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diff --git a/gnqa/paper1_eval/src/data/responses/test/response01.json b/gnqa/paper1_eval/src/data/responses/test/response01.json new file mode 100644 index 0000000..890f75a --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response01.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-03-29T08:12:04.175184Z", + "data": { + "amplify": false, + "answer": "The text mentions two severe asthma clusters, SC2 and SC3, but does not specify individual genes. SC2 is characterized by high T2 inflammation, high FeNO, bronchoalveolar lavage and blood eosinophilia, and up-regulated expression of T2 signature and mast cell genes. SC3 is characterized by lower levels of T2 clinical biomarkers and gene expression, a later age of onset, increased incidence of nasal polyposis, and higher levels of neutrophilic inflammation.", + "context": { + "00da4d7c-879c-47f8-8648-89b2fdb19eda": [ + { + "document_id": "00da4d7c-879c-47f8-8648-89b2fdb19eda", + "text": "\n\nWe present an analytical technique designed to test a priori defined gene sets (for example, pathways) for association with disease phenotypes.We apply this method to gene expression profiles of human diabetic muscle, identifying a set of genes whose expression is correlated with insulin resistance and aerobic capacity.These results suggest hypotheses about pathways contributing to human metabolic disease and, more generally, show the value of incorporating information about functional relationships among genes in the analysis of microarray data." + } + ], + "0af8f2bf-8caf-4459-823b-06e22e637cc8": [ + { + "document_id": "0af8f2bf-8caf-4459-823b-06e22e637cc8", + "text": "\n\nPathway and gene ontology analysis for select phenotypes and envionmental factors showing GxE interactions." + } + ], + "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427": [ + { + "document_id": "14cad5a7-e53a-4ab8-9d4f-8f0b827ae427", + "text": "\n\nNext, the genes that correlated with FeNO (n = 549) were used to objectively cluster asthma subjects into subgroups.In agreement with Moore et al., most of the severe asthma patients clustered into 2 subject clusters (SCs) (SC2 and SC3).One severe asthma cluster (SC2) had high T2 inflammation, as evidence by a high FeNO, bronchoalveolar lavage and blood eosinophilia, and up-regulated expression of T2 signature and mast cell genes.The other severe asthma cluster (SC3) had lower levels of T2 clinical biomarkers and gene expression, in addition to a later age of onset, increased incidence of nasal polyposis and higher levels of neutrophilic inflammation.Roughly 1/2 of all asthma subjects had evidence of high T2 inflammatory response (by clinical biomarkers and gene expression), confirming the prior findings of Woodruff et al. in a more severe and steroid-treated patient population.In general, both severe asthma clusters (SC2 and SC3) were older and more obese than the other non-severe subclusters.Further, both of the severe SCs demonstrated suppression of genes associated with cilia function, neuronal function, cell adhesion and wound repair.These findings suggested that airway epithelial defense, repair, neuronal function are an integral part of a healthy epithelial layer and perhaps prevention of severe asthma." + } + ], + "18d12255-3cc6-415b-bd30-ff94bb087813": [ + { + "document_id": "18d12255-3cc6-415b-bd30-ff94bb087813", + "text": "These\ngenes are high priority candidates, although we acknowledge that causal variants may lie in non-coding\nregions. For each of these high priority candidates we then examined which GO:biological processes\n(Consortium, 2015) and KEGG pathways (Kanehisa et al. , 2012) the gene was annotated as being part of,\nand highlighted those which may relate to our phenotypes. We also reviewed known effects of mutations\nusing the Mouse Genome Informatics (MGI) Phenotypes, Alleles and Disease Models Search\n(www.informatics.jax.org/allele) (Bello et al. , 2015)." + } + ], + "19aeec76-3ae4-4039-a887-407738ad4298": [ + { + "document_id": "19aeec76-3ae4-4039-a887-407738ad4298", + "text": "Results were displayed as a matrix with all phenotypes/diseases associated with\n\n173\n\nmouse models and human genes found for the candidate gene list. 174\n175\n\n2.6. Expression-phenotype correlations\n\n176\n\nFor each gene discovered after filtering, an adequate probe within the well-curated INIA Amygdala\n\n177\n\nCohort Affy MoGene 1.0ST (Mar11) RMA, Hippocampus Consortium M430v2 (Jun06) PDNN,\n\n178\n\nVCU BXD Prefrontal Cortex M430 2.0 (Dec06) RMA, INIA Hypothalamus Affy MoGene 1.0ST\n\n179\n\n(Nov10), and INIA Adrenal Affy MoGene 1.0ST (Jun12) RMA Databases was identified using\n\n180\n\nGeneNetwork (http://www.genenetwork.org; Williams and Mulligan, 2012))." + } + ], + "1f2060d9-353b-4de8-9172-edf15881f40f": [ + { + "document_id": "1f2060d9-353b-4de8-9172-edf15881f40f", + "text": "\n\nThe GeneNetwork website contains extensive phenotypic datasets ranging from behavioral to morphological to pharmacological.To identify phenotypes associated with Gsto1 variation, we queried the BXD phenotype database in GeneNetwork, which contains nearly 3000 phenotypes, to look for the phenotypes that are most closely related to hippocampal expression of Gsto1 (probe set 1416531_at)." + } + ], + "36858807-1395-4b2f-a3ee-e054f9b0149d": [ + { + "document_id": "36858807-1395-4b2f-a3ee-e054f9b0149d", + "text": "\n\nTo examine known causal genes that have been reported in the literature, including related genes and pathways, a gene list was generated consisting of 6264 genes categorized by disorders, pathways, expression, AmiGO terms, and other into 26 sublists (supplemental data).This list was manually collected from different database sources covering all aspects of insulin-and glucose-related genes and disorders.This was done through an extensive literature review using PubMed, Ovid®, GeneCards®, and the National Center for Biotechnology Information (NCBI).Gene and protein expression databases such as BioGPS and The Human Protein Atlas were used.Protein interactions and gene network databases, such as AmiGO, BioGRID, GIANT, KEGG, and Reactome, were also used.Knockout mouse databases, such as MGI and IMPC, were also used.However, filtering against the gene list will not replace the manual screening for all variants called; therefore, we did not consider the results of our gene list alone.Once the raw data were obtained, they were filtered and investigated individually.As shown in Fig. 1, mutations went through serial steps ending up with a single nucleotide polymorphism mutation as a potential explanation.Pathogenicity scores were determined by SIFT, PolyPhen-2, PROVEAN, and PhD-SNP." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Chesler, E. J., Wang, J., Lu, L., Qu, Y., Manly, K. F., and Williams, R. W. (2003). Genetic correlates\nof gene expression in recombinant inbred strains: a relational model system to explore\nneurobehavioral phenotypes. Neuroinformatics 1, 343–357. doi:10.1385/NI:1:4:343. Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., et al. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26, 1205–1210. doi:10.1093/bioinformatics/btq126. Farrar, C. A., Zhou, W., and Sacks, S. H. (2016). Role of the lectin complement pathway in kidney\ntransplantation. Immunobiology 221, 1068–1072. doi:10.1016/j.imbio.2016.05.004. Gene Ontology Consortium (2015)." + }, + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In this\nmanuscript we will outline some simple use cases, and show how a small number of plausible\ncandidate genes can be identified for an immune phenotype. 1. Data\nOnce you have navigated to genenetwork.org, there 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." + } + ], + "58714c13-954b-46b3-bd0e-69ccadd9dc6a": [ + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "Protein interaction data: There is a growing body of protein-interaction data and this data is a useful\nextension to inferences of functional interaction between disease gene candidates and co-expressed genes. Ontologies for Functional Annotation: This project will lead to a small subset of genes of interest for asthma\nand AD.. Ontologies are key in making automated and vocabulary controlled statements about function and it\nwill be interesting to interface the analytical framework presented in the proposal with contemporary\nadvances in gene ontology methodology." + }, + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "A network or interaction model will be generated using methods of graphical modelling\nwith both inhouse data and public databases to propose predictive models for epithelial cells and characterise critical\nmolecular interactions within asthma and AD biology. Finally, supporting and extending methodologies from above\nwill contribute to (E) Future Directions of the study and include interfacing and data exchange with contemporary\npublic databases. D(a) Disease Association and eQTL Mapping\nMapping the human genome for regions and positions that are responsible for disease susceptibility and\ndifferential gene expression is central to this project." + }, + { + "document_id": "58714c13-954b-46b3-bd0e-69ccadd9dc6a", + "text": "For example, time series data sets potentially capture relationships and\ndependencies of gene expression within and between time points which may suggest causative co-regulation. These\ndependencies and interactions could be better uncovered using statistical modelling approaches such as Bayesian\nmodel based methods that aim to identify co-expressed clusters of genes under a model of temporal dependence\nbetween observations, that is utilising gene expression measures in time to better judge cluster membership11,12. Secondly, the asthma and AD expression dataset of sibpairs inherently contains underlying structures of\nshared genetic disease risk." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "Genes are arranged based\non their genetic positions, and genes annotated to be involved in the module are colored red. Genes with absolute GMAS over 0.268 are\nconsidered significantly associated. DDT, BOLA3, and ARID1A are labeled. B, Venn diagram of novel genes associated with respiratory electron transport module in human, mouse and rat. 707 genes were predicted\nto be mito-proteins by G-MAD in all three species." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Chesler, E. J., Wang, J., Lu, L., Qu, Y., Manly, K. F., and Williams, R. W. (2003). Genetic correlates\nof gene expression in recombinant inbred strains: a relational model system to explore\nneurobehavioral phenotypes. Neuroinformatics 1, 343–357. doi:10.1385/NI:1:4:343. Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., et al. (2010). PheWAS: demonstrating the feasibility of a phenome-wide scan to discover genedisease associations. Bioinformatics 26, 1205–1210. doi:10.1093/bioinformatics/btq126. Farrar, C. A., Zhou, W., and Sacks, S. H. (2016). Role of the lectin complement pathway in kidney\ntransplantation. Immunobiology 221, 1068–1072. doi:10.1016/j.imbio.2016.05.004. Gene Ontology Consortium (2015)." + }, + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In this\nmanuscript we will outline some simple use cases, and show how a small number of plausible\ncandidate genes can be identified for an immune phenotype. 