siblings of patientswith type 1 diabetes: prospective twin study. Bmj 1999:318: 698 702.\n31. L\nEVY-M ARCHAL C, P ATTERSON C, G REEN A. Variation", + "title": "2003 -Genetic epidemiology of type 1 diabetes.pdf", + "version": "v0", + "chunk_order": 122, + "document_id": "cbc7f2d3-3f65-50ba-b281-96dd1c77f2c0", + "extraction_id": "592fd011-4dfb-5a78-8973-482e35541468", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "0cd29c12-48e8-5f9f-9744-6b8acfaae0c7", + "score": 0.7090156847825352, + "metadata": { + "text": "Studies in twins have demonstrated that 5070 % in the body mass index (BMI) variance may be explained by genetics ( Allison et al., 1996 ), and T2DM concordance was reported ranging from 1737 % in dizygotic to 5070 % in monozygotic twins ( Kaprio et al., 1992 ; Medici et al., 1999 ; Poulsen et al., 1999 ). In addition, family and adoption studies have reported heritability ranging from 2060 % for obesity ( Rice et al., 1999 ; Stunkard et al., 1986 ) and 3070 % for T2DM ( Meigs", + "title": "2011 - Lifestyle and Genetics in Obesity and type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 14, + "document_id": "a16d3328-039c-530a-bfe5-f6f80ecf2ad0", + "extraction_id": "551087b1-8e80-5a7b-839a-304f566a6417", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "3a9e7574-8914-5a96-86b6-b7b87a89b894", + "score": 0.7063656203310993, + "metadata": { + "text": "Monozygotic twins exhibit numerous epigenetic differences: clues to twindiscordance? Schizophr Bull 29: 169178.\n8. Oates NA, van Vliet J, Duffy DL, Kroes HY, Martin NG, et al. (2006) Increased\nDNA methylation at the AXIN1 gene in a monozygotic twin from a pair\ndiscordant for a caudal duplication anomaly. Am J Hum Genet 79: 155162.\n9. Kuratomi G, Iwamoto K, Bundo M, Kusumi I, Kato N, et al. (2008) Aberrant\nDNA methylation associated with bipolar disorder identified from discordant", + "title": "2013 - Continuous Aging of the Human DNA Methylome.pdf", + "version": "v0", + "chunk_order": 106, + "document_id": "34783678-d4c2-566f-9b5b-0db18a72e86d", + "extraction_id": "a0916b04-3463-5247-94da-0c97fd5da20d", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + }, + { + "id": "af680560-47c6-5556-bb80-c7584d762f66", + "score": 0.7053382782537412, + "metadata": { + "text": "5\nE/C128orts to estimate the heritability of T2D by a comparison of the concordance rates in\nmono- and dizygotic twins have varied greatly as a result of di/C128erences in ascertainment\nscheme, diagnostic criteria and follow-up duration.69Concordance for diabetes is\ngenerally higher in identical twins (supporting a genetic basis for disease), although the\nextremely high concordance rates in some early studies6were undoubtedly inated by\nascertainment bias.\nEvidence from population studies", + "title": "2001 - The genetics of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "8ab06972-1c6c-5d68-a270-65fb0af0917b", + "extraction_id": "737e4fe2-91ba-50c5-8f64-1149944fb60c", + "associatedQuery": "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIHJknEcr96E1ybbJw2DE0EMMQI1v", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent studies on the interaction between genetic variants and environmental factors in diabetes development have identified several potential therapeutic targets. These include:\n\n1. **Primary Regulators of Insulin Secretion and Action**: Several type 2 diabetes (T2D) risk variants have been identified as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. This suggests that targeting these pathways could be a potential therapeutic strategy [6].\n\n2. **Specific Genetic Variants**: Newly discovered single nucleotide variants (SNVs) allow for better characterization of abnormalities in early insulin processing and secretion. Genes such as TCF7L2, SLC30A8, and C2CD4B have been highlighted as potential targets due to their roles in these processes [6].\n\n3. **Gene-Environment Interactions**: The interaction between genetic susceptibility and environmental factors such as physical activity and dietary fat has been shown to modify the risk of glucose homeostasis and T2D. This indicates that interventions targeting these environmental factors could potentially mitigate the genetic risk [7].\n\nThese findings underscore the importance of considering both genetic and environmental factors in developing therapeutic strategies for diabetes.", + "role": "assistant" + } + } + ], + "created": 1728919836, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 231, + "prompt_tokens": 1238, + "total_tokens": 1469 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b092c8b9-edb1-55fb-ae16-c67e3298946e", + "score": 0.7191900443548004, + "metadata": { + "text": "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "title": "2008 - Glossary of Genetics Genomics Terms.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "c66d2572-071d-5aaf-829c-b3ca6cf6d697", + "extraction_id": "53e868dd-b318-5cf3-8b2e-98a548aab7cf", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "55f842a4-506a-5992-9b6e-47c81aee6809", + "score": 0.7178966234860804, + "metadata": { + "text": "ponse to thiazolidinedione therapy and candidate genes \n[100103]. Results from pharmacogenetic studies could \npotentially provide physicians with a powerful tool to \nadjust therapy appropriately for those individuals carry\ning variants known to affect a given medication. Distefano \nand Watanabe have recently reviewed the pharmaco\ngenetics of diabetes [104].\nGenegene and geneenvironment interactions are also \nlikely to be helpful to the clinician in making therapeutic", + "title": "2011 - Inherited destiny Genetics and gestational diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "6d341cd2-ae56-5807-9aff-39298efc4d06", + "extraction_id": "48c3e4a4-db23-5fca-9c46-775e80894655", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "728c47bb-e8e2-5359-9ff5-9ad9b13f999c", + "score": 0.7136673160593114, + "metadata": { + "text": "Genomics of T2D\nDiet, lifestyle, environment, and even genetic variation influence an individuals response to disease therapy. Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for iden -\ntifying genetic variants responsible for patient differ -", + "title": "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "extraction_id": "52a000e5-d790-55f2-9eac-14554d426173", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "15872da6-8175-5db6-b741-10ae3cf85088", + "score": 0.7108629385408409, + "metadata": { + "text": "ease caused by interactions between multiple genetic\nand environmental factors. Significant progress has been\nmade in understanding the genetic architecture of T2D\nover the past 10 years [1]. A number of genome-wide as-\nsociation studies in diverse human populations have\nidentified more than 60 common variants and loci asso-\nciated with risk for T2D [2]. These studies have also\nrevealed a significant overlap between traits and pheno-\ntypes of monogenic diabetes with related common", + "title": "2017 - Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "18a8a000-69ed-5d34-b13f-f5ae016d1067", + "extraction_id": "b24927c4-ee83-51a8-b431-b43be7d3b678", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "53fd1ea0-5ca7-5066-bb07-e7469c640e22", + "score": 0.7107654041508094, + "metadata": { + "text": "21582171 (2014).\n 29. Wood, A. R. et al. A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of \ntype 2 diabetes variants. Diabetes 66, 22962309 (2017). 30. Pickrell, J. K. Joint analysis of functional genomic data and genome- \nwide association studies of 18 human traits. Am. J. Hum. Genet. 94, \n559573 (2014).\n 31. Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets", + "title": "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "version": "v0", + "chunk_order": 97, + "document_id": "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "extraction_id": "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "027f0c97-d38d-551d-add3-4a759a406895", + "score": 0.7100871032528355, + "metadata": { + "text": "by GWASs [ 16,28,29]. A wide variety of network-based approaches have been applied to investigate the\nextent to which the genetics of T2D predisposition converge on a restricted set of biological pathways.\nSeveral T2D risk variants have been identied as primary regulators of insulin secretion, insulin\naction, and pancreatic islet transcription factors. [ 10,16]. The newly discovered SNVs allow the better\ncharacterization of abnormalities in early insulin processing and secretion. TCF7L2 ,SLC30A8 ,C2CD4B ,", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 26, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "455b92f7-6156-5735-8586-29a66af0f9e5", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "155260c5-ba90-540f-8d48-bafece83fa47", + "score": 0.709456205368047, + "metadata": { + "text": "[10] , many environmental factors [11] , and the interac-\ntions among those genetic and environmental factors. Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM \n[12] and there is reason to believe that a significant pro-\nportion of the susceptibility genes identified by GWASs will interact with these environmental factors to influ-ence the disease risk. Florez et al.", + "title": "2010 - Evidence of Interaction between Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "1a33b1d1-23ee-5b33-b42d-c745c8210166", + "extraction_id": "d2de4ed1-897b-5e5b-bc29-c03310096d64", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "3d00ac57-9828-5146-a895-9840de9af5f7", + "score": 0.7094038302061773, + "metadata": { + "text": "interactions suggest a way by which genetic risk may beameliorated, these environmental factors are of great relevanceto public health, and are the focus of a growing number of studies\n[7].\nEnvironmental factors, such as diet and lifestyle, are important\nin the onset, development and progression of T2D and its related\nphenotypes [8,9]. The interactions of environmental factors with", + "title": "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "8c310d76-0a3b-574c-9859-859258870ee5", + "extraction_id": "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "518d294f-67c5-5870-9f28-3cb4dfa81e42", + "score": 0.7045328860918147, + "metadata": { + "text": "cases. J Am Med Assoc. 1956;161:1628 30.\n3. Duncan LE, Keller MC. A critical review of the first 10 years of\ncandidate gene-by-environment interaction research in psychiatry.\nAm J Psychiatry. 2011;168:1041 9.\n4. Brito EC et al. Previously associated type 2 diabetes variants may\ninteract with physical activity to modify the risk of impaired glu-\ncose regulation and type 2 diabetes: a study of 16,003 Swedish\nadults. Diabetes. 2009;58:1411 8.", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "cb5c4aab-77ed-58cd-98b8-9e1ba64eb9cf", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + }, + { + "id": "6b83f0af-1145-5679-9dae-0f645771d25d", + "score": 0.7044464945793202, + "metadata": { + "text": "this occurs. Findings to date, however, indicate\nthat behavioral changes can substantially\nmitigate diabetogenic and obesogenic effects\nof individual or multiple risk alleles, which\nhas much broader clinical and public health\nimplications.We have seen considerable progress in our\nunderstanding of the role that both environ-\nment and genetics play in the development of\nT2D. Recent work suggests that the adverse\neffect of some established T2D-associated\nloci may be greatly attenuated by appropriate", + "title": "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "extraction_id": "89bf4316-d0cc-5310-a45e-1dd8b8aefe1b", + "associatedQuery": "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIHJu3dzRIlHnqdmlSb6lzwzfrntr", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes by potentially explaining some of the \"missing heritability\" that common variants identified through genome-wide association studies (GWAS) do not account for. While GWAS have identified many common variants associated with Type 2 diabetes, these explain only a fraction of the heritability of the disease [4]. The missing heritability could be located in low-frequency and rare variants, particularly in noncoding regions of the genome [1]. However, studies have shown that rare coding variants, especially when clustered in a small number of genes, are unlikely to account for much of the missing heritability [10]. Instead, if rare coding variants are significant, they are likely scattered across many genes [10]. Therefore, while rare variants may contribute to the heritability of Type 2 diabetes, their exact role and impact remain to be fully elucidated, and larger multi-population studies are needed to reliably identify rare variants exclusively associated with Type 2 diabetes [6].", + "role": "assistant" + } + } + ], + "created": 1728919846, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 213, + "prompt_tokens": 1205, + "total_tokens": 1418 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "935ff4d1-9840-5fab-8ad8-82e668319e91", + "score": 0.7924776920702824, + "metadata": { + "text": "and rare coding variants do not account for much of theheritability of type 2 diabetes. Under this scenario, themissing heritability could be located in common orlow-frequency and rare variants in noncoding regionsof the genome. Recent studies that jointly modeled dia-betes or obesity risk as a function of genetic relatednessacross all of the GWAS SNPs have suggested that much\nof the heritability of these traits can be explained by", + "title": "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "version": "v0", + "chunk_order": 110, + "document_id": "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "extraction_id": "01778b74-61b2-5f64-be8e-775c79af171d", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "b6905dfe-c622-58cd-b9ac-2cdcedada7e4", + "score": 0.7733684547731772, + "metadata": { + "text": "T2D heritability.\n3. Uncovering the Signicance of Rare-Coding and Non-Coding Genetic Variants in the Etiology\nof Type 2 Diabetes\nAs previously stated, GWASs have uncovered many new genetic associations that are relevant to\nT2D, but GWAS ndings represent common and mid-frequency genetic variations, thus excluding\nrare frequency variants and also cumulative effect of many variants with small effect sizes. Missing\nheritability refers to the portion of genetic variance that cannot be explained by all signicant", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 34, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "0f2a8ab2-1666-50c0-b0b0-2a37e1f6917f", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "0ab59821-8bf4-50d3-92e7-b0dd593883a8", + "score": 0.7672769794679619, + "metadata": { + "text": "could be accounted for by low-frequency and rare variants\nof moderate effect in a small number of genes. Our whole-exome sequencing study has explicitly addressed thisquestion. Additionally, we did not examine whether thereare fewer than 20 genes involved in type 2 diabetes butrather looked at whether rare coding variants in fewerthan 20 genes account for much of the heritability. In\nsuch a model, any number of other genes that do not", + "title": "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "version": "v0", + "chunk_order": 101, + "document_id": "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "extraction_id": "01778b74-61b2-5f64-be8e-775c79af171d", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "e266cecd-e881-5c64-8ce3-4894cbe47db5", + "score": 0.7656311988830621, + "metadata": { + "text": "contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common \nvariants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. \nHere, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES \nconsortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome", + "title": "2016 - The genetic architecture of type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "d7e2a9de-46f1-5191-9cb0-dd68eb9f365a", + "extraction_id": "c266fa33-e779-514b-9337-636a69c6e6a4", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "aacfbc09-f4ec-5b71-a4a8-efd43cf8b6db", + "score": 0.7605376982368425, + "metadata": { + "text": "One common disease that has been subjected to intense\ngenetic study is type 2 diabetes.\n32The heritability of type 2\ndiabetes has been estimated to be around 30%.3335\nThrough GWASs, 63 loci have been reproducibly associ-ated with type 2 diabetes.\n36However, as for other complex\ntraits, the associated SNPs can only account for <20% of\nthe heritability estimated from family studies.36\nHere, we seek to evaluate the role that rare coding vari-", + "title": "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "extraction_id": "89a75bbb-f0f6-5391-98fd-56631343a38e", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "0977e7bd-4c4c-5c6c-a4d1-3b5f6fda03c5", + "score": 0.7549468053893237, + "metadata": { + "text": "prevalence of T2D. These authors found rare variants that were not detected previously in population\nstudies, but none of them were associated with T2D [ 49]. Larger multi-population studies and more\nadvanced study methods are needed to reliably identify rare variants that are exclusively associated\nwith T2D to eventually uncover missing T2D heritability.\n3.2. Genetic Variants in Familial Studies of Type 2 Diabetes\nThe development of T2D is driven by the combined effect of environmental factors and a", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 50, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "524594ab-31ca-5f5c-8126-7c58060bb73e", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "9a3d06ce-e86f-511f-82ac-97e486618e47", + "score": 0.7514202370816429, + "metadata": { + "text": "variance in disease risk that can be accounted for bythe 63 previously identied associations with commonvariants.\nOur empirical and simulation results are compatible\nwith a variety of different genetic architectures for type2 diabetes. First, if rare coding variants are responsiblefor the majority of the heritability of the trait, the variants\nare most likely scattered across many ( >20) different", + "title": "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "version": "v0", + "chunk_order": 107, + "document_id": "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "extraction_id": "01778b74-61b2-5f64-be8e-775c79af171d", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "451c2da6-3fd5-53f4-a58e-32b4f1d2cbbd", + "score": 0.7468359772162465, + "metadata": { + "text": "Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D. However, only close to 10% heritability is explained by these variants. Other genetic variants, particularly those which are rare but with significant effects need to be identified.", + "title": "2015 - Genetics, genomics and personalized medicine in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 120, + "document_id": "d8b85c3e-62f3-5e67-99b0-d0a2f225aff0", + "extraction_id": "5cd40b2d-72d6-5386-be94-b4e8188e4114", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "40f471a6-3615-52f3-a306-9f3568680409", + "score": 0.7453516429243513, + "metadata": { + "text": "and rare sequence variants associated with elevated \nor reduced risk of type 2 diabetes. Nat. Genet. 46, \n294298 (2014).\n168. Lek, M. etal. Analysis of protein-coding genetic \nvariation in 60,706 humans. Nature 536, 285291 \n(2016).169. Xue, A. etal. Genome-wide association analyses \nidentify 143 risk variants and putative regulatory \nmechanisms for type 2 diabetes. Nat. Commun. 9, \n2941 (2018).\n170. Huyghe, J. R. etal. Exome array analysis identifies", + "title": "2020 - Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics..pdf", + "version": "v0", + "chunk_order": 168, + "document_id": "2a386c81-8f24-5993-8e48-0e89d7fb4fec", + "extraction_id": "36db7673-abd7-55b1-9caf-b66498e19e78", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + }, + { + "id": "a5469aca-198e-56f5-ab92-16fd00c5e0fc", + "score": 0.7422869512692748, + "metadata": { + "text": "diabetes. In particular, our study suggests that when clus-tered in a small number of genes, rare coding variants ofmoderate to strong effect are unlikely to account formuch of the missing heritability. Rather, if rare coding var-iants are an important factor in type 2 diabetes risk, theyare most likely scattered across many genes. Our resultshave important implications for the design and interpreta-\ntion of future medical resequencing studies.\nSubjects and Methods\nStudy Populations", + "title": "2013 -Whole-Exome Sequencing of 2,000 Danish Individuals.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "2f2f6ff1-8f05-510d-bcf6-b1860fd9350c", + "extraction_id": "89a75bbb-f0f6-5391-98fd-56631343a38e", + "associatedQuery": "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIHK1iKM6Po4mTFBDwGSbImYGY94p", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The latest findings on the role of non-coding RNAs in the pathogenesis of diabetes highlight several key aspects:\n\n1. **Role of lncRNAs in Diabetes**: Long non-coding RNAs (lncRNAs) are implicated in mediating complex pathological mechanisms of diabetes. They are involved in post-transcriptional regulation and are associated with orchestrated networks that influence diabetes pathogenesis [5]. LncRNAs are considered better therapeutic targets due to their specific functions in regulating cellular pathways and their expression patterns that correlate with the progression of diabetes [7].\n\n2. **Epigenetic Influence**: Non-coding RNAs, including microRNAs and lncRNAs, can influence epigenetic mechanisms. They can promote the expression of pathological genes through post-transcriptional and post-translational mechanisms, contributing to metabolic memory and sustained gene expression in diabetic conditions [4].\n\n3. **Regulation of Islet Function**: LncRNAs have been shown to regulate pancreatic islet function, which is central to understanding diabetes pathophysiology. For instance, the lncRNA H19 has been implicated in islet development and function [8].\n\n4. **MicroRNAs in Disease**: MicroRNAs (miRs) play critical roles in various diseases, including diabetes, by influencing proliferation, differentiation, and development [2].\n\nThese findings underscore the importance of non-coding RNAs as regulatory players in diabetes and its complications, offering potential avenues for therapeutic intervention.", + "role": "assistant" + } + } + ], + "created": 1728919853, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 295, + "prompt_tokens": 1346, + "total_tokens": 1641 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "1eb14f0c-3d81-53bc-91c8-98acf2e014b7", + "score": 0.719383716583252, + "metadata": { + "text": "13\nDe Rosa et al.\nType 2 Diabetes and CVD\nFrontiers in Endocrinology | www.frontiersin.org January 2018 | Volume 9 | Article 2176. Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentia-\ntion and development. Nat Rev Genet (2014) 15:721. doi:10.1038/nrg3606 \n177. Wang KC, Chang HY . Molecular mechanisms of long noncoding RNAs. Mol Cell (2011) 43:90414. doi:10.1016/j.molcel.2011.08.018 \n178. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet (2011) \n12:86174. doi:10.1038/nrg3074", + "title": "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf", + "version": "v0", + "chunk_order": 186, + "document_id": "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2", + "extraction_id": "097b0feb-4ffa-5ad6-8140-5e404e83c80b", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "92a20945-b038-52a4-8cc8-ffb70e6f7559", + "score": 0.7168975045146058, + "metadata": { + "text": "Epigenetic Mechanisms in Diabetic Complications 16 \nother non-coding RNAs can also in teract with transcriptional co -regulators and thereby further 337 \ninfluence epigenetics and tran scriptional regulation (82, 104). 338 \n Recent findings have demonstrated a critical role for miRs in various diseases. They have 339 \nbeen found to play key roles in proliferation, di fferentiation, development, and in cancer, where 340", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 71, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "b1d2c95c-d639-5c75-8c52-278f1e187675", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "9c11148d-9f7a-5d84-aa05-2b67e7a8f1f3", + "score": 0.7107552117559452, + "metadata": { + "text": "Beltrami, C., Angelini, T.G., Emanueli, C., 2015. Noncoding RNAs in diabetes vascular\ncomplications. J. Mol. Cell. Cardiol. 89, 42 50.https://doi.org/10.1016/j.yjmcc.\n2014.12.014 .\nBrookheart, R.T., Michel, C.I., Listenberger, L.L., et al., 2009. The non-coding RNA gadd7\nis a regulator of lipid-induced oxidative and endoplasmic reticulum stress. J. Biol.Chem. 284, 7446 7454. https://doi.org/10.1074/jbc.M806209200 .\nCarter, G., Miladinovic, B., Patel, A.A., et al., 2015. Circulating long noncoding RNA", + "title": "2018 - Pilot genome-wide association study identifying novel risk loci for type 2.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "e2c6283c-d95f-574a-9dab-345a708d388c", + "extraction_id": "a166bf29-6be5-54ff-b869-9d4ff087d1e5", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "19d9d3a6-c982-5c57-a16c-226b8aa76ed5", + "score": 0.7056097388267517, + "metadata": { + "text": "Noncoding RNAs that are induced by diabetic conditions can also promote \ntheexpression of pathological genes via various post-transcriptional and \npost-translational mechanisms\n These epigenetic mechanisms and noncoding RNAs can lead to persistently \nopen chromatin structures at pathological genes and sustained gene \nexpression, which can also be a mechanism for metabolic memory\n Key epigenetic regulators, microRNAs and long noncoding RNAs could serve", + "title": "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "be05127e-1be8-5573-b571-51a11c3b2be2", + "extraction_id": "79ce3e1f-1c6a-51dc-b5ad-848173af4e69", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "b774bf7b-4546-56d2-ae7b-7bc2c9f2fb08", + "score": 0.696410080683532, + "metadata": { + "text": "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes,\nlncRNAs and their associated orchestrated networks are implicated in mediating complex pathological\nmechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and\n172 iScience 19, 162176, September 27, 2019", + "title": "2019 - Development and Genome Sequencing.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "18820c9e-f7ae-57ae-897d-0d9c3f616b6a", + "extraction_id": "d971dced-935c-566b-a4a2-11bcf99b9c84", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "94eed8ea-cc78-52d0-a188-442380512b85", + "score": 0.687047004699707, + "metadata": { + "text": "coding RNAs [18]. A number of indirect lines of evi-dence point to the involvement of epigenetic changes indiabetic nephropathy. Murine models of disease progres-sion displaying temporal variation in gene expressionhave indicated these supra-sequence devices may beinvolved in the pathogenesis [19]. Gene expressionchanges reflect dynamic alterations in gene transcription\nand also messenger RNA stabi lity, which may be influ-", + "title": "2010 - Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "23f0ee09-5536-5f63-bf15-bce1894b5fed", + "extraction_id": "f9500ec9-0600-5e2c-b64e-b062fb7a7552", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "2d9e043b-a3fa-52dc-9a4e-71ed49f9ec1d", + "score": 0.6849800944328308, + "metadata": { + "text": "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 88, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "14656f4f-b0bd-5f4f-a67a-aeb902f24757", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "66b05301-179b-597c-bb68-e6fd0e0d1d5a", + "score": 0.6833786368370056, + "metadata": { + "text": "NAs to be mapped to diabetic susceptible loci [49 52], \nall suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications.\nLncRNAs asregulators ofislet function\nThe pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 42, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "96a78d74-ac6d-513e-a5a7-b22ef95ea041", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "4a8a2861-62b9-520c-8833-45fb8bd3ffd7", + "score": 0.6825283358974269, + "metadata": { + "text": "expected to rise due to the increasing incidence of diabetes, which necessitates the need for exploration of new molecular aspects of DR to expand the current scope of therapy.\nIn the last two decades, the rapid advent of high-throughput genomic technology has made it evident that \nmore than 97% of the human genome is comprised of non-protein-coding elements, such as non-coding RNAs \n(ncRNAs)\n6. Although significant research has been conducted in annotating the transcripts that arise from these", + "title": "2018 - MALAT1 An Epigenetic Regulator of Inflammation in Diabetic Retinopathy.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "cd4dd3bc-bcea-5670-a40f-bc95c319f3ed", + "extraction_id": "2d8abaf8-9f48-5b9a-b50e-897fd4751b7b", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + }, + { + "id": "25d3616b-1ba4-59ce-a11b-38d108d5b387", + "score": 0.6798065304756165, + "metadata": { + "text": "regulation, control of mRNA decay, and sequestration of transcription factors. Although the underlying causes that define \nthe diabetic phenotype are extremely intricate, most of the studies in the last decades were mostly centered on protein-coding \ngenes. However, current opinion in the recent past has authenticated the contributions of diverse lncRNAs as critical regula -\ntory players during the manifestation of diabetes. The current review will highlight the importance of lncRNAs in regulating", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "efc73cf6-99c6-5272-9bb0-7bd6a34633f0", + "associatedQuery": "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIHKAjqtg6gr5hkyEsdT3wwz3yXTB", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The interaction between multiple polygenic risk scores (PRS) can improve the prediction of Type 1 and Type 2 diabetes by combining information from various genetic loci associated with these diseases. This approach allows for a more comprehensive assessment of an individual's genetic risk. Specifically, combining information from common risk polymorphisms has been shown to improve disease prediction for Type 2 diabetes [3]. Additionally, partitioning polygenic scores according to factors of disease heterogeneity and mapping genetic loci to different immune-cell subtypes can enhance the predictive power of PRS, particularly for Type 2 diabetes [9]. These strategies leverage the aggregation of genetic risk from multiple sources, thereby capturing a larger proportion of the genetic variance underlying these traits and improving early diagnosis, intervention, and prevention efforts [4].", + "role": "assistant" + } + } + ], + "created": 1728919862, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 154, + "prompt_tokens": 1370, + "total_tokens": 1524 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "748c1d81-0c27-515a-8bf1-12e717645e66", + "score": 0.7702065706253052, + "metadata": { + "text": "review of polygenic risk scores for type 1 and type 2 diabetes. Int J Mol \nSci. 2020;21(5):1703.\n 48. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. \nGenome wide polygenic scores for common diseases identify \nindividuals with risk equivalent to monogenic mutations. Nat Genet. \n2018;50:121924.\n 49. Ding Y, Hou K, Burch KS, Lapinska S, Priv F, Vilhjalmsson B, et al. Large \nuncertainty in individual polygenic risk score estimation impacts PRS", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 176, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "02701cd5-d2ce-560c-b5a9-e694fecdb3c2", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "score": 0.7527217864990234, + "metadata": { + "text": "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter \nvention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most \nwidely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non\nEuropean populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "3b9e0030-8bf9-5d63-9813-3cf18e98be3b", + "score": 0.7513685822486877, + "metadata": { + "text": "prediction of type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008).\n 45. Weedon, M. N. et al. Combining information from common type 2 diabetes \nrisk polymorphisms improves disease prediction. PLoS. Med. 3, e374 (2006).\n 46. Euesden, J., Lewis, C. M. & OReilly, P . F. PRSice: Polygenic Risk Score \nsoftware. Bioinformatics 31, 14661468 (2015).\n 47. Gatineau, M. et al. Adult obesity and type 2 diabetes (Public Health England,", + "title": "2018 - Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "ab2868dd-62f6-5350-994c-fcea4328e8a3", + "extraction_id": "9190d1c1-41a4-5af3-a570-7fea6a15e71a", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "score": 0.7487341388682768, + "metadata": { + "text": "(GWAS) in diverse populations have identified hundreds \nof genetic loci associated with T2D [79]. Polygenic risk \nscores (PRS), which aggregate the genetic risk of individ -\nual alleles across the genome, are thus promising to pre -\ndict future T2D occurrence and improve early diagnosis, \nintervention, and prevention of T2D [1015]. However, \nto date, T2D PRS were most widely developed and vali -\ndated in individuals of European descent. Given that the \npredictive performance of PRS often attenuates in non-", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "17c49e58-c89a-5495-b17f-adcade90a4c6", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "a374d88e-458e-5252-8b3a-5ca162fa6982", + "score": 0.7476893217748821, + "metadata": { + "text": "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk\nanalysis using the sum of the weight of each risk-associated locus of genomic sequence\nobtained from the corresponding evidence. These weights are assessed from the regression\ncoefcient associated with each locus. These combined genetics features and correlation\nmatrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "3c30b33b-8928-5cee-9c37-c70642fff75c", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "a551335d-c3ed-5d12-a611-9991d192cc1e", + "score": 0.7324631214141846, + "metadata": { + "text": "performance.\nConclusions: By integrating T2D GWAS from multiple populations, we developed and validated a transancestry PRS, \nand demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts \nrepresent the first step towards the implementation of the T2D PRS into routine healthcare.\nKeywords: Polygenic risk score, Type 2 diabetes, Diverse populations, Clinical implementation", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "17c49e58-c89a-5495-b17f-adcade90a4c6", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "score": 0.7306916438708781, + "metadata": { + "text": "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. \nA polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the \ngenetic variance underlying a trait, the overall effect size", + "title": "2021 - Genetics and genomics of arrhythmic.pdf", + "version": "v0", + "chunk_order": 81, + "document_id": "462ed035-e4fb-5847-a92d-927f05a2b58b", + "extraction_id": "ada410d0-6b91-5959-b834-cc3389e29c5f", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "635180f9-540f-5533-9d61-c5cfe14657fa", + "score": 0.7235454320907593, + "metadata": { + "text": "8.Padilla-Mart nez, F., Collin, F., Kwasniewski, M., and Kretow-\nski, A. (2020). Systematic review of polygenic risk scores for\ntype 1 and type 2 diabetes. Int. J. Mol. Sci. 21, 1703 .\n9.Rao, A., and Knowles, J. (2019). Polygenic risk scores in coro-\nnary artery disease. Curr. Opin. Cardiol. 34, 435440 .\n10.Dikilitas, O., Schaid, D.J., Kosel, M.L., Carroll, R.J., Chute,\nC.G., Denny, J.A., Fedotov, A., Feng, Q., Hakonarson, H., Jar-vik, G.P., et al. (2020). Predictive utility of polygenic risk scores", + "title": "2022 - Stability of polygenic scores across.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "30af2d38-7941-5d0a-9da1-a8ad2dc22329", + "extraction_id": "a548bb25-cbff-5466-b932-afe160bfbe32", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "fd7ccb09-2768-5ceb-8b29-9b29cdef57a8", + "score": 0.7130113840103149, + "metadata": { + "text": "partitioned polygenic scores according to factors of disease heteroge-\nneity, as successfully demonstrated for type 2 diabetes (32). Another\nstrategy could be the mapping of statistically associated genetic loci to\ndifferent immune-cell subtypes according to gene expression patterns\nderived from single-cell RNA sequencing (33).\nAutoimmune PRS, possibly in combination with other genetic and\nnongenetic predictors, may be of importance to manage the risk of", + "title": "2022 - Coming of Age Human Genomics.pdf", + "version": "v0", + "chunk_order": 36, + "document_id": "45506895-eef1-57f4-8ca1-79fe23a2493f", + "extraction_id": "d2add072-cb41-54f8-9583-9616b11e4ae3", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + }, + { + "id": "cc476583-54c8-5607-95bd-d06ae875dfb8", + "score": 0.705722591451484, + "metadata": { + "text": "genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated ( n= 4271) and subsequently tested\nassociations of these scores with 3-year lipid changes in adolescents ( n= 620), carotid intima-media thickness (cIMT)\nin adult women ( n= 781), dyslipidemia ( n= 7723), and coronary heart disease (CHD) ( n= 2374 cases and 6246\ncontrols) in type 2 diabetes (T2D) patients.\n(Continued on next page)", + "title": "2021- Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "ce8040c7-157f-54c5-b28b-3224e8871415", + "extraction_id": "5f2ac528-4965-5d5e-86d0-8862032bb7b9", + "associatedQuery": "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIHKFuXAocol6QH0B6QHJlkuJdiDC", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent single-cell RNA-sequencing studies have provided significant mechanistic insights into beta-cell failure pathways. These insights include:\n\n1. **De-differentiation Signatures**: Single-cell analyses of human islet cells have revealed de-differentiation signatures, suggesting that beta cells may lose their specialized functions and revert to a more progenitor-like state, which contributes to their dysfunction in diabetes [1].\n\n2. **Transcriptional Regulation**: Advances in single-cell genomic profiling have enhanced our understanding of transcriptional regulation in non-beta cell types, which may play crucial roles in the hallmark features of beta-cell insufficiency and dysfunction in type 2 diabetes (T2D) [2].\n\n3. **ER Stress and Heterogeneity**: Single-cell transcriptomic analyses have identified subpopulations of beta cells experiencing endoplasmic reticulum (ER) stress. This stress is implicated in the dysfunction of both alpha and beta cells, contributing to diabetes pathogenesis [8].\n\nThese findings highlight the complexity of beta-cell failure and underscore the importance of single-cell technologies in unraveling the molecular mechanisms underlying diabetes.", + "role": "assistant" + } + } + ], + "created": 1728919867, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 223, + "prompt_tokens": 1528, + "total_tokens": 1751 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b7812a7a-5504-57ca-8755-969dee45717e", + "score": 0.7292243838310242, + "metadata": { + "text": "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc-\ning: new developments and medical applications. Cell Biosci \n9:53. https ://doi.org/10.1186/s1357 8-019-0314-y\nTeo AKK etal (2018) Single-cell analyses of human islet cells reveal \nde-differentiation signatures. Cell Death Discov 4:14. https ://doi.\norg/10.1038/s4142 0-017-0014-5\nTheis FJ, Lickert H (2019) A map of beta-cell differentiation pathways \nsupports cell therapies for diabetes. Nature 569:342343. https ://", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "7f7a7f30-2e4e-50aa-bbcb-9f211c371e38", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "d5c2a32a-b869-59c1-8a63-45ab620669de", + "score": 0.7201639413833618, + "metadata": { + "text": "4. PRECISE CELLULAR GENOMICS\nElucidating the molecular mechanisms that lead to beta cell\ndysfunction and T2D pathogenesis has been a major focus of diabetes\nresearch for decades. However, advances in single cell genomic\nproling techniques have led to greater understanding of non-beta cell\ntype transcriptional regulation and suggest that they may play\nimportant roles in hallmark features of beta cell insuf ciency and", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 55, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "7a2a9981-4096-5049-a717-3e69eb609777", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "ab373b7e-8c0b-59d8-9408-3e09ac76761e", + "score": 0.6964799761772156, + "metadata": { + "text": "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol \n(Oxf) 211:273284\n 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose \nintolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142\n 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "8bbfb009-87b7-54ae-8465-8796db8c271a", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "a2adc65b-035b-568f-a0ae-9f7821ef45bc", + "score": 0.6789516997962189, + "metadata": { + "text": "understand each cell type s genomic architecture and better charac-\nterize their roles in islet resilience and failure. Experimental manipu-\nlation of the regulatory elements and/or the target genes identi ed by\n(epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g.,\nEndoC-\nbH1-H3) is essential to progress from correlation to causation.