1. Data\nOnce you have navigated to genenetwork.org, there 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." + } + ], + "85ee9743-b34d-4d49-9017-d7d2e5d4b996": [ + { + "document_id": "85ee9743-b34d-4d49-9017-d7d2e5d4b996", + "text": "6\n\nPhenotype-matched reports\n\n7\n\nThe framework implementation we have presented uses only genomic\ninformation to generate a patient or research report. Of course, the\nclinical features of the sample offer vital clues as to which gene is\nlikely responsible for the disease. It would therefore make sense to include phenotype-based gene filtering or prioritization to the report. To\nmake this possible, associations of Human Phenotype Ontology (HPO)\nterms[292] to their known disease genes could be integrated into the\nsystem. Users can enter HPO terms that match the phenotypes observed in a patient to shorten their list of candidate genes." + } + ], + "98d443c7-8d99-4139-a27d-e447b0f6630f": [ + { + "document_id": "98d443c7-8d99-4139-a27d-e447b0f6630f", + "text": "Predicted transcriptome association test\n\nWe used the PrediXcan 16 framework to identify genes that might mediate associations between genetic variants and asthma risk.PrediXcan is a software tool that estimates tissue-specific gene expression profiles from an individual's SNP genotype profile by use of prediction models trained in large reference databases of genotypes and tissue-specific gene expression profiles.With these genotype-imputed expression profiles, PrediXcan can perform gene-based association tests that correlate predicted expression levels with phenotypes (eg, asthma) to identify candidate causal genes from GWAS data.We used a summary version of PrediXcan, which has high concordance with the individual-level version (r²>0•99). 17or predictions, we downloaded elastic net models trained with reference transcriptome data from the Genotype-Tissue Expression consortium 18 for 49 tissues (appendix pp 9, 47)." + } + ], + "b72caae5-bb5a-4317-8d4d-21b41d60df21": [ + { + "document_id": "b72caae5-bb5a-4317-8d4d-21b41d60df21", + "text": "\n\nGene selection was based on searches conducted using the Genetic Association Database (geneticassociationdb.nih.gov).Only genes with multiple, independent indicators of function were included.aPhenotype available for one cohort only." + } + ], + "ed140f66-fbad-4fd7-8ae3-4d9cac4f63ac": [ + { + "document_id": "ed140f66-fbad-4fd7-8ae3-4d9cac4f63ac", + "text": "The results from the phenotype-driven searches\nshould then be linked to gene names associated with a\ngiven phenotype. These genes are presented as a list\nfrom which the user can choose the genes of interest\nand save them in a shopping cart. It is then possible to\nfeed the genes into the gene-centric use-case and perform a more detailed data mining or meta-analysis. The description and further development of the phenotype-driven use-case may represent a very useful\nconcept for scientists and clinicians outside the mouse\ncommunity." + } + ], + "fcd522a5-43ad-413b-abd9-5e3c9ccaca9f": [ + { + "document_id": "fcd522a5-43ad-413b-abd9-5e3c9ccaca9f", + "text": "\n\nAs a demonstration of the utility of the web interface, we entered the 9 genes that reached suggestive significance in a recent genome-wide association study of opioid cessation (Cox et al. 2020).The graph view of the search results are shown in Fig. 3. Genes and keywords are all shown as circles and lines connecting them show the number of abstracts containing the 2 circles they connect.Keywords under the same main category are shown with the same color in the graphic output.Clicking on the lines brings up a new page that displays all sentences containing the keywords that line connects.An alternative tabular view of the same results is also available, where genes, the keywords, and number of abstracts are shown as separate columns." + } + ] + }, + "data_source": [], + "document_id": "C58C7981111D600F0D509F387DB6CCF7", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "asthma", + "genes", + "phenotype", + "FeNO", + "T2&inflammation", + "eosinophilia", + "gene&expression", + "PrediXcan", + "GWAS", + "Human&Phenotype&Ontology&HPO" + ], + "metadata": [], + "question": "List genes related to asthma with their phenotype descriptions.", + "subquestions": null, + "task_id": "C58C7981111D600F0D509F387DB6CCF7", + "usage": { + "chatgpt": 5108, + "gpt-4": 4004, + "gpt-4-turbo-preview": 2992 + }, + "user_id": 2 + }, + "document_id": "C58C7981111D600F0D509F387DB6CCF7", + "task_id": "C58C7981111D600F0D509F387DB6CCF7" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response02.json b/gnqa/paper1_eval/src/data/responses/test/response02.json new file mode 100644 index 0000000..f6fc655 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response02.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T11:43:39.700633Z", + "data": { + "amplify": false, + "answer": "Mouse studies popular in genenetwork often focus on ailments such as diabetes, glaucoma, obesity-associated diabetes, and metabolic diseases. These studies utilize genetically modified mouse models, inbred mouse models, and polygenic models. They also use specific strains like DBA/2J, Goto-Kakizaki (GK) rat, and KK mice.", + "context": { + "08a6ce71-133e-426d-adfe-600ff52802a2": [ + { + "document_id": "08a6ce71-133e-426d-adfe-600ff52802a2", + "text": "A major\nadvantage of the mouse as an animal model is the availability of\nwell-characterized inbred strains that enable functional genomics\non defined genetic backgrounds. Currently, however, exploiting\nthe full utility of mice to study human diseases is hampered by the\nlack of gene targeting resources for multiple inbred mouse strains. DBA/2J is a common inbred mouse strain critical in studying a\ndiverse range of human diseases. For example, it is widely used as\nan inherited model of glaucoma. Glaucoma is a neurodegenerative\ndisorder that affects 70 million people worldwide." + } + ], + "14a9de52-cff1-4397-bb2c-8c2e34bb05bf": [ + { + "document_id": "14a9de52-cff1-4397-bb2c-8c2e34bb05bf", + "text": "The\nnetwork is driven by a common regulator,\nEbi2 (also known as Gpr183), which is conserved in rats and humans, is expressed in\nmacrophages and is associated in GWASs\nwith human type 1 diabetes48. Such systemsgenetics studies are possible in rats because\nof the ready availability of ex vivo tissues and\nthe statistical power gained from studies of\ninbred strains in controlled environments. Overall, these vignettes provide clear\nexamples of the translational focus of the\nrat genetics community in an era of unprecedented scientific opportunity enabled\nby ultra-high-throughput genomics and\nmathematical biology." + } + ], + "1bf337a1-ffed-4199-a11f-c5a62df47980": [ + { + "document_id": "1bf337a1-ffed-4199-a11f-c5a62df47980", + "text": "\n\nInbred animal models with homogeneous genetic backgrounds have been a powerful adjunct to human studies, providing a sufficiently large number of samples required for an unconstrained genetic analysis.Several polygenic NIDDM rodent models have been developed.These include the Goto-Kakizaki (GK) rat, the Otsuka Long-Evans Tokushima Fatty (OLETF) rat, the Nagoya Shibata Yasuda mouse, the New Zealand Obese mouse (reviewed in Kim et al., 1998), and the Tsumura-Suzuki Obese Diabetes mouse (Suzuki et al., 1999).The underlying genetic factors in these animal models have been studied by quantitative trait locus (QTL) mapping analysis, and several QTLs associated with glucose intolerance, defective insulin secretion, or parameters defining glucose homeostasis have been located (reviewed in Kim et al., 1998;Hirayama et al., 1999;Ueda et al., 1999)." + } + ], + "2a7da18e-3756-45c5-b18c-a2231685fefd": [ + { + "document_id": "2a7da18e-3756-45c5-b18c-a2231685fefd", + "text": "In as much\nas it is quite difficult to conduct certain infectious disease studies in humans, there has\nbeen a critical need for small animal models for infectious diseases. Appreciating the\nlimitations of existing models, we developed several novel and complementary mouse\nmodels that are ideal for use in systems genetics studies of complex diseases. These\nmodels not only allow biological validation of known genetic associations, but importantly they afford an unbiased tool for discovering novel genes and pathways contributing to disease outcomes, under different environments. 2008 Genetic effects on environmental vulnerability to disease." + } + ], + "3776e53f-5f7d-4cf4-ab7c-5fe06a1c0570": [ + { + "document_id": "3776e53f-5f7d-4cf4-ab7c-5fe06a1c0570", + "text": "Generalities\n\nMouse models have been developed to give new insights into human diseases.Mouse models can be classified into two main classes: 1) genetically modified mouse models, animals that lack (knockout) or overexpress a specific gene and the protein that is encoded for, 2) mice that acquire a disease/symptom following an experimental procedure, such as diet, chemical injections and specific surgery." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "4439ac39-e421-482f-9aa9-9ad11fa641c1": [ + { + "document_id": "4439ac39-e421-482f-9aa9-9ad11fa641c1", + "text": "In\nother cases, the rat phenotypes have proved more\nrobust and consistent, such as pristane-induced\narthritis as a model for rheumatoid arthritis\n(Holmdahl et al. 2001) and cresentic glomerulonephritis (Aitman et al. 2006). Decades of careful\nphenotyping and detailed analyses in rat experimental crosses have led to the localization of hundreds of rat physiological quantitative trait loci\n(pQTLs) containing genes that confer susceptibility\nto complex disease phenotypes, including hypertension, type 2 diabetes, autoimmune disorders, and\ncancer (Flint et al. 2005). The availability of the rat genome sequence in\nJune 2003 (Gibbs et al." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": ", et al. , Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse\nModels: A Path toward Precision Medicine. Neuron, 2019. 101(3): p. 399-411 e5. Beura, L.K. , et al. , Normalizing the environment recapitulates adult human immune traits in laboratory mice. Nature, 2016. 532(7600): p. 512-6. Kleinert, M., et al. , Animal models of obesity and diabetes mellitus. Nat Rev Endocrinol, 2018. 14(3): p. 140-162. Kebede, M.A. and A.D. Attie, Insights into obesity and diabetes at the intersection of mouse and human genetics. Trends Endocrinol Metab, 2014. 25(10): p. 493-501.\nvon Scheidt, M., et al." + }, + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "Researchers have access to all the tissue samples in mice, especially those highly relevant in\ndiseases, which is impossible in most human studies because of ethical issues. 8. Mouse models can be used to capture the disease progression stages in longitudinal studies. 9. Mouse genetic populations are able to model the genetic diversity of human populations, and require\nfewer individuals for genetic association analyses. 10. Unlike human genetic studies where data should always be kept highly confidential, data from mouse\nstudies can be made public available to facilitate its re-analysis to the fullest extent." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nPolygenic models of obesity.Polygenic models of obesity may provide a more accurate model of the human condition.A variety of different polygenic mouse models of obesity, glucose intolerance and diabetes exist, allowing a variety of genotypes and susceptibilities to be studied.However, unlike the monogenic models, there are no wild-type controls.In addition, the male sex bias is more extreme in these models (Leiter, 2009).These polygenic models have been used in a wide variety of studies that have aimed to reverse the symptoms of type 2 diabetes (Chen et al., 2009;Fukaya et al., 2009;Guo et al., 2010;Mochizuki et al., 2011;Yoshinari and Igarashi, 2011), understand more about the interplay of obesity and glucose homeostasis (Kluth et al., 2011) (Jurgens et al., 2007) or study diabetic complications (Cheng et al., 2007;Fang et al., 2010;Buck et al., 2011;Lee et al., 2011a).KK mice.KK mice are a mildly obese and hyperleptinaemic strain derived from wild-derived ddY mice in Japan by Kondo in 1957 (Clee and Attie, 2007).They develop severe hyperinsulinaemia and demonstrate insulin resistance in both muscle and adipose tissue.The pancreatic islets are hypertrophic and degranulated.This mouse strain also shows signs of diabetic nephropathy (Ikeda, 1994)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": ", 2008) and specific genetic factors for predisposition to DN were\nrecently identified in several diabetic sibling studies (Bleyer et al. , 2008; Schelling et\nal.,2008; Tanaka et al. , 2005). Similar to humans, inbred strains of mice exhibit differences in their susceptibility to\ndiabetes, renal and cardiovascular diseases (Krolewski et al. , 1996). More recently,\ndifferential susceptibilities to DN have also been observed in well-defined strains of\n\n23" + } + ], + "84b037c5-8e75-434f-aad1-d270257963f6": [ + { + "document_id": "84b037c5-8e75-434f-aad1-d270257963f6", + "text": "\n\nThe third advantage of the mouse model is that after identification of a candidate gene, direct genetic evidence for its involvement in a pathophysiology can be obtained in mice, but very rarely in humans.Thus, inbred mouse models are ideally suited for the investigation of the obesity-associated diabetes.However, the genetic homogeneity of the inbred strains is not only an advantage, it also limits their potential.Individuals of an inbred mouse line are genetically identical, and it cannot be expected that a single strain carries more than a small portion of all relevant gene variants.Currently, more than 2000 mouse QTL for different traits have been identified in crosses between inbred stains, but only about 1 % has been characterized on molecular level (Flint et al. 2005).Thus, more than one model and new resources, e.g., systems biology may be required for a complete genetic analysis of complex traits.Previous and ongoing research supports the view that the combination of individual genomes-by intercross of inbred strains and by the generation of congenic lineswill reveal effects of many more genes and gene interactions than can be observed in a single inbred strain.Because the cross-breeding experiments are time consuming and expensive, selecting the ''right'' models of the obesity-associated diabetes is of crucial importance (Leiter 2009).Another advantage of mouse studies in comparison to human studies is the ability to control the environment and to investigate effects of diets, exercise, and intestinal microbiota." + } + ], + "8604652e-2477-4552-8f43-f5f19e421df2": [ + { + "document_id": "8604652e-2477-4552-8f43-f5f19e421df2", + "text": "Introduction\nRodents, particularly mouse and rat have been widely used for biomedical research in models of\nhuman diseases since it is known that almost of all of genes in mouse and rat are similar to that of\nhumans. However, not every genetic pathway or molecular mechanism of diseases or drugs discovered\nto be efficacious in these models can be extrapolated to human diseases. Thus, while much data from\nanimal studies have been successfully applied to humans, some have not. The present study aims to\nexplore the degrees of differences in the causal pathways for lung fibrosis between humans and mice." + } + ], + "90015638-c92d-4506-95b5-b789f08d613a": [ + { + "document_id": "90015638-c92d-4506-95b5-b789f08d613a", + "text": "\n\nThese limitations support the increasing need of experimental systems to characterize the fundamental biological mechanisms responsible for diabetes inheritance and the function of risk genes.In the context of diabetes pathogenesis, in vitro systems are useful but often limited, in particular to assess glucose tolerance, insulin sensitivity, islet architecture and function and diabetes complications.The laboratory mouse provides a wide range of experimental models for diabetes gene discovery and for in vivo post-GWAS studies of diabetes that develops either spontaneously or following gene editing [5].The laboratory rat is also a powerful system to implement phenotyping methods required to record biological variables relevant to common chronic diseases.The rat is the preferred model to perform phenotyping procedures that are often technically challenging in mice or require the collection of large volumes of blood or organs.For these reasons, rat models of type 2 diabetes or hypertension have been successfully used to localise in the genome genes controlling endophenotypes relevant to these complex diseases.This review addresses strategies used to map the genetic determinants of physiological and molecular phenotypes relevant to type 2 diabetes pathogenesis and to characterize their biological function in vivo through examples derived from genetic and genomic research in the Goto-Kakizaki (GK) rat strain." + } + ], + "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001": [ + { + "document_id": "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001", + "text": "However, many of the phenotypes of the homozygous null mutations\nwere extreme and/or did not model the complexity of the metabolic syndrome. For example, IR knockout (IR2/2) mice died\nbecause of developmental effects (Accili et al. , 1996), which precluded analysis of adult mice. Likewise, GLUT42/2 mice exhibited only moderate insulin resistance and were not overtly diabetic, suggesting compensatory mechanisms (Katz et al. , 1995). Monogenic GEMMs furthermore ignore the polygenic nature of\nmetabolic diseases, resulting from genetic and environmental\nfactors impacting at multiple levels in signaling cascades. Oligogenic mouse models remedied some of these shortcomings." + }, + { + "document_id": "ab1a324f-3c9c-4b41-bb1d-5d5ca216a001", + "text": "Since glucokinase2/2 mice are embryonic lethal, this collection of glucokinase mutants is useful for dissecting the pathogenesis of MODY2. Genetic reference populations (GRPs)\nPerhaps the most ‘‘refreshing’’ mouse resource for investigating\ncomplex diseases is the construction of mouse crosses using\ninbred mice and the subsequent QTL mapping. Inbred mice\nhave an inherent wealth of variation due to past spontaneous\nmutation events, which have been preserved through systematic and uninterrupted brother-sister matings (Paigen, 2003). Inbred mice are appealing since they are genetically identical\nwithin a strain but are diverse between strains." + } + ], + "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65": [ + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "text": "Mouse Models of Oxidative Stress and Mitochondrial\n\nDysfunction in Aging.Genetically engineered mouse models provide great systems to directly dissect the complex relationship between oxidative damage, mitochondrial dysfunction, and aging.Although it is difficult to manipulate mitochondrial genome, genetic engineering of nuclear genes that are involved in oxidative stress response and mitochondrial function has been utilized to study mitochondrial biology and aging." + } + ], + "dee36885-b2f4-4311-b70a-17e228034820": [ + { + "document_id": "dee36885-b2f4-4311-b70a-17e228034820", + "text": "Rodent models of glaucoma have gained favor in the research community due to their ease of handling and the lower costs associated with acquisition and care. In particular, the mouse provides a number of useful genetic\napproaches to create models and to test specific molecular interactions associated with the disease process. Furthermore, the mouse genome is relatively\nconserved compared to the human genome." + } + ], + "df542302-18b9-43c2-a421-cba1dba0b3be": [ + { + "document_id": "df542302-18b9-43c2-a421-cba1dba0b3be", + "text": "Better Mouse Models. A key point to bear in mind in assessing the usefulness of mouse models is the relative plasticity displayed by rodents faced with gene deletions.Thus, differences between the penetrance of mutations in human genes linked to monogenic forms of diabetes, including maturity onset diabetes of the young (MODY), between humans and mice, are usually observed [114] with the mouse equivalents showing far less marked disturbances in glycemia or changes which are seen only after deletion of both alleles.This clearly reflects the limitations of the use of mice (weight ∼25 g, life expectancy ∼3 years) for comparisons with human subjects.Nonetheless, and although the phenotypes of the above murine models are thus often more subtle than the human counterparts, they remain useful models for the study of diabetes, allowing single-targeted gene deletions which are impossible in man.For example, human populations with different genetic backgrounds have different susceptibility to the R235W ZnT8 polymorphism.We should not, therefore, find surprising the results that different genetic backgrounds and different diet reveal different phenotypes in ZnT8 knockout models." + } + ] + }, + "data_source": [], + "document_id": "E7F8082FA9D839C33A51A833B8FC1A89", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "DBA/2J", + "glaucoma", + "genetically&modified&mouse&models", + "diet", + "chemical&injections", + "specific&surgery", + "diabetes", + "Goto-Kakizaki", + "Otsuka&Long-Evans&Tokushima&Fatty", + "Nagoya&Shibata&Yasuda&mouse", + "New&Zealand&Obese&mouse", + "Tsumura-Suzuki&Obese&Diabetes&mouse" + ], + "metadata": [ + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "Data suggest that urine AQP5/creatinine ratio is significantly higher in patients with diabetic nephropathy than in control subjects, subjects diabetes, or subjects with nephropathy of unknown etiology; urine AQP5/creatinine ratio increases with stage of diabetic nephropathy; this biomarker may improve clinical models in distinguishing diabetic nephropathy from normal controls and subjects with type 2 diabetic alone.