\nSimilarly, transitioning from themouse (C57BL/6) to multiple mouse", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 97, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "117cc1a5-d236-56b2-a69d-9c0a2fb9053d", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "887e1f7e-5044-5be8-a506-588ca7afa004", + "score": 0.6767109632492065, + "metadata": { + "text": "therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome andRNA-sequencing data was employed to extensively characterize the genomic and molecularlandscape of insulinomas relative to normal beta cells. Here, we show at the pathway levelthat the majority of the insulinomas display mutations, copy number variants and/or dys-regulation of epigenetic modifying genes, most prominently in the polycomb and trithoraxfamilies. Importantly, these processes are coupled to co-expression", + "title": "2017 - Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "6cf1eb8d-a91e-58a2-b6f4-29653678d0d3", + "extraction_id": "dee54186-e75e-5ed2-818d-cd6f4370b153", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "4bfcfbd6-f45e-553d-a043-a12e7abeff61", + "score": 0.6755803823471069, + "metadata": { + "text": "gesting that changes in alpha cell identity may ultimately lead to theirdysfunction. Analysis of normal and T2D islet single cells with\nsimultaneous RNA-seq and patch clamping (patch-seq) also revealed\nsubpopulations of alpha cells with varying enrichment for ER stressresponse genes (e.g., DDIT3, XBP1, PPP1R15A )[30]. Interestingly, this\ntranscriptomic heterogeneity was consistent in normal and T2D islets", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 58, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "7a2a9981-4096-5049-a717-3e69eb609777", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "d32d6338-6cda-5f58-999d-2b4287ee4a77", + "score": 0.668185830116272, + "metadata": { + "text": "RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. https ://doi.org/10.15252 /msb.20188 746\nMa L, Zheng J (2018) Single-cell gene expression analysis reveals \n-cell dysfunction and deficit mechanisms in type 2 diabe-tes. BMC Bioinform 19:515. https ://doi.org/10.1186/s1285 \n9-018-2519-1\nMacaulay IC, Ponting CP, Voet T (2017) Single-cell multiom-\nics: multiple measurements from single cells. Trends Genet 33:155168. https ://doi.org/10.1016/j.tig.2016.12.003", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "10e4029f-0324-55c9-8fe8-023a924d1732", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "ef0b8934-2af1-5848-88f9-ff5a2e4f3cc1", + "score": 0.6666788260127561, + "metadata": { + "text": "peak current. Prior single cell transcriptomic analyses have also notedsubpopulations of ER-stressed beta cells [31,32] which implicates the\ndysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to beReview\nS18MOLECULAR METABOLISM 27 (2019) S15 eS24/C2112019 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).\nwww.molecularmetabolism.com", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "7a2a9981-4096-5049-a717-3e69eb609777", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "46ed97d7-7b3e-5be2-a409-04a37d105ef2", + "score": 0.6642862558364868, + "metadata": { + "text": "to understanding human development using single-cell tran-scriptomics. Development 144:1584. https ://doi.org/10.1242/dev.15045 8\nCamp JG, Wollny D, Treutlein B (2018) Single-cell genomics to guide \nhuman stem cell and tissue engineering. Nat Methods 15:661667. https ://doi.org/10.1038/s4159 2-018-0113-0\nCarrano AC, Mulas F, Zeng C, Sander M (2017) Interrogating islets \nin health and disease with single-cell technologies. Mol Metab \n6:9911001. https ://doi.org/10.1016/j.molme t.2017.04.012", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "f740892a-7817-58b0-bec4-8648086b2353", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + }, + { + "id": "f06bcc81-6ef9-5874-8ef9-6bcb3c34b0d0", + "score": 0.6579534011469308, + "metadata": { + "text": "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? \n1 3\nBrissova etal. 2018; Tritschler etal. 2017). Moreover, an \nincrease in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; \nTheis and Lickert 2019; Tritschler etal. 2017).\nNotably, single-cell transcriptome profiling has been", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 62, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "65471d38-cd13-5de2-8c19-1eb72d24d6f5", + "associatedQuery": "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIHKMXGqZPQ6g67pYsMpeLJDEH7VZ", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The epigenetic landscape of key metabolic tissues shows several changes when comparing diabetic individuals to non-diabetic individuals:\n\n1. **DNA Methylation Changes**: In diabetic individuals, increased DNA methylation has been observed in the promoter region of the PPARGC1A gene in both islets and skeletal muscle [3]. This suggests a potential mechanism by which gene expression related to metabolism is altered in diabetes.\n\n2. **Histone Modifications**: There are disruptions in histone methylation patterns in diabetic states. While healthy individuals maintain stable histone methylation patterns, these can be disrupted in diabetes, indicating changes in the epigenome associated with inflammation and metabolic memory [2].\n\n3. **Impact on Gene Expression**: Epigenetic modifications, such as DNA methylation, have been linked to reduced expression of genes involved in diabetes and metabolism. Variations in DNA methylation have been noted near diabetes susceptibility genes and enhancers [6].\n\n4. **Tissue-Wide Epigenetic Changes**: Diabetes mellitus, characterized by high glucose stress, leads to epigenetic changes across most tissues impacted by the disease, including the cardiovascular system and immune system [7].\n\n5. **Adipose Tissue**: In subjects with type 2 diabetes, altered DNA methylation and differential expression of genes influencing metabolism and inflammation have been observed in adipose tissue [9].\n\nThese findings collectively suggest that diabetes is associated with specific epigenetic alterations across various metabolic tissues, which may contribute to the pathophysiology of the disease.", + "role": "assistant" + } + } + ], + "created": 1728919874, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 309, + "prompt_tokens": 1337, + "total_tokens": 1646 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "d198d71b-34ac-5402-b166-b476c15e2eef", + "score": 0.7108756648450297, + "metadata": { + "text": "To date, the overwhelming majority of studies including and assessing\ngenetic variation have pro led the steady state patterns of epigeneticmodi cations and gene expression in islets or their constituent cell\ntypes. Others have compared how these steady state measures differ\nbetween T2D and non-diabetic (ND) individuals [13,16,40 e44]. Sur-\nprisingly, these studies, especially transcriptome analyses, haveidenti ed only modest alterations despite clear phenotypic differences", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "043ee0bf-ec42-57dd-aa0e-4f4f5aac2437", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "d032460f-1bab-5710-8a79-872651f9c36e", + "score": 0.6921367416218828, + "metadata": { + "text": "T1D and resulting complications (99). These epig enomic profiling studies suggest that, while a 415 \nreasonably stable histone methylation pattern is maintained in healthy individuals over time in a 416 \ncell-type specific setting, this pa ttern can be disrupted in a dis ease state. Moreover, they also 417 \nprovide a glimpse of the inflammatory cell epig enome under the diabetic state and suggest that 418 \nnew information about diabetes, its complicatio ns and metabolic memory can be obtained by 419", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 88, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "efbaf00f-0cb1-531f-a9fd-2844670ec92c", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "a6efcd76-42f1-5d4c-bcfa-f5e63ac9eb4c", + "score": 0.6888615674459012, + "metadata": { + "text": "hyperglycaemia, epigenetic changes have also been noted in\nother experimental settings of hyperglycaemia. For example,\nincreased DNA methylation has been described for the promoter\nregion of the peroxisome proliferator-activated receptor-\ng(PPAR g)\ncoactivator-1 agene (PPARGC1A) in diabetic islets ( Ling et al., 2008 ).\nSimilar hypermethylation in the promoter region of the PPARGC1A\ngene has been noted in the skeletal muscle from diabetic patients,", + "title": "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "version": "v0", + "chunk_order": 116, + "document_id": "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "extraction_id": "daf2d7fd-e789-5ceb-9984-d95656b5dd91", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "29bdfc8d-cb06-5ce5-8db6-f92b2f106b7d", + "score": 0.6861002819418571, + "metadata": { + "text": "and correlated with mitochondrial content ( Barr /C18es et al., 2009 ).\nEpigenetic changes have also been suggested to be responsible forthe legacy effect of reduced risk of vascular complications after a\nperiod of sustained tight glucose control, or metabolic memory of\ntransient hyperglycaemia and increased risk of diabetic vascular\ninjury ( Pirola et al., 2010 ). Histone methylation variations have\nbeen noted in monocytes cultured in high glucose, as well as blood", + "title": "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "extraction_id": "daf2d7fd-e789-5ceb-9984-d95656b5dd91", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "cf4f3239-dd62-5eef-b5fc-85f4780e3f48", + "score": 0.6848712751346079, + "metadata": { + "text": "Epigenetic Mechanisms in Diabetic Complications 17 \nInterestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 \nregulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 \nand HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362", + "title": "2010 - The Role of Epigenetics in the Pathology of Diabetic Complications.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "766edfd5-4756-51bf-b636-c94b041d030c", + "extraction_id": "41ac576d-b850-5ee8-9753-ba9b060ba798", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "1c0b9205-340f-568f-990f-785c66154732", + "score": 0.6813801120889132, + "metadata": { + "text": "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber-\nrant DNAme has also been reported in the reduced expression\nof genes involved in diabetes and metabolism, and DNAme\nvariations have also been noted near diabetes susceptibility\ngenes and enhancers [ 15,47].\nGenomic DNA from diabetic patients with nephropa-\nthy relative to those without displayed differential meth-\nylation at several genes, including UNC13B , which had", + "title": "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "470f1f94-792d-5273-a88f-7e06084951c5", + "extraction_id": "44d96546-84c3-51f1-85f9-22790a91d105", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "64f12ea8-a441-5fb5-a80e-1acbfb596a1e", + "score": 0.680983085573729, + "metadata": { + "text": "of diabetes mellitus on the body is a high glucose stressed condition, altering substrate metabolism and causing systemic inflammation [60]. Due to this environmental change, researchers have shown how epigenetic changes occur across most, if not all, tissues that are impacted by diabetes mellitus [49, 61].\nIn the cardiovascular system, the heart, circulatory \nsystem, and regulating immune system are all tran\n-", + "title": "2019 - Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics.pdf", + "version": "v0", + "chunk_order": 144, + "document_id": "332ac2ec-accc-5370-a4d2-6fec9ce7e072", + "extraction_id": "aff84b9e-3855-5960-accd-dcac6b362346", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "8f447303-efd0-5564-9213-2cdc8fce12dd", + "score": 0.6785546136763935, + "metadata": { + "text": "nephropathy. Exp. Physiol. 98, 934945 (2013).\n48. Reddy, M.A., Tak Park, J. & Natarajan, R. \nEpigenetic modifications in the pathogenesis \nofdiabetic nephropathy. Semin. Nephrol. 33, \n341353 (2013).\n49. Li, S.L. etal. Enhanced proatherogenic \nresponses in macrophages and vascular smooth \nmuscle cells derived from diabetic db/db mice. \nDiabetes 55, 26112619 (2006).\n50. El-Osta, A. etal. Transient high glucose causes \npersistent epigenetic changes and altered gene", + "title": "2014 - Diabetic nephropathy\u2014emerging epigenetic mechanisms.pdf", + "version": "v0", + "chunk_order": 156, + "document_id": "be05127e-1be8-5573-b571-51a11c3b2be2", + "extraction_id": "cbbe696b-8541-537a-ac5f-77b82cdb8201", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "7aafd7e0-2446-57a4-bb1e-1f77b4f4559e", + "score": 0.6759020283280606, + "metadata": { + "text": "exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fatoverfeeding. Diabetologia 2014;57:1154-1158.\n53. Nilsson E, Jansson PA, Perfilyev A, et al. Altered DNA methylation and differential\nexpression of genes influencing metabolism and inflammation in adipose tissue\nfrom subjects with type 2 diabetes. Diabetes 2014;63:2962-2976.\n54. Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies", + "title": "2016 - NIH working group report using genomic information to guide weight management From universal.pdf", + "version": "v0", + "chunk_order": 125, + "document_id": "0ee28c8a-3618-559e-be0a-30f2579a0d1f", + "extraction_id": "68f800e3-8a0c-50ee-ba8b-ceb361f14fa5", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + }, + { + "id": "cf775ed3-457e-52a7-b54b-5c1b5c179819", + "score": 0.6755989387274433, + "metadata": { + "text": "etal. Hyperglycemia induces a dynamic cooperativity of histone methylase \nand demethylase enzymes associated with gene-activating epigenetic marks that coexist on the lysine tail. Diabetes (2009) 58:122936. doi:10.2337/\ndb08-1666 \n111. Keating S, Plutzky J, El-Osta A. Epigenetic changes in diabetic and cardio-vascular risk. Circ Res (2016) 118:170622. doi:10.1161/CIRCRESAHA. \n116.306819 \n112. Paneni F, Volpe M, Lscher TF, Cosentino F. SIRT1, p66(Shc), and Set7/9 in", + "title": "2018 - Type 2 Diabetes Mellitus and Cardiovascular Disease Genetic and Epigenetic Links.pdf", + "version": "v0", + "chunk_order": 150, + "document_id": "3e82a2e5-4b2c-59c0-99cd-f3b06d8dabf2", + "extraction_id": "9e010393-b98f-5f6c-a62d-fc0646ba8667", + "associatedQuery": "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIHKSpSdna9OyEUtDVRTMkIkEtBS5", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo include the use of CRISPR-mediated homology-directed repair (HDR) to correct specific genetic mutations associated with diabetes. For instance, CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method employed in iPSCs is CRISPR/Cas9-based HDR, where a Cas9-mediated cut is generated adjacent to the site of interest, and a homologous donor template with the intended nucleotide change is recombined by HDR [9]. Additionally, there has been a successful correction of a variant in the Wolfram syndrome 1 (WFS1) gene using CRISPR-mediated HDR, which improved insulin secretion in iPSC-differentiated beta-like cells [3]. These advancements highlight the potential of CRISPR-based genome editing to correct monogenic forms of diabetes by targeting specific genetic mutations in vivo.", + "role": "assistant" + } + } + ], + "created": 1728919880, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 204, + "prompt_tokens": 1367, + "total_tokens": 1571 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "f42c0f84-d2a8-5bf9-89c2-3dd182bfb235", + "score": 0.72869388632142, + "metadata": { + "text": "A variety of cellular and animal models have been developed and applied\nover the past few years to experimentally manipulate cis-regulatory\nelements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at\nwill. Unsurprisingly, CRISPR (epi)genome editing tools can and have\nbeen used to target putative T2D target genes [54] orcis-REs[55] in beta", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 79, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "57736895-897e-54e5-a735-aadcbd77cb63", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "2af36592-3e59-583c-a9c7-d612175f4afc", + "score": 0.723567528937313, + "metadata": { + "text": "(276279). Through CRISPR-mediated HDR and base editing, it\nis possible to correct the vast majority of genetic variants, if notall. Conversion of GWAS-identi ed non-coding variants has not\nbeen conducted/documented in the diabetes eld, but it seems\ninevitable that such work will be carried out in the near futureHu et al. Genome Editing of Pancreatic Beta Cells\nFrontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 11", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 140, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "75b937b2-1e0b-5d63-b542-618ad91bbd1f", + "score": 0.7071702516529192, + "metadata": { + "text": "Cas9 editing to restore insulin production in differentiated iPSCcells that mimicked neonatal diabetes ( 251,252). Likewise, Shi\net al. converted a patient-speci c mutation in GATA6 gene and\nshowed that the mutation involved (GATA6\nR456C) has a similar\neffect to GATA6 knockout ( 21). Most recently, correction of a\nvariant in the Wolfram syndrome 1 ( WFS1 ) gene by CRISPR-\nmediated HDR improved insulin secretion in iPSC-differentiatedb-like cells ( 253). Studies on GWAS identi ed genetic variants", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 127, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "1f114642-3f77-5346-89e8-394c433f66ff", + "score": 0.6914571935806998, + "metadata": { + "text": "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model\n(230,231). However, the use of iPSC is controversial and there\nare some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275).\nManipulation of hESC/iPSC cells via CRISPR-Cas9\ntechnology provides a platform for the correction of genomic\nmutations not only in diabetes but in other disease elds as well", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 139, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "df30dab3-a490-5497-a079-2741f9039f87", + "score": 0.6890247278824299, + "metadata": { + "text": "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin\ngenes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is\nincreasingly becoming the favored choice for genome editing inhPSCs [16,52] .\n3.2. Employing hPSCs and genome editing tools to study diabetes\nand metabolic syndromes\nIn general, the strategy to carry out in vitro disease modeling of dia-", + "title": "2016 - Dissecting diabetes metabolic disease.pdf", + "version": "v0", + "chunk_order": 40, + "document_id": "eee2f79d-e093-52fb-871a-798fd859235e", + "extraction_id": "998a92ba-e7fc-5553-b629-7b5797fbfafe", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "eadf2320-de70-5499-ade0-7aa9930ac091", + "score": 0.6886285953896413, + "metadata": { + "text": "Due to its simplicity and adaptability, CRISPR has rapidly\nbecome the most popular genome editing tool available for the\nmammalian genome ( 50,63). Because NHEJ DNA repair often\nintroduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the\ndiabetes eld, CRISPR has also been adopted to study several\ngenes in bcell lines and in human ES-derived bcells ( 21,151,", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 76, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "fe5bf2df-2eda-5ef0-8aad-79bbc5b898d6", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "57b9550d-0258-5a87-be57-976f471e5763", + "score": 0.685957871193632, + "metadata": { + "text": "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265).\nUsing CRISPR-Cas9, leptin and leptin receptor knockout mice\nhave been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via\nHDR to achieve cell-speci c deletion of the gene ( 266).\nGenome Editing: Clinical Application\nin Diabetes\nAn important goal in genetic research is to identify the genetic", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 133, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "ab61a462-21d3-50dc-afb3-3e1cdeb15b1f", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "1859f32b-8f5c-5c3c-9f4d-54193d37645d", + "score": 0.6847404241561938, + "metadata": { + "text": "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes.\nKeywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young,\nstem cells, mouse models\nINTRODUCTION\nType 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise\nto 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "5f8a0ddd-a0c7-5151-9b6a-e0980bb94aa6", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "99ccc9a2-865f-5d11-9b08-b26261d02fc9", + "score": 0.682744264602666, + "metadata": { + "text": "samples ( 236).\nCRISPR technology has been used recently to correct point\nmutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC\nis CRISPR/Cas9-based homology-directed repair (HDR). Here, a\nCas9-mediated cut is generated adjacent to the site of interest. A\nhomologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "0a3e3095-4789-505a-96b7-123a05078e95", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + }, + { + "id": "83053df5-47ac-59da-9c30-69740a64372d", + "score": 0.679858367434644, + "metadata": { + "text": "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System.\nJ Vis Exp JoVE (2017). doi: 10.3791/56260\n277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells:\nTools for Advancing Diabetes Disease Modeling and Beta-Cell Development.\nCurr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells\nFrontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 280, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "a36cee80-5961-55e5-8ea4-8d4e1bc501a9", + "associatedQuery": "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIHKYN37xsXdGCjQ8Ms8PgKZ10CIR", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes by combining genetic, epigenetic, transcriptomic, and phenotypic information. This integration helps identify genes and novel metabolic pathway targets that are crucial for understanding mechanistic relationships with insulin resistance and pancreatic islet failure [1]. Additionally, complementary systems-level data, such as protein-protein interactions and gene expression, provide insights into the mechanisms underlying the pathogenesis of complex traits like type 2 diabetes (T2D) [8]. This multi-omics approach allows for a more comprehensive understanding of the genome-to-phenome correlation in T2D, which is essential for examining the disease's complex genetic architecture [9].", + "role": "assistant" + } + } + ], + "created": 1728919886, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 149, + "prompt_tokens": 1144, + "total_tokens": 1293 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "7302a27a-6e56-589d-a579-635f25fc46a3", + "score": 0.7181355953216553, + "metadata": { + "text": "The integration of genetic, epigenetic, transcriptomic and phenotypic information allows to identify\ngenes and novel metabolic pathway targets that deserve further attention to elucidate mechanistic\nrelationships with insulin resistance and pancreatic islet failure. Although the GWASs and EWASs\nshed light onto (epi)genomic landscape of T2D to a great extent, these methods have still explicit\nlimitations to conquer, such as sample size, small effect size, low allele frequency, genetic heterogeneity", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 121, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "978df5a8-acb4-53d3-b351-66a3bc613c78", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "4d780759-36bb-5295-a63a-16dab6aeab8c", + "score": 0.7155009508132935, + "metadata": { + "text": "map of the human genome, spurred larger multi-institutional programs (e.g., 1000 Genomes Projects,\nEncyclopedia of DNA Elements [ENCODE], and Roadmap Epigenomics), that have the goal of tracking\ngenomic and epigenomic changes across multiple populations [ 8]. Aforementioned studies enabled\nGWASs for complex diseases such as T2D. DNA amplication, Sanger sequencing, and microarray\nstudies have shed light on the genetics of diabetes but have only provided a limited amount of data. An", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "aba850e8-8c0d-5256-b2ba-fa1dfc221114", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "ac4d8521-b492-59b5-9978-891f5a5ce0c5", + "score": 0.7094894859042614, + "metadata": { + "text": "Abstract\nWhile genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that\ncontribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under-\nsurveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait", + "title": "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "8c310d76-0a3b-574c-9859-859258870ee5", + "extraction_id": "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "81fb2df2-4154-58a7-b217-b07153a6c921", + "score": 0.694022536277771, + "metadata": { + "text": "The advancement that has taken place in Genome-Wide Association Studies (GWAS)\nholds tremendous information related to various gene patterns associated with divergent\nillnesses that are complex and challenging to perform reductive analysis from a single locus,\nas stated by Cho Ys [6] and Coron [7]. The evolution of GWAS has focused on integrating\ndata related to multi-locus across the gene that would assist in predicting complex illnesses", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "3c30b33b-8928-5cee-9c37-c70642fff75c", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "263ea999-9662-5518-a606-939f69d09f90", + "score": 0.688255786895752, + "metadata": { + "text": "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk \nfactors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par -\nticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "title": "2020 - Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "5dd7d700-03db-595d-b1a5-beca77f9579e", + "extraction_id": "2c601441-443d-5c47-95bb-6343378dd5dc", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "c807fc8b-966e-56a9-91ce-07b9baf940d9", + "score": 0.6872100234031677, + "metadata": { + "text": "1. Introduction\nGenome wide association studies (GWAS) of type 2 diabetes\nmellitus and relevant endophenotypes have shed new light on the\ncomplex etiology of the disease and underscored the multiple\nmolecular mechanisms involved in the pathogenic processes\nleading to hyperglycemia [1]. Even though these studies have\nsuccessfully mapped many diabetes risk genetic loci that could not\nbe detected by linkage analysis, the risk single nucleotide poly-", + "title": "2017 - Genomic regulation of type 2 diabetes endophenotypes Contribution.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "fef1ae33-b3af-50ea-909c-f1b57f7fe981", + "extraction_id": "3dc37987-5204-5414-92ee-9d97af221261", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "ef027493-6063-5abd-9ee7-0c9a37379317", + "score": 0.6856701970100403, + "metadata": { + "text": "how they will continue to expand our understanding of the\ngenetic risk factors and underlying biology of diabetes.\nKeywords Genotyping .Genome-wide association .\nSequencing .Imputation .Exome .Genome .\nFine-mapping .Diabetes .Quantitative traits .Metabochip .\nSingle nucleotide polymorphism\nIntroduction\nGWA studies have made progress toward understanding the\ninherited basis of type 1 and type 2 diabetes by detecting\ndisease-associated DNA variants, usually with allele fre-", + "title": "2012 - What will Diabetes Genomes Tell Us.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "38b3b7ab-d13e-5986-9a3a-54abe8a3e1e9", + "extraction_id": "50a110f8-e91d-5985-9fe9-62a373a58c9d", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "869d46b4-e379-54f8-bd71-143d9f31fa93", + "score": 0.6849029251990971, + "metadata": { + "text": "complementary systems level data such as that related to protein-\nprotein interactions and to and gene expression can provideinsights into the mechanisms underlying pathogenesis of complextraits [2224]. Here, we have combined these approaches towarddeciphering genome to phenome correlation in T2D ( Figure 1 ).\nGiven that T2D GWAS genes do not directly relate to disease", + "title": "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "extraction_id": "8dd91a24-2ac7-57b3-9cb3-f8ac74b1885c", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "b92b959c-2f31-5177-8a21-627f3ee81b6c", + "score": 0.6839315483610912, + "metadata": { + "text": "phenotypes [2,6]. The recently accomplished deep sequencing of\nhuman exomes has indeed suggested that rare variations\ncontribute substantially to human phenotypic variation and\ndisease susceptibility [73]. Availability of post-GWASs era data\nfor T2D will be crucial in examining genome to phenomecorrelation in greater details. Emerging methods in pathway-wide\nanalysis and integrative network based analysis of genetic\nassociation data in complex disorders will further help accelerate", + "title": "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "extraction_id": "f6926cab-e00d-5972-a815-2ecc9f8c35d5", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + }, + { + "id": "7fd80e84-ec0c-564c-8e8b-278b8c622abb", + "score": 0.6818853210279167, + "metadata": { + "text": "Abstract\nGenome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a\ncommon complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target\ntissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories", + "title": "2013 - Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "ea7c2799-c259-5d0e-b40b-ecebe0a9fc9f", + "extraction_id": "9369222f-e125-58c0-8f2b-cf5daa867f77", + "associatedQuery": "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIHKdF53rZo0tRRSpImOeG4mHUbkt", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Genomic imprinting has a significant impact on the susceptibility and progression of diabetes. Imprinting can influence the expression of genes involved in metabolic processes, which are crucial in the development of diabetes. For instance, changes in imprinting status at specific loci, such as the KCNQ1 locus, have been linked to type 2 diabetes susceptibility, indicating that temporal changes in imprinting can affect the function of pancreatic islets and contribute to diabetes risk [6]. Additionally, imprinting defects have been associated with specific forms of diabetes, such as transient neonatal diabetes, suggesting that imprinted genes play a role in the disease's onset and progression [4]. Furthermore, the effects of maternal diabetes on the offspring's epigenome, including alterations in DNA methylation profiles, highlight the role of imprinting in the intergenerational transmission of diabetes risk [3], [7]. These epigenetic changes can lead to a permanent programming of the developing offspring, increasing the risk of diabetes in subsequent generations [8]. Overall, genomic imprinting is a critical factor in understanding the genetic and epigenetic mechanisms underlying diabetes susceptibility and progression.", + "role": "assistant" + } + } + ], + "created": 1728919891, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 225, + "prompt_tokens": 1415, + "total_tokens": 1640 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "10776283-4b6d-544c-89ac-0225c65bec1e", + "score": 0.7016704260307309, + "metadata": { + "text": "maternal diabetes reduces the precision of gene regulation in exposed individuals. Loss of precision in embry-onic gene regulation may include changes to the epigenome via deregulated expression of chromatin-modify-ing factors. Unraveling the mechanisms underlying such epigenetic modications in diabetic pregnancies willhelp to understand how teratogenic insults compromise embryonic development and possibly provide ave-nues for therapeutic intervention. Birth Defects Research (Part A) 88:601611, 2010.", + "title": "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "aa74b552-7e06-5596-8dec-298c40ad558c", + "extraction_id": "a9352adc-46d0-5947-a70d-940a7686008d", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "dc64e623-a130-5814-b54a-dd5f787f10d5", + "score": 0.6944549083709717, + "metadata": { + "text": "and metabolic imprinting: the ongoing effects of maternal hyper-glycemia. Diabetes Care 30:2287 2292\n9. Clausen TD, Mathiesen ER, Hansen T et al (2008) High prevalence\nof type 2 diabetes and pre-diabetes in adult offspring of women withgestational diabetes mellitus or type 1 diabetes: the role of intrauter-\nine hyperglycemia. Diabetes Care 31:340 346\n10. Solomon CG, Willett WC, Carey VJ et al (1997) A prospective\nstudy of pregravid determinants of gestational diabetes mellitus.\nJAMA 278:1078 1083", + "title": "2018 - Genetic variants of gestational diabetes mellitus a study of 112 SNPs among 8722 women in two independent populations.pdf", + "version": "v0", + "chunk_order": 92, + "document_id": "3b301dd1-17bd-5632-9a96-d6294c6d7650", + "extraction_id": "6ca1166c-ba51-5437-b325-5299e3e8fcef", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "5495230d-c26d-5633-90e8-028912e5298a", + "score": 0.6944146959309192, + "metadata": { + "text": "M. Gestational diabetes alters offspring DNA methylation profiles in human and rat: Identification of key \npathways involved in endocrine system disorders, insulin signaling, diabetes signaling, and ILK signaling. \nEndocriniology 2015;156:2222 -38. \n[33] Murphy SK, Huang Z, Hoyo C. Differentially methylated regions of imprinted genes in prenatal, \nperinatal and postnatal human tissues. PLOS ONE 2012;7:e40924.", + "title": "2017 - Genome-wide DNA methylation variation in maternal and cord blood of gestational diabetes population.pdf", + "version": "v0", + "chunk_order": 79, + "document_id": "e02a2e19-3527-5466-b8d6-69e62f657698", + "extraction_id": "971ff653-c42a-5366-ae2b-080df9aa679f", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "4ecf5607-8d58-5908-aa1b-4416af202e69", + "score": 0.6941716877869277, + "metadata": { + "text": "12. Kim JK, Samaranayake M, Pradhan S. Epigenetic mechanisms in\nmammals. Cell Mol Life Sci. 2009;66:596-612.\n13. Horsthemke B, Buiting K. Genomic imprinting and imprinting\ndefects in humans. Adv Genet. 2008;61:225-246.\n14. Iacobuzio-Donahue CA. Epigenetic Changes in Cancer. Annu\nRev Pathol. 2009;4:229-249.\n15. Temple IK. Imprinting in human disease with special reference\nto transient neonatal diabetes and Beckwith-Wiedemann syn-\ndrome. Endocr Dev. 2007;12:113-123.", + "title": "2010 - Autism Spectrum Disorders and Epigenetics.pdf", + "version": "v0", + "chunk_order": 115, + "document_id": "6b435185-b16c-5b05-826b-eb98ca7bf806", + "extraction_id": "dcc77767-4641-5969-b3c1-4ea96a644a74", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "a5412cf9-367c-518e-bb4f-77d8deb00a32", + "score": 0.6896819081807708, + "metadata": { + "text": "and Knowler W C. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study \nof discordant sibships. Diabetes 2000;49:2208 -11. \n[11] Feil R and Fraga MF. Epigenetics and the environment: Emerging patterns and implications. Nature \nReviews Genetics 2012;13:97 -109. \n[12] Recillas -Targa F. DNA Methylation, Chromatin boundaries, and mechanisms of genomic imprinting. \nArchives of Medical Research 2002;33:428 -38.", + "title": "2017 - Genome-wide DNA methylation variation in maternal and cord blood of gestational diabetes population.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "e02a2e19-3527-5466-b8d6-69e62f657698", + "extraction_id": "a17ed56f-20d4-56be-9aec-ac0b4943d19a", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "9814f4a0-2701-5920-bfd7-df5e1f3b134e", + "score": 0.6849690675735474, + "metadata": { + "text": "53. T ravers,M.E. etal. Insights into the molecular \nmechanism for type2 diabetes susceptibility at the \nKCNQ1 locus from temporal changes in imprinting \nstatus in human islets. Diabetes 62, 987992 (2013).\n54. Gulli,G., Ferrannini,E., Stern,M., Haffner,S. \n&DeFronzo,R.A. The metabolic profile of NIDDM \nisfully established in glucose-tolerant offspring of \ntwoMexican-American NIDDM parents. Diabetes 41, \n15751586 (1992).\nPRIMER\nNATURE REVIEWS | DISEASE PRIMERS VOLUME 1 | 2015 | 17", + "title": "2015 - Type 2 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 211, + "document_id": "415516ba-5365-501b-84ce-0789045862f8", + "extraction_id": "bbe952b1-6cc2-56a8-b5e8-5ca6b44b4316", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "4f7b210f-26f7-5726-baff-8d469b2cc3df", + "score": 0.6846446990966797, + "metadata": { + "text": "Gaudet, D., Hivert, M.F., Brisson, D., Bouchard, L., 2013 Sep. Gestational diabetesmellitus epigenetically affects genes predominantly involved in metabolic dis-\neases. Epigenetics 8 (9), 935 e943.\nSalbaum, J.M., Kappen, C., 2012 Oct. Responses of the embryonic epigenome to\nmaternal diabetes. Birth Defects Res. A Clin. Mol. Teratol. 94 (10), 770 e781.\nSalbe, A.D., Lindsay, R.S., Collins, C.B., Tataranni, P.A., Krakoff, J., Bunt, J.C., 2007 Feb.", + "title": "2015 - Maternal diabetes, gestational diabetes and the role of epigenetics in their long term effects on offspring.pdf", + "version": "v0", + "chunk_order": 242, + "document_id": "3e92bd8e-fbf7-5bc4-9395-0a6dd0b0934e", + "extraction_id": "e7e97f1e-d947-5b94-b2a9-5ac4b443628c", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "8267bc80-1791-5e21-b228-053cba0629fd", + "score": 0.6832183192242206, + "metadata": { + "text": "environment are probably mediated by a permanent program-ming of the developing offspring, e.g. by the mechanism ofimprinting. Of interest, the increased risk of diabetes continuesinto subsequent generations, suggesting the changes also affectthe germ cell line [143]. \nConclusions \nThere is little doubt that some animal models of diabetes have", + "title": "2005 - Animal models of diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "2fd381ac-2898-5a8c-af93-bcc86e7dec14", + "extraction_id": "f7b36272-9780-52e8-9cb3-62d1c6c8c3b6", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "4bb50efe-65b0-5c3c-9f58-03b423c93c0d", + "score": 0.6831094622612, + "metadata": { + "text": "tal diabetes and later onset diabetes: a case of inher -\nited insulin resistance. Arch. Dis. Child. 72:5657.\n 6. Temple, I.K., et al. 1995. An imprinted gene(s) for \ndiabetes? Nat. Genet. 9:110112.\n 7. Temple, I.K., et al. 1996. Further evidence for an \nimprinted gene for neonatal diabetes localised to chro -", + "title": "2004 - Impaired glucose homeostasis in transgenic mice expressing the human transient neonatal diabetes mellitus locus.pdf", + "version": "v0", + "chunk_order": 111, + "document_id": "268bc8e3-7787-5bc0-8f7d-fffe20194dca", + "extraction_id": "f68a90b3-5e03-57f4-8cb6-252e3a3fa132", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + }, + { + "id": "f703ae7e-5f64-52ee-860e-7b91b3066477", + "score": 0.6825436760511218, + "metadata": { + "text": "1994; Martinez-Frias et al., 1998). The underlying mecha-nisms are not well understood, but are thought to\ninvolve various responses of the embryonic genome to\nthe adverse intrauterine environment (Greene, 2001;Loeken, 2008).\nTo explore how conditions of maternal diabetes affect\ngene expression in the embryo, we recently conducted\nexpression proling experiments on embryos from dia-betic dams compared to embryos from normal dams(Pavlinkova et al., 2009). We were able to demonstrate", + "title": "2010 - Neural tube defect genes and maternal diabetes during pregnancy.