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab213643" + }, + { + "object": "these pathological and molecular changes induced by diabetes were eliminated in JNK2-/- diabetic mice compared to JNK2-/- control mice, and C66 treatment did not further affect these parameters in JNK2-/- diabetic mice. Our results indicate that C66 ameliorates diabetic cardiomyopathy by inhibiting JNK2 relative pathways.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab908673" + }, + { + "object": "We used young, leptin receptor deficient Db/Db mice to mimic the effect of diet and diabetes on adolescents. Db/Db and Control mice were fed either Western or Control diets, and were sacrificed at 3 months of age. Db/Db mice were obese, while only female mice developed diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1014541" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "Microarray analysis of Chlamydia psittaci infected C57BL/6J and DBA/2J mice shows that proinflammatory cytokine and chemokine Cxcl1 KC, Cxcl2 Mip2, and Cxcl11 were up-regulated >10-fold in susceptible DBA/2J mice. Upstream regulators of inflammation such as TLRs and MyD88 were increased in resistant C57BL/6J vs susceptible DBA/2J mice. >100-fold increase in ifi204 and ifi203 proliferation of macrophage and >10-fold for NK cell activating C57BL/6J vs DBA/2J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab3623" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "MDM2 controls diabetic and non-diabetic renal antioxidant capacity via NRF2. NRF2 predominantly mediates MDM2's action on diabetic kidney disease. P53 is a key factor through which MDM2 activates NRF2 in diabetic kidney disease.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab473349" + }, + { + "object": "Data suggest expression of Sirt3 in developing skeletal muscle can be regulated by diet; maternal low-protein diet down-regulates Sirt3; postnatal high-fat diet up-regulates Sirt3; these two factors increase susceptibility of pups to type 2 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab203342" + }, + { + "object": "diabetic subjects with diabetic foot showed in comparison with diabetics without diabetic foot higher IL-6 and resistin plasma levels, lower adiponectin plasma levels", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab228757" + } + ], + "question": "Which types of mouse studies are popular in genenetwork, where a type an ailment, e.g. asthma, allergies, aging, heart failure, diabetes, glaucoma?", + "subquestions": null, + "task_id": "E7F8082FA9D839C33A51A833B8FC1A89", + "usage": { + "chatgpt": 6380, + "gpt-4": 4204, + "gpt-4-turbo-preview": 3247 + }, + "user_id": 2 + }, + "document_id": "E7F8082FA9D839C33A51A833B8FC1A89", + "task_id": "E7F8082FA9D839C33A51A833B8FC1A89" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response03.json b/gnqa/paper1_eval/src/data/responses/test/response03.json new file mode 100644 index 0000000..c14ab82 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response03.json @@ -0,0 +1,205 @@ +{ + "created_at": "2024-03-25T10:53:10.812694Z", + "data": { + "amplify": false, + "answer": "Yes, the studies identified significant quantitative trait loci associated with both type 1 and type 2 diabetes. These include loci such as TCF7L2, DNER, SRR, HNF1A, KCNQ1, CDKN2A/CDKN2B, JAZF1, CDC123, CAMK1D, TSPAN8, LGR5, THADA, ADAMTS9, NOTCH2, and others. However, the significance of some loci varied across different populations and studies.", + "context": { + "0bcb68e6-4da3-457c-a68c-981b14d1ded8": [ + { + "document_id": "0bcb68e6-4da3-457c-a68c-981b14d1ded8", + "text": "\n\nAdditional large-scale meta-analysis predicated upon increased sample size, were carried out on existing datasets from the WTCCC [18] and the Genetics of Kidneys in Diabetes (GoKinD) study [69][70][71] plus control data derived from the National Institute of Mental Health.These investigators observed significant association of previously observed loci.Importantly, they did not observe evidence of new T1D loci reaching the threshold for genome-wide significance.Instead they re-analyzed the most nominally significant associated SNP in an independent British cohort of approximately 6000 cases, 7000 controls and in 2800 families, where they uncovered four additional loci, BACH2 (previously reported [67]), 10p15 harboring protein kinase C theta (PRKCQ), 15q24 harboring nine genes including the cathepsin H (CTSH), complement 1q (C1q), tumor necrosis factor related protein 6 (C1QTNF6) and somatostatin receptor 3 (SSTR3) genes.Table 1 summarizes the 16 T1D loci reported to date.An example of a tag-SNP that captures the association with T1D in each instance is highlighted together with its relative minor allele frequency in controls and what magnitude of risk or protection it confers.Key references regarding the role of each locus in the context of the disease are included and along with the chromosomal band where each locus resides, the main candidate gene (symbol and full name) is highlighted." + } + ], + "0de85e11-dcbb-4538-b043-ee18a30e9f14": [ + { + "document_id": "0de85e11-dcbb-4538-b043-ee18a30e9f14", + "text": "Detection of established loci\n\nWe explored the extent to which previously reported type 2 diabetes association signals could be detected in African-descent individuals.Based on the previously reported effect sizes and the effect allele frequency and sample size from our African meta-analysis, we had sufficient power (80%) to detect three signals (TCF7L2, DNER and SRR) at genome-wide significance (p < 2.5 × 10 −8 ) (ESM Table 2).Only the TCF7L2 variant reached genome-wide significance in our study, whereas both variants in DNER (rs1861612) and SRR (rs391300), originally discovered in Pima Indians and East Asians, respectively, had p > 0.1 (ESM Table 2)." + } + ], + "1c2f4eb9-5880-418a-be08-4c33ec3a8889": [ + { + "document_id": "1c2f4eb9-5880-418a-be08-4c33ec3a8889", + "text": "\n\nOn the basis of the combined stage 1-3 analyses, we found that six signals reached compelling levels of evidence (P ¼ 5.0 Â 10 -8 or better) for association with T2D (Table 2).As in all linkage disequilibrium (LD)-mapping approaches, characterization of the causal variants responsible, their effect sizes and the genes through which they act will require extensive resequencing and fine-mapping.However, on the basis of current evidence, we found that the most associated variants in each of these signals map to intron 1 of JAZF1, between CDC123 and CAMK1D, between TSPAN8 and LGR5, in exon 24 of THADA, near ADAMTS9 and in intron 5 of NOTCH2." + } + ], + "33c5de8c-7efc-41df-a540-22729d8b7d2c": [ + { + "document_id": "33c5de8c-7efc-41df-a540-22729d8b7d2c", + "text": "\n\nReplication study of newly identified type 1 diabetes risk loci" + } + ], + "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f": [ + { + "document_id": "3675ae2a-18d5-4f2b-97e1-1827eddc0f6f", + "text": "\n\nAlthough these are considered to be loci convincingly associated with susceptibility to type 2 diabetes in populations of European descent, other genes related to susceptibility to the disease are probably still unidentified, particularly those for populations of other ancestries.In order to uncover genetic variants that increase the risk of type 2 diabetes, we conducted a genome-wide association study in Japanese individuals with type 2 diabetes and unrelated controls.We first genotyped 268,068 SNPs, which covered approximately 56% of common SNPs in the Japanese, in 194 individuals with type 2 diabetes and diabetic retinopathy (case 1) and in 1,558 controls (control 1) collected in the BioBank Japan.We compared the allele frequencies of 207,097 successfully genotyped SNPs and selected the 8,323 SNPs showing the lowest P values.We then attempted to genotype these 8,323 SNPs in 1,367 individuals with type 2 diabetes and diabetic retinopathy (case 2) and for 1,266 controls (control 2) (stage 2), and successfully obtained data for 6,731 SNPs (the P value distribution in the second test is shown in Supplementary Fig. 1a online).The results of principal component analysis 8 in the stage 1 and 2 samples and HapMap samples revealed that there was no evidence for population stratification between the case and control groups throughout the present tests (Supplementary Fig. 1b,c).We selected the 9 SNP loci showing P values o0.0001 (additive model in stage 2, Table 1) and genotyped a third set of cases and controls comprising 3,557 Japanese individuals with type 2 diabetes (cases 3,4,5) and 1,352 controls (controls 3,4).We evaluated the differences in the population structure among these three sets of case and two sets of control groups by Wright's F test.As the results indicated that there was no difference in the population structure among these groups (Supplementary Table 1b online), we combined these populations for the third test of case-control study.The third set of analysis identified the significant associations for six SNPs (Table 1), including the CDKAL1 locus at 6p22.3 (rs4712524, rs9295475 and rs9460546), the IGF2BP2 locus at 3q27.2 (rs6769511 and rs4376068) and the KCNQ1 locus at 11p15.5 (rs2283228).The remaining three SNPs (rs13259803, rs612774 and rs10836097) had P values of 40.05 in the third test and were not further examined.CDKAL1 and IGF2BP2 were previously reported as susceptibility genes for type 2 diabetes in the Japanese population 9 .Therefore, we focused on the KCNQ1 locus, which was highly associated with type 2 diabetes." + } + ], + "3a066437-9d88-46c7-bc55-9992728847a7": [ + { + "document_id": "3a066437-9d88-46c7-bc55-9992728847a7", + "text": "\n\nWe consider these data as an interesting preliminary result that surely requires additional independent studies including a higher number of patients in order to confirm and clarify the possible contribution of this locus to the development of T2DM complications." + } + ], + "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a": [ + { + "document_id": "3bd9d1c6-6b4b-42dc-915a-b3323f1fb98a", + "text": "DISCUSSION\n\nTaken together, our full second-stage approach and combined meta-analysis have revealed additional loci associated with type 1 diabetes.Clearly the risks are relatively modest compared with previously described associations, and it was only with this sample size at our disposal that we could we detect and establish these signals as true positives through an independent validation effort." + } + ], + "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc": [ + { + "document_id": "3ce10e4a-3ddc-4c7c-8897-84285ccfeedc", + "text": "Identification of susceptibility loci\n\nThe degree of evidence for all reported T2D loci was quantified as follows: a locus with a logarithm of odds ratio (LOD) score of 3 or more was considered significant, a LOD score between 2.2 and 3 was considered suggestive and a LOD score between 1 and 2.2 was considered nominal.For T2D, only those loci were included that were significant at least once, or were suggestive in at least one study and at least nominal in two or more studies.The inclusion of the second category of loci was based on a study by Wiltshire et al. [72], in which it was postulated that locus counting