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "aa74b552-7e06-5596-8dec-298c40ad558c", + "extraction_id": "a9352adc-46d0-5947-a70d-940a7686008d", + "associatedQuery": "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?," + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIHKkTED9VE0du8urGhS0MeefXMR7", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Longitudinal genomics studies are crucial for understanding gene-environment interactions in diabetes onset and management because they allow researchers to observe how genetic variations interact with environmental factors over time. This approach helps in identifying temporal patterns and causal relationships that are not possible to discern from cross-sectional studies [2]. By incorporating genotype measurements into longitudinal studies, researchers can gain significant insights into the genetic basis of diseases like diabetes, which can lead to better prediction, targeted prevention, and stratified treatment of type 2 diabetes (T2D) [3]. Additionally, these studies can help stratify T2D into subclasses that can be treated more effectively by understanding gene-lifestyle interactions [8]. Overall, longitudinal genomics studies provide a comprehensive framework to explore how genetic predispositions and environmental exposures collectively influence diabetes-related traits and disease progression.", + "role": "assistant" + } + } + ], + "created": 1728919898, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 163, + "prompt_tokens": 1342, + "total_tokens": 1505 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "ee24ad01-f93a-55c4-8c2c-9dea6a6a84d5", + "score": 0.7031167922546404, + "metadata": { + "text": "genome-wide association scans on type 2 dia-betes (Lango et al, 2008 ; van Hoek et al, 2008 ).\nBoth studies found a similar predictive value\nshowing only a marginal improvement in the\nprediction of type 2 diabetes beyond classicalclinical characteristics.\nThus, despite overwhelming signicances and\nrepeated replications, the explained variance andpredictive value of the currently identied sus-\nceptibility loci is too low to be clinically useful.\n5 GeneEnvironment Interactions\nin Obesity and Diabetes", + "title": "2010 - Candidate Gene and Genome-Wide Association Studies in Behavioral Medicine.pdf", + "version": "v0", + "chunk_order": 4330, + "document_id": "17637a6f-804e-50e4-9cf5-37318e17f15c", + "extraction_id": "3bf3c6a7-de03-5114-bad8-d53fd76d0fba", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "de2af111-7fad-5dc1-baae-4742ccc8ba0d", + "score": 0.7029099045449565, + "metadata": { + "text": "actions between genetic variation and environmental exposures\nand medical therapies has important implications for the predic-\ntion, targeted prevention, and s tratified treatment of T2D and\nmany other diseases.\nThe literature on gene-e nvironment interactions in\ndiabetes-related traits is extensive, but few studies are accom-\npanied by adequate replication data or compelling mechanistic\nexplanations. Moreover, most studies are cross-sectional,\nfrom which temporal patterns and causal effects cannot be", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 8, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "08acfe03-73b3-5533-b8e4-9caa031d33dd", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "e07d8080-aba7-5216-8a75-e078201b8c0a", + "score": 0.7002737313427437, + "metadata": { + "text": "ined for a range of disorders, from diabetes, cancer and in ammatory bowel disease to \ndepression. We refute the contention that incorporating the measurement of genotype into longitudinal-epidemiological studies is wasteful or unlikely to yield signi cant \nbene ts.\n2008 Genetic effects on environmental vulnerability to disease. Wiley, Chichester (Novartis Foundation Symposium) p 128142\nSlow progress understanding the genetic basis of many common diseases has been", + "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "version": "v0", + "chunk_order": 986, + "document_id": "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "extraction_id": "cfc4760c-755e-5693-8d7b-4332fb6c45e5", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "e76c1d0c-33b7-5d9e-958f-fce6adfe81aa", + "score": 0.6990422010421803, + "metadata": { + "text": "In principle, each of these loci provides an opportunity to define \nthe genetic architecture and pathophysiology of these traits.\nThe earliest successes for genetic discovery in diabetes and \nobesity arose from the study of monogenic and syndromic \nforms of disease, for which the segregation of rare, but highly \npenetrant, alleles could be tracked using family-based linkage \napproaches that are well suited to that setting. Maturity-onset \ndiabetes of the young, for example, accounts for ~12% of cases", + "title": "2012 - The Genetic and Epigenetic Basis of Type 2 Diabetes and Obesity.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "d74ac751-712b-5970-98e6-bd348adc1dee", + "extraction_id": "50bde36d-2968-5eaa-9713-924e73383427", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "30728ec3-882c-5bb0-8f41-4c74dfafdf13", + "score": 0.6983946145537738, + "metadata": { + "text": "wide GxE interactions in explaining the variance of diabetes-related traits.\nCitation: Zheng J-S, Arnett DK, Lee Y-C, Shen J, Parnell LD, et al. (2013) Genome-Wide Contribution of Genotype by Environment Interaction to Variation of\nDiabetes-Related Traits. PLoS ONE 8(10): e77442. doi:10.1371/journal.pone.0077442\nEditor: Maria Eugenia Saez, CAEBi, Spain\nReceived April 10, 2013; Accepted September 3, 2013; Published October 28, 2013", + "title": "2013 - Genome-Wide Contribution of Genotype by Environment Interaction.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "8c310d76-0a3b-574c-9859-859258870ee5", + "extraction_id": "f3975a2c-8a66-582e-a4b8-868b1f4722d4", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "f7ed49ac-f617-5c13-851e-98d1583e020f", + "score": 0.690583825111394, + "metadata": { + "text": "data sharing to advance complex disease research. \nNat. Rev. Genet. 17, 535549 (2016).\n82. Franks,P .W., Pearson,E. & Florez,J.C. Gene-\nenvironment and gene-treatment interactions in \ntype2 diabetes: progress, pitfalls, and prospects. \nDiabetes Care 36, 14131421 (2013).\n83. Hagberg,J.M., Jenkins,N.T . & Spangenburg,E. \nExercise training, genetics and type2 diabetes-\nrelated phenotypes. Acta Physiol. 205, 456471 \n(2012).\n84. Langenberg,C. etal. Gene-lifestyle interaction and", + "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "version": "v0", + "chunk_order": 138, + "document_id": "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "extraction_id": "512ae4b5-27c8-509c-87ad-abd64d4295a6", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "151c185f-3300-5518-810c-3fb0d6715f2c", + "score": 0.6864032336233131, + "metadata": { + "text": "Genomics and geneenvironment interactions\nEven though many cases of T2DM could be prevented \nby maintaining a healthy body weight and adhering to a \nhealthy lifestyle, some individuals with prediabetes mel -\nlitus are more susceptible to T2DM than others, which \nsuggests that individual differences in response to life -\nstyle interventions exist76. Substantial evidence from \ntwin and family studies has suggested a genetic basis \nof T2DM77. Over the past decade, successive waves of", + "title": "2018 - Global aetiology and epidemiology of type 2 diabetes mellitus and its complications.pdf", + "version": "v0", + "chunk_order": 57, + "document_id": "8bc8f3d4-968f-5252-ab4c-832b92e9ec0d", + "extraction_id": "df2a8699-692f-5f25-94b3-508f9ed2f210", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "cc98a5b9-131e-5b60-919e-82e86b7a37a7", + "score": 0.6857945101506102, + "metadata": { + "text": "DNA variation with disease processes in a range of settings, from cell\nlines to human populations, and major advances have been made in\ncoupling these complex datasets with information about extrinsic\nenvironmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions.\nDoing so may teach us about disease etiology and help stratify type 2\ndiabetes (T2D) into subclasses that can be treated more effectively, with", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "c362793d-c70f-5225-afe5-88098042daef", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "a94c609e-4816-5e10-96fd-ba8d79218405", + "score": 0.6852905154228259, + "metadata": { + "text": "fuel subsequent functional and clinical translation studies.\nThis is important, because diabetes medicine may rely increas-\ningly on genomic stratification of patient populations and\ndisease phenotype, for which gene-environment interaction\nstudies might prove highly informative.\nHow Are Gene-Environment Interactions Defined?\nThe term gene-environment interaction has different meanings\nto different biomedical re searchers (see Supplement 1for\nglossary of terms used). However, here, we focus on the", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "08acfe03-73b3-5533-b8e4-9caa031d33dd", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + }, + { + "id": "1d13cf78-3215-5873-b910-cbcac141779b", + "score": 0.6837809274327215, + "metadata": { + "text": "Nutrients 2014, 6 5362 \n \n48. Cornelis, M.C.; Hu, F.B. Gene -enviroment interactions in the development of type 2 diabetes: \nRecent progress and continuing challenges. Annu. Rev. Nutr. 2012, 32, 245259. \n49. Lee, Y.C.; Lai, C.Q.; Ordovas, J.M.; Parnell, L.D. A database of gene -enviroment interactions \npertaining to blood lipid traits, cardiovascular disease and type 2 diabetes. J. Data Mining \nGenomics Proteomics 2011, 2, 106, doi:10.4172/2153- 0602.1000106.", + "title": "2014 - Nutrigenetics and Nutrigenomics Insights into Diabetes Etiopathogenesis.pdf", + "version": "v0", + "chunk_order": 181, + "document_id": "ce4f171c-494c-53f2-a770-c3edd3561c40", + "extraction_id": "232f9536-eeac-5739-a57d-770cf5b32947", + "associatedQuery": "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIHKoCrJvacxorigznvNb5BV4LGGI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent integrative genomics approaches, such as the combination of single-cell RNA sequencing and epigenomic profiling, have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. These approaches have allowed researchers to delve deeper into the transcriptional regulation of non-beta cell types, which may play crucial roles in the hallmark features of beta cell insufficiency and dysfunction associated with Type 2 Diabetes (T2D) [1]. \n\nSingle-cell RNA sequencing has been particularly instrumental in high-throughput diabetes research by enabling the sequencing of individual cells from human pancreatic islets. This is important given the heterogeneity within the islets of Langerhans, which consist of various cell types. By tracking genetic changes in individual cells, researchers can better understand the complex cellular landscape and the specific contributions of different cell types to diabetes pathogenesis [2].\n\nFurthermore, epigenomic profiling adds another layer of complexity by revealing how epigenetic changes can modulate gene expression without altering the DNA sequence. These changes are crucial for maintaining the secretory capacity, survival, and functional identity of pancreatic islets, as well as their response to insulin [8]. The integration of these genomic and epigenomic data helps identify regulatory elements and pathways that could be targeted for therapeutic interventions, moving from correlation to causation in understanding diabetes [10].", + "role": "assistant" + } + } + ], + "created": 1728919902, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 271, + "prompt_tokens": 1500, + "total_tokens": 1771 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "d5c2a32a-b869-59c1-8a63-45ab620669de", + "score": 0.7126145958900452, + "metadata": { + "text": "4. PRECISE CELLULAR GENOMICS\nElucidating the molecular mechanisms that lead to beta cell\ndysfunction and T2D pathogenesis has been a major focus of diabetes\nresearch for decades. However, advances in single cell genomic\nproling techniques have led to greater understanding of non-beta cell\ntype transcriptional regulation and suggest that they may play\nimportant roles in hallmark features of beta cell insuf ciency and", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 55, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "7a2a9981-4096-5049-a717-3e69eb609777", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "1c659cb4-085b-55b9-be3c-6332c36cbeba", + "score": 0.6997478008270264, + "metadata": { + "text": "Genes 2018 ,9, 374 7 of 19\n4. Single-Cell RNA-seq as a Novel Approach in High-Throughput Type 2 Diabetes Research\nIslets of Langerhans are heterogeneous structures that consist of different cell types. Further\nresearch is needed to track genetic changes in individual pancreatic islet cells and in sorted cell\npopulations. The massive development of NGS allowed the sequencing of single cells from human\npancreatic islets. Considering the cell-type heterogeneity within Langerhans islets, such an approach", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 56, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "f06bcc81-6ef9-5874-8ef9-6bcb3c34b0d0", + "score": 0.6991837233061711, + "metadata": { + "text": "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? \n1 3\nBrissova etal. 2018; Tritschler etal. 2017). Moreover, an \nincrease in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; \nTheis and Lickert 2019; Tritschler etal. 2017).\nNotably, single-cell transcriptome profiling has been", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 62, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "65471d38-cd13-5de2-8c19-1eb72d24d6f5", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "b7812a7a-5504-57ca-8755-969dee45717e", + "score": 0.6979928016662598, + "metadata": { + "text": "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc-\ning: new developments and medical applications. Cell Biosci \n9:53. https ://doi.org/10.1186/s1357 8-019-0314-y\nTeo AKK etal (2018) Single-cell analyses of human islet cells reveal \nde-differentiation signatures. Cell Death Discov 4:14. https ://doi.\norg/10.1038/s4142 0-017-0014-5\nTheis FJ, Lickert H (2019) A map of beta-cell differentiation pathways \nsupports cell therapies for diabetes. Nature 569:342343. https ://", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "7f7a7f30-2e4e-50aa-bbcb-9f211c371e38", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "ab373b7e-8c0b-59d8-9408-3e09ac76761e", + "score": 0.6955804824829102, + "metadata": { + "text": "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol \n(Oxf) 211:273284\n 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose \nintolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142\n 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "title": "2018 - Lnc\u2011ing non\u2011coding RNAs with metabolism and diabetes roles.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "019efefb-65db-55f5-a3a7-4f224473f51f", + "extraction_id": "8bbfb009-87b7-54ae-8465-8796db8c271a", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "7a5c8fad-97c5-59d2-8e5e-ee72d3dc2362", + "score": 0.6935632594152429, + "metadata": { + "text": "24. Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human\npancreatic beta cell transcriptome. Genome Res. 23, 1554 1562 (2013).\n25. Takane, K. K., Bender, A. & Stewart, A. F. Speci c targeting and sorting of\npuried human beta cells: de ning the human beta cell transcriptome. ADA\nScienti c Sessions, San Francisco (2014).\n26. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation\nnetwork analysis. BMC Bioinformatics 9, 559 (2008).", + "title": "2017 - Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas.pdf", + "version": "v0", + "chunk_order": 203, + "document_id": "6cf1eb8d-a91e-58a2-b6f4-29653678d0d3", + "extraction_id": "bdf327a6-decb-5c7a-a981-a7969206b455", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "b7c1d2be-88c5-5f33-b812-b05e842f1647", + "score": 0.6914374048374549, + "metadata": { + "text": "5. Genome-Wide Proling of Epigenetic Changes in Pancreatic Islets and Peripheral Tissues\nEpigenetic data added another layer of complexity to our understanding of the genomic bases\nof T2D. Given that a variable epigenetic pattern can modulate the link between the SNP and trait,\nconsideration of this interplay is critically important. Molecular epigenetics involves changes in\ngene function that occur without a change in the nucleotide sequence via DNA methylation, histone", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 60, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "11a5527b-8d22-5e69-8a84-6d9180517d81", + "score": 0.6867216042710319, + "metadata": { + "text": "and model organisms. The combination of data from high-throughput approaches and association\nstudies has provided compelling evidence that some epigenetic markers contribute to the risk of\nT2D [ 57,58]. Epigenetic alterations have been shown to affect the expression of genes that are crucial\nfor maintaining pancreatic islet secretory capacity, survival, and functional identity and the proper\nresponse to insulin in peripheral tissues [ 59,60]. Furthermore, several epigenetic signatures, such", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 64, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "52e8a636-ced9-5c14-a7e5-0c30b7f05107", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "db06230d-31c0-5947-8c1c-f58c48b6f439", + "score": 0.6851639339230112, + "metadata": { + "text": "Epigenomic approaches: applications in diabetic\ncomplications research\nEpigenetic studies in human disease have been greatly accel-\nerated as a result of advances in whole-genome and epige-\nnome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is\nanalysed using bisulfite conversion of genomic DNA, immu-\nnoprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "title": "2015 - Epigenetic mechanisms in diabetic complications and metabolic memory.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "470f1f94-792d-5273-a88f-7e06084951c5", + "extraction_id": "312b1856-e1b1-5ae7-8cba-370becf5f7cb", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + }, + { + "id": "a2adc65b-035b-568f-a0ae-9f7821ef45bc", + "score": 0.6835848877677401, + "metadata": { + "text": "understand each cell type s genomic architecture and better charac-\nterize their roles in islet resilience and failure. Experimental manipu-\nlation of the regulatory elements and/or the target genes identi ed by\n(epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g.,\nEndoC-\nbH1-H3) is essential to progress from correlation to causation.\nSimilarly, transitioning from themouse (C57BL/6) to multiple mouse", + "title": "2019 - (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.pdf", + "version": "v0", + "chunk_order": 97, + "document_id": "b9bc63a5-e366-5685-bd7a-4732a8eeffb7", + "extraction_id": "117cc1a5-d236-56b2-a69d-9c0a2fb9053d", + "associatedQuery": "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + } + } + ], + "kg_search_results": null + } + } + } +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json b/gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json new file mode 100644 index 0000000..ed04cba --- /dev/null +++ b/gnqa/paper2_eval/data/responses/gpt4o/de_gn_responses.json @@ -0,0 +1,3222 @@ +{ + "1": { + "results": { + "completion": { + "id": "chatcmpl-AIGrF12QPoEwoc2D22aSA5ivwYW2D", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent advancements in network-based integrative genomics have significantly enhanced our understanding of complex trait architectures by enabling a more comprehensive analysis of the interactions between genetic and environmental factors. These advancements allow for the integration of gene variants and environmental factors to better understand the networks controlling complex traits [1]. The use of systems genetics approaches facilitates the connection of different trait networks with networks of gene and environmental variants, which helps in identifying the global genetic modulation of complex phenotypes [1].\n\nMoreover, the integration of genotypic, expression, and other data within a Bayesian network framework has been shown to improve our mechanistic understanding of how DNA perturbations can induce changes in gene networks that ultimately affect disease outcomes [2]. This integrative approach provides a more detailed view of the polygenic and pleiotropic networks that modulate complex traits, such as those affecting the nervous system [5].\n\nOverall, these advancements in network-based integrative genomics offer a more nuanced and interconnected view of complex trait architectures, moving beyond single-gene analyses to consider the broader network of interactions that contribute to phenotypic outcomes.", + "role": "assistant" + } + } + ], + "created": 1728918069, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 217, + "prompt_tokens": 1160, + "total_tokens": 1377 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "2fe235ff-90ab-5f21-8e51-cbfb0e13713a", + "score": 0.7168012598989943, + "metadata": { + "text": "It is important to integrate the gene variants and environmental factors to the trait to understand the network controlling that trait. In systems genetics approach, different trait networks are related to different networks of gene and environmental variants to find global genetic modulation of the complex phenotype. The availability of genetic reference panels makes it easy to acquire diverse phenotypic data and advanced computational models make it possible to analyse their relationship. 2.2.1.", + "title": "2012 - Genetic regulation of adult hippocampal neurogenesis A systems genetics approach using BXD recombinant inbred mouse strains.pdf", + "version": "v0", + "chunk_order": 149, + "document_id": "c54da858-9620-588e-8e41-76a960af2ff6", + "extraction_id": "ebea9717-52a1-5eb8-8b5a-67afb90c95f8", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "e26ebc1e-e05d-56fb-8718-604275994a84", + "score": 0.6923868243463851, + "metadata": { + "text": "Processing Large-Scale, High-Dimension Genetic 325\nanother. We anticipate these types of networks becoming increasingly important in\nthe human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks\nthat cause disease. The integration of genotypic and expression and other data have\nrecently been shown, in a Bayesian network framework [76], to enhance the overall", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "b17b43c6-1ba8-5849-8664-3b5cd78877b5", + "score": 0.6905426957718185, + "metadata": { + "text": "2. GENETICAL GENOMICS\nIn recent years, there has been growing interest in uniting genetic and genomic\napproaches to enable more comprehensive dissections of complex traits and their\ngenetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "title": "2005 -Pomp- GenomeExploitation.pdf", + "version": "v0", + "chunk_order": 641, + "document_id": "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "extraction_id": "80f97b13-9dd9-5d52-9d55-0abac724605e", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "037c669c-da80-5e1e-abe3-c4344145a4ed", + "score": 0.6905426957718185, + "metadata": { + "text": "2. GENETICAL GENOMICS\nIn recent years, there has been growing interest in uniting genetic and genomic\napproaches to enable more comprehensive dissections of complex traits and their\ngenetic architecture. Jansen and Nap (2001) termed this synthesis genetical ge-", + "title": "2006 - Marker Assisted Backcrossing .pdf", + "version": "v0", + "chunk_order": 641, + "document_id": "5efc1bdf-f847-5eaf-a808-9cf71b9399ce", + "extraction_id": "da78b007-359c-548c-8cb0-ba4a3dab0f86", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "fb5944f3-bb0e-599e-827c-a8b7c6934746", + "score": 0.6871884055621492, + "metadata": { + "text": "42.Chesler EJ, et al. 2005. Complex trait analysis of gene expression uncovers\npolygenic and pleiotropic networks that modulate nervous system func-tion. Nat. Genet. 37:233242.\n43.Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, develop-\ning a resource for mammalian systems genetics: a status report of theWellcome Trust cohort. Mamm. Genome 19:379 381.\n44.Xiao J, et al. 2010. A novel strategy for genetic dissection of complex traits:", + "title": "2013 - Host Genes and Resistance.pdf", + "version": "v0", + "chunk_order": 1268, + "document_id": "cc6cf2b5-0440-51e8-aad4-d0b4b5331ab2", + "extraction_id": "661e7fb0-804c-53e2-b948-6512c372ac57", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "a860695e-fe40-55eb-9eb8-072e1daf5cf2", + "score": 0.684656180780394, + "metadata": { + "text": "multiple-SNP analysis of GWAS summary statistics identiesadditional variants inuencing complex traits. Nat Genet\n44(369375):S1S3. doi: 10.1038/ng.2213\nYang J, Zaitlen NA, Goddard ME et al (2014) Advantages and pitfalls\nin the application of mixed-model association methods. NatGenet 46:100106. doi: 10.1038/ng.2876\nYazbek SN, Buchner DA, Geisinger JM et al (2011) Deep congenic", + "title": "2014 - Fine-mapping QTLs in advanced intercross lines and other.pdf", + "version": "v0", + "chunk_order": 217, + "document_id": "eb30392e-f079-511d-8c6c-a6e6c98d2167", + "extraction_id": "a5c455c9-50f6-5f12-84cd-26d335001e6b", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "22301737-122c-57be-a2f1-9d631ad101b3", + "score": 0.675520160732255, + "metadata": { + "text": "10. \nThe power of integrating all these genetic and genomic data has now been well documented, offering a glimpse of what the future of com-plex trait genetics will look like. Model systems that are genetically more complex, including extensive eight-strain crosses\n11,12 and haplotype \nassociation studies using large panels of regular inbred strains of mice, and even humans, are", + "title": "2007 - Latexin is a newly discovered regulator of hematopoietic stem cells.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "63467ba8-940b-59f6-bbd5-0b0ce7883d49", + "extraction_id": "91470df2-7451-59d2-af9f-98cdf2f85486", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "101c1f27-4a98-5d1c-b013-c5f1950aee95", + "score": 0.6743107230292936, + "metadata": { + "text": "tive analysis of omics summary data reveals putative mechanisms \nunderlying complex traits. Nat Commun 9:918\n 33. Yang J, Hong Lee S, Goddard ME, Visscher PM (2011) GCTA: \na tool for genome-wide complex trait analysis. Am J Hum Genet \n88:7682\n 34. Zeisel A, Hochgerner H, Lnnerberg P, Johnsson A, Memic F, \nvan der Zwan J etal (2018) Molecular architecture of the mouse \nnervous system. Cell 174:999.e221014.e22\n 35. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ (2016) RVTESTS:", + "title": "2020 - Large?scale pathway specific polygenic risk and transcriptomic.pdf", + "version": "v0", + "chunk_order": 147, + "document_id": "96119357-a6dd-5ea3-8bcb-9c047f0a336e", + "extraction_id": "a53c7001-432d-5289-9cc1-b3d75a721da4", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "91ac7cb9-ec59-5bd6-9f24-aa840caf2c27", + "score": 0.6738772379379455, + "metadata": { + "text": "used to identify molecular traits involved in the p athology of diseases and to eluci- \ndate the networks underlying complex phenotypes. Re cent studies have pushed the \ngenetical genomics concept further towards data int egration and interpretation \nwithin and across molecular levels, and have also r evealed remaining challenges. \nThe focus of this review is to discuss these challe nges and their possible solutions in", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 618, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "4a7ed73c-26c7-5852-8a02-a39cd0d611ec", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + }, + { + "id": "6e933f07-26d6-5cf1-8ee0-9bf6ec68b1ff", + "score": 0.6721933278807058, + "metadata": { + "text": "2 large populations. The new methods have allowed us to dissect the genetic architecture of \ncomplex disorders including the identification of the causal genomic loci, estimation of \nthe disease heritability, estimation of effect sizes of different loci and their non-additive \ninteractions. \nLinkage analysis \n \n The earlier breakthroughs in linking genotype with phenotype involved studies of \nMendelian disorders that can be mapped to a single gene and a single mutation. These", + "title": "2015 - Functional Analysis of Genomic Variation and Impact on Molecular and Higher Order Phenotypes.pdf", + "version": "v0", + "chunk_order": 56, + "document_id": "263d327b-f5db-54e4-a215-b3f8a51cd7d6", + "extraction_id": "cec82840-0f05-5fc5-bfcf-a4b928124fef", + "associatedQuery": "How do recent advancements in network-based integrative genomics alter our understanding of complex trait architectures?" + } + } + ], + "kg_search_results": null + } + } + }, + "2": { + "results": { + "completion": { + "id": "chatcmpl-AIGrO4A8FLvBSq44CsQgHQD11jLhN", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org include the addition of data from 10 species, multi-omics analysis, updated code, and new tools, making it a valuable resource for predictive medicine and systems genetics [1]. These enhancements allow for more comprehensive analyses and facilitate the study of correlations between traits and data mining in genomic regions containing candidates for quantitative trait genes [4]. Additionally, GeneNetwork is continuously updated to support systems genetics analyses, which can aid in the discovery, replication, validation, and translation of gene-environment interactions [8].", + "role": "assistant" + } + } + ], + "created": 1728918078, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 114, + "prompt_tokens": 1190, + "total_tokens": 1304 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.7165116836719714, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "8999a4c7-e5de-539f-bee4-fd00cb69e7bb", + "score": 0.6932196799981797, + "metadata": { + "text": "analytical method, have been used to discover gene-\nenvironment interactions; some approaches address similar\nobjectives, whilst others are complementary and can be ap-\nplied in sequence. Below we describe several of these ap-\nproaches, and refer the reader to another excellent review of\ngene-environment interaction methods [ 31].\n(a)Established statistical approaches\nUntil 2008, almost all studies of gene-environment interac-\ntions focused on testing hypotheses based on existing biolog-", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "9639b49f-d3be-5592-bb0e-a0341a7caa06", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "e5320abf-d018-51c2-a386-aa822f605e1a", + "score": 0.6798188686370898, + "metadata": { + "text": "ulated by non-genetic factors. Thus, the once esoteric\ntopic of gene-environment interaction is now becoming\nmainstream and appealing to investigators across diversedisciplines; this has propelled major methodological in-\nnovations for the discovery, replication, validation and\ntranslation of gene-environment interactions. The expo-\nnentiation of data resources for these purposes has\ndemanded analytical solutions that address data dimen-\nsionality reduction. Although not yet extensively imple-", + "title": "2016 - Putting the Genome in Context Gene-Environment Interactions.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "ea43bb66-b6fe-5682-8f48-90568c080401", + "extraction_id": "c362793d-c70f-5225-afe5-88098042daef", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6637628479286614, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "e71e1073-2800-5598-917f-00c3c08ed274", + "score": 0.6632876000828993, + "metadata": { + "text": "Eaves LJ 2006 Genotype x environment interaction in psychopathology: fact or artifact? Twin \nRes Hum Genet 9:18\nHunter DJ 2005 Geneenvironment interactions in human diseases. Nat Rev Genet \n6:287298\nIoannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG 2001 Replication validity \nof genetic association studies. Nat Genet 29:306309\nIoannidis JP, Gwinn M, Little J et al 2006 A road map for ef cient and reliable human genome \nepidemiology. Nat Genet 38:35", + "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "version": "v0", + "chunk_order": 205, + "document_id": "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "extraction_id": "ad295276-e94f-53a4-9278-e6b93888ab10", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "1c26e6f6-680b-5877-9600-fee25a42c943", + "score": 0.6603226864145597, + "metadata": { + "text": "GeneNetwork is an open-access database that collates genomic\ninformation of diverse experimental crosses and reference panels\nas well as phenotypic data from miscellaneous research groups\n[26].\nStatistics\nData generation, statistical analysis and graph creation were\nperformed with SPSS Statistics 21 (IBM, Ehningen, Germany). As\nappropriate, mean and median values were further used for QTLanalysis. Phenotypic robustness for each strain was assessed by the", + "title": "2014 - Systems Genetics of Liver Fibrosis Identification of Fibrogenic and Expression Quantitative Trait Loci in the BXD Murine Reference Population.pdf", + "version": "v0", + "chunk_order": 29, + "document_id": "125d9cd4-5297-5173-9b16-9073cd3bcc71", + "extraction_id": "8c423789-3641-5853-9cf3-f4a026ffb446", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "8f299e3a-a7bc-5258-8f4d-0e964f89b35e", + "score": 0.6585033739505199, + "metadata": { + "text": "NU32CH13-Hu ARI 18 June 2012 13:45\neffectively scan the entire genome for interac-\ntions with environment. Although innovative,\nthe most effective study design and statistical\napproach for conducting gene-environment-\nwide interaction studies (GEWIS) remains\nunresolved (88). The greatest challenge for\nGEWIS involves nding a balance between\nrejecting true ndings resulting from stringent\nmultiple-testing correction and reporting\nfalse-positive results. Several novel methods", + "title": "2012 - Gene-Environment Interactions in the Development of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 66, + "document_id": "ea9601ed-ad83-506e-b1b7-e7211671ff73", + "extraction_id": "a043fdc7-8228-5f22-b6db-d3b1ad4eb6ae", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "f3f859bb-d066-5552-b07e-eefcb489d8f5", + "score": 0.6570818628620003, + "metadata": { + "text": "1 \n GeneNetwork: a continuously updated tool for systems genetics analyses \n \nPamela M. Watson1, David G. Ashbrook1 \n \n1Department of Genetics, Genomics and Informatics, University of Tennessee Health Science \nCenter, Memphis, TN 38163, USA \n \nAbstract \n \nGeneNetwork and its earlier iteration , WebQTL, have now been an important database and \ntoolkit for quantitative trait genetics research for two decades. Recent improvements to", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "8744d4f9-5566-5435-98ce-2afae5f59ad3", + "score": 0.6550541335279783, + "metadata": { + "text": "13 132. Geneenvironment interaction: \novercoming methodological challenges\nRudolf Uher\nMRC Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings \nCollege London, UK\nAbstract. While interacting biological effects of genes and environmental exposures (G \n E) form a natural part of the causal framework underlying disorders of human health, \nthe detection of G E relies on inference from statistical interactions observed at popu-", + "title": "2008 - Genetic Effects on Environmental Vulnerability to Disease Novartis Foundation Symposium 293.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "5d65e407-34e5-5c1c-b394-989b7a09b57d", + "extraction_id": "a52959f6-733a-58bb-93c0-65403fb72c83", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + }, + { + "id": "82539c96-b4a2-50e2-bbdd-4458ff3f0d2a", + "score": 0.6547671361336992, + "metadata": { + "text": "A number of recent developments in twin methodology have taken\nplace based on the incorporation of measured genotype information. Thisenables twin models to estimate how much of the genetic variation is dueto variation in a specific gene. Gene-environment interaction studies, link-Copyright National Academy of Sciences. All rights reserved.Cells and Surveys: Should Biological Measures Be Included in Social Science Research?\nhttp://www.nap.edu/catalog/9995.html", + "title": "2001 - Demography in the age of genomics.pdf", + "version": "v0", + "chunk_order": 436, + "document_id": "0f07fa43-feb6-5656-b7e7-b8faa86f5623", + "extraction_id": "efc8c74e-a862-5c6e-b947-96b664aaf0d0", + "associatedQuery": "What are the latest methodological improvements in evaluating gene-environment interactions using GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "3": { + "results": { + "completion": { + "id": "chatcmpl-AIGrUMBGxTc4nmy408W8WUAr2t9TQ", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets by allowing for the construction of biological networks that are predictive of molecular interactions and phenotypic outcomes. When data on DNA variation is combined with gene expression, protein interactions, and DNA-protein binding information, it enables the creation of comprehensive models that can predict complex phenotypes more accurately [6]. This integration facilitates the study of relationships between genes, pathways, and phenotypes, providing a more holistic view of the biological systems involved [2]. Additionally, the use of sophisticated statistical and computational tools in conjunction with multi-omics data allows for the genetic dissection and synthesis of traits, further enhancing predictive accuracy [7].", + "role": "assistant" + } + } + ], + "created": 1728918084, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 142, + "prompt_tokens": 1160, + "total_tokens": 1302 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6984842419624329, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "f53306e0-447d-5640-b26f-6b617ce35a46", + "score": 0.6796948313713074, + "metadata": { + "text": "Conclusion \nGeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. \nHere we have used GeneNetwork to explore an inflammatory phenotype, and identified a small \nnumber of plausible candidate genes. A similar workflow can be used for any trait on \nGeneNetwork, or for any phenotype collected by an investigator in a genetically diverse \npopulation. GeneNetwork can allow users to study relationships between genes, pathways, and \nphenotypes in an easy to use format.", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "c08af10b-f2ad-540b-be15-7cc101bf2dbc", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "da10a7f5-6d13-504c-8db9-d67a48a3193e", + "score": 0.6796948313713074, + "metadata": { + "text": "Conclusion \nGeneNetwork is an excellent tool for exploring complex phenotypes with systems genetics. \nHere we have used GeneNetwork to explore an inflammatory phenotype, and identified a small \nnumber of plausible candidate genes. A similar workflow can be used for any trait on \nGeneNetwork, or for any phenotype collected by an investigator in a genetically diverse \npopulation. GeneNetwork can allow users to study relationships between genes, pathways, and \nphenotypes in an easy to use format.", + "title": "2020 - Gene network a continuously updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 131, + "document_id": "374fd6d3-e6c1-560c-a421-a4b393ba23b2", + "extraction_id": "046a82bb-8f86-5ecd-8879-34e569630a21", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6671545703915029, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "d500c4bd-50b1-5271-b7a6-42591225de7a", + "score": 0.6594731408200607, + "metadata": { + "text": "connect Genotype with Gene2 and Phenotype, knowledge of the \nGenotype still influences the predicted values of these variables. \nFor example, Genotype = 1 may cause a decrease in Gene1 and this \ndecrease in Gene1 will subsequently cause a reduction in Gene2.\n4 Discussion\nNetwork modeling of biological datasets is often limited by the \nnumber of samples within a dataset, and the available data does not \nsupport the construction of precise and reliable large-scale networks", + "title": "2017 - Precise network modeling of systems genetics data using the Bayesian network webserver.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "c80b6981-5243-55a2-b5d8-0d7ffb2f4505", + "extraction_id": "803030b1-07ab-5b8c-97cb-297339488484", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "a9508122-3b14-5365-979c-ba580bdcb78f", + "score": 0.6555604338645935, + "metadata": { + "text": "GeneNetwork http://www.genenetwork.org is anexample of a bioinformatics tool that can be used to\nexplore systems genetics data.\nThe importance of defining biological networks and\npredicting molecular interactions has been emphasized\nby several reports [1,2]. Such studies emphasize that\nwhen knowledge about DNA variation within popula-\ntions is interfaced with data on gene expression, protein\ninteractions and DNA-protein binding, biological\nnetworks can be constructed that are predictive of the", + "title": "2010 - Systems genetics analyses predict a transcription role for P2P-R Molecular confirmation that P2P-R is a transcriptional co-repressor.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "e4d1e2e9-f267-5814-8c7b-dc11d7eec9bf", + "extraction_id": "ec624ebb-489a-5437-a721-f01cf981d0a7", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "a24d4dd1-29f8-596e-bc8b-f0dafaa82858", + "score": 0.6548624634742737, + "metadata": { + "text": "metadata (data about the data) are combined with\nsophisticated statistical and computation tools for\nthe genetic dissection and synthesis of single traitsor entire systems of traits.\nOne challenge facing investigators in the inter-\npretation of the massive data sets on GeneNetworkand elsewhere is deciding how much confidence toplace in QTL extracted from still noisy array andproteomic platforms after having conducted many\nthousands of statistical tests with poorly understood", + "title": "2005 - How replicable are mRNA expression QTL.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "699171c5-d983-50de-bcd2-fc3e117ff444", + "extraction_id": "0a4dc047-3b00-5657-b414-885d99b55d19", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "score": 0.6539399027824402, + "metadata": { + "text": "accuracy of predictive networks [40, 5153]. We have also recently demonstrated\nhow this class of network can be used to inform associations identied in GW Astudies [40].\n9 Summary\nThe signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "1e9adc57-45b4-5ac1-a0bf-a0b5ce07fef1", + "score": 0.6534584760665894, + "metadata": { + "text": "members o f pathway modules [78]. Other studies applied gene network modeling algorithms to identify the \npotential regulators in complex di seases, for example cardiomyopathy [79], hepatic steatosis [80], as well as \ncoronary artery disease [81]. \n Finally, there are many other integrative approaches available for the analysis of multi -omics data, but \nhave not yet been applied in mouse systems genetics studies. Examples include the transcriptome -wide", + "title": "2019 - Systems genetics approaches to probe gene function.pdf", + "version": "v0", + "chunk_order": 123, + "document_id": "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "extraction_id": "8ef4c3cf-8018-5334-9f82-19c9e86739a5", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + }, + { + "id": "d7e5ef8a-d43a-587d-8ffd-cd5e8e63f6ab", + "score": 0.6517401156735328, + "metadata": { + "text": "gathered together into an easily accessible format, not siloed into disparate data pools that \ncannot easily be integrated, valid ated, o r extended. This approach will allow us to make animal \nmodels of so called precision medicine, although perhaps more accurately, we want \npredictive medicine , where a phenotypic outcome (such as disease) can be predicted , and \navoided . \nGeneNetwork (genenetwork.or g; GN) is one tool for systems genetics and predictive medicine,", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How do multi-omics data integration techniques enhance the prediction accuracy of phenotypic traits in GeneNetwork datasets?" + } + } + ], + "kg_search_results": null + } + } + }, + "4": { + "results": { + "completion": { + "id": "chatcmpl-AIGraUSt4UjtI0mL9sXfXnJsapOUk", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Analyzing large-scale transcriptomic data within GeneNetwork.org presents several computational challenges and solutions:\n\n1. **Computational Complexity and Memory Requirements**: The analysis of large networks is computationally complex and requires significant memory resources. Traditional methods may not be ideal for handling such large datasets [5].\n\n2. **Scalable Algorithms**: To address these challenges, scalable parallel algorithms have been developed. These algorithms are designed to handle fixed-parameter tractable (FPT) problems, which are crucial for managing large-scale data efficiently [6].\n\n3. **High-Performance Implementations**: Genome-scale algorithms and high-performance implementations are utilized to facilitate the genetic analysis of gene transcription, making it possible to manage and analyze large volumes of data effectively [2].\n\n4. **Visualization and Biological Validation**: Visualization methods and biological validation are essential to translate complex data into understandable results. These methods help in comparing graphs collected in a systems genetic context, reflecting differences in time, tissue, and treatment effects [3].\n\n5. **Tools for Large-Scale Network Construction**: Tools like WGCNA, GeneNet, ARACNE, and SPACE are specifically designed to construct gene networks at very large scales. These tools also provide functionalities for analyzing and visualizing resulting networks, including identifying subnetworks [5].\n\nThese solutions collectively help in overcoming the computational challenges associated with large-scale transcriptomic data analysis within GeneNetwork.org.", + "role": "assistant" + } + } + ], + "created": 1728918090, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 281, + "prompt_tokens": 1295, + "total_tokens": 1576 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.700504800692344, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "aafbe14f-7ad3-5ad4-9951-90edecaceaa3", + "score": 0.6961420981926354, + "metadata": { + "text": "Combinatorial Genetic Regulatory Network Analysis\nTools for High Throughput Transcriptomic Data\nElissa J. Chesler1and Michael A. Langston2\n1Life Sciences Division, Oak Ridge National Laboratory,\nP.O. Box 2008, Oak Ridge, TN 37831-6124, USA\n2Department of Computer Science, University of Tennessee,\nKnoxville, TN 379963450, USA\nAbstract: A series of genome-scale algorithms and high-performance implementations\nis described and shown to be useful in the genetic analysis of gene transcription. With", + "title": "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "d9038328-bfea-5f73-87aa-6077b697e4db", + "extraction_id": "47c06e52-1923-58d0-9286-9674893a502a", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "ac2029ae-498b-5ec0-ae10-f5729344cb5b", + "score": 0.6620665389915272, + "metadata": { + "text": "Combinatorial Genetic Regulatory Network Analysis Tools 163\nIn addition to expansive volumes of data, there is a growing complexity to the types\nof research questions that can be asked. We are presently developing approaches to\ncompare graphs collected in a systems gene tic context to reect differences in time,\ntissue and treatment effects. Visualizatio n methods and compelling biological validation\nof novel results are essential to translate these methods and deliver them to the broader", + "title": "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "version": "v0", + "chunk_order": 1029, + "document_id": "5ded506d-7935-53f9-a118-57a9f3943376", + "extraction_id": "5e93e58f-a415-5ead-9356-c749891269cc", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6521361068527733, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "2e404112-d767-58f9-9bd3-f0220733759c", + "score": 0.644407372343323, + "metadata": { + "text": "larger networks well. Because of the computational complexity aswell as the memory requirements, these methods as currentlyimplemented are not the ideal choice for such large networks.WGCNA, GeneNet, ARACNE and SPACE, on the other hand,were designed to construct the gene network at very large scales.Also, it worth mentioning that the WGCNA package providesseveral useful tools to facilitate the analysis and visualization of\nresulting networks, including tools to identify subnetworks and an", + "title": "2012 - Comparing Statistical Methods for Constructing Large Scale Gene Networks.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "ea0b9f5f-b1cf-5774-98aa-0f022c831fb8", + "extraction_id": "2a75bfb9-6beb-54ef-b72b-25045ee3222d", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "8bb5a6fb-9528-59cb-bc79-a1a52584abfa", + "score": 0.64085860970195, + "metadata": { + "text": "Proc Natl Acad Sci U S A 100: 94409445.\n32. Chesler E, Langston MA (2005) Combinatorial Genetic Regulatory Network\nAnalysis Tools for High Throughput Transcriptomic Data. Proceedings,RECOMB Satellite Workshop on Systems Biology and Regulatory Genomics.\n17 p.33. Abu-Khzam F, Langston M, Shanbhag P, Symons C (2006) Scalable Parallel\nAlgorithms for FPT Problems. Algorithmica 45.\n34. Langston M, Perkins A, Saxton A, Scharff J, Voy B (2006) Innovative", + "title": "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "version": "v0", + "chunk_order": 196, + "document_id": "1a20f715-5068-5c61-8396-59e6096fa7de", + "extraction_id": "29446d6f-fb32-5a6e-a51a-179c888091b2", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "59c4b4b6-6b08-5182-a493-e7f753b7eb87", + "score": 0.6403773832208723, + "metadata": { + "text": "computational methods for transcriptomic data analysis. SAC 06: Proceedings\nof the 2006 ACM symposium on Applied computing.\n35. Csardi G, Nepusz T (2006) The igraph software package for complex network\nresearch. InterJournal Complex Systems 1695.\n36. Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene Suite for gene list\nenrichment analysis and candidate gene prioritization. Nucleic Acids Res 37:W305311.\n37. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant", + "title": "2012 - Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex functional and mechanistic implications.pdf", + "version": "v0", + "chunk_order": 197, + "document_id": "1a20f715-5068-5c61-8396-59e6096fa7de", + "extraction_id": "29446d6f-fb32-5a6e-a51a-179c888091b2", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "9c01962f-fcac-57b3-a17d-487e37323230", + "score": 0.640213031775148, + "metadata": { + "text": "plenary lecture, with a focus on the computational challengesin analyzing large datasets. The type of datasets discussed by\nWilliams included the microarray type outputs first suggestedby Jansen and Nap ( 2001 ) for inclusion in genetical genomics\nanalyses and are now extended to cross-platform datasets\n(Damerval et al. 1994;\n Ciobanu et al. 2010 ). A framework\nfor carrying out the genetic analyses was described as being\navailable through the GeneNetwork and WebQTL software", + "title": "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "extraction_id": "3bdf080c-2715-5acc-bba4-717283851240", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "1e19020c-c664-560b-8d2a-ef53ab8cb996", + "score": 0.6394572448568718, + "metadata": { + "text": "32. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kru glyak L, Bumgarner RE, \nSchadt EE: Integrating large-scale functional genomic data to dissect the \ncomplexity of yeast regulatory networks . Nat Genet 2008, 40 (7):854-861. \n33. Vera G, Jansen RC, Suppi RL: R/parallel--speeding up bioinformatics \nanalysis with R . BMC bioinformatics 2008, 9:390. \n34. Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC: A statistical \nmultiprobe model for analyzing cis and trans genes in genetical", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 311, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "368bb4b5-bc26-5a39-95fc-561f58eb0e08", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + }, + { + "id": "1755868d-9b84-5a6e-b6db-db70cb413656", + "score": 0.6389806841892619, + "metadata": { + "text": "Processing Large-Scale, High-Dimension\nGenetic and Gene Expression Data\nCliona Molony, Solveig K. Sieberts, and Eric E. Schadt\nAbstract The now routine generation of large-scale, high-throughput data in mul-\ntiple dimensions (genotype, gene expression, and so on) provides a signicant\nchallenge to researchers who desire to integrate data across these dimensions in", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 0, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "bee70000-17e9-5352-8c9c-349c78dfaa23", + "associatedQuery": "What are the computational challenges and solutions in analyzing large-scale transcriptomic data within GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "5": { + "results": { + "completion": { + "id": "chatcmpl-AIGrg63GEuWBoLBB21tTvYo1XKFpy", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The inclusion of data from diverse populations on GeneNetwork.org aims to enhance the generalizability of findings by addressing the limitations associated with genetic studies that predominantly focus on less diverse populations. The goal is to increase the diversity of underrepresented populations, with targeted recruitment aimed at over 50% non-European ancestry [9]. This effort is expected to improve the applicability of genetic findings across different populations, thereby enhancing the generalizability of the research outcomes. By incorporating data from a broader range of genetic backgrounds, GeneNetwork.org can provide more comprehensive insights into genetic networks and pathways, which are applicable to a wider array of populations.", + "role": "assistant" + } + } + ], + "created": 1728918096, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 124, + "prompt_tokens": 1112, + "total_tokens": 1236 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6721890568733215, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "c2225b34-e4a6-5147-998d-c2a5132d7a08", + "score": 0.6535828709602356, + "metadata": { + "text": "GeneNetwork provided the platform for correlation analysis,\nprincipal component generation, and linkage analysis. In\ngeneral, datasets were queried for gene symbols, downloaded\nfrom GeneNetwork, and additional analysis was performed\nin R whenever necessary. P-values mentioned in relation to\nPearsons coecient throughout this paper are based on pair-\nwise comparisons. All p-values were Bonferroni-adjusted for\n36,012 genes, which is equal to the number of genes captured", + "title": "2016 - Genetic networks in mouse retinal ganglion cells.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "ca0d3a29-7814-5d09-ad9d-e4143e87900d", + "extraction_id": "194c0d73-a9b7-5b5e-ac92-7dd689da6fc0", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "dc8fdfb1-539c-5941-bd4d-b595164cce9b", + "score": 0.6535828709602356, + "metadata": { + "text": "GeneNetwork provided the platform for correlation analysis,\nprincipal component generation, and linkage analysis. In\ngeneral, datasets were queried for gene symbols, downloaded\nfrom GeneNetwork, and additional analysis was performed\nin R whenever necessary. P-values mentioned in relation to\nPearsons coecient throughout this paper are based on pair-\nwise comparisons. All p-values were Bonferroni-adjusted for\n36,012 genes, which is equal to the number of genes captured", + "title": "2018 - Genetic Networks Activated by Blast Injury to the Eye.pdf", + "version": "v0", + "chunk_order": 323, + "document_id": "57e3820f-7a5d-51f1-a0c6-ecfbdf546005", + "extraction_id": "b881d0e1-11d4-578d-8560-0106c77d7a23", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "score": 0.6387626363322022, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "score": 0.6387483692915231, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "33dc52df-73a5-514e-8edb-33ae5046b8af", + "score": 0.638529918281442, + "metadata": { + "text": "network. Cell 9, 12121226 (2014).\n12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic \npathways. N. Engl. J. Med. 0, 16991701 (2009).\n13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: \na review of statistical methods and recommendations for their application. \nAm. J. Hum. Genet. 8, 622 (2010).\n14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional \nnetwork of yeast genes. Science 0, 15551558 (2004).", + "title": "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "extraction_id": "46616368-74e6-5605-9e43-9789e8e1bea1", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "312eae52-ede7-5c13-8974-fce0126426cf", + "score": 0.6373084403688657, + "metadata": { + "text": "addition to this, GeneNetwork can be used to study correlations between traits and to\nperform data mining in genomic regions containing candidates for quantitative trait genes\n(Hoffman et al., 2011). All datasets in GeneNetwork are linked to a materials and methods\ninformation page that summarizes experimental details relating to the dataset.\nDatabases within GeneNetwork include the transcriptome database, the BXD published", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 98, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "0b2bd83d-680a-52d2-8116-50cce4f35cc3", + "score": 0.6338300704956055, + "metadata": { + "text": "al., 2005). GeneNetwork is designed primarily as a web service for exploratory and\nstatistical analysis of large published phenotype and genome datasets, and includes data\nfrom several species (see Supplementary Discussion). GeneNetwork includes extensive\nphenotype data extracted from the literature and submitted by users, which makes it\npractical to compare data on drug responses with gene expression patterns. Gene expression", + "title": "2011 - Using the PhenoGen Website for \u201cIn Silico\u201d Analysis of Morphine-Induced Analgesia Identifying Candidate Genes.pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "eb266fa1-8dec-5c56-a3d5-b508bd6bd448", + "extraction_id": "0e3a5e40-06b0-58d4-b495-3093954ed17b", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "e17f1d54-7ea8-5a44-95b7-5d07f348574c", + "score": 0.6304648142776015, + "metadata": { + "text": "limit the applicability of genetic ndings in more diversepopulations. In the next phase of the network, the goalis to increase the diversity of underrepresented popula-tions, with targeted recruitment aimed at over 50% non-European ancestry. The lessons from enrollment andRoRs to diverse populations, even limited, will inform\nour next phase as we continue to strive for a more represen-", + "title": "2021 - Lessons learned from the eMERGE Network balancing genomics.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "cd0002dd-dcf1-567a-bf41-61eb0d6d982b", + "extraction_id": "8aecb357-2d62-51f9-9256-6fdf8c73791e", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + }, + { + "id": "d519a13a-b6a0-505d-9a90-dd8f974721b4", + "score": 0.6274190171111227, + "metadata": { + "text": "data available across all contributing consortia will facilitate systematic \nexploration of these correlated phenotypes with more sophisticated \nstatistical methods for joint analysis5254, yielding greater insight into \nthe underlying pathways and genetic networks they represent. As data \nfrom human genetic networks accrue, we will be better placed to test \nwhether there is support for the notion of hub genesthat is, genes \nhighly connected with others in the network, proposed by experi", + "title": "2012 - Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.pdf", + "version": "v0", + "chunk_order": 125, + "document_id": "879c61e9-2efa-550b-b7ca-f88d67eb2199", + "extraction_id": "bc862e34-d30b-5882-9cc9-69f2bce72239", + "associatedQuery": "How has the inclusion of data from diverse populations impacted the generalizability of findings on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "6": { + "results": { + "completion": { + "id": "chatcmpl-AIGrl5sKA3HUkZ2rgn7crnu6ec7EE", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The insights obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders include the identification of two fundamental yet distinct genetic components shared by major neuropsychiatric disorders. The first component is involved in central nervous system (CNS) development, neural projections, and synaptic transmission [1]. Additionally, the polygenicity of psychiatric illnesses has been highlighted, indicating that psychiatric disorders are influenced by multiple genes, and there is a degree of single nucleotide polymorphism (SNP) sharing among disease cases, which helps estimate the common, inherited portion of these disorders [2]. Furthermore, shared and unique genetic factors have been identified, which highlight key gene sets and molecular processes that may lead to improved diagnosis and treatment of psychiatric disorders [3].", + "role": "assistant" + } + } + ], + "created": 1728918101, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 147, + "prompt_tokens": 1272, + "total_tokens": 1419 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "2aaaf2f2-8ea8-5f34-82ce-60cdce021b1c", + "score": 0.7398017644882202, + "metadata": { + "text": "Lotan et al. Neuroinformatics of major neuropsychiatric disorders\nWe demonstrated that although these disorders share a rela-\ntively small set of genes, there are two fundamental yet distinctgenetic components, or vectors, that are both shared by all sixdisorders. While the rst component is involved in CNS develop-\nment, neural projections and synaptic transmission, the second", + "title": "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "38896019-c47e-5288-88a9-302779568cd3", + "extraction_id": "0749dafa-17cf-5434-aad9-151a128e357b", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "06a4a00d-2b22-557a-b744-e4ac1fa8a5a2", + "score": 0.730652928352356, + "metadata": { + "text": "genetic variation) for any psychiatric disorder\n(Fig. 1), there is sufficient information to drawsome general conclusions.\nThe polygenicity of psychiatric illness\nIn addition to finding specific genes, molecu-\nlar genetics can provide information about theheritability of psychiatric disease, an approach\nthat has led to some important insights about\nthe genetic architecture of psychiatric illness.The degree of SNP sharing among disease cases\nestimates the common, inherited portion of a", + "title": "2015 - Somatic mutation in cancer.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "0801355e-6f92-5526-a0b7-85a2bc859c51", + "extraction_id": "feb6add1-ae89-5c82-8d59-6d4d66ea6779", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "cf9ea924-eb96-5444-9a8b-ed45c932b130", + "score": 0.7138526439666748, + "metadata": { + "text": "of shared and unique genetic factors highlights key gene sets and molecular processesthat may ultimately translate into improved diagnosis and treatment of these debilitating\ndisorders.\nKeywords: major neuropsychiatric disorders, neuroinformatics, cross-species, translational, genetic components,\ngenome wide association studies, enrichment\nINTRODUCTION\nCommon psychiatric disorders including attention-", + "title": "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "38896019-c47e-5288-88a9-302779568cd3", + "extraction_id": "300d8f31-5e42-5c17-a801-2f7afad3995e", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "88756a11-58d2-59ec-8eed-08a96fc24ca0", + "score": 0.7116725444793701, + "metadata": { + "text": "6. D. H. Geschwind, J. Flint, Genetics and genomics of psychiatric\ndisease. Science 349, 1489 1494 (2015). doi: 10.1126/science.\naaa8954 ; pmid: 26404826\n7. S. Cichon et al ., Genomewide association studies: History,\nrationale, and prospects for psychiatric disorders.\nAm. J. Psychiatry 166, 540 556 (2009). doi: 10.1176/\nappi.ajp.2008.08091354 ; pmid: 19339359\n8. A. Battle et al., Genetic effects on gene expression across\nhuman tissues. Nature 550, 204 213 (2017). doi: 10.1038/\nnature24277 ; pmid:\n29022597", + "title": "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "version": "v0", + "chunk_order": 158, + "document_id": "24caaa62-2368-534f-8c42-f088c3409510", + "extraction_id": "82c75078-0fc5-508c-95ba-f2975fdec2c5", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "f771b6cd-babd-56c2-a536-fbafc07c9be7", + "score": 0.7088691765743997, + "metadata": { + "text": "the Psychiatric Genomics Consortium found that the results were highly correlated between \nmethods in a comparison of methods applied across several psychiatric disorders ( Network \nPathway Analysis Subgroup of Psychiatric Genomics Consortium 2015 ). A second \nlimitation of pathway-based analysis is that it is still biased by our incomplete prior \nknowledge of gene function in the etiology of psychiatric illness.\nDespite these challenges, pathway-based analyses have identified biological pathways", + "title": "2019 - Beyond Genome-wide Significance Integrative Approaches to the Interpretation and Extension of GWAS Findings for Alcohol Use Disorder.pdf", + "version": "v0", + "chunk_order": 39, + "document_id": "f59b3e10-a887-5708-b520-c5e8adb48dcd", + "extraction_id": "f623501d-c824-5334-98d7-dd599d0c063d", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "fd183495-c22b-5b6e-af12-ec216a838141", + "score": 0.7040727348349316, + "metadata": { + "text": "Lotan et al. Neuroinformatics of major neuropsychiatric disorders\nGENES FROM THE NHGRI-CROSS-DISORDER SET CLUSTER IN THREE\nCO-EXPRESSION MODULES WITH DISTINCT SPATIO-TEMPORALEXPRESSION PATTERNS AND FUNCTIONAL BIASES\nOne of the major properties of genes involved in regulation of", + "title": "2014 -Neuroinformatic analyses of common and distinct genetic components associated with major neuropsychiatric disorders.pdf", + "version": "v0", + "chunk_order": 69, + "document_id": "38896019-c47e-5288-88a9-302779568cd3", + "extraction_id": "b3e6daa0-872e-546c-bee5-873b8f716c77", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "224463d2-e8a3-5a17-ab9b-9d6a39a081b8", + "score": 0.700661301612854, + "metadata": { + "text": "Genet. 2009; 85:847861. [PubMed: 19931040] \nBrownlee DJ, Fairweather I. Exploring the neurotransmitter labyrinth in nematodes. Trends Neurosci. \n1999; 22:1624. [PubMed: 10088995] \nBucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI Jr, Reich T, \nSchmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage \nstudies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994; 55:149158. [PubMed: \n8189735]", + "title": "2017 - Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-response Behaviors in Model Organisms.pdf", + "version": "v0", + "chunk_order": 142, + "document_id": "045eff7e-5ff3-5b0e-9858-76eb8560e9d4", + "extraction_id": "4c500aa5-faeb-5273-83a9-c5c91a27c697", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "18de97fd-e46c-5600-b45d-82de340e0d6b", + "score": 0.6984201253402171, + "metadata": { + "text": "with shared effects on ve major psychiatric disorders: a genome-\nwide analysis. Lancet 381(9875):13711379\nDavis S, Meltzer P (2007) Geoquery: a bridge between the gene\nexpression omnibus (geo) and bioconductor. Bioinformatics\n14:18461847\nde Mooij-van Malsen AJG, Vinkers CH, Peterse DP, Olivier B, Kas\nMJH (2011) Cross-species behavioural genetics: a starting point\nfor unraveling the neurobiology of human psychiatric disorders.\nProg Neuropsychopharmacol Biol Psychiatr 35(6):13831390", + "title": "2014 - Analyzing_gene_expression_data_in_mice_w.pdf", + "version": "v0", + "chunk_order": 75, + "document_id": "643f0642-d9c6-52f8-8b86-e469e778c003", + "extraction_id": "848a85f6-382c-54e8-947b-670d71bb0639", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "366961c5-4349-5d93-abf5-203de53a4928", + "score": 0.6969206748557505, + "metadata": { + "text": "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function\nof tissue-speci c gene networks.\nMolecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7\nINTRODUCTION\nSeveral psychiatric disorders of developmental origin are char-", + "title": "2022 -Restrepo- Predict impulsivity in children.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "15c3ab55-d6e6-532e-a655-759059ab7c07", + "extraction_id": "10e3b0c3-e7cc-52e9-a6c2-e721a848bae5", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + }, + { + "id": "d7155850-29e4-5fec-b5a2-974f8ead2fef", + "score": 0.6969206748557505, + "metadata": { + "text": "systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease)associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function\nof tissue-speci c gene networks.\nMolecular Psychiatry (2022) 27:27422750; https://doi.org/10.1038/s41380-022-01533-7\nINTRODUCTION\nSeveral psychiatric disorders of developmental origin are char-", + "title": "2022 - Corticolimbic DCC gene co-expression networks as predictors of impulsivity in children.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "fdecd4db-5e3a-5a3a-8145-28d05392822e", + "extraction_id": "8c7a2723-caa8-5ae1-a47c-c0c889443919", + "associatedQuery": "What novel insights have been obtained from GeneNetwork.org regarding the genetic basis of psychiatric disorders?" + } + } + ], + "kg_search_results": null + } + } + }, + "7": { + "results": { + "completion": { + "id": "chatcmpl-AIGrrCJF0xy80I2fCpFw4lJ55PYWM", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues by enabling the prediction of cell-type proportions from bulk genomics data. This computational deconvolution is crucial for understanding the relative abundance of various cell types within a tissue, which is a key step in analyzing gene expression data from complex tissues [1]. Additionally, machine learning methods, such as decision tree methods, are explored to model functional dependencies and predict co-expressed gene profiles, which can further aid in the deconvolution process by identifying regulatory elements and signals that vary with disease status [4]. These advancements allow for more accurate and insightful analysis of gene expression data, facilitating the identification of transcriptional changes and regulatory networks in complex tissues.", + "role": "assistant" + } + } + ], + "created": 1728918107, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 147, + "prompt_tokens": 1216, + "total_tokens": 1363 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "5a61091b-7128-5326-a08c-9e53506eb0f4", + "score": 0.60247802734375, + "metadata": { + "text": "The method takes as input a large cohort of individuals, wherethe input for each individual includes: (1) genotyping; (2) bulk ex-pression of genes in a certain tissue; (3) the relative abundance(proportions) of the various cell types in the tissue (it is possible\nto use computational deconvolution methods to predict cell-type\nproportions from bulk genomics data ( Newman et al. 2015 )). In", + "title": "2018 - Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics.pdf", + "version": "v0", + "chunk_order": 70, + "document_id": "89534971-8c50-51ee-b2c4-35957579f911", + "extraction_id": "79e0c3a8-7d1b-5372-a776-7e9a76d09691", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "1de27ae0-e471-5f99-baeb-6d53071de37b", + "score": 0.5995309710223072, + "metadata": { + "text": "Filtering out the latter class of technical difficulty im-proved the recovery of genuine cis-modulated transcripts\nand thus to identify genes that are relevant to further down-stream regulation of gene expression and more complex phe-notypes (Ciobanu et al. 2010 ).\nWilliams also discussed the power of a structured mapping\npopulation in model organisms and presented the Complex4 Funct Integr Genomics (2012) 12:1 9", + "title": "2012 - Advances in biotechnology and linking outputs to variation in complex traits Plant and Animal Genome meeting January 2012.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "c81c86b5-c5ab-5abf-83c0-415b0950fd51", + "extraction_id": "3bdf080c-2715-5acc-bba4-717283851240", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "92e845b4-fbdf-52e8-8ebd-39392ccdfeb7", + "score": 0.5966375470161438, + "metadata": { + "text": "genomic hybridization microarrays (8), can complement RNA expression data \nand result in novel discoveries. With the evolution and maturation of proteom ics, \ncertainly combining serum- or tissue-based patterns of protein expression \nwith RNA expression holds promise. Finally, other rich sources of complex \ndata such as the literature can be used to complement our analysis of microar ray \ndata (39). These analyses face significant challenges with respect to gene", + "title": "2008 - Gene Expression Profiling.pdf", + "version": "v0", + "chunk_order": 1781, + "document_id": "59f3b969-089b-5258-93ad-892dbc9ffa9c", + "extraction_id": "00906abf-f4ca-53f2-a2b6-20359686e9ec", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "d192b3fd-5ece-570a-a905-f94eef684af2", + "score": 0.5949562191963196, + "metadata": { + "text": "data. To model the functional dependence we shall explore machine learning methods16, such as decision tree methods \nto predict the co-expressed gene profiles. As part of this study and in (E) Future work, see below, we will investigate \nthe benefit of using comparative genomics in helping to lo cate and characterise the regul atory elements and signals. \n \nD(d) Integration and Modelling to infer regulato ry systems co-varying with disease status", + "title": "2005 - Part I Previous Research Track Record.pdf", + "version": "v0", + "chunk_order": 64, + "document_id": "1875d68b-adeb-5f91-8a67-91d881906238", + "extraction_id": "0853c5ab-3d98-565c-ba1f-50e5bd91d14c", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "16baa529-fa53-5760-96b2-38779cab00e0", + "score": 0.59277476646825, + "metadata": { + "text": "derived from complex tissue such as brain show a high level of correspondence24,25. Such\nstructure can be used to inform a new level of neuroscientific investigation that is not\npossible using standard analysis of differential expression2225.\nFor example, one of the first such studies23 showed that gene networks could be used to\nprovide a unifying method of identifying transcriptional targets of human brain evolution in", + "title": "2009 - Neuroscience in the era of functional genomics and systems biology.pdf", + "version": "v0", + "chunk_order": 82, + "document_id": "08e29201-f2cc-5fd5-9c28-bc4b8aaaa936", + "extraction_id": "52f30738-038c-58b4-af90-3e1c8735e729", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "38245be7-bd5c-5711-94ba-794c16247aa9", + "score": 0.591386079788208, + "metadata": { + "text": "profiling of a multicellular organism,\" Science, vol. 357, no. \n6352, pp. 661 -667, 2017. \n[68] X. Guo, W. Li, and F. Iorio, \"Convolutional neural networks \nfor steady flow approximation,\" in Proceedings of the 22nd \nACM SIGKDD international conference on knowledge \ndiscovery and data mining , 2016, pp. 481 -490. \n[69] V. Ntranos, L. Yi, P. Melsted, and L. Pachter, \"A \ndiscriminative learning approach to differentia l expression \nanalysis for single -cell RNA -seq,\" Nature Methods, vol. 16,", + "title": "2022 -Madadi- AI RNA.pdf", + "version": "v0", + "chunk_order": 181, + "document_id": "03b9b993-8dd5-5b0d-9493-99fb9a624948", + "extraction_id": "ebd9b396-f870-5c65-9460-7f3da6c11e6c", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "14ac602a-df31-53c4-95cf-6ff078ddec34", + "score": 0.5903836488723755, + "metadata": { + "text": "levels can influence the ability to call differential gene expression\n(Oshlack and Wakefield 2009), we also included, as a feature, the\naverage expression level of the genes in the young samples.\nAll machine-learning algorithms assigned genes to the correct\ntranscriptional change with age 67% 81% of the time on average,\nsignificantly above that of a random classification (50%) (Fig. 3B,C;\nSupplemental Fig. S3B,C ;Supplemental Table S3A,B ). Models de-", + "title": "2019 - Remodeling of epigenome and transcriptome.pdf", + "version": "v0", + "chunk_order": 30, + "document_id": "87ffccee-fc33-5373-948d-67736aa0f069", + "extraction_id": "4e757e70-c73b-59b2-8129-d253c4620f49", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "c810e291-415f-5bee-a54b-1548ff0bacd5", + "score": 0.5893268585205078, + "metadata": { + "text": "DNA. Microarray technology is helpful in capturing biological \ngenetic information to computer data. Computational techniques \ncan be applied on those large set of genetic data of every \nindividuals with or without disease, so that the genes that are \nresponsible for the disease occurrence can be po inted out. \nDifferentially Expressed Genes (DEG) are identified using many \ntechniques. Machine Learning (ML) algorithms plays a \nsignificant role in identifying the distinction between normal", + "title": "2018 - A survey on machine learning approaches in gene expression classification in modelling computational diagnostic system for complex diseases.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "8355d7b5-9da9-5bb8-8a3e-6f77c667599c", + "extraction_id": "c7cd8df0-306c-5b1d-97b8-42410f4b82ed", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "5057d65b-2c37-5344-b757-3af91d22c690", + "score": 0.586599588394165, + "metadata": { + "text": "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that", + "title": "2005 -Pomp- GenomeExploitation.pdf", + "version": "v0", + "chunk_order": 1058, + "document_id": "a77aefe9-379e-54a2-b029-8f5f3e798e64", + "extraction_id": "d813f94e-cbde-502a-b387-a5cfd585ecca", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + }, + { + "id": "8a074429-2464-5b19-8eb8-6775d588b24f", + "score": 0.586599588394165, + "metadata": { + "text": "mapping, several sophisticated analyses will be required to extract full value fromthe enormous amount of collected data, and gain valuable insight into geneticcontrol of gene expression. As recently noted by Ariel Darvasi (2003), I expect thatthe combining of genetic information and gene expression will hasten the day whengenomics delivers on its promise to improve health care. But we must continuestriving to develop and apply sophisticated analytical tools for interpreting the vast,complex data sets that", + "title": "2006 - Marker Assisted Backcrossing .pdf", + "version": "v0", + "chunk_order": 1058, + "document_id": "5efc1bdf-f847-5eaf-a808-9cf71b9399ce", + "extraction_id": "99f23be3-af56-5ae5-9577-ae940bfd9653", + "associatedQuery": "How do advancements in machine learning algorithms contribute to the deconvolution of gene expression data in complex tissues?" + } + } + ], + "kg_search_results": null + } + } + }, + "8": { + "results": { + "completion": { + "id": "chatcmpl-AIGrwafXsxRn06hAraC16E8hpnzWh", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Enhancer-promoter interactions play a significant role in the regulation of gene networks by influencing gene expression levels and patterns. These interactions are crucial for determining cell-specific gene expression, as enhancers can regulate genes over long distances and are involved in complex regulatory networks [4]. Approximately 90,000 enhancer-promoter interactions have been identified, with a majority occurring within the same topologically associating domains (TADs), which suggests a structured and hierarchical organization of these interactions within the genome [3]. Genes with more enhancers tend to have higher expression levels, indicating that enhancers contribute to the regulation of gene expression by interacting with promoters [3]. Additionally, enhancer-promoter interactions can involve long-range interactions, making the prediction of specific enhancer-target relationships challenging [1]. These interactions are part of the broader gene networks that include various regulatory elements and factors, highlighting their importance in the regulation of gene networks as uncovered through platforms like GeneNetwork.org.", + "role": "assistant" + } + } + ], + "created": 1728918112, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 188, + "prompt_tokens": 1276, + "total_tokens": 1464 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "54f0e8c3-0322-51a6-b129-5850d0586c84", + "score": 0.718110935800617, + "metadata": { + "text": "dynamic16,17, and several studies have proposed that impaired\nenhancer activation could be at the origin of disease1821. Besides\ninteracting with nearby promoters, enhancers also engage in\nlong-range interactions. Indeed, it is estimated that approximately\n3540% of all promoter-enhancer interactions are intervened by\nat least one gene22, which makes exact enhancer-target prediction\nchallenging. Long-range enhancers interactions can be identi ed\nby chromosome conformation capture methods23,24.", + "title": "2020 - Skeletal muscle enhancer interactions identify genes controlling whole-body metabolism.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "fa738c86-1026-50f5-aebb-285ec92b209c", + "extraction_id": "1a87b58e-d091-582c-b96d-adac454fdf9d", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "b713e667-ba32-514b-8373-0aebd9702cfc", + "score": 0.6770162774910773, + "metadata": { + "text": "motifs found in its promoter (gene-to-sequence). We will referto the ensemble of these inuence interactions as genenetworks.