is a useful additional tool for the evaluation of genome scan data for complex trait loci.We used the same two criteria to determine the loci from the five papers published on obesity since 2004 and combined these loci with those from Bell et al. [7].As obesity phenotypes, BMI, serum leptin levels, abdominal subcutaneous and visceral fat, and percentage body fat were included.All of these phenotypes were used as continuous quantitative traits, as well as with various cut-off levels." + } + ], + "4be1d780-404a-4826-ba06-80b2c15e705b": [ + { + "document_id": "4be1d780-404a-4826-ba06-80b2c15e705b", + "text": "\n\nToday, more than 100 loci for type 2 diabetes and glycemic traits have been identified through numerous GWA studies of common and rare variation in populations of diverse ancestral origins [31]; however, to date, very few GWA studies have been published in cohorts of Mexican ancestry.The first GWA study performed in a non-European cohort was published in 2007 and comprised 561 Mexican American type 2 diabetes cases and controls drawn from the Starr County Health Studies [32].Although no loci reached genome-wide significance, several loci identified in prior GWA studies in Europeans were replicated [32].This analysis was subsequently expanded (N = 1273) and meta-analyzed with a cohort from Mexico City (N = 1310) in 2011 [33,34].The most significant variants observed in this meta-analysis included known regions near HNF1A and KCNQ1.Top association signals were then meta-analyzed with the DIAGRAM and DIAGRAM+ datasets of European ancestry individuals, resulting in two regions reaching genome-wide significance: HNF1A and CDKN2A/CDKN2B (Table 1).Top association signals in both studies were annotated to explore their roles as expression quantitative trait loci (eQTL) in both adipose and muscle tissues, revealing a marked excess of transacting eQTL in top signals in both tissue types." + } + ], + "5293f814-f4a7-48e0-b4e5-b1f13fdc8516": [ + { + "document_id": "5293f814-f4a7-48e0-b4e5-b1f13fdc8516", + "text": "\n\n75±79 The main conclusion is that there is no major locus for T2D (analogous to HLA in type 1 diabetes).This is not surprising given the modest l s for T2D (approximately 3.5 in Europeans), imposing a limit on the magnitude of any single gene eect. 4Many scans have consequently been signi®cantly underpowered to detect the modest gene eects anticipated.Certainly, few T2D scans have reported linkages meeting the established criteria for genomewide signi®cance. 80This modest power, combined with the diversity of the pedigrees sampled and the analytical techniques used, means that the replication of positive ®ndings between data sets has been the exception rather than the rule." + } + ], + "711e3d33-a196-4072-bc31-ffaa6bb3efa0": [ + { + "document_id": "711e3d33-a196-4072-bc31-ffaa6bb3efa0", + "text": "Quantitative Trait Analysis\n\nExploration of putative T2DM variants with quantitative glycemic traits in a subset of African-American samples (n = 671 from the IRAS and IRASFS control samples, Table S5) revealed limited insight into the biological mechanism associated with T2DM risk.In addition, the five putative African-American T2DM susceptibility loci were tested for association with quantitative measures of glucose homeostasis in the European Caucasian population, in silico, by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC; [16]).These results did not provide further insight into the probable role these variants may have in disease susceptibility (Table S6).The most significantly associated SNP in African Americans, rs7560163, failed quality controls filters and was not included in analysis likely due to being monomorphic as seen in a representative Caucasian population from the HapMap project (Table S4)." + } + ], + "91d6996a-319d-461e-ae78-3c64a70832cc": [ + { + "document_id": "91d6996a-319d-461e-ae78-3c64a70832cc", + "text": "\n\nDiscovery of novel loci for T2D susceptibility.We tested for T2D association with ~27 million variants passing quality-control filters, ~21 million of which had a minor allele frequency (MAF) < 5%.Our meta-analysis identified variants at 231 loci reaching genomewide significance (P < 5 × 10 −8 ) in the BMI-unadjusted analysis (N eff 231,436) and 152 in the smaller (N eff 157,401) BMI-adjusted analysis.Of the 243 loci identified across these two analyses, 135 mapped outside regions previously implicated in T2D risk (Methods, Fig. 1 and Supplementary Table 2)." + } + ], + "ad88aed6-75ba-469d-b96b-7be4a65be8fc": [ + { + "document_id": "ad88aed6-75ba-469d-b96b-7be4a65be8fc", + "text": "\n\nGenetic studies performed since 2012 have identified many additional T2D loci based on risk alleles common in one population but less common in others.Studies in African Americans identified RND3-RBM43 (28), HLA-B and INS-IGF2 (29).Studies in South Asians identified TMEM163 (30) and SGCG (31).One locus, SLC16A11-SLC16A13, was simultaneously identified in Japanese and Mexican Americans (32,33), and studies in East Asians identified ANK1 (34), GRK5 and RASGRP1 (35), LEP and GPSM1 (32), and CCDC63 and C12orf51 (36).A study of individuals from Greenland identified TBC1D4 (37), and a sequencing-based study of Danes with follow-up in other Europeans identified MACF1 (38).Finally, the largest GWAS to date in American Indians identified DNER at near genome-wide significance (P = 6.6 × 10 −8 ) (39).Three of these studies imputed GWAS data using the 1000 Genomes Project sequence-based reference panels, providing better genome coverage (29,32,33,40).Taken together, these studies highlight the value of diverse populations, including founder and historically isolated populations, to detect risk loci." + } + ], + "b973bd17-aac9-4d68-8ac4-1c683165b68f": [ + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nFinally, a recent study identified additional susceptibility loci for type 2 diabetes by performing a meta-analysis of three published GWAs. 21As acknowledged by the authors, GWAs are limited by the modest effect sizes of individual common variants and the need for stringent statistical thresholds.Thus, by combining data involving 10,128 samples, the authors found in the initial stages of the analysis highly associated variants (they followed only 69 signals out of over 2 million metaanalyzed SNPs) with P values Ͻ10 Ϫ4 in unknown loci, and 11 of these type 2 diabetes' associated SNPs were taken forward to further stages of analysis.Large stage replication testing allowed the detection of at least six previously unknown loci with robust evidence for association with type 2 diabetes." + }, + { + "document_id": "b973bd17-aac9-4d68-8ac4-1c683165b68f", + "text": "\n\nSurprisingly, data about previous published loci associated with type 2 diabetes were not sufficiently powerful to reach a significant P value in individual scans.For example, variants at SLC30A8 and PPARG were significantly associated with type 2 diabetes only when pooling all the GWAs data, whereas in a single genome scan (DGI), no gene showed a positive signal (P value: 0.92 and 0.83, respectively).Thus, this may suggest that GWAs are still underpowered to find SNPs with small effect size." + } + ], + "d86525a8-0a2f-44a8-b343-61a5df8d6e68": [ + { + "document_id": "d86525a8-0a2f-44a8-b343-61a5df8d6e68", + "text": "\nBackground: The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci, but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.Methods: We analysed data from two sources: (1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); (2) the Rapid Response 2 (RR2) project of the T1DGC, which genotyped 4417 individuals from 1062 non-overlapping families, including 2059 affected individuals (mostly sibling pairs) for the 1536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.Results: One locus, mapping to a linkage disequilibrium (LD) block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect LD with each other (r 2 = 1).We obtained a joint p value of 1.3610 26 , which exceeds by an order of magnitude the conservative threshold of 3.26610 25 obtained by correcting for the 1536 single nucleotide polymorphisms (SNPs) tested in our study.Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83610 29 .Conclusions: A novel type 1 diabetes locus was discovered.It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and T cell receptor (TCR) signalling by activating the Ras signalling pathway." + } + ], + "dad48e98-2dcc-41ae-866a-139f5540a24c": [ + { + "document_id": "dad48e98-2dcc-41ae-866a-139f5540a24c", + "text": "\n\nFinally, we examined whether genes identified using our association studies were enriched within diabetes-related pathways.We collated a list of 42 genes to which 53 CpG sites associated with T2D traits (CS score ≥1.77, combined P < 0.017) mapped.Even in this small dataset, pathway analysis (Supplementary Material, Table S12) indicated significant enrichment in 31 pathways (Fisher's exact P < 0.05), including those related to circadian clock (P = 0.005), adipocytokine signaling (P = 0.009), leptin pathway (P = 0.023), HDL-mediated lipid transport (P = 0.031) and insulin signaling (P = 0.033)." + } + ], + "e88b610f-8afa-46f7-a03c-d7bd579a7496": [ + { + "document_id": "e88b610f-8afa-46f7-a03c-d7bd579a7496", + "text": "\n\nIn recent years, progress has been made in following up mechanistic studies of GWAS type 2 diabetes-association signals [6,7,9,[25][26][27][28][29][30], but challenges remain in sifting through the many associated variants at a locus to identify those influencing disease.We hypothesized that a common variant with modest effect underlies the association at the CDC123/CAMK1D locus and evaluated the location of high LD variants (r 2 $.7; n = 11) at the locus relative to known transcripts and to putative DNA regulatory elements.We identified two variants that overlapped putative islet and/or liver regulatory regions and none located in exons.We did not assess variants in lower LD (r 2 ,.7), and additional functional SNPs may exist at this locus acting through alternate functional mechanisms untested in the current study." + } + ], + "fdbabc3c-ec60-45ce-9f5c-683f745c4d00": [ + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "\n\nMeta-analysis results for T2D SNPs for insulin and glucose-related traits." + }, + { + "document_id": "fdbabc3c-ec60-45ce-9f5c-683f745c4d00", + "text": "A r t i c l e s\n\nBy combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P < 5 × 10 −8 .These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A).The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation.We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits." + } + ] + }, + "data_source": [], + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "TCF7L2", + "DNER", + "SRR", + "HNF1A", + "KCNQ1", + "CDKN2A", + "CDKN2B", + "JAZF1", + "CDC123", + "CAMK1D" + ], + "metadata": [ + { + "object": "We identified a Congenital long QT syndrome LQTS family harboring three compound mutations in different genes KCNQ1-R174C, hERG-E1039X and SCN5A-E428K. IKs-like, IKr-like, INa-like currents and the functional interaction between KCNQ1-R174C and hERG-E1039X channels were studied using patch-clamp.Expression of KCNQ1-R174C alone showed no IKs. Co-expression of KCNQ1-WT + KCNQ1-R174C caused a loss-of-function in IKs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007244" + }, + { + "object": "Pancreatic cancer was induced in adult mice by the combination of KRASG12D overexpression and loss of Tp53 and Cdkn2a only if Cdkn2b was concomitantly inactivated. inactivation of both Cdkn2b and Cdkn2a was necessary for Rb phosphorylation and to encompass oncogene-induced cellular senescence.