\nThe interaction between two genes in a gene network does\nnot necessarily imply a physical interaction, but can also referto an indirect regulation via proteins, metabolites and ncRNA\nthat have not been measured directly. Inuence interactions\ninclude physical interactions, if the two interacting partnersare a transcription factor, and its target, or two proteins in the", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 6, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "640aa5eb-9b93-541a-ba5a-c1179c157c95", + "score": 0.6645434698734611, + "metadata": { + "text": "~90,000 enhancer-promoter interactions (fig.S36). As expected, ~75% of enhancer-promoterinteractions occurred within the same TAD, and\ngenes with more enhancers tended to have high-\ner expression (Fig. 5B and fig. S36). We inte-grated the Hi-C data with QTLs; surprisingly,\nQTLs involving SNPs distal to eGenes but linked\nby Hi-C interactions showed significantly stron-ger associations (as indicated by the QTL Pvalue)\nthan those with SNPs directly in the eGene pro-\nmoter or exons (Fig. 5C and fig. S37).", + "title": "2018 - Comprehensive functional genomic resource and integrative model forthe human brain.pdf", + "version": "v0", + "chunk_order": 105, + "document_id": "24caaa62-2368-534f-8c42-f088c3409510", + "extraction_id": "213169b2-a4b0-5d5c-a297-c9a5896652ad", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "b6a01191-0181-547f-b37c-139a841296e4", + "score": 0.6513058751043167, + "metadata": { + "text": "histone-modifying proteins, and other factors to regulate polymerase-II activity. Such factors can bind in close prox-\nimity to promoters to influence gene expression. However, \nthere is substantial evidence that additional genetic elements \nreferred to as enhancers play major roles in determining cell-\nspecific patterns of gene expression.\n1517 Initially identified \n>30 years ago, enhancer elements can be located at various \ndistances from promoters, typically between 1 and 50 kilo-", + "title": "2013 - Genetic and Genomic Approaches to Understanding Macrophage Identity and Function.pdf", + "version": "v0", + "chunk_order": 25, + "document_id": "1526d201-2f4e-5e6c-b2c8-8c825e741401", + "extraction_id": "4c2afa3b-cf31-58ba-8ae8-2bf609f25dbc", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "958ecf38-a371-5a53-920f-b28dddea3fe4", + "score": 0.6482539982644332, + "metadata": { + "text": "involved in the regulation of the target genes of both networks, but that the \ninteraction partners through which this regulation is established differs for both \ntarget genes.", + "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "version": "v0", + "chunk_order": 441, + "document_id": "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "extraction_id": "d2dd2002-c8f6-5e2e-a06a-a8a20268c637", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "ec2195b2-3ecd-5a55-a085-db9bb844f818", + "score": 0.6482315869193335, + "metadata": { + "text": "variants in epigenomic features using a systematic, data-driven approach.\nBioinformatics 31,26012606 (2015).\n13. Schug, J. et al. Promoter features related to tissue specicity as measured by\nShannon entropy. Genome Biol. 6,R33 (2005).14. He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer-promoter\ninteractome in human cells. Proc. Natl Acad. Sci. USA 111, E2191E2199 (2014).\n15. Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specic gene", + "title": "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "version": "v0", + "chunk_order": 156, + "document_id": "0046a766-21c6-582a-b868-685a24920faf", + "extraction_id": "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "dac1a702-ecf9-5fe8-bb31-ea3c13bc94d9", + "score": 0.6480490775079096, + "metadata": { + "text": "regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA\n110, 1792117926 (2013).\n16. Quang, D. X., Erdos, M. R., Parker, S. C. J. & Collins, F. S. Motif signatures in\nstretch enhancers are enriched for disease-associated genetic variants. Epigenet.\nChromatin 8,23 (2015).\n17. Whyte, W. A. et al. Master transcription factors and mediator establish\nsuper-enhancers at key cell identity genes. Cell153, 307319 (2013).", + "title": "2016 - The genetic regulatory signature of type 2 diabetes in human skeletal muscle.pdf", + "version": "v0", + "chunk_order": 157, + "document_id": "0046a766-21c6-582a-b868-685a24920faf", + "extraction_id": "9da4c40c-fa6f-557f-b78d-7ffdb9bb9d41", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "c9155893-bf1f-516c-b509-f6d2014d275e", + "score": 0.6478024931071714, + "metadata": { + "text": "networks. In fact, several of the higher-order networks we describe below rely on having multiple reliable and interoperable transcriptional activators and repressors for proper functioning.\nEven so, these engineered transcription factors have not yet been \nfully characterized, and if they are to be used as building blocks for complex gene networks, then knowledge of their in vivo kinetics and", + "title": "2009 - Next generation synthetic gene networks.pdf", + "version": "v0", + "chunk_order": 36, + "document_id": "0d620c5e-a9ae-5b19-851b-37e40292ab8d", + "extraction_id": "38e443bd-610e-5a1d-9f32-082e808d016a", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "55660a79-e4ed-5fc7-8232-aa1401bfd3e8", + "score": 0.6460842893694578, + "metadata": { + "text": "BMC Genomics 2008, 9:310 http://www.biomedcen tral.com/1471-2164/9/310\nPage 10 of 17\n(page number not for citation purposes)A gene regulatory network comprising the regulatory\ninteractions of the significant genes and the significant\nand enriched TFs is shown in Figure 5. Obvious are the\nfive hubs, the core regulatory circuit derived from [17].\nWell-regulated candidates can be identified like Acly and\nFabp4 . Target and regulator at the same time is Ipf1.\nDiscussion", + "title": "2008 - Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "4060609b-1464-55fa-93cd-fefaf2cac900", + "extraction_id": "c9ae0334-a2f7-5063-81aa-f313c77e4b65", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + }, + { + "id": "a85fbdc3-7bb7-5d61-9d14-e15cc49fc28a", + "score": 0.645416340505104, + "metadata": { + "text": "32. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, et al. Systematic \ndissection of regulatory motifs in 2,000 predicted human enhancers using a massively parallel \nreporter assay. Genome research. 2013:gr. 144899.112. \n \n33. Rands CM, Meader S, Ponting CP, Lunter G. 8.2% of the human genome is constrained: \nvariation in rates of turnover across functional element classes in the human lineage. PLoS \ngenetics. 2014;10(7):e1004 525.", + "title": "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf", + "version": "v0", + "chunk_order": 413, + "document_id": "6acebf19-b80c-5352-8201-99d5634fcc80", + "extraction_id": "7f3f1b6c-9fcd-5e8e-a4e0-d53da591d706", + "associatedQuery": "What role do enhancer-promoter interactions play in the regulation of gene networks uncovered through GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "9": { + "results": { + "completion": { + "id": "chatcmpl-AIGs1N0h1lzkHw7McrwTnV7iXLWUI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The integration of ATAC-seq data with RNA-seq data can provide valuable insights into chromatin accessibility and gene regulation by combining information about open chromatin regions with gene expression profiles. ATAC-seq is a technique that characterizes accessible chromatin regions, which are often associated with transcriptional activity [1]. This method can simultaneously profile open chromatin, transcription factor-binding footprints, and nucleosome positioning [2]. By integrating this data with RNA-seq, which measures gene expression levels, researchers can relate chromatin accessibility to gene expression patterns.\n\nFor example, by creating a reference map using single-cell RNA sequencing (scRNA-seq) and assigning cell-type identities, researchers can relate cell-type-resolved accessible chromatin to gene expression [3]. This integration helps in identifying cis-regulatory programs by aggregating reads from cells within each ATAC-seq cluster and linking them to gene expression data.\n\nOverall, the integration of ATAC-seq and RNA-seq data allows for a comprehensive understanding of how chromatin accessibility influences gene regulation, providing insights into the regulatory elements that control gene expression in different cellular contexts.", + "role": "assistant" + } + } + ], + "created": 1728918117, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 224, + "prompt_tokens": 1316, + "total_tokens": 1540 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "9172db35-cec2-5970-8e5d-d73357f13abe", + "score": 0.6670019626617432, + "metadata": { + "text": "high-throughput sequencing (ATAC-seq) allows the characterization of accessible chromatin re-\ngions,whichcorrespondtoareasoftranscriptionactivity(149).Examiningthethree-dimensional\norganization of the genome can facilitate the association between regulatory elements and\ntheir target genes by dividing the genome into discrete functional blocks, commonly known as\ntopologically associating domains (139). The Encyclopedia of DNA Elements (ENCODE) and", + "title": "2020 - The Genomics of Auditory.pdf", + "version": "v0", + "chunk_order": 58, + "document_id": "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "extraction_id": "0c7a27ef-7a65-5b32-8129-b168a336018a", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "2020244c-6b6e-5613-900e-d7e32f6c4d57", + "score": 0.6658084988594055, + "metadata": { + "text": "variants, it is still unclear how multiple independent variants\ninfluence gene networks through changes in chromatin states.\nThe Assay for Transpose Accessible Chromatin (ATAC-seq)\nwas recently developed to address the need for sensitive as-\nsays requiring less starting material, which also has the ability\nto simultaneously profile open chromatin, transcription factor-\nbinding footprints, as well as nucleosome positioning in a\nsingle assay [ 57]. Given the limited availability of primary", + "title": "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "extraction_id": "203710b7-3267-5ecf-9397-b5becdaeead1", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "f4ae7779-bbfc-5a13-bcd2-2e6724011eb8", + "score": 0.6634333729743958, + "metadata": { + "text": "Data Fig.4a). To relate cell-type-resolved accessible chromatin to gene \nexpression, we created a single-cell RNA sequencing (scRNA-seq) refer -\nence map of peripheral blood and pancreas. We assigned cell-type identi -\nties for 90,495 cells to 29 clusters, which identified similar cell types and \nproportions to snATACseq (Extended Data Fig.5ac).\nTo characterize cis-regulatory programs, we aggregated reads from \ncells within each snATACseq cluster and identified accessible chroma -", + "title": "2021 - Interpreting type 1 diabetes risk.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "9f13ec69-195b-55eb-a549-b3eb3dc0f321", + "extraction_id": "607a959f-6f63-5f18-8935-b76d87aa4820", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "1bdc47f8-9b31-5f89-8381-2238c4aec987", + "score": 0.6631792783737183, + "metadata": { + "text": "DNA methylation and ATAC-seq data (Supplementary Fig. 3). Integration across gene- and coordinate-centric views helps users examine genomic events in different \nchromosome contexts. For example, Xenas \nVisual Spreadsheet can help elucidate whether a gene amplification is part of a chromosomal arm duplication or a focal \namplification (Supplementary Fig. 6).", + "title": "2020 - Visualizing and interpreting cancer genomics.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "eaae9d37-9c40-5e1c-9de9-d5ebcce9eae3", + "extraction_id": "ffc72db8-67ea-508a-aba1-d2592bd00ea2", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "6b16574f-b513-5361-a0a8-a19f86ef6316", + "score": 0.6621679067611694, + "metadata": { + "text": "matin accessibility assay ATAC-seq has been applied to single\ncells and has been shown to capture a higher order chromatin\nstructure resembling the profiles generated by Hi-C [ 72].\nAdditionally, for CAD candidate genes that are transcrip-\ntion factors (TF), such as TCF21 and STAT3, protein-DNA\ninteractions could be studied on a genome-wide scale using\nchromatin immunoprecipitation sequencing (ChIP-Seq).\nRecently, ChIP-Seq performed against TCF21 in human cor-", + "title": "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "version": "v0", + "chunk_order": 43, + "document_id": "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "extraction_id": "2e588b06-841f-50d7-b161-330199d5c4cf", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "5297cd77-3ccf-570e-9ff9-bdb778638793", + "score": 0.6546493768692017, + "metadata": { + "text": "seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Formaldehyde-\nAssisted Isolation of Regulatory Elements (FAIRE-seq) and DNase I hypersensitive sites sequencing \n(DNase-seq). \nThe integration of DNA methylation data (WGBS) and chromatin accessibility data (ATAC-seq) \nwith established ChIP-seq mark ers have provided an opportunity to create high-resolution", + "title": "2018 - High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes.pdf", + "version": "v0", + "chunk_order": 90, + "document_id": "1cb0c4ac-c1fe-55c2-919c-52cd5018c00d", + "extraction_id": "9cd48835-a7bf-50aa-928f-adb817e229d4", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "a49d3e49-6005-5890-ba75-8e5d59df13e5", + "score": 0.638329029083252, + "metadata": { + "text": "94. Mumbach MR, et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919922. doi: \n10.1038/nmeth.3999.\n 95. Kumasaka N, et al. Fine-mapping cellular QTLs with RASQUAL and ATAC-\nseq. Nat Genet. 2016;48:206213. doi: 10.1038/ng.3467.\n 96. Buenrostro JD, et al. ATAC-seq: a method for assaying chromatin acces-\nsibility genome-wide. Curr Protoc Mol Biol. 2015;109:21.29.121.29.9. \ndoi: 10.1002/0471142727.mb2129s109.", + "title": "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 153, + "document_id": "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "extraction_id": "786d21d6-5544-5357-8163-1a1a96f6a791", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "eafc949f-7238-5776-bfef-5ccd9f91787e", + "score": 0.6377456378264919, + "metadata": { + "text": "CpG sites. Single nucleus Assay for Transposase-Accessible Chromatinusing sequencing (snATACseq) was informative of chromatin opennessin various kidney cell types. The RegulomeDB is a database with exten-sive epigenetic annotation for SNPs. The promoter capture HiC (PCHiC)\nsequencing data identified sequence interaction with gene promoters,", + "title": "2022 - Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.pdf", + "version": "v0", + "chunk_order": 20, + "document_id": "b9194555-5fdb-549e-9edb-d108132a7dd1", + "extraction_id": "d26b98eb-66cc-5185-9061-cda1fe904ba6", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "c93bf9e1-39bd-59a9-8dd1-1b67a0853b8c", + "score": 0.6230217218399048, + "metadata": { + "text": "a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21.29.2121.29.29. https ://doi.org/10.1002/04711 42727 .mb212 9s109 \nBysani M etal (2019) ATAC-seq reveals alterations in open chromatin \nin pancreatic islets from subjects with type 2 diabetes. Sci Rep 9:7785. https ://doi.org/10.1038/s4159 8-019-44076 -8\nCamp JG etal (2015) Human cerebral organoids recapitulate gene \nexpression programs of fetal neocortex development. Proc Natl", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 115, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "f740892a-7817-58b0-bec4-8648086b2353", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + }, + { + "id": "6442bc7c-4e2e-553f-82c4-b2f09e01823e", + "score": 0.6181412935256958, + "metadata": { + "text": "genes are involved with multiple biological features. RNA sequencing has been coupled with protein quantication (DNA barcoded antibodies to quantify protein \nexpression) and ATAC-seq to facilitate the study of genes involved with chromatin accessibility remodeling. \ntheir environment [14 , 31 , 88 , 95 , 105] . Advances in multiplexed gene \nediting and transcriptional programing will also enable CRISPR screens", + "title": "2021 - Moving from in vitro to in vivo CRISPR screens.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "31d137b9-90a3-5b5a-ba4f-7a4d5b2c61bc", + "extraction_id": "6078715b-9964-5922-8fc9-5f105d0001ca", + "associatedQuery": "How can the integration of ATAC-seq data with RNA-seq data on GeneNetwork.org inform about chromatin accessibility and gene regulation?" + } + } + ], + "kg_search_results": null + } + } + }, + "10": { + "results": { + "completion": { + "id": "chatcmpl-AIGs9vl6ZxGFt8u7h4G1USup0nUIZ", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org involve several advanced methodologies. One approach is the use of Bayesian network inference, which has been advanced to generate causal networks from observational biological data [2]. This method allows for the modeling of probabilistic relationships between genes and can help infer causality from complex datasets.\n\nAdditionally, there is a focus on using genetic markers to orient causal inference in genome-wide association studies, which is critical for understanding the genetic basis of phenotypes [5]. This involves identifying genetic variants that can serve as markers to infer causal pathways.\n\nAnother strategy involves the use of network deconvolution, a general method to infer direct dependencies in networks, which can be applied to gene networks to clarify causal relationships [2].\n\nFurthermore, the integration of multi-omics data and the use of updated tools on platforms like GeneNetwork.org enhance the ability to perform predictive medicine and systems genetics analyses, which are crucial for inferring causal relationships in gene networks [10].\n\nThese strategies collectively leverage statistical, computational, and biological insights to improve the inference of causal relationships in gene networks.", + "role": "assistant" + } + } + ], + "created": 1728918125, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 222, + "prompt_tokens": 1308, + "total_tokens": 1530 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "83b84d63-4942-5c91-b93e-3ea1164c600e", + "score": 0.6962268533571462, + "metadata": { + "text": "genetic data which are shifting the paradigm of net work inferences by providing \nstatistical evidence to support directed links betw een genes, proteins, metabolites or \ndiseases. In Chapter 6 , different approaches using genetic data for gene network \ninference that have been proposed are reviewed. Chapter 7 examines the statistical \npotential of such methods under different realistic settings: varying population sizes \nand in the presence or absence of hidden factor var iation and suggests ways to", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 74, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "d0102d97-2e08-50c3-86f4-d1103da9cca1", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "05de9482-4937-5a26-b7fc-0a3cd86c4c40", + "score": 0.6899891680485518, + "metadata": { + "text": "73. Yu,J., Smith,V.A., Wang,P .P ., Hartemink,A.J. & \nJarvis,E.D. Advances to Bayesian network \ninference for generating causal networks from \nobservational biological data. Bioinformatics 20, \n35943603 (2004).\n74. Sachs,K., Perez,O., Peer,D., Lauffenburger,D. A. & \nNolan,G. P . Causal protein signaling networks derived \nfrom multiparameter single cell data. Science 308, \n523529 (2005).\n75. Feizi,S., Marbach,D., Mdard,M. & Kellis,M. \nNetwork deconvolution as a general method to", + "title": "2015 - Quantitative and logic modelling of molecular and gene networks.pdf", + "version": "v0", + "chunk_order": 163, + "document_id": "8db6a373-be03-5653-beaf-1b2ae1d98c31", + "extraction_id": "e23eae56-f71e-55fb-b443-e95adfe8ef22", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "b0b9c2ba-ff4b-5b2c-854f-70007eba8fd4", + "score": 0.6785443212337111, + "metadata": { + "text": "Causal Inference of Regulator-Target Pairs by Gene Mapping 97\n1.2 Background: Inferring Regula tory Networks from Correlated\nGene Expression\nIndependent of the data sets described so far, large collections of gene expres-\nsion over time course (Spellman et al., 1998) or varying environmental con-\nditions (Gasch et al., 2000; Hughes et al., 2000) have been studied to reveal\ndependent variation among genes and thereby deduce regulatory relationships.", + "title": "2005 - Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data.pdf", + "version": "v0", + "chunk_order": 634, + "document_id": "5ded506d-7935-53f9-a118-57a9f3943376", + "extraction_id": "2d776c48-9d99-5feb-9c18-113416c86d96", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "951c0969-df10-5038-b235-1bf4fa358ebb", + "score": 0.6777965618493235, + "metadata": { + "text": "data, to infer possible pathways and help build a link from the phe-notype back to a causal gene. In many cases, such interaction data are already available in public archives and need not be generated anew by the researcher [ \n1 ]. These different sources of interaction \ndata can be collated into network models ( see Note 1 ) which \nallow analysis using techniques borrowed from graph theory.", + "title": "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "version": "v0", + "chunk_order": 5, + "document_id": "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "extraction_id": "3292d5e1-b06c-5041-8190-44119ec0fdf0", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "09527834-da5e-5c34-9439-cf078f40870f", + "score": 0.6764813258361038, + "metadata": { + "text": "relationships with a causal inference test . BMC Genet 2009, 10 :23. \n60. Chaibub Neto E, Ferrara CT, Attie AD, Yandell B S: Inferring causal \nphenotype networks from segregating populations . Genetics 2008, \n179 (2):1089-1100. \n61. Li Y, Tesson BM, Churchill GA, Jansen RC: Critical preconditions for \ncausal inference in genome-wide association studies under review 2010. \n62. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 696, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "f71776c8-e5c9-55e0-ad54-3725550dea19", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "98fdd553-df98-510e-8e0d-62739abf5518", + "score": 0.6709361868538757, + "metadata": { + "text": "T, Samson L, T I (2006) A systems approach to mapping DNAdamage response pathways. Science 312:10541059\nYu J, Smith V A, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to\nbayesian network inference for generating causal networks fromobservational biological data. Bioinformatics 20:35943603How to infer gene networks from expression proles\nM Bansal et al\n10Molecular Systems Biology 2007 &2007 EMBO and Nature Publishing Group", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 117, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "452b1ade-c691-5feb-9a12-cfe83ae314af", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "29e3d52a-5651-5cdc-94a7-babb6142e244", + "score": 0.6694398916435371, + "metadata": { + "text": "with the data.\nTo cope with this problem, Siegenthaler et al. proposed a novel\nassessment procedure that incorporates the inferability of gene\nregulatory interactions by redening the confusion matrix interms of inferability of the network, i.e., the possibility of the\nnetwork to be determined from data. The inferability of GRNs\nwas analyzed based on the causal information that could beextracted from experiments. Authors used data from the DREAM", + "title": "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "f64cf13c-d989-50da-be0d-81e34a735a42", + "extraction_id": "b5c98115-372f-5bee-8517-80dc9b6838ee", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "6bda096f-f5e1-51c9-9818-6c13cdfc8fe9", + "score": 0.6672160227766035, + "metadata": { + "text": "and can thus be helpful in determining the causal structure of gene networks. Often, such data have already been gathered previously in the form of single-gene experiments and other links can be gleaned from a search of the published literature. In a few cases, a relevant database exists which can be used as a data source. Links \nof this type will all be directed edges from gene to phenotype (where the phenotype is the same as used as the seed).", + "title": "2016 - Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.pdf", + "version": "v0", + "chunk_order": 28, + "document_id": "8c395e40-b6b9-5b00-9f32-ca35a598c595", + "extraction_id": "c2a8a3ab-2531-55c1-920b-d908fa07c027", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "fbae4b79-573c-5b0b-ba0f-3761dbb22590", + "score": 0.6666336456944478, + "metadata": { + "text": "tional methodologies in gene regulatory net-works. IGI Global, Hershey, PA, pp 127\n11. Roy S, Das D, Choudhury D, Gohain GG,\nSharma R, Bhattacharyya DK (2013) Causality\ninference techniques for in-silico gene regu-latory network, Mining intelligence and knowl-edge exploration. Springer, New York,\npp 432443\n12. Olsen C, Meyer PE, Bontempi G (2009) Infer-\nring causal relationships using information the-oretic measures. In Proceedings of the 5th\nBenelux Bioinformatics Conference (BBC09)", + "title": "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "version": "v0", + "chunk_order": 61, + "document_id": "f64cf13c-d989-50da-be0d-81e34a735a42", + "extraction_id": "ae0e55f7-f33c-5179-ba14-8221c2a07be8", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6610835396597672, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the latest strategies for inferring causal relationships within gene networks using data from GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "11": { + "results": { + "completion": { + "id": "chatcmpl-AIGsGaW9DtpbrFAp6kQFqInDl6kUP", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Advancements in single-nucleus RNA sequencing (snRNA-seq) provide more granular insights into cell-type-specific gene expression networks by allowing for the analysis of gene expression within the nuclei of cells, rather than relying on intact cells as in single-cell RNA sequencing (scRNA-seq) [1]. This method is particularly useful for profiling gene expression in complex tissues from frozen samples at the single-cell level, which can be challenging with other techniques [1]. \n\nAdditionally, snRNA-seq can help clarify cell-type proportions and corresponding transcriptional profiles, as demonstrated in studies involving postmortem human brain tissue [9]. By isolating nuclei and performing snRNA-seq, researchers can achieve finer cell subtype resolution, which is crucial for understanding the heterogeneity within cell populations [7]. This level of detail is essential for constructing accurate cell-type-specific gene expression networks, as it allows for the identification of transcriptional changes and cell-type-specific gene expression patterns that might be obscured in bulk tissue analyses [3]. \n\nOverall, snRNA-seq enhances our ability to dissect complex tissues into their constituent cell types and understand the unique gene expression networks within each type, providing a more detailed and nuanced view of cellular function and interaction.", + "role": "assistant" + } + } + ], + "created": 1728918132, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 245, + "prompt_tokens": 1260, + "total_tokens": 1505 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "615beb0f-6b0a-59a6-a2fe-0be884c43d55", + "score": 0.7195009803623257, + "metadata": { + "text": "On the other hand, single-nucleus RNA-seq (snRNA-seq)\nprovides an alternative method for gene expression proling\nin complex tissues from frozen samples at single cell levels\n(Grindberg et al., 2013). Compared to scRNAseq, snRNA-seq\nanalyze gene expression within the nuclei instead of intact cells.\nIt should be noted that there could be potential dierences\nbetween the RNA type and expression levels between nucleus\nand cytosol. As observed in a previous study comparing nuclear", + "title": "2021 - New Technologies to Study Functional Genomics of Age-Related Macular Degeneration.pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "419ee941-2cd6-56ae-8221-aed1c22a8ee2", + "extraction_id": "453f1ace-3591-50a3-afa5-86404632ace3", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "732b8fa8-8832-5002-bea1-bdde2bc61c64", + "score": 0.7094707911891229, + "metadata": { + "text": "most genetic and epigenetic mechanisms are yet to be probed with single-cell resolution. To understand the finer details at the level of a singular cell, sophisticated genomic and epigenomic next-generation sequencing (NGS) technologies have increased the potential for research output immensely (see Clark etal. 2018; Clark etal. 2016; Kelsey etal. 2017; \nMacaulay etal. 2017; Stuart and Satija 2019). These would", + "title": "2020 - Advances of single?cell genomics and epigenomics in human disease.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "afe53f5a-3962-520f-be55-9df5bfdaad70", + "extraction_id": "60355441-16f5-53a2-9b24-9616624f8d00", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "849c1df7-4164-5164-b3be-6cdeb62ee555", + "score": 0.6886525339691333, + "metadata": { + "text": "of the disease, profiling gene expression in only bulk tissue sam-ples may obscure biologically relevant cell-type specific changes.\nWhile single-cell RNA-seq allows us to evaluate transcriptional\nchanges within cell-types, it is prohibitively costly to executeon large cohorts (i.e. hundreds of individuals). To circumvent\nthis issue, we developed a framework that leverages single-", + "title": "2020 - Integrative genomics approach identifies conserved.pdf", + "version": "v0", + "chunk_order": 23, + "document_id": "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "extraction_id": "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "3069c1d1-6b89-513a-83c3-e64cce07043f", + "score": 0.687616845596339, + "metadata": { + "text": "2019). The traditional RNA sequencing technology (bulk RNA-seq) is\napplied to determine gene expression pro les, isoform expression,\nalternative splicing and single-nucleotide polymorphisms on basis oftissue samples, which contains various cell types ( Kuksin et al., 2021 ).\nOn the contrast, single-cell RNA sequencing (scRNA-seq), a noveltechnology can detect the gene expre ssion patterns for each transcript\nwithin single cell and distinguish cell subtypes ( Lhnemann et al., 2020 ).", + "title": "2023 - Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.pdf", + "version": "v0", + "chunk_order": 16, + "document_id": "8505ccf0-3138-5b83-b36d-8ebd7506a3a4", + "extraction_id": "59e0781d-994c-5ef5-b2f4-073f4a73743b", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "504a960d-e669-52d1-b6c0-439b4f981d5f", + "score": 0.6853773593902588, + "metadata": { + "text": "sion from smaller amounts of RNA enabled cell typespecific analyses.Specific cell types can beisolated using flow cytometry, for example, using endogenously expressed fluorescent markers,\nwith or without combining with antibodies for cell surface proteins. Transcriptomic analysis by\neither microarray or bulk RNA sequencing then follows (39,67,68,104,145).Such analyses can\n280 Taiberetal.\nAnnu. Rev. Genom. Hum. Genet. 2022.23:275-299. Downloaded from www.annualreviews.org", + "title": "2020 - The Genomics of Auditory.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "f56b6ae4-e05a-5851-9c10-4bd62f237778", + "extraction_id": "16c769c7-b6ad-5b50-8d81-92c6768595f5", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "769d2c00-d882-59a6-aa69-feb575c9fe1a", + "score": 0.6814767122268677, + "metadata": { + "text": "Recent applications\nSingle-cell RNA sequencing has had a profound impact\non our understanding of neuronal and hematopoietic\ncell types, as well as the immune system. Examples of\nnovel insights in immunity include a window on to an\nunexpected plethora of dendritic cells in mouse immun-\nity [25] and new regulators and subpopulations of CD4+\nT cells [26 28]. In hematopoiesis, much single-cell tran-\nscriptomics work has focused on hematopoetic stem\ncells and the single-cell perspective has provided reso-", + "title": "2016 - Single-cell genomics coming of age.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "dca877e8-cbb9-561e-9b3c-6085228af97d", + "extraction_id": "8d4d3a2d-0aca-5880-98e7-92638c72dd31", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "1fa406bc-fb29-5b60-90bc-1e77bd499df6", + "score": 0.6796850168611117, + "metadata": { + "text": "single- nucleus RNAseq makes them a valuable complement to the find-\nings published by Orozco, Chen et al. (Orozco et al., 2020 ). Furthermore, \nYan et al. (2020) used cell sorting to enrich for cell types with a high \ndegree of heterogeneity, resulting in finer cell subtype resolution for \nnon-photoreceptor cell types such as RGCs. \nIn addition to neural retina, our understanding of the choroidal", + "title": "2022 - Systems genomics in age-related macular degeneration.pdf", + "version": "v0", + "chunk_order": 57, + "document_id": "e8cf1e00-cf22-54cb-a0de-790a822c62d1", + "extraction_id": "e488a94d-d7b3-5d56-bd56-95ac6e89d3ed", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "5f508353-ff30-5dfc-9bac-4bb8c6627391", + "score": 0.672986289053747, + "metadata": { + "text": "using sequencing (ATAC-seq),95,96 that can map chro-\nmatin interactions and accessibility with higher resolu-tion than previous methods will improve our ability to \ndisentangle GWAS loci; while single-cell RNA sequenc-\ning\n97,98 and CRISPR-based pooled gene perturbation \nmethods99103 provide unprecedented opportunities for \nstudies of how RNA expression patterns differ between cells within tissues and how those tissues and cells react \nto perturbation of multiple genes in parallel.", + "title": "2018 - Human Genetics of Obesity and Type 2 Diabetes Mellitus.pdf", + "version": "v0", + "chunk_order": 92, + "document_id": "2083de31-17c6-5d1e-9aa6-2efc6c1d9ac2", + "extraction_id": "74048afb-68c3-520a-b661-1d347e9d2fcd", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "42cf70a7-610a-5792-be62-58114dfc505a", + "score": 0.6666386127471924, + "metadata": { + "text": "cell RNA-seq data from a smaller cohort in conjunction withco-expression network analysis in order to estimate cell-typespecific transcriptomic changes in large, bulk tissue RNA-seq\ndatasets.\nWe isolated nuclei and performed single-nuclei RNA-seq\n(snRNA-seq, n= 27 321 nuclei) on postmortem human brain\ntissue from aged, neurologically healthy controls ( n=5 ,6 7t o9 0 +\nyears old, PFC, Supplementary Material, Table S1 ) to clarify cell-\ntype proportions and the corresponding transcriptional profiles", + "title": "2020 - Integrative genomics approach identifies conserved.pdf", + "version": "v0", + "chunk_order": 24, + "document_id": "704a4d4c-3655-5cc0-8d2b-5f4723db13ff", + "extraction_id": "863ce70a-3bcd-5a6c-a63f-620a9fdcdfdf", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + }, + { + "id": "908fad18-f471-5067-8bfc-f49951bdb4d1", + "score": 0.665932834148407, + "metadata": { + "text": "without the biases of probe sequence selection and\nhybridization reactions. The second innovation is cell-specific\nRNA profiling methods [79] that make it practical to generate\ncomparatively accurate expression data for individual cell\ntypes in genetically engineered lines of mice. We can soon\nexpect far more comprehensive and specific lists of genes for\nseveral important cell and tissue types that can be used to\nassemble multicellular expression networks in eye.ACKNOWLEDGMENTS\nDr. Eldon E.", + "title": "2009 - Gene expression in the mouse eye an online resource for genetics using 103 strains of mice.pdf", + "version": "v0", + "chunk_order": 253, + "document_id": "85241c56-1338-5b42-8b33-10b14514f169", + "extraction_id": "65c45e96-da39-59d8-9b9e-0679df8b1472", + "associatedQuery": "How do advancements in single-nucleus RNA sequencing provide more granular insights into cell-type-specific gene expression networks?" + } + } + ], + "kg_search_results": null + } + } + }, + "12": { + "results": { + "completion": { + "id": "chatcmpl-AIGsO45INZIWjU37FcOiRroinBDZj", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The context provided does not explicitly mention the impact of recent discoveries in non-coding RNA regulation on refining gene interaction maps on GeneNetwork.org. However, there are some relevant points that can be inferred:\n\n1. GeneNetwork.org has been updated with new tools and data, including multi-omics analysis, which could potentially incorporate non-coding RNA data to enhance gene interaction maps [2].\n2. The integration of gene expression data sets, particularly for mouse and rat, into GeneNetwork.org suggests that the platform is equipped to handle complex genetic data, which may include non-coding RNA interactions [7].\n3. There is ongoing research and data collection on non-coding RNAs, as indicated by references to databases like Rfam and cisRED, which could contribute to refining gene interaction maps by providing insights into regulatory networks [9], [10].\n\nWhile the specific impact of non-coding RNA discoveries on GeneNetwork.org is not detailed, the platform's enhancements and the broader research context suggest that such discoveries could play a role in improving the accuracy and depth of gene interaction maps.", + "role": "assistant" + } + } + ], + "created": 1728918140, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 215, + "prompt_tokens": 1346, + "total_tokens": 1561 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "302feae2-3bab-5fb8-8483-0cea906c83e8", + "score": 0.6653348207473755, + "metadata": { + "text": "52.Zhu J et al. (2007) Increasing the power to detect causal associations by combining genotypicand expression data in segregating populations. PLoS Comput Biol 3:e69\n53.Zhu J et al. (2008) Integrating large-scale functional genomic data to dissect the complexity ofyeast regulatory networks. Nat Genet 40:854861\n54.Kim JK et al. (2005) Functional genomic analysis of RNA interference in C. elegans. Science308:11641167", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 161, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "29c980ec-bd6e-5ae7-a61c-5abd67d0ef67", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6633270978927612, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "0374a059-20c1-5b75-a7a7-bf69ce03740c", + "score": 0.6616688966751099, + "metadata": { + "text": "expression and its effect on disease . Nature 2008, 452 (7186):423-428. \n12. Chen LS, Emmert-Streib F, Storey JD: Harnessing naturally randomized \ntranscription to infer regulatory relationships amo ng genes . Genome Biol \n2007, 8(10):R219. \n13. Aten JE, Fuller TF, Lusis AJ, Horvath S: Using genetic markers to orient \nthe edges in quantitative trait networks: the NEO s oftware . BMC Syst \nBiol 2008, 2:34. \n14. Millstein J, Zhang B, Zhu J, Schadt EE: Disentangling molecular", + "title": "2011 - Genetical genomics approaches for systems genetics.pdf", + "version": "v0", + "chunk_order": 606, + "document_id": "de78a01d-8d03-5afb-af5b-ce2ed2167766", + "extraction_id": "c0983224-1ade-5c10-9f2b-847e9b33f706", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "860be786-e27d-5dd1-96bf-4bcc48957b4d", + "score": 0.6570985317230225, + "metadata": { + "text": "and unknown function by large-scale coexpression analysis.\nPlant Physiol 2008, 147:41-57.\n98. Wolfe CJ, Kohane IS, Butte AJ: Systematic survey reveals gen-\neral applicability of \"guilt-by-a ssociation\" within gene coex-\npression networks. BMC Bioinformatics 2005, 6:227.\n99. Lee NH: Genomic approaches for reconstructing gene net-\nworks. Pharmacogenomics 2005, 6:245-58.\n100. Goutsias J, Lee NH: Computational and experimental\napproaches for modeling ge ne regulatory networks. Curr", + "title": "2009 - Genes and gene expression modules associated with caloric.pdf", + "version": "v0", + "chunk_order": 316, + "document_id": "893ba204-2e69-563f-9046-7246ca61494f", + "extraction_id": "56129761-d500-59b9-bd9b-cd9cbcada21c", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "4488c0f4-c24a-5b6d-814a-a30b15cc4c03", + "score": 0.6562051177024841, + "metadata": { + "text": "the discovery of interface genes. These mRNA transcripts regulate expression of genes\nin those structures, and thereby couple multiple networks a nd biological processes. The\ndetection of these transcripts and the analysis of their gen es regulatory polymorphisms\n37", + "title": "2009 - Visual analytics for relationships in scientific data (1).pdf", + "version": "v0", + "chunk_order": 242, + "document_id": "a6642ef1-8aa2-5305-9cc8-8a6263bb2b0c", + "extraction_id": "d64d8cf5-5b57-5a29-99b4-a8d2ab4bda21", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "9f6fb84a-f487-5ea6-a84e-403642b6d76e", + "score": 0.6557608842849731, + "metadata": { + "text": "Rev. Genet 2007;8:437449. [PubMed: 17510664] A review of theory and approaches to mapping\ngenetic interaction networks.\n16. Bork P, et al. Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol 2004;14:292\n299. [PubMed: 15193308]\n17. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. I.\nAccuracy assessment. Genome Res 1998;8:175185. [PubMed: 9521921]", + "title": "2007 - Integrating physical and genetic maps from genomes to interaction networks.pdf", + "version": "v0", + "chunk_order": 112, + "document_id": "a9a113e2-d5e5-5903-91de-4b45b37d870f", + "extraction_id": "ba1a83a3-d0e9-5f1e-870f-228abdae771d", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "0858b8f7-66f3-5741-ae7e-4504bca7292f", + "score": 0.6545545664695175, + "metadata": { + "text": "CC represents a dramatic improvement over existinggenetic resources for mammalian systems biology appli-\ncations (Adam et al. 2007 ; Chesler et al. 2008 ). A number\nof gene expression data sets from microarray experiments,particularly those for mouse and rat, have been integrated\ninto GeneNetwork ( http://www.genenetwork.org ), which is\nessentially a web knowledgebase in which the entire dataset and relevant metadata (data about the data) are com-\nbined with sophisticated statistical and computation tools", + "title": "2010 - Systems genetics, bioinformatics and eQTL mapping.pdf", + "version": "v0", + "chunk_order": 53, + "document_id": "27c922c6-e449-5f83-868a-3ad7284facc8", + "extraction_id": "298ee1f5-58a9-567c-86ba-8ac5967e1718", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "a02b4589-65ec-50e1-9849-090971ddb2b0", + "score": 0.6540389060974121, + "metadata": { + "text": "gene, and the first f unctional anti -sense miRNA, Lastly, we have used \ncomparative genomics to infer regulatory networks based on individual \nconserved instances of regulatory motifs, which show functional enrichments \nsimilar and sometimes higher to genome -scale experimental met hods such as \nChIP -chip. As part of the ENCODE and modENCODE projects, we are now \nstudying dynamics of developmental and cell -differentiation networks in", + "title": "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "version": "v0", + "chunk_order": 239, + "document_id": "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "extraction_id": "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "7d3e3705-c5e7-5a37-91c1-a87842f5b9a7", + "score": 0.6538775162064292, + "metadata": { + "text": "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu-\nlatory sites from the cisRED database (Robertson et al. , 2006). There is much to do\nin both of these emerging areas but even preliminary data have already given new\ninsights into mammalian biology: it seems there is high lineage specic expansion of\nsome ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another\ngrowing area of activity is in cataloguing the genetic variation present in human", + "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 499, + "document_id": "045edae8-468b-5725-be06-8cb4b8f6a92b", + "extraction_id": "a3ae6875-b0fc-5a4e-866f-4fee99c7d2a2", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + }, + { + "id": "73198d17-f9ce-5528-89d8-f6e466258708", + "score": 0.6538775162064292, + "metadata": { + "text": "(ncRNAs) from the Rfam database (Grifths-Jones et al. , 2005) and predicted regu-\nlatory sites from the cisRED database (Robertson et al. , 2006). There is much to do\nin both of these emerging areas but even preliminary data have already given new\ninsights into mammalian biology: it seems there is high lineage specic expansion of\nsome ncRNA classes relative to protein-coding genes (Birney et al. , 2006). Another\ngrowing area of activity is in cataloguing the genetic variation present in human", + "title": "2007 - Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 499, + "document_id": "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "extraction_id": "9c89683f-aca5-57f9-b28d-62e9eb64377b", + "associatedQuery": "What impact have recent discoveries in non-coding RNA regulation had on refining gene interaction maps on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "13": { + "results": { + "completion": { + "id": "chatcmpl-AIGsUMf0eTPsxD8TBs1unBQPLXIKg", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The provided context does not explicitly mention the integration of spatial transcriptomics approaches into GeneNetwork.org. However, it does describe GeneNetwork as a resource for systems biology and systems genetics, which includes large transcriptome datasets from multiple tissues [2], [9]. The platform is used to study relationships among markers, genes, and phenotypes, and to analyze genetic regulatory commonality and tissue structure and function [3], [4]. While spatial transcriptomics is not directly referenced, the existing capabilities of GeneNetwork in handling multi-omics data and performing systems genetics mapping [1], [5] suggest that it could potentially support spatial transcriptomics approaches to enhance understanding of tissue architecture and function.", + "role": "assistant" + } + } + ], + "created": 1728918146, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 136, + "prompt_tokens": 1062, + "total_tokens": 1198 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6523095965385437, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "dcb29dfe-ba22-54bc-91f7-af3261a18fd2", + "score": 0.6367458701133728, + "metadata": { + "text": "of importance in the emergence of precision medicine ( Curtis, 2015 ; Desautels et al., 2014 ; \nGlade Bender et al., 2015 ; Jorgensen, 2015 ; Kummar et al., 2015 ; Marquet et al., 2015 ; \nRubin, 2014 ) wherein therapeutic strategies need to be aligned with specific properties of \ntumors.\nMethods\nGeneNetwork and WebGestalt\nGeneNetwork is an open access, online data analysis resource for systems biology and \nsystems genetics. It contains a large number of microarray datasets from multiple tissues of", + "title": "2015 - Cell cycle gene expression networks discovered using systems biology Significance in carcinogenesis.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "6f354254-4f4d-52ad-bed7-9356f43c0b20", + "extraction_id": "5b6d04d2-3aa2-5a43-814a-b13e60e3bb1d", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "6beb1115-9f40-555f-a6b4-3c73945101a0", + "score": 0.636005699634552, + "metadata": { + "text": "GeneNetwork, a public web source used to study relations amongmarkers, genes, and phenotypes. We made use of large transcriptomedata sets for the amygdala, hippocampus, ventral tegmental area", + "title": "2015 - Identification of candidate genes that underlie the QTL on chromosome 1 that mediates genetic differences in stress-ethanol interactions.pdf", + "version": "v0", + "chunk_order": 25, + "document_id": "eecf4236-efca-577d-ba62-c20c9768950e", + "extraction_id": "26045fea-cd20-5e3d-be07-e8a8e9ca603a", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "6e2695ed-e652-52e1-b896-0bbbb585bb60", + "score": 0.6356210302001548, + "metadata": { + "text": "ject to mapping analysis. We examine the connectivity among these sets and analyze\nthe molecular, biochemical and genetic regulatory commonality of connected genes us-ing novel and existing bioinformatics tools. We also develop data-driven hypotheses to\nexplain the mechanisms of genetic perturbations and variation as a means of dening\nglobal consequences of individual differences on tissue structure and function.\nMuch of our work is motivated by prior studies of brain gene expression and mRNA", + "title": "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "version": "v0", + "chunk_order": 13, + "document_id": "d9038328-bfea-5f73-87aa-6077b697e4db", + "extraction_id": "f1181fc1-fe08-53b1-bda7-00423a568234", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "7ce6c0fe-8b0a-5ce9-83d1-6e6b99b4f24d", + "score": 0.6329202870774109, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2020 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "d11a87ca-4989-59af-95e3-ab90af7d9212", + "extraction_id": "4ca2fc9e-7d42-5ea3-b1b7-a296bfbc6a09", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "30e2423f-2b2b-5c7d-8808-b025242fa0c7", + "score": 0.6328813275240315, + "metadata": { + "text": "including correlation and network analysis to compare associations \nbetween tissues and between other rodent or human data sets[32]\nMany of the Data Sets are amenable to systems genetics mapping and other methods and are accessible at GeneNetwork. The Description and Usage column provides details about the data set and potential", + "title": "2017 - GeneNetwork a toolbox for systems genetics.pdf", + "version": "v0", + "chunk_order": 264, + "document_id": "682c3a51-0aa5-54a3-a6e7-a09b81c0e8b6", + "extraction_id": "7dd82b3f-58bd-5915-9eea-250f11412ff2", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "bd4b772b-4df4-588e-a7bd-2d5d9484f945", + "score": 0.6269146203994751, + "metadata": { + "text": "weighted gene co-expression network are described in[54]. Consensus network analysis was carried out with Rfunction blockwiseConsensusModules in the WGCNA R\npackage [54].\nOur online R software tutorial easily permits the user\nto identify tissue-specific age related modules and CpGs.\nGene ontology enrichment analysis", + "title": "2012 - Aging effects on DNA methylation modules.pdf", + "version": "v0", + "chunk_order": 163, + "document_id": "cdd3bf57-3c36-5673-bd78-1e53f384d539", + "extraction_id": "bf37d9e2-c9a3-5886-88db-103264c4cecb", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "9bf34d9a-9c54-5376-a38e-7f32daba8107", + "score": 0.6185001730918884, + "metadata": { + "text": "approach employed in the construction of large expression data\nsets, such as those provided by GeneNetwork,39treats gene\nexpression as a continuous variable across RI strains, rather than asa categorical one (knockout model). Hence, we believe that using\nthese complementary, yet conceptually distinct, approaches\nenhanced our ability to propose mechanistic insights.\nA limitation of the current study relates to the non-trivial\nrelationship between structural and functional brain connectivity.4", + "title": "2016 - Alterations in the expression of a neurodevelopmental gene exert long-lasting effects on cognitive-emotional phenotypes and functional brain networks translational evidence from the stress-resilient Ahi1 knockout mouse.pdf", + "version": "v0", + "chunk_order": 162, + "document_id": "8cd3e767-17b8-5868-b335-fdb6cc2ff02c", + "extraction_id": "ea5fd027-559f-568f-9c4d-a4615730426a", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "225f0aa2-c185-5b36-923a-a24e545b866f", + "score": 0.6179440845284423, + "metadata": { + "text": "GeneNetwork ( http://www.genenetwork.org ; Williams and Mulligan, 2012)). These databases 180 \nrepresent transcriptome datasets for different tissues of recombinant inbred mice. If several probes 181 \nfor the same gene were available, probes with higher maximum likelihood ratio statistic (LRS, a 182 \nmeasurement of the association or linkage between differences in traits and differences in particular 183 \ngenotype markers values) were used. 184", + "title": "2018 - Metanalysis of genome-wide association studies for panic disorder suggest pathways and mechanisms of pathogenesis.pdf", + "version": "v0", + "chunk_order": 38, + "document_id": "e4b2f5dc-6df6-5af8-9ca9-3ccb6518d300", + "extraction_id": "434963e5-549e-5986-90a9-cbf4a5f7f06e", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + }, + { + "id": "b6b401f6-66c1-5e0d-ab68-09f6f6d7e10f", + "score": 0.6176329255104065, + "metadata": { + "text": "pathways.TheGeneNetworkdatabaseisauniqueresourceforco-expressionanalysisusingdatafromavarietyof\ntissues acrossgeneticallydistinctinbredmice.However,extractionofbiologicallymeaningfulco-expressedgenesets\nischallengingduetovariabilityinmicroarrayplatforms,probequality,normalizationmethods,andconfounding\nbiologicalfactors.Inthisstudy,wetestedwhetherliteraturederivedfunctionalcohesioncouldbeusedasanobjectivemetricinlieuofgroundtruthtoevaluatethequalityofprobesandmicroarraydatasets.", + "title": "2019 -Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "0a22eed8-cdda-52de-a73f-d82b3f73b78d", + "extraction_id": "dab0ce13-0d90-514c-9220-8edd64eceb6c", + "associatedQuery": "How are spatial transcriptomics approaches being integrated into GeneNetwork.org to enhance understanding of tissue architecture and function?" + } + } + ], + "kg_search_results": null + } + } + }, + "14": { + "results": { + "completion": { + "id": "chatcmpl-AIGsZPO5vngTlUtdx2Hky3vN1AZqv", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent developments in quantitative trait locus (QTL) mapping have significantly refined our understanding of gene regulatory variants in several ways:\n\n1. **Identification of eQTLs**: QTL mapping of gene expression traits allows for the identification of expression quantitative trait loci (eQTLs), which are genomic regions that have a regulatory effect on gene expression traits. These eQTLs can be categorized into local eQTLs, which are located near the gene encoding the transcript, and distant eQTLs, which are located elsewhere in the genome [2]. This distinction helps in understanding the regulatory architecture of the genome.\n\n2. **Increased Resolution and Confidence**: With advancements in DNA sequencing and the availability of whole-genome databases and gene expression data from various tissues, researchers can use bioinformatic tools to identify candidate genes with greater confidence for further functional validations [1]. This enhances the precision of QTL mapping in pinpointing regulatory variants.\n\n3. **Functional Mapping and Hypothesis Generation**: QTL mapping studies, such as those beginning with yeast, have used gene expression as the phenotype to infer regulatory control. Although these studies are not conclusive, they help narrow down potential regulatory candidates, generate hypotheses for further testing, and construct regulatory networks in silico [3].\n\n4. **Detection of Secondary QTLs and Epistatic Interactions**: Recent developments allow for the identification of large numbers of less strong, secondary QTLs that were previously obscured by background noise. This opens up new possibilities for analyses, such as identifying epistatic interactions, which can reveal pathways of genetic control within the studied tissue [4].\n\n5. **Integration with Expression Analysis**: The integration of genetic variation in associated loci with expression analysis data through eQTL studies helps define regulatory relationships. This approach provides insights into the physiological consequences of causal variants, aiding in the translation of findings into diagnostic tests and risk evaluation [8].\n\nOverall, these advancements in QTL mapping enhance our understanding of the complex regulatory mechanisms underlying gene expression and trait variation.", + "role": "assistant" + } + } + ], + "created": 1728918151, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 412, + "prompt_tokens": 1259, + "total_tokens": 1671 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "93d79fcd-e841-5cd0-baa7-69ad032cacef", + "score": 0.766341714103023, + "metadata": { + "text": "to as quantitative trait loc us (QTL) mapping study. QTL studies inform us region s on the \nchromosome where existing polymorphisms or SNPs are highly correlated with variation of the \ntrait of interest. With the advancement in DNA sequencing, whole genome database of several \nmouse strains as well as gene expression data from several tiss ues are available. This allows us to \nuse bioinformatic tools to identify candidate genes with greater confidence for further functional \nvalidations .", + "title": "2019 - Novel Genetic Loci Control L5 Vertebral Trabecular Bone and the Response to Low Calcium Intake in Growing BXD Recombinant Inbred Mice.pdf", + "version": "v0", + "chunk_order": 217, + "document_id": "de8dda5e-0e2f-5aa9-bb13-851c526b36a5", + "extraction_id": "16fdf35c-ab83-53db-9f76-e817326c6067", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "248ac859-2589-5afa-824d-e1357bf23e59", + "score": 0.7579537178305676, + "metadata": { + "text": "differences, allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation. QTL mapping of gene expression traits \nallows us to identify eQTLs; genomic regions that have a regulatory effect on \nthose expression traits. Two types of eQTLs can be distinguished, i.e., those \nthat map near (less than 10 Mb from) the gene which encodes the transcript \n(local ) and those that map elsewhere in the genome ( distant ).\n18 Together, local", + "title": "2011 - EXPLOITING NATURAL AND INDUCED GENETIC VARIATION TO STUDY HEMATOPOIESIS.pdf", + "version": "v0", + "chunk_order": 266, + "document_id": "6f250b15-61b3-57ed-8900-5aa4a173fa8c", + "extraction_id": "76e22011-da6d-5af7-a74f-2b4d0f11e879", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "32497309-fb89-58c9-b828-6a16fa55c11d", + "score": 0.7529518449988656, + "metadata": { + "text": "simultaneously. Beginning with a study in yeast (Brem et al. 2002), QTL mapping has been \ndone with gene expression as the phenotype. In such a study, the genomic loci responsible for variation in gene expression can be used to infer regulatory control. While such a study is not conclusive, it can be used to narrow the potential regulatory candidates, generate \nhypotheses for further testing and construct regulatory networks in s ilico.", + "title": "2010 - Genome-wide analysis of transcriptional regulation in the murine liver.pdf", + "version": "v0", + "chunk_order": 181, + "document_id": "8a115c1b-662c-5062-b77f-bbde0532bbe9", + "extraction_id": "957166a3-0298-5324-a24a-02b59ec3427f", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "ea5eaca1-c91d-59f6-af5b-5490749d950a", + "score": 0.7513671665568968, + "metadata": { + "text": "is that one can now identify large numbers of less strong, second-ary QTLs which were previously lost to background noise, and this information opens up a whole new range of possible analy-ses, such as the identi cation of epistatic interactions ( Figure 5), \nthat promise to uncover pathways of genetic control within the tissue studied.\nTraditionally, QTL mapping starts with a phenotype of inter-", + "title": "2009 - Genetics of the hippocampal transcriptome in mouse a systematic survey and online neurogenomics resource.pdf", + "version": "v0", + "chunk_order": 93, + "document_id": "33437cc7-ee4e-59b9-b3e6-ed50eb504b52", + "extraction_id": "a47731b3-bb43-5d9c-a7eb-bfea5eea557e", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "56ba9ce8-4cdd-5d4d-83c1-a370e9c8f959", + "score": 0.7499000281274282, + "metadata": { + "text": "and quantitative trait loci (QTL) regulatory models. A major goal is to identify which,among a set of candidate genes, are the most likely regulators of trait variation. These\nmethods are applied in an effort to identify multiple-QTL regulatory models for large\ngroups of genetically co-expressed genes, and to extrapolate the consequences of thisgenetic variation on phenotypes observed across levels of biological scale through the", + "title": "2007 - Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data.pdf", + "version": "v0", + "chunk_order": 2, + "document_id": "d9038328-bfea-5f73-87aa-6077b697e4db", + "extraction_id": "47c06e52-1923-58d0-9286-9674893a502a", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "cd33f83f-d19c-5419-a157-c2f1d8148347", + "score": 0.7476117616625401, + "metadata": { + "text": "distal regions into even finer regulatory loci. This influence on gene expression may be \nthe reason why so many classical QTLs have been mapped to Qrr1 . \n \nThe complexity highlighted by Qrr1 may very well be the rule rather than the \nexception for loci that modulate complex traits. Efforts to fine -map a single QTL have \noften been confronted by clusters of multiple small effect QTLs within the original \ninterval (Legare et al., 2000; Demarest et al., 2001) . This poses a serious challenge, and", + "title": "2009 - Multiscale Genomic Analysis of the Corticolimbic System_ Uncoveri (1).pdf", + "version": "v0", + "chunk_order": 614, + "document_id": "3d0df5a3-7d7c-5edc-b94d-cae582f59c12", + "extraction_id": "3296b30e-7dd3-576d-a2df-442406caa472", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "e5354b88-c1ec-54e1-ab61-c30689e30ea1", + "score": 0.7471424642631507, + "metadata": { + "text": "genotypes, availing of genetic markers across the whole\ngenome, and allow the identication of QTLs with signi-\ncant effects on the disease (Darvasi 1998 ; Manolio 2010 ).\nQTLs are genetic regions closely linked to a gene with a\nquantitative effect on the phenotype. QTL mapping is\nbased on the concept that phenotypic differences between\ninbred mouse strains can be used to demonstrate theimportance of genetic effects on complex phenotypes\n(Andreux et al. 2012 ; Hillebrandt et al. 2002 ). The standard", + "title": "2015 - Exploring multiple quantitative trait loci models of hepatic fibrosis in a mouse intercross.pdf", + "version": "v0", + "chunk_order": 9, + "document_id": "464d119c-ba16-5716-8b69-169940f090b4", + "extraction_id": "121f6744-a773-5a59-b8c7-7e7e85e2b067", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "3fa64113-fa70-575c-81ae-0769dff93a27", + "score": 0.7451775377777227, + "metadata": { + "text": "of the variants within associated loci through expression-quantitative trait locus (eQTL) studies will combine the genetic variation in associate d loci with expression analysis\ndata to define regulatory relationships. Studies designed to understand the functional effect of any causal variants in relevant cell systems and an imal models will give\ninsight to physiological consequence. These advances will underpin efforts to translate the findings through development of diagnostic tests, ris k evaluation and", + "title": "2008 - Type 2 diabetes new genes, new understanding.pdf", + "version": "v0", + "chunk_order": 91, + "document_id": "3992c53c-c48c-597d-8d96-70b1026deb70", + "extraction_id": "31a1546b-c160-5b22-a3fb-1e26ab2861c3", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "662c7b64-e34e-5faa-b920-6b59334ef372", + "score": 0.7442948748801843, + "metadata": { + "text": "illustrating the potential of functional mapping for effici ently establishing \nassociations between existing QTL, as well as for novel QTL discovery.\nReferences\n1. Damerval C, Maurice A, Josse JM, De Vienne D: Quantitative trait\nloci underlying gene product va riation: a novel perspective\nfor analyzing regulation of genome expression. Genetics 1994,\n137:289-301.\n2. Brem RB, Yvert G, C linton R, Kruglyak L: Genetic dissection of\ntranscriptional regulation in budding yeast. Science 2002,\n296:752-755.", + "title": "2008 - Towards systems genetic analyses in barley Integration of phenotypic, expression and genotype data into GeneNetwork.pdf", + "version": "v0", + "chunk_order": 85, + "document_id": "8513abbe-65ed-5f35-9f86-ba93cfc5a194", + "extraction_id": "9b24b22c-0f8c-5b3c-9479-518aa6dfab01", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + }, + { + "id": "f9ca5851-0871-54ae-8d01-752c806bd081", + "score": 0.7398491966994066, + "metadata": { + "text": "over a decade ago, using new genometypes for the BXD family of murine strains, as well\nas new statistical tools, showing that we can identify new quantitative trait loci (QTLs),\nresulting in highly plausible candidate genes.\nQuantitative trait locus (QTL) mapping has been carried out in numerous species to associate\nregions of the genome to phenotypes even before the structure of the genome was well understood\n(e.g., [ 3]). Rodents, especially mice, have been the species most prominently used for biomedi-", + "title": "2022 -Chunduri- Drugs Animal Models.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "9cfa4f4c-37ce-5c0f-9da6-3bbb075fdc45", + "extraction_id": "b93f76a6-6928-564a-a9a0-149c8ef774d9", + "associatedQuery": "How do recent developments in quantitative trait locus (QTL) mapping refine our understanding of gene regulatory variants?" + } + } + ], + "kg_search_results": null + } + } + }, + "15": { + "results": { + "completion": { + "id": "chatcmpl-AIGsiGGBCVBLOOrTQrnGlB4EM7iVd", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org has several implications:\n\n1. **Enhanced Functional Analysis**: By integrating epigenetic data, the predictive functional analysis of SNPs can be improved. This is because epigenetic data can highlight regions of DNA that are accessible or inaccessible to protein binding by transcription factors and other regulatory proteins, which can affect gene expression and regulation [4].\n\n2. **Prioritization of Regulatory Variants**: The incorporation of genome-wide histone modification signatures, as revealed by collaborative efforts like the ENCODE Project and Roadmap Epigenomics, allows for the prioritization of functional regulatory variants. This can be particularly useful in mapping studies and databases, enhancing the understanding of regulatory features in various tissues and cell lines [2].\n\n3. **Understanding Gene Expression Variation**: By studying the genetics of epigenetics, it is possible to reveal genes that directly or indirectly affect epigenetic gene states. This approach can help estimate the percentage of variation in gene expression that can be explained by different epigenetic conformations, thus providing a deeper understanding of gene regulation [3].\n\n4. **Resource for Predictive Medicine and Systems Genetics**: The integration of multi-omics data, including epigenomic data, makes GeneNetwork.org a valuable resource for predictive medicine and systems genetics. This integration supports more comprehensive analyses and enhances the platform's utility for research and clinical applications [7].\n\nOverall, incorporating epigenomic data into GeneNetwork.org enriches the platform's analytical capabilities, offering deeper insights into gene regulation and expression, and supporting advanced research in genetics and epigenetics.", + "role": "assistant" + } + } + ], + "created": 1728918160, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 336, + "prompt_tokens": 1318, + "total_tokens": 1654 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a87fa6ff-4bc0-50ac-b654-f7d734bbbf02", + "score": 0.6593109369277954, + "metadata": { + "text": "frequent usage of terms like epigenetic or chromatin land-scape. New methods for high-throughput mapping ofgenome-wide histone modifications and protein-DNA inter-\nactions were developed over the last few years (Blecher-Gonen\net al., 2013; Garber et al., 2012).\nHistone Modifications Associated with Gene\nEnhancersChromatin can be modulated by covalent histone modifica-", + "title": "2016 - Next Generation Transcriptomics.pdf", + "version": "v0", + "chunk_order": 48, + "document_id": "56cafe26-2b36-50d6-a5c7-c7d947473b61", + "extraction_id": "b3a2e390-fad2-5afc-b8ca-6f0b57b9cac1", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "66fa4c5e-0b26-5c01-b5ec-d199a4da11bb", + "score": 0.6519500613212585, + "metadata": { + "text": "orative efforts of the ENCODE Project [ 42] and Roadmap\nEpigenomics [ 43] consortia have already revealed a\ncompendia of genome-wide histone modification signatures\nfor various regulatory features in multiple primary tissues\nand cell lines. These datasets have been applied to global\nmapping studies and databases to prioritize functional regula-\ntory variants [ 44,45]. While these assays have been employed\nextensively in LCLs, and tumor cell lines to follow-up auto-", + "title": "2016 - Genetics and Genomics of Coronary Artery Disease..pdf", + "version": "v0", + "chunk_order": 33, + "document_id": "23a1b7be-9541-5e16-b9cc-24ea420a4961", + "extraction_id": "203710b7-3267-5ecf-9397-b5becdaeead1", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "77ae8cce-6686-5930-a6a1-291143cba4c5", + "score": 0.6497661858657721, + "metadata": { + "text": "genetical genomics) and the genetics of epigeneticscould be studied simultaneously, thus revealing genes\nthat directly or indirectly affect epigenetic gene states.\nAn additional issue that could be addressed by such anapproach is to estimate the percentage of variation in\ngene expression that can be explained by different\nepigenetic conformations.\nThe level of complexity could be further increased by\nincluding different cell types in the analysis, such as the", + "title": "2008 - Combining transcriptional profiling and genetic linkage analysis to uncover gene networks operating in hematopoietic stem cells and their progeny.pdf", + "version": "v0", + "chunk_order": 86, + "document_id": "af6e0103-849d-542f-bca7-0251082bc0b3", + "extraction_id": "767fd341-f407-5322-a932-9b1cecb869e0", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "9c31e888-0660-507d-927f-e54f98a7248f", + "score": 0.6428762674331665, + "metadata": { + "text": "Incorporating epigenetics into genetic analysis can also enhance the predictive\nfunctional analysis of SNPs by highlighting regions of DNA that are accessible or\ninaccessible to protein binding by transcription factors and other regulatory pro-\nteins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG)\nmethylation sites. Rakyan et al. (2004) suggested that such an event might affect the\noverall methylation prole of a locus and, consequently, promoter activity and gene", + "title": "2007 - Bioinformatics_for_Genetices_MAZEN_SAEED.pdf", + "version": "v0", + "chunk_order": 1274, + "document_id": "139463d1-c63c-5c51-bf9c-9ccc356768e0", + "extraction_id": "9515bd16-96d4-5b09-b23c-63a1cc5d19ae", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "5935ee2f-4621-577d-8d9b-e47d2d0699e2", + "score": 0.642857394048132, + "metadata": { + "text": "Incorporating epigenetics into genetic analysis can also enhance the predictive\nfunctional analysis of SNPs by highlighting regions of DNA that are accessible or\ninaccessible to protein binding by transcription factors and other regulatory pro-\nteins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG)\nmethylation sites. Rakyan et al. (2004) suggested that such an event might affect the\noverall methylation prole of a locus and, consequently, promoter activity and gene", + "title": "2007 - Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 1274, + "document_id": "4ea8e1a8-e113-5f02-ad78-880b9c51a101", + "extraction_id": "522373ca-3ce6-5fe5-b062-ee097f378397", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "0f00daa0-2bb4-5a3f-8d51-a1cd2957bef4", + "score": 0.6428248882293701, + "metadata": { + "text": "Incorporating epigenetics into genetic analysis can also enhance the predictive\nfunctional analysis of SNPs by highlighting regions of DNA that are accessible or\ninaccessible to protein binding by transcription factors and other regulatory pro-\nteins. SNPs may also lead to loss or gain of cytosineguanine dinucleotide (CpG)\nmethylation sites. Rakyan et al. (2004) suggested that such an event might affect the\noverall methylation prole of a locus and, consequently, promoter activity and gene", + "title": "003 -Barnes- Bioinformatics_for_Geneticists.pdf", + "version": "v0", + "chunk_order": 1274, + "document_id": "045edae8-468b-5725-be06-8cb4b8f6a92b", + "extraction_id": "c5468773-a09b-510d-bcdf-f685d7714106", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6355242133140564, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "03e25c07-34a0-5b1f-a5f9-ba9a0e2c0d91", + "score": 0.63309069374474, + "metadata": { + "text": "374. Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F ., Ren, B. et al.\n(2010), The NIH Roadmap Epigenomics Mapping Consortium, Nat.\nBiotechnol. V ol. 28, pp. 10451048.\n375. Portela, A. and Esteller, M. (2010), Epigenetic modications and\nhuman disease, Nat. Biotechnol. V ol. 28, pp. 10571068.\n376. Esteller, M. (2007), Cancer epigenomics: DNA methylomes and\nhistone-modication maps, Nat. Rev . Genet. V ol. 8, pp. 286298.\n377. Gilad, Y ., Rifkin, S.A. and Pritchard, J.K. (2008), Revealing the archi-", + "title": "2011 - Human genetics and genomics a decade after the release of the draft sequence of the human genome.pdf", + "version": "v0", + "chunk_order": 475, + "document_id": "6d475ac7-7094-5268-96ce-ae8f50f42cd2", + "extraction_id": "3960aec4-df25-57cd-9c60-5561f876a795", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "2e2d861b-4662-5ba5-80e6-ff0e4d9e80b4", + "score": 0.6326631903648376, + "metadata": { + "text": "likely to be part of regulatory elements. Our global map of histone marks will serve as an important resource forunderstanding the epigenetic basis of type 2 diabetes.\n[Supplemental material is available online at http:/ /www.genome.org. The ChIP-seq and gene expression data from this\nstudy have been submitted to ArrayExpress (http:/ /www.ebi.ac.uk/microarray-as/ae/) under accession nos. E-MTAB-189\nand E-MTAB-191, respectively.]\nGenetic and epigenetic factors determine cell fate and function.", + "title": "2010 - Genome-wide analysis of histone modifications.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "68bfce04-818d-5122-91c2-13a4a3ba0229", + "extraction_id": "6b5317f7-aa3f-5dfe-8e50-ef90619b6707", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + }, + { + "id": "47eea0dd-b899-5ed2-8b16-150b976f1f0a", + "score": 0.6322832703590393, + "metadata": { + "text": "these with other epigenetic mechanisms. This section will describe each method and provide the reader with technologies and recommendations to aide in the design and implementation of an epigenetic study . \n Histone Modifi cation Analysis \n Histone modi cation signals can be captured with chromatin immunoprecipita-\ntion (ChIP), which provides modi cation position approximation on the genome", + "title": "2011 - Molecular Genomic Research Designs.pdf", + "version": "v0", + "chunk_order": 151, + "document_id": "ced08e27-8655-59a4-bf63-0ba746f139b7", + "extraction_id": "d3fe612e-6d4a-5410-9e60-cd2ef8fff897", + "associatedQuery": "What are the implications of incorporating epigenomic data, such as histone modification maps, into the gene networks on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "16": { + "results": { + "completion": { + "id": "chatcmpl-AIGsp8i4dh5GAWf5RuA0Au0DHwVdV", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Recent findings on 3D genome organization have significantly enhanced our understanding of functional genomic networks in several ways:\n\n1. **Co-regulation through Spatial Organization**: The 3D chromatin structure is known to couple nuclear compartmentalization of chromatin domains with the control of gene activity, which contributes to cell-specific gene expression [1]. This spatial organization within the nucleus suggests that chromosomal and spatial co-localization may indicate co-regulation of genes, thereby influencing functional genomic networks.\n\n2. **Regulation by Distant Elements**: There is a growing awareness that the three-dimensional juxtaposition of DNA regions within nuclei allows genes to be regulated by elements located at a distance from the gene itself [4]. This understanding helps explain how disease-associated SNPs can fall within gene regulatory elements, thus affecting genomic networks and potentially leading to disease.\n\n3. **Integration with Functional Annotations**: Advances in identifying functional genomic elements through various annotations, such as those from the ENCODE project, have been complemented by insights into 3D genome organization. This integration helps in identifying potential regulatory variants and understanding their roles within genomic networks [2].\n\nThese findings collectively contribute to a more comprehensive understanding of how genes are regulated within the complex spatial architecture of the genome, thereby enhancing our knowledge of functional genomic networks.", + "role": "assistant" + } + } + ], + "created": 1728918167, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 259, + "prompt_tokens": 1395, + "total_tokens": 1654 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "996bbbd1-c605-5733-bf20-42367be2244c", + "score": 0.6783368991814304, + "metadata": { + "text": "genomes. Hence, chromosomal and spatial co-localization in\nthe nucleus may indicate co-regulation. It was previously shown\nthat 3D chromatin structure couples nuclear compartmentaliza-tion of chromatin domains with the control of gene activity ( Gue-\nlen et al., 2008 ) and thus contributes to cell-specic gene\nexpression ( Zullo et al., 2012 ). In this context, it is noteworthy\nthat cellular senescence is associated with modications of theglobal chromatin interaction network ( Chandra et al., 2015 ). To", + "title": "2015 - Insights into Sex Chromosome Evolution and Aging from the Genome of a Short-Lived Fish.pdf", + "version": "v0", + "chunk_order": 63, + "document_id": "def32424-2f9d-5c4b-9c03-be2d8bd53a24", + "extraction_id": "516bdde0-cafe-5eac-a627-32ef9e262e32", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "c7834fbb-eb9a-5f55-ac26-67af248e7179", + "score": 0.665553887751468, + "metadata": { + "text": "2 \n \nIntroduction \nRecent scientific advances have enabled the identification of functional genomic elements \nthrough a diverse set of functional annotations, including proteins functional scores (1, 2) , \nevolutionary conservation scores (3-5), and epigenetics scores from the Encyclopedia of DNA \nElements (ENCODE) (6). Other initiatives such as the R oadmap Epigenomics project (7) and \nFANTOM5 project (8, 9) also provide evidence for potential regulatory v ariants in the human", + "title": "2021 - Modern Statistical Methods for Genetics and Genomic Studies.pdf", + "version": "v0", + "chunk_order": 44, + "document_id": "6acebf19-b80c-5352-8201-99d5634fcc80", + "extraction_id": "9309edf2-5e2d-5567-ae78-f6681b866410", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "score": 0.6593453491124315, + "metadata": { + "text": "accuracy of predictive networks [40, 5153]. We have also recently demonstrated\nhow this class of network can be used to inform associations identied in GW Astudies [40].\n9 Summary\nThe signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "5543c99f-6542-55b8-b62c-e34a03b9c2fe", + "score": 0.6573882903225261, + "metadata": { + "text": "a growing awareness that the three-dimensional juxtaposition of DNAregions within nuclei means that genes can be regulated by regulatory\nelements that are located at some distance from the gene ( Fig. 5 )\n(Javierre et al., 2016 ;Kadauke and Blobel, 2009 ). As a result of this,\ndisease associated SNPs have been shown to fall in gene regulatory\nelements ( Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014;\nLee et al., 2014; Schierding et al., 2015 ).", + "title": "2018 - The genetic architecture of type 1 diabetes mellitus.pdf", + "version": "v0", + "chunk_order": 77, + "document_id": "341261db-b38a-5bd2-8d8d-fc04a0b3da30", + "extraction_id": "5b8b3673-7fd4-5989-9982-a6d5ea374c8d", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "33dc52df-73a5-514e-8edb-33ae5046b8af", + "score": 0.6539934690006413, + "metadata": { + "text": "network. Cell 9, 12121226 (2014).\n12. Hirschhorn, J.N. Genomewide association studiesilluminating biologic \npathways. N. Engl. J. Med. 0, 16991701 (2009).\n13. Cantor, R.M., Lange, K. & Sinsheimer, J.S. Prioritizing GWAS results: \na review of statistical methods and recommendations for their application. \nAm. J. Hum. Genet. 8, 622 (2010).\n14. Lee, I., Date, S.V., Adai, A.T. & Marcotte, E.M. A probabilistic functional \nnetwork of yeast genes. Science 0, 15551558 (2004).", + "title": "2015 - Selecting causal genes from genome-wide association studies via functionally coherent subnetworks.pdf", + "version": "v0", + "chunk_order": 72, + "document_id": "af43f4ac-7211-52f0-8f6b-e4bde73bbe4a", + "extraction_id": "46616368-74e6-5605-9e43-9789e8e1bea1", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "e26ebc1e-e05d-56fb-8718-604275994a84", + "score": 0.6511586904525803, + "metadata": { + "text": "Processing Large-Scale, High-Dimension Genetic 325\nanother. We anticipate these types of networks becoming increasingly important in\nthe human genetics space to gain a mechanistic understanding of how a given DNAperturbation induces changes in one or more genes that go on to affect networks\nthat cause disease. The integration of genotypic and expression and other data have\nrecently been shown, in a Bayesian network framework [76], to enhance the overall", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 129, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "fb3452e6-4584-5c3f-92de-9bc44d30b21c", + "score": 0.6485294136054384, + "metadata": { + "text": "regions correlated with functional noncoding elements, including enhancers, better than did\nregions identified solely on the basis of nucleotide sequence. These results support the idea that\nthe molecular shape of DNA is under selection and can identify evolutionary history.\nGenomic sequences that code for proteinsare relatively well understood but make\nup only ~2% of the human genome ( 1).