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab580373" + }, + { + "object": "Twenty-five different variants were identified in GCK gene 30 probands-61% of positivity, and 7 variants in HNF1A 10 probands-17% of positivity. Fourteen of them were novel 12- GCK /2- HNF1A . ACMG guidelines were able to classify a large portion of variants as pathogenic 36%- GCK /86%- HNF1A and likely pathogenic 44%- GCK /14%- HNF1A , with 16% 5/32 as uncertain significance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab977086" + }, + { + "object": "We found that CDKN2B was a virtual target of miR-15a-5p with potential binding sites in the 3'UTR of CDKN2B 77-83 bp. We also showed that miR-15a-5p could bind to the CDKN2B 3'UTR. The data revealed a negative regulatory role of miR-15a-5p in the apoptosis of smooth muscle cells via targeting CDKN2B, and showed that miR-15a-5p could be a novel therapeutic target of AAA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004682" + }, + { + "object": "For each gene and the four pathways in which they occurred, we tested whether pancreatic cancer PC patients overall or CDKN2A+ and CDKN2A- cases separately had an increased number of rare nonsynonymous variants. Overall, we identified 35 missense variants in PC patients, 14 in CDKN2A+ and 21 in CDKN2A- PC cases.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab300370" + }, + { + "object": "we investigated the effects of KCNQ1 A340E, a loss-of-function mutant. J343 mice bearing KCNQ1 A340E demonstrated a much higher 24-h intake of electrolytes potassium, sodium, and chloride. KCNQ1, therefore, is suggested to play a central role in electrolyte metabolism. KCNQ1 A340E, with the loss-of-function phenotype, may dysregulate electrolyte homeostasis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1008629" + }, + { + "object": "Results show that C-FOS directly binds to rs7074440 TCF7L2. Its knockdown decreases TCF7L2 gene expression proving evidence that c-FOS protein regulates TCF7L2 through its binding to rs7074440.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab661049" + }, + { + "object": "This review provides an update of the latest research advances on JAZF1 and its regulatory network in T2 diabetes mellitus T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab935511" + }, + { + "object": "JAZF1 was identified as a direct miR-1275 target. miR-1275 supresses migration and invasion of gastric cancer cells in vitro and in vivo, which was restored by JAZF1 overexpression. Moreover, JAZF1 was recognized as a direct regulator of Vimentin.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab434327" + }, + { + "object": "DNA hypermethylation of KCNQ1 promoter resulted in its downregulation in hepatocellular carcinoma HCC. Bioinformatic analysis indicated a regulatory role of KCNQ1 in the epithelial-to-mesenchymal transition process. Gain-of-function study showed that KCNQ1 exhibited remarkable inhibitory roles on tumor metastasis in vitro and in vivo.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab235904" + } + ], + "question": "Do we find significant quantitative trait loci in the studies for diabetes?", + "subquestions": null, + "task_id": "B7084C90C3CF93908B3FB34BBA00743B", + "usage": { + "chatgpt": 9426, + "gpt-4": 4931, + "gpt-4-turbo-preview": 3991 + }, + "user_id": 2 + }, + "document_id": "B7084C90C3CF93908B3FB34BBA00743B", + "task_id": "B7084C90C3CF93908B3FB34BBA00743B" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response04.json b/gnqa/paper1_eval/src/data/responses/test/response04.json new file mode 100644 index 0000000..ea08b61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response04.json @@ -0,0 +1,189 @@ +{ + "created_at": "2024-03-25T14:00:01.668047Z", + "data": { + "amplify": false, + "answer": "Several environmental factors can affect mice and influence diabetes. These include the stress response, which can be greater in male mice and can confound feeding behavior, the unique gut microbiota of rodents from different production facilities, cage position, single versus group housing, the skill level of the researcher, ambient room temperature, and the type of cage bedding. Diet, particularly high-fat feeding, can also induce obesity and diabetes. Additionally, the intrauterine environment can impact the subsequent development of diabetes amongst offspring.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ae5d2bb-b09d-4646-922a-277188b53cbb": [ + { + "document_id": "0ae5d2bb-b09d-4646-922a-277188b53cbb", + "text": "\n\nIn these models, adult offspring of diabetic animals were noted to have normal development of the endocrine pancreas (Aerts et al., 1997;Ma et al., 2012).However, they develop glucose intolerance and impaired insulin response to glucose challenge, and display insulin resistance, mainly in the liver and muscle, highlighting the presence of both insulin resistance and b-cell dysfunction (Aerts et al., 1988;Holemans et al., 1991a,b).The key role of the intrauterine environment was demonstrated by a series of embryo transfer experiments, which showed that the diabetes risk in a low genetic risk strain can be substantially increased by the hyperglycaemic environment of a dam with a high genetic risk of diabetes (Gill-Randall et al., 2004)." + } + ], + "20771d36-aa57-46ad-b3c6-80f5b038ba43": [ + { + "document_id": "20771d36-aa57-46ad-b3c6-80f5b038ba43", + "text": "\n\nDiabetes-obesity syndromes in rodents" + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "However, in other contexts, B6 mice are more likely\nthan D2 to spontaneously develop diabetic syndromes,\nAging Clin Exp Res\n\nindicating that risk factors exist on both genetic backgrounds [29]. QTL mapping studies indicate that these\nmurine metabolic traits have a complex genetic architecture that is not dominated by any single allele [29–31],\nmuch like humans [32, 33]. Prior work identified candidate genes on Chr 13 that might\nunderlie diabetes-related traits, including RASA1, Nnt, and\nPSK1. RASA1 show strong sequence differences between\nB6 and D2 strains [34]. Rasche et al." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nOther diet-induced rodent models of type 2 diabetes.Although rats and mice are the most commonly used models for studies of type 2 diabetes, other rodents have also been identified as useful models.These include the desert gerbil and the newly described Nile grass rat, both of which tend to develop obesity in captivity." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSince the obesity is induced by environmental manipulation rather than genes, it is thought to model the human situation more accurately than genetic models of obesityinduced diabetes.High fat feeding is often used in transgenic or knock-out models, which may not show an overt diabetic phenotype under normal conditions, but when the beta cells are 'pushed', the gene may be shown to be of importance.It should be noted that the background strain of the mice can determine the susceptibility to diet-induced metabolic changes, and thus, effects could be missed if a more resistant strain is used (Surwit et al., 1995;Bachmanov et al., 2001;Almind and Kahn, 2004).It has also been reported that there is heterogeneity of the response to high fat feeding within the inbred C57BL/6 strain, indicating that differential responses to a high-fat diet are not purely genetic (Burcelin et al., 2002)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "Other considerations and limitations\n\nA myriad of factors affect animal experiments.Men elicit a greater stress response in mice than women 292 , likely confounding feeding behaviour.Rodents from different production facilities (for example, Jackson Laboratory and Taconic) have unique gut microbiotas 293 , perhaps contributing to differences in their susceptibility to DIO and related diabetic complications 293 .Similarly, cage position within a rack of cages, single versus group housing, the skill level of the researcher, ambient room temperature or the type of cage bedding can all affect experimental outcomes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nWe believe there are several factors that researchers should consider when conducting obesity and diabetes mellitus research in rodents (FIG.2).Although our list is by no means an exhaustive, it demonstrates the complexity and interconnectedness of the myriad of factors that can confound experimental outcomes.Although it is impossible to control for everything, researchers should accurately detail all experimental conditions and methods to allow for better interpretation of the results and, importantly, for better reproducibility." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nFigure2| Important experimental parameters and potential confounders of experimental outcomes in obesity and diabetes research and their interrelatedness.Countless factors influence experimental outcomes when using animal models, and what is enumerated here is by no means a complete list.This figure is one depiction of the multifactorial and interconnected genetic and environmental matrix that makes it virtually impossible to design the perfect experiment.For example, single-housing mice to obtain more accurate food intake data introduces a stress that in turn affects food intake.The severity of this stress response is both strain-specific and sex-dependent.What is important is to be aware of these challenges and to control for them in the most optimal manner.It is equally, if not more, important to accurately and comprehensively detail all experimental conditions in research papers, as these have bearing on the interpretation and reproducibility of the published results.DIO, diet-induced obesity." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAnother concern pertains to control mice.Compared with free-living mice in the wild, laboratory control mice with ad libitum access to food are sedentary, overweight, glucose intolerant and tend to die at a younger age 297 .Comparisons between mice with DIO and control mice might be analogous to investigating the genetic cause of obesity-resistance by comparing humans who are overweight or obese.This potential problem with control mice could explain why the use of DIO diets that have 40% to 60% of total energy from fat is so prevalent, as this might be necessary to achieve divergent weight gains.With free access to running wheels, C57BL/6J mice voluntarily run 5-10 km per day 298,299 .As is the case with humans 300 , mice get health benefits from regular physical activity including weight loss, decreased adiposity and improved insulin sensitivity 301,302 .Physical activity might also affect the epigenome over several generations 303 .An enriched physical and social cage environment alone improves leptin sensitivity and energy expenditure in mice, independent of physical activity 304,305 .Overall, these data suggest that with standard mouse husbandry, chow-fed laboratory mice are not the ideal healthy and lean control group for meaningful obesity research." + } + ], + "8cd81e24-a326-4443-bc37-0e6e421e70b2": [ + { + "document_id": "8cd81e24-a326-4443-bc37-0e6e421e70b2", + "text": "\n\nTo better address these points, various animal models have been developed.For example, using HFD-T2DM male rats, the F1 female offspring showed reduced β cell area and insulin secretion, together with glucose intolerance, without changes in body weight [145].The islets of the F1 female offspring showed differential expression of many genes involved in Ca 2+ , mitogen-activated protein kinase and Wnt signaling, apoptosis and cell cycle regulation [145].Similarly, in pregnant C57BL6J mice, food deprivation resulted in β cell mass reduction and an increased risk of β cell failure in offspring [146]." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "They are probably typical of those\nfew mice that develop diabetes more slowly and do\nnot tax the pancreatic insulin supply as severely early\nin the course of the disease. Attempts at therapy. Attempts to keep the weight\nof diabetic mice within normal limits by total or\npartial food restriction resulted in premature deaths. After it was discovered that gluconeogenesis is greatly\nincreased in diabetic mice, attempts were made to\nregulate blood sugar levels and also weight gain by\nfeeding rations devoid of carbohydrate." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "The degree\nof dependence of adiposity, hyperglycemia, and islet\nhypertrophy on food consumption varies among these\nmice, but in all, the increase in islet volume and consequent fi-eell hyperplasia appears to be an effective\n\n247\n\nmeans of maintaining blood sugar concentrations at\nnear normal levels. I n contrast, neither the diabetic\nsand rat [5] nor the diabetic mouse has hypertrophied\nislets and neither effectively controls blood sugar levels." + }, + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "HV~MEI,: Studies with the Mutation, Diabetes\n\nalmost undetectable. Similarly, the activities of citrate\nlyase and glucose-6-phosphate dehydrogenase were\ngreatly decreased in these older diabetic as compared\n\nDiabetologia\n\nthe diabetic mice have attained m a x i m u m weight,\nafter which no further accumulation of adipose tissue\nis noted. Fig. 8." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Animal models of diabetes in pregnancy and the role of intrauterine environment\n\nAnother important field of diabetes research that has relied heavily on animal experimentation is the study of diabetes in pregnancy and the role of the intrauterine environment on the subsequent development of diabetes amongst offspring." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "obesity", + "insulin&resistance", + "glucose&intolerance", + "high-fat&diet", + "environmental&factors", + "mouse&models", + "genetic&background", + "intrauterine&environment", + "diet-induced&obesity" + ], + "metadata": [ + { + "object": "Data suggest that secretion of insulin by beta-cells is related to insulin resistance in complex manner; insulin secretion is associated with type 2 diabetes in obese and non-obese subjects, but insulin resistance is associated with type 2 diabetes only in non-obese subjects. Chinese subjects were used in these studies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab210958" + }, + { + "object": "Data, including data from studies using knockout/transgenic mice, suggest that PrPC is involved in development of insulin resistance and obesity; PrPC knockout mice fed high-fat diet present all the symptoms associated with insulin resistance hyperglycemia, hyperinsulinemia, and obesity; transgenic mice overexpressing PrPC fed high-fat diet exhibit normal insulin sensitivity and reduced weight gain.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab215504" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "FNDC5 attenuates adipose tissue inflammation and insulin resistance via AMPK-mediated macrophage polarization in HFD-induced obesity. FNDC5 plays several beneficial roles in obesity and may be used as a therapeutic regimen for preventing inflammation and insulin resistance in obesity and diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab299408" + }, + { + "object": "WISP1 can be involved in glucose/lipid metabolism in obese youth, which may be modulated by IL-18. Increased WISP1 levels may be a risk factor of obesity and insulin resistance, and WISP1 has a potential therapeutic effect on insulin resistance in obese children and adolescents", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1017591" + }, + { + "object": "Obesity interacted with the TCF7L2-rs7903146 on Type 2 DiabetesT2D prevalence. Association of TCF7L2 polymorphism with T2D incidence was stronger in non-obese than in obese subjects. TCF7L2 predictive value was higher in non-obese subjects. We created obesity-specific genetic risk score with ten T2D-polymorphisms and demonstrated for the first time their higher strata-specific predictive value for T2D risk.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab541919" + }, + { + "object": "LCN-2 expression and serum levels could discriminate IGT from NGT and type 2 diabetes mellitus T2DMfrom IGT obese women and early predicting T2DM among obese women. While, LCN-2 expression level was the independent predictor of IGT in obese women. Combination of both LCN-2 expression and serum levels improved their diagnostic value in early detection of IGT and T2DM among obese women", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab445589" + }, + { + "object": "Gestational obesity and gestational diabetes mellitus may contribute to elevated serum chemerin. Serum chemerin in pregnancy was associated with insulin resistance and triglycerides. Chemerin gene may play a role both in obese and gestational diabetes mellitus patients", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321477" + }, + { + "object": "SGNE1 genetic variation does not contribute to obesity and common forms of Type 2 diabetes but may worsen glucose intolerance and insulin resistance, especially in the background of severe and early onset obesity", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab702715" + }, + { + "object": "our results indicate that UC-MSCs can alleviate insulin resistance in part via production of IL-6 that elicits M2 polarization. Additionally, human obesity and insulin resistance were associated with increased pro-inflammatory ATMs infiltration. Thus, MSCs may be a new treatment for obesity-related insulin resistance and T2D concerning macrophage polarized effects", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab560980" + } + ], + "question": "What environmental factors effect mice that also influence diabetes?", + "subquestions": null, + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "usage": { + "chatgpt": 6673, + "gpt-4": 3552, + "gpt-4-turbo-preview": 2599 + }, + "user_id": 2 + }, + "document_id": "F2F9D8F0AD775EA291F0358E622D33D4", + "task_id": "F2F9D8F0AD775EA291F0358E622D33D4" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response05.json b/gnqa/paper1_eval/src/data/responses/test/response05.json new file mode 100644 index 0000000..9a49068 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response05.json @@ -0,0 +1,191 @@ +{ + "created_at": "2024-03-25T14:02:33.179370Z", + "data": { + "amplify": false, + "answer": "1. Diabetes incidence study where mice were tested for diabetes monthly by blood glucose and weekly by urine assessment.\n2. Studies involving transgenic/knockout animal models of type 2 diabetes to study the role of genes and their effects on peripheral insulin action.\n3. Study involving male type-2 diabetic db/db mice and genetic control non-diabetic db/ mice.\n4. Study involving young adult mice fed a high-fat or Western diet to elicit DIO and insulin resistance.\n5. Study involving Akita mouse genotyping.\n6. Study involving 12-week-old male type 2 diabetic db/db mice and contemporary control wild-type mice.\n7. Study involving transgenic mice to create specific models of type 1 and type 2 diabetes.\n8. Study involving AKITA mice derived from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene.\n9. Study monitoring mice for the development of diabetes.", + "context": { + "02a9d5a9-41a4-4d70-b828-c4bda13fa01c": [ + { + "document_id": "02a9d5a9-41a4-4d70-b828-c4bda13fa01c", + "text": "Methods\n\nMouse models of diabetes.All animal studies were conducted according to a protocol approved by the Institutional Animal Care and Use Committee at the Beckman Research Institute of City of Hope.Male type-2 diabetic db/db mice (T2D leptin receptor deficient; Strain BKS.Cg-m þ / þ lepr db/J) and genetic control non-diabetic db/ þ mice (10-12 weeks old), were obtained from The Jackson Laboratory (Bar Harbor, ME) 11,17 .Male C57BL/6 mice (10 week old, The Jackson Laboratory) were injected with 50 mg kg À 1 of STZ intraperitoneally on 5 consecutive days.Mice injected with diluent served as controls.Diabetes was confirmed by tail vein blood glucose levels (fasting glucose 4300 mg dl À 1 ).Each group was composed of five to six mice.Mice were sacrificed at 4-5 or 22 (ref.17) weeks post-induction of diabetes.Glomeruli were isolated from freshly harvested kidneys by a sieving technique 11,17 in which renal capsules were removed, and the cortical tissue of each kidney separated by dissection.The cortical tissue was then carefully strained through a stainless sieve with a pore size of 150 mm by applying gentle pressure.Enriched glomerular tissue below the sieve was collected and transferred to another sieve with a pore size of 75 mm.After several washes with cold PBS, the glomerular tissue remaining on top of the sieve was collected.Pooled glomeruli were centrifuged, and the pellet was collected for RNA, protein extraction or for preparing MMCs 11,17 .Male Chop-KO mice were also obtained from the Jackson Laboratory (B6.129S(Cg)-Ddit3 tm2.1Dron /J).Based on our previous experience, sample size was determined to have enough power to detect an estimated difference between two groups.With minimum sample size of 5 in each group, the study can provide at least 80% power to detect an effect size of 2 between diabetic and non-diabetic groups or treated and untreated groups at the 0.05 significant level using two-sided t-test.Since we expected larger variation between groups especially for the mice with oligo-injection, we used more than 5 mice in each group (with 6 mice in each group, we have 80% power to detect an effect size of 1.8 at the 0.05 confidence level).Our actual results with current sample size did show