\nMany functions are encoded in the remaining\n~98% noncoding portion of the genome, but little", + "title": "2009 - Rare Variants of IFIH1, a Gene Implicated in Antiviral Responses, Protect Against Type 1 Diabetes.pdf", + "version": "v0", + "chunk_order": 48, + "document_id": "7d9d5ce7-7bfe-5fe2-a325-fe97db015a10", + "extraction_id": "9893879f-6b73-5dc6-b274-f48ba8163644", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "a02b4589-65ec-50e1-9849-090971ddb2b0", + "score": 0.6415468071969278, + "metadata": { + "text": "gene, and the first f unctional anti -sense miRNA, Lastly, we have used \ncomparative genomics to infer regulatory networks based on individual \nconserved instances of regulatory motifs, which show functional enrichments \nsimilar and sometimes higher to genome -scale experimental met hods such as \nChIP -chip. As part of the ENCODE and modENCODE projects, we are now \nstudying dynamics of developmental and cell -differentiation networks in", + "title": "2013 - Pathways, Networks and Systems Medicine Conferences.pdf", + "version": "v0", + "chunk_order": 239, + "document_id": "b50a9732-7d01-5d4d-8f33-a9d43dbc7df3", + "extraction_id": "4cdc439f-bd23-5978-9f34-a34e1cb33cf4", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "38405f05-93df-579e-a8e4-c6b0c13e86a6", + "score": 0.639809391509928, + "metadata": { + "text": "References\n1. Cremer T, Cremer M, Dietzel S, Muller S, Solovei \nI, Fakan S. Chromosome territoriesa function-al nuclear landscape. Curr Opin Cell Biol 2006; 18:307-16.\n2. Misteli T. Beyond the sequence: cellular organization of genome function. Cell 2007; 128:787-800.\n3. Schneider R, Grosschedl R. Dynamics and interplay of nuclear architecture, genome organization and gene expression. Genes Dev 2007; 21:3027-43.", + "title": "2009 - Loss of A-type lamins and genomic instability.pdf", + "version": "v0", + "chunk_order": 52, + "document_id": "eab22335-5688-5e37-9f65-c4b58d6d95c8", + "extraction_id": "a6f16fce-1813-5d38-899f-6eb04c7d0007", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + }, + { + "id": "67365e1f-c588-56b5-aae0-44604958f8e1", + "score": 0.6392257428630755, + "metadata": { + "text": "enhancers in the control of cell identity and disease. Cell(2013) 155:934 47.\ndoi: 10.1016/j.cell.2013.09.053\n45. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear\norganization. Genes Dev (2012) 26:11 24. doi: 10.1101/gad.179804.111\n46. Schmitt AD, Hu M, Ren B. Genome-wide mapping and analysis of\nchromosome architecture. Nat Rev Mol Cell Biol (2016) 17:743 55.\ndoi: 10.1038/nrm.2016.104\n47. Javierre BM, Burren OS, Wilder SP, Kreuzhuber R, Hill SM, Sewitz S, et al.", + "title": "2020 - Functional Genomics in Pancreatic \u03b2 Cells Recent Advances in Gene Deletion and Genome Editing Technologies for Diabetes Research.pdf", + "version": "v0", + "chunk_order": 171, + "document_id": "51350055-d53c-5692-ab53-337b8a8bafd6", + "extraction_id": "f4955281-f174-562e-bc8a-170b701beffc", + "associatedQuery": "How do recent findings on 3D genome organization contribute to our understanding of functional genomic networks?" + } + } + ], + "kg_search_results": null + } + } + }, + "17": { + "results": { + "completion": { + "id": "chatcmpl-AIGsxUUcXG8q6ZckzX5v3uoIBTYQl", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Artificial intelligence (AI) has several potential applications in improving the annotation and interpretation of gene networks:\n\n1. **Inference of Gene Functions**: AI techniques, such as network inference algorithms, can help infer the putative functions of unknown genes by linking them to genes with known functions that exhibit similar expression patterns. This approach can also prioritize candidate variants and predict disease inheritance modes to some extent [3].\n\n2. **Network Inference Techniques**: AI-driven network inference techniques can be utilized to infer biological processes and the potential phenotypic impact of variants in genes of unknown function. These techniques can provide powerful approaches to inferring phenotypic information where direct links to phenotype do not exist [4].\n\n3. **Computational Approaches**: AI, particularly through computational approaches using statistical, machine learning, or soft-computing techniques, serves as a discovery tool for finding gene networks. These approaches can complement literature-based methods that gather published information on genes and their interrelationships [6].\n\n4. **Pattern Recognition and Predictive Modeling**: Deep learning models, a subset of AI, can be used for pattern recognition in gene sequences to identify potential future illnesses. There is also a demand for explainable AI models that are interpretable in decision-making, which can enhance the understanding and application of genomic data [8].\n\nThese applications demonstrate how AI can significantly enhance the annotation and interpretation of gene networks by providing insights into gene functions, biological processes, and potential phenotypic impacts.", + "role": "assistant" + } + } + ], + "created": 1728918175, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 295, + "prompt_tokens": 1164, + "total_tokens": 1459 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "df726361-271a-5dbb-b6d1-03dab5a63006", + "score": 0.6485917568206787, + "metadata": { + "text": "[111], and for generation of networks based on known gene \ninteractions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi-\ncation by regression (NIR) [119] has been used to identify", + "title": "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "extraction_id": "53c57cc4-4d43-505a-974c-442d06e144df", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "ee9014b2-ff70-50d1-a022-7a5792383700", + "score": 0.6399985933303006, + "metadata": { + "text": "Here we will focus on gene network inference algorithms\n(the inuence approach). A description of other methods\nbased on the physical approach and more details oncomputational aspects can be found in (Beer and Tavazoie,2004; Tadesse et al, 2004; Faith and Gardner, 2005; Prakash\nand Tompa, 2005; Ambesi and di Bernardo, 2006; Foat et al,\n2006). We will also briey describe two improper reverse-engineering tools (MNI and TSNI), whose main focus is not", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "1b4abf11-ed4b-5169-9ba9-8569bc5c10f7", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "6d8b4af6-6baf-58ff-9e1d-003862f53edd", + "score": 0.6375796984904918, + "metadata": { + "text": "NIA[360] may help to infer a putative function by linking unkn own\ngenes to genes known from previous studies to show a similar e xpres-\nsion pattern. We can also characterize unknown genes by thei r evolu-\ntionary, loss-of-function and network interaction proper ties to prioritize\ncandidate variants[184] and even predict disease inherita nce mode to a\ncertain degree[153].\nTaking this approach a step further, GeneNetwork[99] is con structed", + "title": "2009 - Detection and interpretation of expression quantitative trait loci (eQTL).pdf", + "version": "v0", + "chunk_order": 99, + "document_id": "ef974b09-4ea2-5382-85e5-c2169f440fda", + "extraction_id": "223e442e-898d-5aea-866a-5cdc0ac915e8", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "e8279254-6a66-5be6-b6ae-c11c20e242f9", + "score": 0.6360610344373575, + "metadata": { + "text": "network inference techniques can be utilized to infer biologicalprocess and the potential phenotypic impact of variants in genes of\nunknown function [71 78]. Thus, pathway and network based\nannotation approaches can be powerful approaches to inferring\nphenotypic information where direct links to phenotype do not exist.\n2.12. De novo association analyses involving multiple genomes\nIn the absence of prior information one might leverage to annotate", + "title": "2011 - Annotating individual human genomes.pdf", + "version": "v0", + "chunk_order": 78, + "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", + "extraction_id": "070421c2-5d23-58b3-9d85-53dd58e7abae", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "137c8fc7-7bc2-543f-a43e-7f819eaaaaa9", + "score": 0.6275167313198216, + "metadata": { + "text": "interaction may be difficult to quantify. Conversely the\ndirections and signs that accompany signalling or regula-\ntory pathways are generally known, but their incorpora-\ntion requires more work. It could nevertheless lead to\nimportant advances for the interpretation of microarray\ndata in cancer studies, for example.\nConclusion\nWe have presented a general framework to analyse gene\nexpression data when a gene network is known a priori .\nThe approach involves the attenuation of the high-fre-", + "title": "2007 - Classification of microarray data using gene networks.pdf", + "version": "v0", + "chunk_order": 83, + "document_id": "639e0456-a445-5e2e-adf5-8eaf987ce2d1", + "extraction_id": "df700ffb-556a-5331-afe6-71f7e77a1fb8", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "394f5f79-0592-52ff-bc83-ea55a95fd17e", + "score": 0.626812219619751, + "metadata": { + "text": "A number of techniques have been proposed for network inference.\nExisting techniques for nding gene networks can be broadly cate-gorized as (i) computational approaches, and (ii) literature-based\napproaches. The computational approach mainly uses statistical,\nmachine learning, or soft-computing techniques [ 14,15] as discov-\nery tools. On the other hand, a literature-based approach gathers\nrelevant published information on genes and their interrelation-", + "title": "2015 - Biological network inference from microarray data, current solutions, and assessments.pdf", + "version": "v0", + "chunk_order": 18, + "document_id": "f64cf13c-d989-50da-be0d-81e34a735a42", + "extraction_id": "c15261b7-54b9-534f-ac95-17c7a5543f31", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "b54b5584-344c-54e5-9442-a7deb099bc76", + "score": 0.6264445781707764, + "metadata": { + "text": "addition, data from linkage or association studies (e.g. GWAS), or from high -throughput genetic screening \nexperiments (e.g. CRISPR screening), or from animal gain -or-loss- of function studies, or from the gene -drug \ninteractions, can also be exploited to predict potential gene functions. Integration of GeneBridge with data from \nthese sources will further enhance the performance for gene function prediction, as is done in STRING [253], \nGeneMANIA [254] and Mitocarta [190, 255].", + "title": "2019 - Systems genetics approaches to probe gene function.pdf", + "version": "v0", + "chunk_order": 494, + "document_id": "1cd18d9c-0fd1-52e3-b0cf-c5e3ad0ff683", + "extraction_id": "f46459a1-592e-5d14-a6d1-f93211353db0", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "09f8c37f-b150-5f07-8275-bd040787f514", + "score": 0.6230763382636412, + "metadata": { + "text": "include the deep learning-driven pattern recognition models for analyzing the gene se-\nquences for identifying the possible future illness and developing mobile applications that\ncan generalize the information from the genomic data. However, there is great demand for\nexplainable Articial Intelligence models that are interpretable in decision-making.\nAuthor Contributions: The authors contributions are as follows, Conceptualization of the study,", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 234, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "29c89d19-3215-54dc-9723-85f96de02b65", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "3152b693-2396-5441-b6ff-6a80eac13ad0", + "score": 0.6213001834571263, + "metadata": { + "text": "Gene network inference algorithms are becoming accurate\nenough to be practically useful, at least when steady-state gene\nexpression data are available, but efforts must be directedin assessing algorithm performances. In a few years, gene\nnetwork inference will become as common as clustering for\nmicroarray data analysis. These algorithms will become moreTable IV Results of the application of network inference algorithms on the experiment data sets\nData sets ARACNE BANJO NIR Clustering Random", + "title": "2007 - How to infer gene networks from expression profiles.pdf", + "version": "v0", + "chunk_order": 103, + "document_id": "5067a047-b97d-522a-9a7e-5372e3bbd102", + "extraction_id": "d4d71d8c-ef2f-5ddb-b3f3-0f5ce8dc0a83", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + }, + { + "id": "c2dae4f8-2305-5d4a-a3f8-c0424d4b80b1", + "score": 0.6208438873291016, + "metadata": { + "text": "accuracy of predictive networks [40, 5153]. We have also recently demonstrated\nhow this class of network can be used to inform associations identied in GW Astudies [40].\n9 Summary\nThe signicant challenge we face in the post-genome era is deciphering the bio-logical function of individual genes, pathways, and networks that drive complexphenotypes like disease. The availability of low-cost, high-throughput technologies", + "title": "2009 - Processing Large-Scale, High-Dimension Genetic and Gene Expression Data.pdf", + "version": "v0", + "chunk_order": 130, + "document_id": "17264155-b665-59db-94cb-f4d67eac20fc", + "extraction_id": "3276b251-2e60-53e8-8fd1-07702f486a43", + "associatedQuery": "What are the potential applications of artificial intelligence in improving the annotation and interpretation of gene networks?" + } + } + ], + "kg_search_results": null + } + } + }, + "18": { + "results": { + "completion": { + "id": "chatcmpl-AIGt6tExGqoQTRXd4fPWOb4MUvYWu", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "The study of genetic pleiotropy has been advanced by data available on GeneNetwork.org through several key developments:\n\n1. **Multi-Omics Analysis and Data from Multiple Species**: GeneNetwork.org has incorporated data from 10 different species and supports multi-omics analysis, which allows researchers to explore genetic pleiotropy across a wide range of organisms and biological data types. This comprehensive approach provides a richer understanding of how genes can influence multiple traits or diseases [4].\n\n2. **Systems Genetics Approach**: The platform enables a systems genetics approach, which contrasts with the traditional candidate gene approach. Instead of focusing on single gene mutations, it explores the relationships between diverse genetic and molecular markers and their resulting phenotypes and diseases. This approach is particularly useful for studying pleiotropy, as it considers the complex interactions and shared pathways that can lead to multiple phenotypic effects from a single genetic locus [5].\n\n3. **Open Web Resource**: GeneNetwork.org is an open web resource, making it accessible to a wide range of researchers. This accessibility facilitates collaborative research and data sharing, which are crucial for advancing the study of pleiotropy by allowing researchers to build on each other's findings and methodologies [8].\n\nThese features collectively enhance the ability to study genetic pleiotropy by providing comprehensive data, advanced analytical tools, and a collaborative platform for researchers.", + "role": "assistant" + } + } + ], + "created": 1728918184, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_6b68a8204b", + "usage": { + "completion_tokens": 271, + "prompt_tokens": 1616, + "total_tokens": 1887 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "b3bb8c8a-a222-5b62-94c5-54910d338fa7", + "score": 0.6981419684717742, + "metadata": { + "text": "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y\n921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; \n922 2010. pp. 767773. doi:10.1534/genetics.110.122549\n923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and \n924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. \n925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "title": "2022 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "version": "v0", + "chunk_order": 230, + "document_id": "4198ec53-60f1-55d1-8759-b9ede1d098c0", + "extraction_id": "2557b3fa-5aed-53f2-a4ca-afbed6154346", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "da910108-9a4b-5482-a4cb-bdb969cf959c", + "score": 0.6981419684717742, + "metadata": { + "text": "920 Diabetologia. 2020;63: 977986. doi:10.1007/s00125-020-05101-y\n921 9. Stearns FW. One hundred years of pleiotropy: A retrospective. Genetics. Genetics; \n922 2010. pp. 767773. doi:10.1534/genetics.110.122549\n923 10. Geiler-Samerotte KA, Li S, Lazaris C, Taylor A, Ziv N, Ramjeawan C, et al. Extent and \n924 context dependence of pleiotropy revealed by high-throughput single-cell phenotyping. \n925 PLoS Biol. 2020;18. doi:10.1371/journal.pbio.3000836", + "title": "2021 - System genetics in the rat HXBBXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose.pdf", + "version": "v0", + "chunk_order": 230, + "document_id": "9ab8b190-fb4f-5bb0-8d04-1cd07a42192a", + "extraction_id": "6b791cd6-0d92-52fb-ac76-d3b0bb4ed535", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "29d6e248-c012-56f7-85c5-1ee104731db0", + "score": 0.6922983297946109, + "metadata": { + "text": "advances, the more examples become known which canbe explained only under the assumption of pleiotropy (Plate 1910, quoted from M cKusick 1976, pp. 301302).\nHis assertion of the extent and importance of pleiotropyhas been a central theme that has been challenged andstrengthened throughout the past 100 years as the way inwhich we study pleiotropy has changed.\nDEVELOPMENT OF PLEIOTROPIC RESEARCH\nOne of the rst experimental studies of the mecha-", + "title": "2010 - One Hundred Years of Pleiotropy A Retrospective.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "c0995711-1389-52b7-a7a9-c92e5709fe43", + "extraction_id": "9b6ebb70-4cc0-5f53-bbbb-815ea191f2fa", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "c63cfaee-749e-547b-9c0a-086266f10670", + "score": 0.6919262409210205, + "metadata": { + "text": "GeneNetwork have reinvigorated it, including the addition of data from 10 species, multi -omics \nanalysis, updated code, and new tools. The new GeneNetwork is now an exciting resource for \npredictive medicine and systems genetics, which is constantly being maintained and improved. \nHere, we give a brief overview of the process for carrying out some of the most common \nfunctions on GeneNetwork, as a gateway to deeper analyses , demonstrating how a small", + "title": "2020 - Gene network a completely updated tool for systems genetics analyses.pdf", + "version": "v0", + "chunk_order": 1, + "document_id": "128224f1-3545-52c3-93cb-77c3cf4ec70a", + "extraction_id": "858f630f-9443-5f13-ac40-8e16eadd9ba1", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "27bb3941-5a92-56a2-b67d-c5e64603c1a3", + "score": 0.6817529201507568, + "metadata": { + "text": "users can take advantage of a systems genetics approach (Rosen et al., 2003, 2007). While\nthe candidate gene approach asks which one gene mutation causes a particular disease, the\nsystems genetics approach explores which phenotypes and diseases result from diverse sets\nof genetic and molecular markers (Rosen et al., 2003, 2007). The majority of data sets in\nGeneNetwork are collected from GRPs consisting of hundreds of diverse, inbred strains of", + "title": "2012 - Using Genome-Wide Expression Profiling to Define Gene Networks Relevant to the Study of Complex Traits From RNA Integrity to Network Topology.pdf", + "version": "v0", + "chunk_order": 96, + "document_id": "1eb6f5b7-a3bc-5455-91f0-6f2eb37be861", + "extraction_id": "3e0c2a06-e6de-5888-a360-a2c483d9f744", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "6c9146cb-b00f-5f4c-8fc0-5a15a41405ec", + "score": 0.677685022354126, + "metadata": { + "text": "34. Pyeritz, R.E. (1989) Pleiotropy revisited: molecular explanations of a classic\nconcept. Am. J. Med. Genet. ,34, 124134.\n35. Gruneberg, H. (1938) An analysis of the pleiotropic effects of a lethal\nmutation in the rat. Proc. R. Soc. Lond. B. ,125, 123144.\n36. Wagner, G.P. and Zhang, J. (2011) The pleiotropic structure of the\ngenotypephenotype map: the evolvability of complex organisms. Nat.\nRev. Genet. ,12, 204213.\n37. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M. and Smoller, J.W.", + "title": "2014 - Mendelian randomization genetic anchors for causal inference.pdf", + "version": "v0", + "chunk_order": 104, + "document_id": "05a32734-5dff-5430-b383-72a3d2e03792", + "extraction_id": "3ac0a087-d982-5d06-b351-d2f1e635c5b0", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "89a8170c-a7b5-5236-8ef3-7d0e6918e584", + "score": 0.6744906500285488, + "metadata": { + "text": "21. Byars, S. G. et al. Genetic loci associated with coronary artery disease harbor\nevidence of selection and antagonistic pleiotropy. PLoS Genet. 13, e1006328\n(2017).\n22. Rodrguez, J. A. et al. Antagonistic pleiotropy and mutation accumulation\ninuence human senescence and disease. Nat. Ecol. Evol. 1, 0055 (2017).\n23. Institute for Health Metrics and Evaluation. Findings from the Global Burden\nof Disease Study 2017 (IHME, 2018).", + "title": "2020 - Multivariate genomic scan implicates novel loci.pdf", + "version": "v0", + "chunk_order": 133, + "document_id": "8529f0c6-a65b-53ed-9663-02d52dd82631", + "extraction_id": "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "12cdef3c-ff25-5349-8ef8-44f08065de4a", + "score": 0.6739315986633301, + "metadata": { + "text": "traits can be due to shared molecular mechanisms and processes (true gene pleiotropy)or covariance can be due to statistical error or to linkage of neighboring, but mechanis-tically independent gene variants. This latter effect is particularly serious and is described\nin more length by Gerlai\n4and in Wang5in the context of RI strains.\nGeneNetwork\nGeneNetwork (GN, www.genenetwork.org ) is an open web resource that enables", + "title": "2018 - The Use of Recombinant Inbred Strains in Systems Genetics and Functional Analyses in Behavioral Pharmacology.pdf", + "version": "v0", + "chunk_order": 15, + "document_id": "337b2462-f1ec-530a-84de-97b13a0b9446", + "extraction_id": "3b23d583-7046-5dce-a506-fab0c2752977", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "a62e58c3-d1a6-54e9-809f-d98488089738", + "score": 0.6705227690623552, + "metadata": { + "text": "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704\n19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018;\n137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333\n20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla-\ntions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/\n10.1038 /ng.3406 PMID: 26414676", + "title": "2022 - System Genetics in the Rat Family.pdf", + "version": "v0", + "chunk_order": 223, + "document_id": "426b5aeb-1550-5039-8f2a-bd83d17c8648", + "extraction_id": "38cbdb87-820c-587e-9511-69d0ba74457a", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + }, + { + "id": "6d34c5df-c9e5-5b22-b2af-2c1f191d984f", + "score": 0.6705227690623552, + "metadata": { + "text": "2019;20 .https://doi.or g/10.118 6/s13059 -019-1628-0 PMID: 30678704\n19. Chesmo reK,Bartlett J,Williams SM.Theubiquity ofpleiotropy inhuman disease. Hum Genet. 2018;\n137: 3944. https://doi.or g/10.100 7/s00439 -017-1854 -zPMID: 29164333\n20. Bulik-Sulli vanB,Finucane HK,Anttila V,Gusev A,DayFR,LohPR,etal.Anatlas ofgenetic correla-\ntions across human diseases andtraits. NatGenet 2015 4711. 2015; 47:12361241. https://doi.or g/\n10.1038 /ng.3406 PMID: 26414676", + "title": "2022 -Senko- Hippocampal neurogenesis serum glucose.pdf", + "version": "v0", + "chunk_order": 223, + "document_id": "bac2ab98-4317-59ed-99ef-deda8c22786d", + "extraction_id": "2e135c0b-af2c-54fa-8661-aa4a3e31c0da", + "associatedQuery": "How has the study of genetic pleiotropy been advanced by data available on GeneNetwork.org?" + } + } + ], + "kg_search_results": null + } + } + }, + "19": { + "results": { + "completion": { + "id": "chatcmpl-AIGtEMdN8awavmFIcxxBrdyWkpsf8", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "GeneNetwork.org studies have identified novel genetic pathways related to aging and lifespan through various approaches. One notable method is the use of network identification by regression (NIR), which has been applied to identify novel pathways in the context of aging and lifespan [2]. Additionally, network-based approaches have revealed six pathways and six key genes that might play pivotal roles in regulating longevity, providing new insights into the mechanisms of longevity [6]. These findings highlight the potential of network-based methods to uncover novel genetic pathways associated with aging and lifespan.", + "role": "assistant" + } + } + ], + "created": 1728918192, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 104, + "prompt_tokens": 1300, + "total_tokens": 1404 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "496d27de-6dd0-5f6a-bedb-64d4c252981d", + "score": 0.7664211392402703, + "metadata": { + "text": "the different pathways linked with aging and even study genenetworks. In such works, GenAge is an adequate resource asit provides a framework for the functional genomics of aging.For example, Xue \net \n \nal \n. (2007) used GenAge to construct a modular\nnetwork of aging and obtain insights into aging, including thefact that genes connecting different modules are more likely toaffect longevity and/or aging, an hypothesis the authors validatedexperimentally in worms (Xue \net \n \nal", + "title": "2009 - The Human Ageing Genomic Resources online.pdf", + "version": "v0", + "chunk_order": 37, + "document_id": "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "extraction_id": "aecbe8a8-aeed-5cfa-b0f3-be29f19d849d", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "df726361-271a-5dbb-b6d1-03dab5a63006", + "score": 0.7302705210286081, + "metadata": { + "text": "[111], and for generation of networks based on known gene \ninteractions such as GeneMania [112] and Cytoscape [113], as well as for identifying cross-species orthology relation-ships [114], network-based thinking has been increasingly applied to the study of aging and lifespan [115-118]. Re-cently, the novel computational method of network identifi-\ncation by regression (NIR) [119] has been used to identify", + "title": "2012 - Genome-Scale Studies of Aging Challenges and Opportunities.pdf", + "version": "v0", + "chunk_order": 41, + "document_id": "b77aace0-fa36-5fd4-8e2a-c8932198acd1", + "extraction_id": "53c57cc4-4d43-505a-974c-442d06e144df", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "300065ff-2ddb-532e-ab5d-a9b0903c8d21", + "score": 0.7285561723377615, + "metadata": { + "text": "network analysis is a useful approach toward identifying genetic \ndeterminants of longevity . PLoS One , 2008 , 3(11), e3802. \n[38] Bell, R.; Hubbard, A.; Che ttier, R.; Chen, D.; Miller, J.P.; Kapahi, \nP.; Tarnopolsky, M.; Sahasrabuhde, S.; Melov, S.; Hughes, R.E. A \nhuman protein interaction network shows conservation of aging \nprocesses between human and invertebrate species . PLoS Genet , \n2009 , 5(3), e1000414. \n[39] Budovsky, A.; Abramovich, A.; Cohen, R.; Chalifa-Caspi, V.;", + "title": "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "version": "v0", + "chunk_order": 84, + "document_id": "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "extraction_id": "e26cef53-9a67-508e-8a29-2f40a6aa45b0", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "4d6876c5-9226-587c-8d3e-d4957ee42dba", + "score": 0.7223046587524035, + "metadata": { + "text": "genes (http://genomics.senescence.info/genes/), more than700 genes have been identified that regulate lifespan inmodel organisms (de Magalha es et al., 2009a). Many ofthese genes and their associated pathwayssuch as theinsulin/IGF1/GH pathwayhave been shown to affect lon-gevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolu-tionarily conserved and may have potential therapeuticapplications (Baur et al., 2006). For example, evidencesuggests the use of", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 65, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "a01ca925-4ccf-5863-a162-7bd4c754fe89", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "15f6d690-61b1-5de3-ac40-10e46777afa8", + "score": 0.7212184403874349, + "metadata": { + "text": "30. Vartiainen, S., Aarnio, V., Lakso, M. & Wong, G. Increased lifespan in\ntransgenic Caenorhabditis elegans overexpressing human -synuclein. Exp.\nGerontol. 41, 871 876 (2006).\n31. Lpez-Otn, C. et al. The hallmarks of aging. Cell153, 1194 1217 (2013).\n32. Kenyon, C. J. The genetics of ageing. Nature 464, 504 512 (2010).\n33. Liberzon, A. et al. The molecular signatures database hallmark gene set\ncollection. Cell Syst. 1, 417 425 (2015).", + "title": "2020 - Multivariate genomic scan implicates novel loci.pdf", + "version": "v0", + "chunk_order": 136, + "document_id": "8529f0c6-a65b-53ed-9663-02d52dd82631", + "extraction_id": "a053b8da-7ec4-5c4f-b4cc-4005e7792d1a", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "9f662099-6f46-5af7-a6c1-4d0945b9a931", + "score": 0.71908362394073, + "metadata": { + "text": "1118 compared to young ones. Overall, our results revealed that six pathways and six key genes might play pivotal roles \nin regulating longevity, and three interacting genes might be implicated in longevity. The results will not only provide new insight into the mechanisms of longevity, but also provide novel ideas for network-based approaches for longevity-related research.\nKeywords Drosophila melanogaster Longevity Gene Pathway Network\nIntroduction", + "title": "2019 - Bioinformatic prediction of critical genes and pathways.pdf", + "version": "v0", + "chunk_order": 4, + "document_id": "01201944-11f2-52d9-ac3e-7af685d4a4c4", + "extraction_id": "4109e561-4721-5f4e-b4d5-4353f8d1741d", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "c96b67f8-ad31-50fd-b053-07b127938ef2", + "score": 0.708771978763106, + "metadata": { + "text": "During the past century, remarkable progress has been \nmade in unveiling the mechanisms of aging. Genetic and molecular pathways that regulate healthspan and lifespan have been identified in various model organisms, provid-ing a rich knowledge base (Longo etal. 2015; Lopez-Otin etal. 2013, 2016; Singh etal. 2019). However, the focus on", + "title": "2020 - Mitonuclear genomics and aging.pdf", + "version": "v0", + "chunk_order": 10, + "document_id": "e05fdc09-c8d8-5134-a1fd-bf07a1564981", + "extraction_id": "e6fb876b-e91c-505a-aa16-7b428ec61f10", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "score": 0.7055839826612516, + "metadata": { + "text": "In addition to aging- and CR-related genes, another\nsource of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations.\nMany longevity-associated genes are related to spe-", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 119, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "d59d7882-333d-5576-86ab-3cfa6354b946", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "a05a46db-5443-566c-9494-212f86ee2eb3", + "score": 0.7047041234550415, + "metadata": { + "text": "been associated with human longevity in genetic asso-ciation studies. The parallel emergence of networkapproaches offers prospects to develop multitargetdrugs and combinatorial therapies. Understandinghow the environment modulates aging-related genesmay lead to human applications and disease therapiesthrough diet, lifestyle, or pharmacological interven-tions. Unlocking the capacity to manipulate humanaging would result in unprecedented health benefits.\nI. Introduction", + "title": "2012 - Genome-Environment Interactions That Modulate.pdf", + "version": "v0", + "chunk_order": 7, + "document_id": "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "extraction_id": "df213743-7428-59be-ba19-2563f8ce5c70", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + }, + { + "id": "016ee489-a313-5648-803d-db50217ae084", + "score": 0.7021947858101385, + "metadata": { + "text": "Network approaches are instrumental in discerning \nglobal properties of aging/lifespan regulators, making com-\nputational predictions and inferring the modularity and rela-tionships of various aging regulators. However, they should be applied with great caution as to avoid bias introduced by \nthe literature, the lack of spatial and temporal information, or \nthe limited coverage of the network [44]. \n4. EPIGENETIC REGULATION OF AGING \n In addition to gene expression changes, the states of epi-", + "title": "2012 - Systems Biology in Aging Linking the Old and the Young.pdf", + "version": "v0", + "chunk_order": 47, + "document_id": "cf7a8c59-4b4d-5e04-94b6-dd97edcb47a8", + "extraction_id": "a74345ec-ceee-5290-990b-ea338e735937", + "associatedQuery": "What novel genetic pathways have been identified in GeneNetwork.org studies related to aging and lifespan?" + } + } + ], + "kg_search_results": null + } + } + }, + "20": { + "results": { + "completion": { + "id": "chatcmpl-AIGtIvgudl04cUWtfjaShHQ8PZDZI", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases by providing a quantitative metric of an individual's inherited risk based on the cumulative impact of many common polymorphisms [7]. These scores aggregate the genetic risk of individual alleles across the genome, which can significantly improve the prediction of future disease occurrence and aid in early diagnosis, intervention, and prevention strategies [5]. PRS can complement established clinical risk factors and intervention paradigms, thereby enhancing early diagnosis and prevention efforts for diseases such as type 2 diabetes (T2D) [6]. Additionally, PRS have emerged as promising biomarkers for predicting disease risk in various areas, including cardiovascular disorders and oncology [8]. By utilizing data from large consortia and genome-wide genotypes, the predictive value of these scores has substantially improved, allowing for a more comprehensive assessment of genetic risk [3].", + "role": "assistant" + } + } + ], + "created": 1728918196, + "model": "gpt-4o-2024-08-06", + "object": "chat.completion", + "system_fingerprint": "fp_a20a4ee344", + "usage": { + "completion_tokens": 180, + "prompt_tokens": 1247, + "total_tokens": 1427 + } + }, + "search_results": { + "vector_search_results": [ + { + "id": "a374d88e-458e-5252-8b3a-5ca162fa6982", + "score": 0.7924221877800128, + "metadata": { + "text": "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk\nanalysis using the sum of the weight of each risk-associated locus of genomic sequence\nobtained from the corresponding evidence. These weights are assessed from the regression\ncoefcient associated with each locus. These combined genetics features and correlation\nmatrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "title": "2022 - Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.pdf", + "version": "v0", + "chunk_order": 17, + "document_id": "be0e50e0-3de8-53c5-8126-a0b618647f80", + "extraction_id": "3c30b33b-8928-5cee-9c37-c70642fff75c", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "bcce1092-32ea-5f65-bc10-4dc1a2dac53a", + "score": 0.7441587753106677, + "metadata": { + "text": "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. \nA polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the \ngenetic variance underlying a trait, the overall effect size", + "title": "2021 - Genetics and genomics of arrhythmic.pdf", + "version": "v0", + "chunk_order": 81, + "document_id": "462ed035-e4fb-5847-a92d-927f05a2b58b", + "extraction_id": "ada410d0-6b91-5959-b834-cc3389e29c5f", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "f36bf430-26bd-5031-a392-14f3c43367ab", + "score": 0.7282247109279222, + "metadata": { + "text": "of genome-wide genotypes and publicly available data from large consortia, GRSs with a larger number of vari-\nants are being used, and the predictive value of these genome-wide polygenic risk scores (PRSs) has substantially improved\n50,51.\nPRSs can be derived using different approaches, however, these require both summary statistics from an exter -", + "title": "2020 - Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.pdf", + "version": "v0", + "chunk_order": 11, + "document_id": "494779f3-1437-5b50-a9b2-3f616a048719", + "extraction_id": "8292e291-87bb-5f04-8e40-fb2228da3927", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "4190e1d8-ae9e-5c42-8842-aa0a60a2bb2c", + "score": 0.7133836150169373, + "metadata": { + "text": "use for estimation of polygenic risk scores (PRS) has grownin recent years. PRS screening may be used to determine therisk of common complex diseases for individuals and theiroffspring, and although it is not widely clinically availablenow, there is an ongoing interest in increasing its utility. Useof GWAS data from European populations for PRS esti-mation would subsequently impose a bias in favor of in-\ndividuals with similar ancestry, whereas limited bene ti s", + "title": "2023 - Clinical, technical, and environmental biases.pdf", + "version": "v0", + "chunk_order": 71, + "document_id": "6a81e435-bd17-558d-850a-44ee3dbab5bd", + "extraction_id": "50731787-cf17-5284-b3f4-2c551cb41c90", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "1677b3ee-7d95-5e10-a6dd-d80b4bb87b29", + "score": 0.7085323544899335, + "metadata": { + "text": "(GWAS) in diverse populations have identified hundreds \nof genetic loci associated with T2D [79]. Polygenic risk \nscores (PRS), which aggregate the genetic risk of individ -\nual alleles across the genome, are thus promising to pre -\ndict future T2D occurrence and improve early diagnosis, \nintervention, and prevention of T2D [1015]. However, \nto date, T2D PRS were most widely developed and vali -\ndated in individuals of European descent. Given that the \npredictive performance of PRS often attenuates in non-", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 12, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "17c49e58-c89a-5495-b17f-adcade90a4c6", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "2c09a46a-20d0-54b4-abcb-608fef7c7f80", + "score": 0.7070292830467224, + "metadata": { + "text": "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter \nvention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most \nwidely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non\nEuropean populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "title": "2022 - Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.pdf", + "version": "v0", + "chunk_order": 3, + "document_id": "4ece243f-acda-569d-b75d-37539260dcb3", + "extraction_id": "f6f0c89d-5c35-5889-8619-a3914e5d2c7e", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "459f7eed-490a-5586-9d2a-20f721daa6bc", + "score": 0.701218843460083, + "metadata": { + "text": "Letters NATure GeNeTicsMethods\nPolygenic score derivation. Polygenic scores provide a quantitative metric of \nan individuals inherited risk based on the cumulative impact of many common polymorphisms. Weights are generally assigned to each genetic variant according to the strength of their association with disease risk (effect estimate). Individuals are scored based on how many risk alleles they have for each variant (for example, zero, one, or two copies) included in the polygenic score.", + "title": "2018 - Genome-wide polygenic scores for common diseases.pdf", + "version": "v0", + "chunk_order": 73, + "document_id": "a8cefcf1-7edf-52cc-8aeb-b4d353acaef5", + "extraction_id": "0a80e61e-648a-5122-9b17-8177bc734674", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "98da512f-fee2-501b-b093-9ee7ab22c5f9", + "score": 0.6958812475204468, + "metadata": { + "text": "(Fig. 1B ). Polygenic risk scores (PRS) have emerged as promising\nbiomarkers for the prediction of disease risk, not only in the area of\ncardiovascular disorders, but also oncology (21). These risk scores also\nhave become increasingly available for a multitude of phenotypes and\nare systematically curated in a free online database (22).\nIt has been shown that certain preexisting autoimmune diseases as\nwell as the occurrence of imAE upon treatment are associated with", + "title": "2022 - Coming of Age Human Genomics.pdf", + "version": "v0", + "chunk_order": 28, + "document_id": "45506895-eef1-57f4-8ca1-79fe23a2493f", + "extraction_id": "ca2e1560-db8f-5c3f-b7bf-dd1beaa94655", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "d27fbbe8-aec0-510f-ab9d-1a0d4f0a1678", + "score": 0.6924620866775513, + "metadata": { + "text": "eases identify individuals with risk equivalent to monogenicmutations. Nat. Genet. ,50, 12191224.\n13. Euesden, J., Lewis, C.M. and OReilly, P.F. (2015) PRSice: poly-\ngenic risk score software. Bioinformatics ,31, 14661468.\n14. Belsky, D.W., Moffitt, T.E., Sugden, K., Williams, B., Houts, R.,\nMcCarthy, J. and Caspi, A. (2013) Development and evalu-\nation of a genetic risk score for obesity.\nBiodemography Soc.\nBiol.,59, 85100.\n15. De Jager, P.L., Chibnik, L.B., Cui, J., Reischl, J., Lehr, S., Simon,", + "title": "2020 - Genome-wide assessment of genetic risk for systemic.pdf", + "version": "v0", + "chunk_order": 102, + "document_id": "af34f0df-a726-5cc4-844f-a5d67273d9a0", + "extraction_id": "9b1cee76-2c59-50d6-a37c-8c593336fe33", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + }, + { + "id": "b3e446bb-e438-5d66-a34c-8e1de0ebb639", + "score": 0.6883896589279175, + "metadata": { + "text": "in tissue-specic regions or use gene co-expression information\nmay provide a more comprehensive view of a specic gene or\na gene networks role in modulating an individuals response\nto environmental variations, compared to that provided by\nthe single candidate gene approach (Gamazon et al., 2015;\nBarth et al., 2020). Expression-based polygenic risk scores\n(ePRS) oer one such approach to understand the underlying\ngenetic background linked to behavioral outcomes (Hari Dass", + "title": "2021 -Potter-Dickey- Genetic Susceptibility.pdf", + "version": "v0", + "chunk_order": 32, + "document_id": "cb119609-daa3-56af-97ff-b809cc39c210", + "extraction_id": "567a2f7e-0ff9-5229-bfeb-066b6e6f50f6", + "associatedQuery": "How do polygenic risk scores (PRS) developed using GeneNetwork.org data enhance the prediction and prevention of complex diseases?" + } + } + ], + "kg_search_results": null + } + } + } +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_1.json b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_1.json new file mode 100644 index 0000000..30a5490 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_1.json @@ -0,0 +1,16 @@ +{ + "question": "What is the significance of the length of telomeres?", + "answer": "The length of telomeres is significant for several reasons: 1. **Protection of Chromosomes**: Telomeres are repetitive nucleotide sequences at the ends of eukaryotic chromosomes that protect them from attrition and damage during replication [1], [2], [6]. They prevent the loss of important genetic information and maintain genomic stability [9]. 2. **Aging and Cellular Senescence**: Telomere length is progressively shortened with each round of genomic replication unless restored by the enzyme telomerase [2], [5]. Short telomeres are considered a sign of advanced age and are associated with cellular senescence, where cells lose their ability to divide and function properly [4], [7], [8]. 3. **Disease Association**: Shorter telomeres have been linked to various diseases. For instance, individuals with Type 2 Diabetes (T2D) have been shown to have shorter leukocyte telomeres, which may be associated with disease progression [1]. Similarly, telomere length in smooth muscle cells has been linked to disease severity in atherosclerosis, and shorter leukocyte telomeres are associated with a higher risk of developing cardiovascular disease [3], [8]. 4. **Genomic Integrity**: Telomeres play a crucial role in maintaining genomic integrity and function. Shortening and erosion of telomeres can lead to chromosome end-to-end fusions and genomic instability, which can cause various cellular dysfunctions and diseases [6], [7]. 5. **Biomarker Potential**: Due to their association with aging and disease, telomere length is being studied as a potential biomarker for various health conditions, including cardiovascular diseases and diabetes [3]. In summary, the length of telomeres is crucial for protecting chromosomes, regulating cellular aging, and maintaining genomic stability. It also has significant implications for understanding and potentially diagnosing age-related diseases and conditions.", + "contexts": [ + "repetitive nucleotide sequences at the end of each eukaryotic chromosome, which protects them from attrition and damage. Although the relationship between leukocyte telomere length (LTL) and diabetes is still questioned 8, different studies have shown that T2D individuals have shorter leukocyte telomeres than non-T2D individuals9, 10 that may be associated with disease progression11. Indeed, the decreased antioxidant capacity described in patients", + "Telomeres are arrays of linked nucleotide hexamer repeats that are found at the ends of chromosomes in a vast clade of organisms [14]. While the sequence of these telomeric repeats can vary between organisms, their biological function is highly conserved, which is to limit damage inflicted on genes during the replica- tion of chromosomes. Telomere length is progressively shortened with each round of genomic replication, unless it is restored through the action of a ribonucleo-", + "telomere length,a phenomenon attributed to higher levels of oxidativestress at the cellular level (70). More recent studies havelinked telomere length in smooth muscle cells with senes-cence and disease severity in patients with atherosclero-sis (141, 150). Leukocyte telomere length was also short ina cohort of similar patients and associated with a higherrisk of developing occult cardiovascular disease (71).More data are needed to understand and validate the useof leukocyte telomere length as a biomarker", + "age telomere length through accumulation of several short telo- meres (Londono-Vallejo et al., 2001; Martens et al., 2000) is responsible for senescence or whether a speci c chromosome arm limits the replication potential of human cells (Hemann et al., 2001). Individual chromosome arms were shown to have large variations in their length (Lansdorp et al., 1996; Benn, 1997; Londono-Vallejo et al., 2001), and chromosome 17p seemed to be equipped with especially short telomeres in hu-", + "Telomeres are specialized structures that protect the ends of linear chromosomes. They shorten during aging due to the unidirectional activity of DNA polymerase, which leaves a section of DNA unrepli-cated on the lagging strand. Telomeres also are subject to shortening by genotoxic stress, such as oxidative damage (33). Among many eukaryotes, the enzyme telomerase maintains telomere length; but telomerase activity varies over the lifespan and between cell types, tissues, and species (34). In most human", + "TTAGGG sequence that cap the ends of chromosomes, protect-ing them from degradation and fusion. The length of telomererepeats is primarily maintained by active telomerase, which iscomposed of Telomerase RNA (TR) and a catalytic subunitTelomerase Reverse Transcriptase (TERT) (Blackburn, 2001).Extensive evidence has shown that telomere shortening anderosion lead to chromosome end-to-end fusions and genomicinstability (Blasco et al ., 1997; Hande et al ., 1999), causing", + "a pivotal role in maintenance of genomic integrity and func-tion (Moyzis et al., 1988; McElligott and Wellinger, 1997; van Steensel and de Lange, 1997). It is generally accepted that telomeres shorten during DNA replication both in vitro and in vivo. In individuals, short telomeres are considered to be a sign of advanced age. Cawthon and coworkers (2003) showed that telomere shortening in hu-", + "Each cell division shortens telomeric DNA until, at a critical length, the cells lose capping function at thechromosomal ends, activating DNA damage check-points, cell senescence, and eventually apoptosis.Telomere shortening has particular relevance in thesetting of CVD. Leukocyte telomere length (LTL) associates signi cantly with vascular cell senescence,", + "nization may directly affect telomere attrition, resulting in accelerated replicative senescence and progeroid phenotypes [180]. Telomeres are regions constituted by tandem repeats of non-coding DNA sequences 5-(TTAGGG)n-3 and a protein complex called shelterin, bound to them. This structure ensures the stability of the genome and protects the chromosomes from a wrong action of the DNA repair machinery [184] by allowing the formation of a chromatin loop called T-Loop [185].", + "telomeres, the repetitive sequence at the end of linear chromosomes, has garnered much attention for its relation to aging. Telomere repeats serve as an internal clock for cycling cells because each round of replication results in the loss of telomeric DNA in the absence of active telomerase (reviewed in [66]). Eventually, this loss over cellular generations culminates in telomere crisis and a permanent state of" + ] +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_2.json b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_2.json new file mode 100644 index 0000000..441fb52 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_2.json @@ -0,0 +1,16 @@ +{ + "contexts": [ + "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", + "Long-lived rodents reveal signatures of positive selection in genes associated with lifespan. PLoS Genet. 14:e1007272. doi: 10.1371/journal.pgen.100 7272 Schchter, F., Faure-Delanef, L., Gunot, F., Rouger, H., Froguel, P., Lesueur-Ginot, L., et al. (1994). Genetic associations with human longevity at the APOE and ACE loci. Nat. Genet. 6, 2932. doi: 10.1038/ng0194-29 Schinaman, J. M., Rana, A., Ja, W. W., Clark, R. I., and Walker, D. W. (2019).", + "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", + "of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin- like growth factor I levels and increased life span. Endocrinology 144:37993810. DOI: https://doi.org/10.1210/en. 2003-0374, PMID: 12933651 de Haan G, Williams RW. 2005. A genetic and genomic approach to identify longevity genes in mice. Mechanisms of Ageing and Development 126:133138. DOI: https://doi.org/10.1016/j.mad.2004.09.012, PMID: 15610771", + "Mulvey L, Sinclair A, Selman C (2014) Lifespan modulation in mice and the confounding effects of genetic background. J Genet Genomics 41:497503. doi: 10.1016/j.jgg.2014.06.002 OConnor TP, Lee A, Jarvis JUM, Buffenstein R (2002) Prolonged longevity in naked mole-rats: age-related changes in metabolism, body composition and gastrointestinal function. Comp Biochem Physiol A 133:835842. doi: 10.1016/S1095-6433(02)00198-8 Opazo JC, Palma RE, Melo F, Lessa EP (2005) Adaptive evolution of", + "/ mice by Lmna heterozy- gosity ameliorates progeroid phenotypes and extends lifespan [143, 174, 175].", + "References 1. Hook Met al.Genetic cartography of longevity in humans and mice: Current landscape and horizons. Biochim. Biophys. Acta1864, 27182732 (2018). 2. Kuningas Met al.Genes encoding longevity: from model organisms to humans. Aging Cell7, 270 280 (2008). [PubMed: 18208581] 3. de Magalhes JP, Wuttke D, Wood SH, Plank M & V ora C Genome-environment interactions that modulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (2012). [PubMed: 22090473]", + "\"Murine chromosomal regions correlated with longevity.\" Genetics 118: 693-704.", + "expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans . Aging Cell, 2009 , 8(4), 370-9. [73] Wang, H.D.; Kazemi-Esfarjani, P.; Benzer, S. Multiple-stress analysis for isolation of Drosophila longevity genes . Proc Natl Acad Sci U S A , 2004 , 101(34), 12610-5. [74] Lin, Y.J.; Seroude, L.; Benzer, S. Extended life-span and stress resistance in the Drosophila mutant methuselah . Science , 1998 , 282(5390), 943-6.", + "sion analysis of mouse liver genes: effect of age and of thelongevity mutant Prop1df. J Gerontol A Biol Sci Med Sci 56: B72B80, 2001. 12.Fabrizio P, Pozza F, Pletcher SD, Gendron CM, and Longo VD. Regulation of longevity and stress resistance by Sch9 in Yeast. Science 292: 288 290, 2001. 13.Haase D, Lehmann MH, Korner MM, Korfer R, Sigusch HH, and Figulla HR. Identi cation and validation of selective" + ], + "answer": "The mouse genes associated with longevity include: 1. **Mouse loci associated with life span**: These loci exhibit sex-specific and epistatic effects [1]. 2. **Mouse growth hormone receptor**: Alterations in this gene result in increased lifespan [3], [4]. 3. **Lmna heterozygosity**: This genetic modification ameliorates progeroid phenotypes and extends lifespan [6]. These references provide evidence of specific genes and loci in mice that have been linked to increased longevity.", + "question": "Which mouse genes have been associated with longevity?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_3.json b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_3.json new file mode 100644 index 0000000..1f78fa9 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_3.json @@ -0,0 +1,65 @@ +{ + "titles": [ + "2009 - The Human Ageing Genomic Resources online.pdf", + "2011 - How pleiotropic genetics of the musculoskeletal system.pdf", + "2021 - Genome-wide association studies identify.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2001 - A genome-wide scan for linkage to human.pdf", + "2007 - Temporal and spatial transcriptional profiles.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2020 - Role of Helicobacter pylori infection in the manifestation of old age-related diseases.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf" + ], + "extraction_id": [ + "7ada6b55-99c2-5e20-bf96-d153f927256c", + "b5b3c74a-90de-5b1e-9580-8031b10be7ec", + "cd7730b6-22dc-5256-9310-79fc348b3226", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "4ea8424f-1cd8-569c-a1df-3f0f54206e70", + "17246c43-2e44-579b-867d-3dc7150ceedd", + "2e42619b-d0b2-5d33-aab8-6f04002ee807", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "e6916baa-9f9d-57aa-b44d-95fb614610a8", + "a01ca925-4ccf-5863-a162-7bd4c754fe89" + ], + "document_id": [ + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "ed31486c-a651-5894-bd96-21fbd78f2646", + "60c2e869-1fee-53ea-b332-26d9c2abc747", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "62b635c3-040e-512a-b016-6ef295308a1e", + "1431984a-82d9-51d4-a23c-5f76a02ab554", + "38f27ec7-08bf-5397-b2b8-bde95e0dc3f8", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e99c68d2-4f35-5591-8072-cfdb31966e68", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec" + ], + "id": [ + "chatcmpl-ABLwW9HA9VG184zgOmenEBU2eMIMc", + "3117c019-7311-53ae-8ab1-927ca822c709", + "0ad664d2-6756-5123-b192-8a56cf6887a5", + "9fa00091-9661-57bd-91c7-f0bf436805a7", + "786d2756-4c4d-5ac0-8d3d-63f914d51664", + "a0672677-71ad-5603-8427-a0648eec407f", + "e0cce1c5-8709-5218-99b6-48a6ba242931", + "bf2cd208-273f-5848-b243-df8b95ea7833", + "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", + "50581d4f-396c-5d12-aec6-5f42e2ab88ef", + "3c369292-4b9c-5156-a80f-4b3301026f30" + ], + "contexts": [ + "It is undisputed that genetic factors influence aging. In a remarkable", + "perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of acomplex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify newcandidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general agingof the organism. This perspective focuses on the", + "population dynamics on the genetic architecture of human longevity. Aging (Albany NY). 2018;10(8):1947 63. 68. Bellenguez C, Kucukali F, Jansen I, Andrade V, Morenau-Grau S, Amin N, et al. Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer disease and highlights potential translational opportunities. medRxiv. 2020. 69. Kojima T, Shimazui T, Hinotsu S, Joraku A, Oikawa T, Kawai K, et al. Decreased expression of CXXC4 promotes a", + "In addition to aging- and CR-related genes, another source of candidate genes and pathways for drug designare human longevity-associated genes (Barzilai andShuldiner, 2001; Browner et al., 2004; Kenyon, 2010).Dozens of genes have now been associated with humanlongevity (de Magalha es et al., 2009a), although only ahandful of genes have been shown to have consistenteffects across populations. Many longevity-associated genes are related to spe-", + "Clinical Genetics and Genomics of Aging", + "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", + "The multifactorial and temporal features of aging can beanalyzed efficiently by genome-wide transcriptional profiling,which has been conducted in various model organisms and hu-mans (Melov and Hubbard 2004). Aging is associated with alter-ations in transcript levels of many genes, including those in-volved in evolutionarily conserved mitochondrial and protea-somal functions (McCarroll et al. 2004), some of which havebeen shown to be directly involved in regulating lifespan in C.", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "Mol Genet Genomic Med. 2020;00:e1157. | 1 of 11 https://doi.org/10.1002/mgg3.1157 wileyonlinelibrary.com/journal/mgg3 1 | INTRODUCTION Aging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which", + "al., 2009; Stanfel et al., 2009). Many of these genesmodulate the response to environmental signals, such asfood availability, and act in signaling pathways that ifunderstood can be targeted (Fig. 1). The genetic regula-tion of aging is therefore an emerging field with multipleapplications in the human nutrition, cosmetic, and phar-maceutical industries. AGING GENES AS TARGETS FOR DRUG DISCOVERY 91" + ], + "answer": "Genetic factors associated with aging include: 1. **Genome-wide association studies (GWAS)**: These studies offer an unbiased approach to identify new candidate genes for human diseases, including those related to aging. Convergent results from multiple aging-related traits can point out the genes responsible for the general aging of the organism [2]. 2. **Longevity-associated genes**: Dozens of genes have been associated with human longevity, although only a handful have shown consistent effects across populations. These genes are often related to specific biological pathways and processes [4]. 3. **Genes involved in mitochondrial and proteasomal functions**: Aging is associated with alterations in the transcript levels of many genes, including those involved in evolutionarily conserved mitochondrial and proteasomal functions. Some of these genes have been shown to be directly involved in regulating lifespan [7]. 4. **Genes with protective functions**: Certain genes that are overexpressed with age seem to have protective functions, helping organisms manage aging. These genes could be targets for manipulation to potentially influence the aging process [8]. These genetic factors highlight the complex interplay of various genes and pathways in the aging process.", + "question": "what genetic factor are associated with aging" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_4.json b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_4.json new file mode 100644 index 0000000..09d1513 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_4.json @@ -0,0 +1,65 @@ +{ + "titles": [ + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2009 - The Human Ageing Genomic Resources online.pdf", + "2012 - Genome-Environment Interactions That Modulate.pdf", + "2011 - Clinical aspects and molecular diagnostics of skin aging.pdf", + "2015 - Transcriptomic profiles of aging in purified.pdf", + "2004 - A Transcriptional Profile of Aging.pdf", + "2020 - A multidimensional systems biology.pdf", + "2011 - Metabolism, Genomics, and DNA Repair in the Mouse Aging Liver.pdf", + "2010 - MicroRNA, mRNA, and protein expression link.pdf", + "2012 - GeneFriends An online co-expression analysis.pdf" + ], + "extraction_id": [ + "d59d7882-333d-5576-86ab-3cfa6354b946", + "52c67b46-63f2-54ae-a78e-e9d54a55f6e4", + "d59d7882-333d-5576-86ab-3cfa6354b946", + "674e1da7-73d5-5101-b5a5-4981e483123c", + "e5fd1ff0-8df5-577f-9f2d-31b0941d5ce5", + "8b47c304-ee91-5c52-8324-79fd0bd32b27", + "9d1656aa-32d2-5094-8232-4817655b1cbd", + "a6a6b5ba-3a72-55c5-91bb-abe747624348", + "fc9974c9-2e48-5a08-9112-0109df9ce096", + "1839dfa6-7080-5de4-96cb-3493ca2056d3" + ], + "document_id": [ + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e43cd3b6-ad8e-5422-ba7c-ceb6e66cc529", + "b1a1997c-e9df-5dc0-9d12-a3977d0c64ec", + "e32f8f2c-d3ad-5dae-a393-9bd87c370ebe", + "50f357a3-e0f2-5e32-a6b2-771fb4b1e1c6", + "4ab656a7-9656-526b-94e1-422875409b44", + "d040bfe3-e409-5b5c-b8f8-f3dd4fc060e3", + "a94fd15d-373e-51c5-ad74-a17e4260d32a", + "c3ae47b0-42dd-5ab0-8fec-a41831d1bbfa", + "be20af52-c782-5098-893a-9a92000bf5a0" + ], + "id": [ + "chatcmpl-ABLwhCwS1z9hZBn1zWSbHm5JcD7CF", + "b719fbc0-94e4-5df0-abb7-0d13fc36214c", + "8fd5ab85-67ed-55e6-bbfa-09436c4fdbfb", + "413f8f54-b5cc-5089-9f5c-d9e3b8bcf594", + "896169ed-4b9b-5ebc-9c9d-1cd2e6f3437c", + "6b4fb407-fd3f-52a3-9cfd-07dc5c891dd5", + "17ce11f7-55df-59bd-a801-a6f38ae9a9ef", + "61baeaa5-d65a-54b5-bfee-9bab8bbf1985", + "3414ff44-7d60-5492-9956-353ab9a94a43", + "b2d47567-09dc-5c77-be72-9448aa954e6b", + "1c3f7772-e1fa-5063-bda7-04b2f7e7b0e3" + ], + "contexts": [ + "lar signatures of mammalian aging. Some of the genes", + "www.ncbi.nlm.nih.gov/homologene) of genes strongly asso-ciated with aging in model organisms. Also included are genesin which mutations result in segmental progeroid syndromes,such as the Werners syndrome gene, as well as genes criticalin pathways previously related to aging, such as the insulin/insulin-like signalling pathway (de Magalhes et al ., 2005a). The", + "overexpressed with age seem to be a response to aging,in that they have been previously found to have protec-tive functions (de Magalha es et al., 2009b). As such,these genes may help organisms manage aging andcould be targets for manipulation. Likewise, gene ex-pression analysis of CR has been conducted to identifyassociated genes (Lee et al., 1999, 2000). A number ofmolecular signatures have emerged from such studiesthat could be useful to identify candidate processes andpathways that affect aging,", + "expression profile of aging in human muscle. Physiol Genomics 2003;14:149-59. 142. Rodwell GE, Sonu R, Zahn JM. A transcriptional profile of aging inthe human kidney. PLoS Biol 2004;e427:2. 143. Hasty P, Campisi J, Hoeijmakers J, van Steeg H, Vijg J. Aging and genome maintenance: lessons from the mouse? Science 2003;299:1355-9. 144. Kyng KJ, May A, Klvraa S, Bohr VA. Gene expression profiling in Werner syndrome closely resembles that of normal aging. Proc Natl Acad Sci U S A 2003;100:12259-64.", + "neurodegenerative diseases. Nature. 2006;443:787 95. 50. de Magalhes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875 81. 51. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, et al. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007;3:e201. 52. Liu LF, Shen WJ, Ueno M, Patel S, Kraemer FB. Characterization of age- related gene expression profiling in bone marrow and epididymal", + "Ly DH, Lockhart DJ, Lerner RA, Schultz PG (2000) Mitotic misregulation and human aging. Science 287: 24862492. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, et al. (2004) Comparing genomic expression patterns across species identies shared transcriptional prole in aging. Nat Genet 36: 197204. Murphy CT, McCarroll SA, Bargmann CI, Fraser A, Kamath RS, et al. (2003) Genes that act downstream of DAF-16 to inuence the lifespan of Caenorhabditis elegans Nature 424: 277283.", + "genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build", + "exhibits important alterations in global gene expressionproles with age. In mice, aging is accompanied by changesin expression of genes associated with increased inamma-tion, cellular stress, brosis, altered capacity for apoptosis,xenobiotic metabolism, normal cell-cycle control, and DNAreplication [ 5]. Lifelong calorie restriction reversed the", + "stance, genes associated with energy production, which decrease their expression during aging across various tissues and species (Zahn et al. 2006, 2007; de Magalha es et al. 2009), start decreasing at this transition point in our data (group 5; Fig. 2A). Hence, 25 yr of age in humans may mark the beginning of systemic change associated with certain senescence processes. Conservation of expression changes with age We observe that both developmental and aging expression pro-", + "p <10 -6; Table 1 shows the top 25 genes. Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30), Fas [20] (p = 2.70-31) and growth hormone receptor Ghr [21] (p = 1.34-19) also showed a significant co-expression. Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10-25) as well as several S100 calcium binding proteins which have been" + ], + "answer": "Genes typically associated with early aging include those involved in segmental progeroid syndromes, such as the Werner syndrome gene [2]. Additionally, genes critical in pathways previously related to aging, such as the insulin/insulin-like signaling pathway, are also associated with early aging [2].", + "question": "which genes are typically associated with early aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_5.json b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_5.json new file mode 100644 index 0000000..c0b3b7f --- /dev/null +++ b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_5.json @@ -0,0 +1,65 @@ +{ + "titles": [ + "2001 - A genome-wide scan for linkage to human.pdf", + "2023 - A transcriptome-based single-cell biological age model.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "2007 - Longevity Genomics Across Species.pdf", + "2020 - Clinical Genetics and Genomics of Aging.pdf", + "2017 - Systems genetic analysis in GeneNetwork.org.pdf", + "2021 - Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.pdf", + "2016 - Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging.pdf", + "2015 - A Chromosome 13 locus is associated with male-specific mortality in mice.pdf", + "2009 - Meta-analysis of age-related gene expression profiles identifies.pdf" + ], + "extraction_id": [ + "17246c43-2e44-579b-867d-3dc7150ceedd", + "0fd46f00-d3e1-54f4-9395-6c3e8294ed51", + "5cc56e3b-53ab-5299-814d-014e2ed31d2f", + "522e2616-daa1-5bf3-8673-a717dfb9b13f", + "5c3840bd-45a5-5928-84ab-a1f2d8536691", + "59121146-02b9-5479-96e2-9fb45cffc81b", + "396683f9-b2e3-5942-bec8-f96fa798c341", + "382122b9-6922-5d85-9e8c-acfa86aff085", + "df0b4be9-3393-5642-a722-ccafffb60df8", + "4d95f551-34bd-5e7a-8702-eb59de73a480" + ], + "document_id": [ + "1431984a-82d9-51d4-a23c-5f76a02ab554", + "9be234b7-f37d-5cd5-8895-bfe676441b2f", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "1ab0b63f-d97c-5f5c-98ee-0bde785fa630", + "62b635c3-040e-512a-b016-6ef295308a1e", + "41be0f9f-a5af-5586-b6cd-16e56fd89cdc", + "4d082da4-fa48-5170-8147-c4fea47a5d4b", + "8cde78ac-cb0e-5983-86ee-91074b2fe1e3", + "ad8f2626-87fb-520e-8cef-ee9a9cc3ab0b", + "5c2cf97f-a57a-5284-85a3-b8d9c5943113" + ], + "id": [ + "chatcmpl-ABLwlxjoJ15UXMdKPBfDnYfvZNLDD", + "e0cce1c5-8709-5218-99b6-48a6ba242931", + "9f9fef49-0bda-5948-93bd-0f8f43bbefdf", + "09da6f9e-b996-5438-91be-41d9438cb930", + "ab0845d4-b4db-53db-927e-b96a52cf7667", + "c2299f0f-9e0b-5279-90e5-37c6bd664976", + "3004d1fd-c5ce-5587-bfab-471e7141952c", + "9082d164-59f8-58a0-ace7-8b3aa9d884e2", + "7abf14d2-cdfe-5c37-8217-6b63bd8fb255", + "380ca35e-b42b-59b4-aef7-aaf2ba3bb59d", + "eea576fd-d766-5ae7-9e63-045869a3f8f7" + ], + "contexts": [ + "effect fundamental mechanisms of aging (14, 16). The drawbacksof such studies include the improbability of picking the right geneto study the myriad of known and unknown genes affecting theprocess of interest (17). The linkage study described heremarkedly improves the efficiency of such association studies bydefining a region likely to contain polymorphism(s) with signif-icant influence on life span. Additional association studies with these families and repli-", + "Map contains 1119 and 1459 curated human and mouse aginggenes, respectively, covering almost all scales of aging, rangingfrom molecular damage to genetic predisposition. Cross-speciescomparison revealed a modest overlap between known humanand mouse aging genes, suggesting both conservation of core sen- escence pathways and fundamental differences in aging between mice and humans (Fig. 2E). Aging-associated genes can alternatively be identified in a", + "11. Gelman R, Watson A, Bronson R et al (1988) Murine chromo- somal regions correlated with longevity. Genetics 118(4):693704 12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci associated with life span exhibit sex-specic and epistatic effects. J Gerontol A Biol Sci Med Sci 57(1):B9B15 13. Foreman JE, Lionikas A, Lang DH et al (2009) Genetic archi- tecture for hole-board behaviors across substantial time intervalsin young, middle-aged and old mice. Genes Brain Behav", + "Along with longevity, a select group of potential aging-related biomarkers will be assayed for each of these mouse models. In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease back- ground. CONCLUSION Our understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems", + "198 the study of age-related diseases for various reasons: (a) mice are closely related to humans, with nearly 99% of human orthologous in mice; (b) their relatively short lifespan and small size allow surveillance of the aging process within a pertinent time frame and make their housing less expensive; (c) the feasibility of performing genetic manipulations facilitates the engineering of transgenic strains (gain- and loss-of function mice) that model premature aging disorders. In this section, we", + "Hsu HC, Lu L, Yi N, Van Zant G, Williams RW, Mountz JD. Quantitative trait locus (QTL) mapping in aging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age of whole-genome association studies. Annual Review of Genetics. 2008; 42:131141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus", + "multiscalar integration of traits. Cell150, 12871299 (2012). [PubMed: 22939713] 33. De Haan G & Van Zant G Genetic analysis of hemopoietic cell cycling in mice suggests its involvement in organismal life span. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 13, 707713 (1999). 34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with longevity. Genetics 118, 693704 (1988). [PubMed: 3163317] 35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).", + "mice to identify genetic factors involved in the regulation of cognitive aging that may have gone undetected in either complex human studies or murine studies utilizing only a single genetic background. Aging is a leading risk factor for age-associated de- mentias such as AD, and our work and others suggest that geneticfactors and mechanisms underlying biological processes during midlife play a key role in determining an individual s susceptibility", + "span and have yielded insights into potential biological pathways and processes related to aging. Despite these successes, several problems are inherent in human longevity studies including potentially high degrees ofenvironmental heterogeneity, genetic diversity, and lack of birth matched controls, among others [ 8]. Inbred mouse strains represent a powerful alternative for identifying genes underlying complex trait genes such as longevity [ 9]. Initial mapping approaches include quanti-", + "Recently, the Atlas of Gene Expression in Mouse Aging Project (AGEMAP) reported gene expression proles with age for 8932genes in 16 mouse tissues (Zahn et al ., 2007). We chose not to" + ], + "answer": "To generate a linkage or association mapping study in mice to understand aging, you can follow these steps: 1. **Select Appropriate Mouse Strains**: Choose inbred mouse strains that are known to have variations in lifespan and aging-related traits. Inbred strains are powerful for identifying genes underlying complex traits such as longevity due to their genetic uniformity [9]. 2. **Identify Aging-Related Traits**: Determine the specific aging-related traits you want to study. This could include longevity, resistance to age-associated diseases, or other biomarkers of aging [4]. 3. **Cross Mouse Strains**: Perform crosses between different inbred strains to generate F1 and F2 generations. This helps in creating genetic diversity necessary for mapping studies [5]. 4. **Phenotyping**: Assay the F2 generation (or other relevant generations) for the aging-related traits of interest. This could involve measuring lifespan, resistance to diseases, or other biomarkers [4]. 5. **Genotyping**: Genotype the mice using molecular markers such as SNPs (Single Nucleotide Polymorphisms) to identify genetic variations across the genome [6]. 6. **Quantitative Trait Locus (QTL) Mapping**: Use QTL mapping techniques to identify chromosomal regions associated with the aging-related traits. This involves statistical analysis to correlate genetic markers with phenotypic traits [6]. 7. **Association Studies**: Conduct association studies to further refine the regions identified by QTL mapping. This involves looking for specific polymorphisms within the identified regions that are significantly associated with the traits [1]. 8. **Replication and Validation**: Validate the findings by replicating the study in different mouse populations or using additional genetic crosses. This helps to confirm the association between the identified genetic regions and aging traits [1]. By following these steps, you can generate a linkage or association mapping study in mice to understand the genetic factors influencing aging.", + "question": "How do I generate a linkage or association mapping study in mice to understand aging?" +}
\ No newline at end of file diff --git a/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_6.json b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_6.json new file mode 100644 index 0000000..8e291e1 --- /dev/null +++ b/gnqa/paper2_eval/data/responses/human/aging/domain_expert_aging_6.json @@ -0,0 +1,65 @@ +{ + "titles": [ + "2010 - A Meta-analysis of Four Genome-Wide Association Studies.pdf", + "2014 - Whole-Genome Sequencing of the World?s Oldest People.pdf", + "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", + "2017 - Four Genome-Wide Association Studies Identify New.pdf", + "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", + "2013 - Genome Instability and Aging.pdf", + "2012 - Genome-wide miRNA signatures of human longevity.pdf", + "2012 - Whole genome sequences of a male and female supercentenarian, ages greater than 114 years.pdf", + "2011 - Genome-wide association study identifies a single major locus contributing to survival into old age the APOE locus revisited.pdf", + "2017 - Genome-wide meta-analysis associates HLA.pdf" + ], + "extraction_id": [ + "8bc54e5b-f45f-54f9-9591-1e26dd80b50d", + "c918522d-c0bf-5b7a-9ced-a69d485b2cb6", + "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", + "b539194c-50bb-55e5-83b2-e779f63ed363", + "402ab5b5-e6fa-58fe-8f32-7c235be7a746", + "f33756b1-7d64-5ab9-bcd6-717deaf05339", + "e79b0811-a0f3-5f44-8004-89fe59aa8a3e", + "402ab5b5-e6fa-58fe-8f32-7c235be7a746", + "a4aa5d3a-81e8-582c-aee6-3ebdd329de86", + "9c6a9e93-5dc5-571d-b3c2-b600ed95e102" + ], + "document_id": [ + "8e452186-a71c-5b62-81b2-7681c87c8e1d", + "d2a5ec28-873a-5ff3-9cf4-dbec3b52dd21", + "05208abc-5ac0-5d4d-b600-2caf59ce75b7", + "c10653f6-b3d7-5b92-9271-ab8fcc7905a7", + "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", + "71e08916-8cc8-5d96-8c06-4461b972b54d", + "18407659-c241-5f37-8ad2-ab59f6a7e288", + "408cdcd5-ab70-520a-b2c4-d9028b0a8d6f", + "05208abc-5ac0-5d4d-b600-2caf59ce75b7", + "3a565ba9-ee5b-5596-b870-ce8c055cb1f1" + ], + "id": [ + "chatcmpl-ABLwzkPUEqxCEqW5L5wugbbowvYPv", + "c2234f77-2268-57d0-a227-e931fc4802c1", + "fb0af8f1-5b2a-5ba1-8a53-ee543a9267bf", + "754929a6-af78-569a-969c-e750d174b952", + "4a6d2b9b-9496-5d90-a24a-43c643c4916b", + "1f4437a7-cee1-5dc2-80e1-9924248857d0", + "91010ff1-43a7-53f6-966d-601913e3b26b", + "63ebd662-9aca-5b8a-b3e3-89860a45da42", + "53a8e33f-da6f-5550-bf18-e45f2779f7a9", + "57227bee-d562-52c9-86dc-f9e2fcea1792", + "b1b9f731-236c-5b4b-8cc6-fcf1e06d866a" + ], + "contexts": [ + "GENOME-WIDE ASSOCIATION STUDY OF LONGEVITY 479 INCREASES in longevity of the general population world - wide are an unprecedented phenomenon with significant health and social impact. Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (17) and in other organisms (8). The protective genetic factors that lead to longevity are likely to involve", + "that any genetic variant that contributes strongly to extremelongevity would also be rare. One possibility is that a specificmutation could alter the protein-coding region in a gene andconfer a significant increase in longevity. Such a mutation couldact in a dominant or recessive fashion, and might be shared by asignificant fraction of the supercentenarian genomes but not bycontrol genomes. We created a computational pipeline todetermine whether our supercentenarian genomes are enrichedfor such a variant", + "ever, natural human and animal longevity is presumed to be acomplex trait (Finch & Tanzi, 1997). In humans, both candidategene and genome-wide genetic association approaches havebeen applied in an attempt to identify longevity loci. The fre-quency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing", + "genetic makeup of extreme longevity is based on a combination of common and rare variants, with common vari-ants that create the background to survive to relatively common old ages, and specific combinations of uncommon and rare variants that add an additional survival advantage to even older ages. Our analy-sis showed that LAVs discovered through a casecontrol study are not necessarily the variants that make someone live to extreme old age, and additional survival analysis is needed to characterize and", + "genetic determination of human exceptional longevity, they arethe rst step toward the generation of a comprehensive referencepanel of exceptionally long-lived individuals. The data also provideinteresting insights into genetic backgrounds that are conduciveto exceptional longevity and allow us to test different models of exceptional longevity. www.frontiersin.org January 2012 | Volume 2 | Article 90 | 1", + "tremely long lived individuals. Longevity has a genetic component, with an estimated heritability of average life expectancy of approximately 25% (105, 106). Family studies of centenarians, thosewho live to 100 years or more, suggest that the relationship between genetics and longevity is stronger in the oldest-old adults (107, 108), supporting the utility of long-lived individuals as a model system for studying genetic variations that predispose people to longevity.", + "because of genetic variation that becomes particularly important for sur- vival at advanced age (Hjelmborg et al. , 2006). Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specic age distribu- tions (Gudmundsson et al. , 2000), frequently show a favorable (healthy) course of the aging process, with the absence or a delayed onset of age-", + "Studies of centenarians have provided strong evidence to sup-port the hypothesis that a genetic contribution to human excep-tional longevity is decisive, although only a small number ofgenetic variants with modest effects have been irrefutably linkedto this phenotype ( Schachter et al., 1994; Barzilai et al., 2003 ; Christensen et al., 2006 ;Wheeler and Kim, 2011 ). The tech- nology of next generation sequencing provides a tool to gen-erate data that may eventually provide an answer ( Metzker, 2009).", + "genetic contribution to human lifespan variation was estimated at 2530% in twin studies (Gudmundsson et al. , 2000; Skytthe et al. , 2003; Hjelmborg et al. , 2006). The most prominent genetic inuence is observed in families in which the capacity toattain a long lifespan clusters (Perls et al. , 2000; Schoenmaker et al. , 2006). Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al. , 2008; Terry et al. , 2008), raising the question whether protective mecha-", + "age, usually de ned by a threshold, such as 90 years). Up to 25% of the variability in human lifespan has been estimated to be genetic1, but genetic variation at only three loci (near APOE , FOXO3A and CHRNA3/5 )25have so far been demonstrated to be robustly associated with lifespan. Prospective genomic studies of lifespan have been hampered by the fact that subject participation is often only recent, allowing insuf cient follow-up time for a well-powered analysis of" + ], + "answer": "Yes, there are specific genetic variations that can contribute to someone living longer. Research has shown that certain genes and their variations can influence longevity. For example, variations near the genes APOE, FOXO3A, and CHRNA3/5 have been robustly associated with lifespan [10]. These genetic variations can affect how our bodies age and how well we can avoid or delay age-related diseases. In simpler terms, think of these genes as parts of a car. Just like how certain parts can make a car run more efficiently and last longer, these genetic variations can help our bodies function better and live longer. However, it's important to note that living a long life is usually a combination of both genetic factors and lifestyle choices, such as diet, exercise, and avoiding harmful habits.", + "question": "is there a specific genetic variation that can cause someone to live longer? please make your answer accessible to a non-expert" +}
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