statistical significance for majority of the miRNAs in the cluster.Histopathological and biochemical analysis of tissues or cells derived from animal models were performed by investigators masked to the genotypes or treatments of the animals." + } + ], + "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d": [ + { + "document_id": "0ffd1f4d-683e-4e44-a6b2-8d2d9849c45d", + "text": "Diabetes incidence study. Mice were kept for 20-28 weeks and tested for diabetes monthly by blood glucose and weekly by urine assessment, with a positive indication being followed by twice-weekly blood testing.Mice were diagnosed as diabetic when the blood glucose concentration was over 260 mg/dl (14.4 mM) after 2-3 h of fasting for two sequential tests.Glucose and insulin tolerance tests were performed by injecting glucose (2 g/kg body weight) or insulin (1 U/kg body weight) intraperitoneally in mice fasted for 6-7 h.Tail vein blood was tested by a Contour glucometer.Assessments of plasma insulin, proinsulin and C-peptide levels were performed using commercial ELISA kits, according to the manufacturer's instructions (insulin, proinsulin and C-peptide mouse ELISA kits, R&D Systems Quantikine).Assays were performed with blinding, with mice coded by number until experimental end." + } + ], + "42e06cda-627e-46f2-a289-c4c1fb6af8f2": [ + { + "document_id": "42e06cda-627e-46f2-a289-c4c1fb6af8f2", + "text": "Animal group and study design\n\nFirst, one set of animals comprising 12-week-old male type 2 diabetic db/db (C57BL/KsJ-db−/db−, n = 8) and contemporary control wild-type (C57BL/KsJ-db+/db−, n = 8) mice (Jackson Laboratories) were included in this study.Their weights and blood glucose levels were analysed to eliminate variation.Erectile functions of the animals were evaluated by the apomorphine-induced penile erection test, according to a previously described protocol (Pan et al. 2014).Afterwards, intracavernous pressure (ICP) investigations and histological measurements were applied to further confirm the results of the function tests.Then, all mice were sacrificed and the corpus cavernosum (CC) was collected from each mouse.Because the tissue of the CC is difficult to crush, we randomly collected the CCs from two mice and mixed them into one subgroup.As a result, four diabetic subgroups (DB groups) and four normal control subgroups (NC groups) were used for molecular measurements.Second, another set of animals, including three T2DMED and three normal control mice that were independent from the original set of animals, were included in the validation experiments using qRT-PCR.Third, another separate set of animals, including five T2DMED and five control mice, were used to verify one of the predicted targets, IGF-1, using ELISA.A luciferase reporter assay was performed to verify the binding of the differentially expressed miRNAs to the target gene IGF-1.All procedures were approved by the Institutional Animal Care and Use committee at Nanjing Medical University." + } + ], + "770beab7-59a4-4bbe-94a5-79a965ab696a": [ + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 2 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "\n\nSummary of rodent models of type 1 diabetes" + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Knock-out and transgenic mice in diabetes research\n\nTransgenic mice have been used to create specific models of type 1 and type 2 diabetes, including hIAPP mice, humanized mice with aspects of the human immune system and mice allowing conditional ablation of beta cells, as outlined above.Beta cells expressing fluorescent proteins can also provide elegant methods of tracking beta cells for use in diabetes research (Hara et al., 2003)." + }, + { + "document_id": "770beab7-59a4-4bbe-94a5-79a965ab696a", + "text": "Genetically induced insulin-dependent diabetes\n\nAKITA mice.The AKITA mouse was derived in Akita, Japan from a C57BL/6NSlc mouse with a spontaneous mutation in the insulin 2 gene preventing correct processing of proinsulin.This causes an overload of misfolded proteins and subsequent ER stress.This results in a severe insulindependent diabetes starting from 3 to 4 weeks of age, which is characterized by hyperglycaemia, hypoinsulinaemia, polyuria and polydipsia.Untreated homozygotes rarely survive longer than 12 weeks.The lack of beta cell mass in this model makes it an alternative to streptozotocin-treated mice in transplantation studies (Mathews et al., 2002).It has also been used as a model of type 1 diabetic macrovascular disease (Zhou et al., 2011) and neuropathy (Drel et al., 2011).In addition, this model is commonly used to study potential alleviators of ER stress in the islets and in this respect models some of the pathology of type 2 diabetes (Chen et al., 2011)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nTo achieve a slow pathogenesis of T2DM, young adult mice 284 or rats 285 are fed a high-fat or Western diet to elicit DIO and insulin resistance.Single or multiple injections with low-dose streptozotocin (~30-40 mg/kg intraperitoneally) then elicit partial loss of β-cells, which results in hypoinsulinaemia and hyperglycaemia.Protocols are being continuously refined and likely differ between species and even strains 283 .The HFD streptozotocin rat is sensitive to metformin, further demonstrating the utility of this model 285 .Downsides of streptozotocin treatment include liver and kidney toxicity and mild carcinogenic adverse effects (TABLE 1)." + } + ], + "785df64a-ebbf-4dca-94dd-0ae27f7ac815": [ + { + "document_id": "785df64a-ebbf-4dca-94dd-0ae27f7ac815", + "text": "Materials and methods\n2.1 Mouse models\n2.1.1 Mouse strains\n2.1.2 Induction of type 1 diabetes\n8\n2.1.3 Insulin treatment on diabetic mice\n2.1.4 Akita mouse genotyping\n2.2 Characterization of diabetic nephropathy in mice\n2.2.1 Proteinuria measurement\n2.2.2 Glomerular cells quantification\n2.2.3 Methenamine silver staining quantification\n\n3. 4. 5. 6." + } + ], + "7e809821-000d-4fff-971d-264650e3612b": [ + { + "document_id": "7e809821-000d-4fff-971d-264650e3612b", + "text": "\n\nii) Rodent models of diabetic retinopathy" + } + ], + "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d": [ + { + "document_id": "8cb13eb6-a9b9-4f9f-8680-9b8add1c453d", + "text": "\n\nThere are some good reviews available in the literatures describing the transgenic/knockout animal models of type 2 diabetes [114][115][116][117][118] .The transgenic and knockout models are developed for studying the role of genes and their effects on peripheral insulin action such as insulin receptor, IRS-1, IRS-2, glucose transporter (GLUT 4), peroxisome proliferator activated receptor-g (PPAR-g) and tumour necrosis factor-a (TNF-a) as well as in insulin secretion such as GLUT-2, glucokinase (GK), islet amyloid polypeptide (IAPP) and GLP-1 and in hepatic glucose production (expression of PEPCK) associated with development of type 2 diabetes.Further, combination or double knockout mouse models including defect in insulin action and insulin secretion (e.g., IRS-1 +/-/GK +/-double knockout) have been produced which clearly illustrate the mechanisms associated with development of insulin resistance and beta cell dysfunction leading to overt hyperglycaemic state in human type 2 diabetes.These above genetically modified animals exhibit various phenotypic features of type 2 diabetes varying from mild to severe hyperglycaemia, insulin resistance, hyperinsulinaemia, impaired glucose tolerance and others as explained in detail elsewhere 6,9,[114][115][116][117][118] .Very recently, tissue specific knockout mouse models have been achieved, allowing further insight into the insulin action with respect to particular target tissues (muscle, adipose tissue and liver) associated with insulin resistance and type 2 diabetes 115,117,118 .The transgenic/knockout animals are currently used mostly for the mechanistic study in diabetes research and not usually recommended for screening programme as they are more complicated and costly." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "Functional deficits refs\n\nNon-Alzheimer-disease mouse [71][72][73][74]76,78,81,85,87 and rat 59,75,77 ,79,95,97 Mouse [81][82][83][84][85] and rat 79,111 Cerebral effects of inducing diabetes or insulin resistance in normal rodents (that is, non-Alzheimer-disease rodent models) and in rodents genetically modified to accumulate amyloidβ in the brain (that is, rodent models of Alzheimer disease). Common intervetions to induce diabetic conditions in rodents included recessive mutations in the leptin gene (Lep; also known as Ob), defects in the leptin receptor (LEPR; also known as OB-R), diet and administration of streptozotocin. Rodents with pancratic overexpression of human amylin spontaneously develop both type 2 diabetes mellitus and dementia-like pathology." + } + ], + "b954224b-333b-4d82-bb9a-6e5b3837849e": [ + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 2 diabetes mellitus" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAs with the KK mouse, the Israeli sand rat model is particularly useful when studying the effects of diet and exercise [120] on the development of Type 2 diabetes." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\n\nAnimal models of Type 1 diabetes" + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "\nAnimal models have been used extensively in diabetes research.Early studies used pancreatectomised dogs to confirm the central role of the pancreas in glucose homeostasis, culminating in the discovery and purification of insulin.Today, animal experimentation is contentious and subject to legal and ethical restrictions that vary throughout the world.Most experiments are carried out on rodents, although some studies are still performed on larger animals.Several toxins, including streptozotocin and alloxan, induce hyperglycaemia in rats and mice.Selective inbreeding has produced several strains of animal that are considered reasonable models of Type 1 diabetes, Type 2 diabetes and related phenotypes such as obesity and insulin resistance.Apart from their use in studying the pathogenesis of the disease and its complications, all new treatments for diabetes, including islet cell transplantation and preventative strategies, are initially investigated in animals.In recent years, molecular biological techniques have produced a large number of new animal models for the study of diabetes, including knock-in, generalized knock-out and tissue-specific knockout mice." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Rodent models of monogenic obesity and diabetes\n\nObesity and the consequent insulin resistance is a major harbinger of Type 2 diabetes mellitus in humans.Consequently, animal models of obesity have been used in an attempt to gain insights into the human condition.Some strains maintain euglycaemia by mounting a robust and persistent compensatory β -cell response, matching the insulin resistance with hyperinsulinaemia.The ob / ob mouse and fa / fa rats are good examples of this phenomenon.Others, such as the db / db mouse and Psammomys obesus (discussed later) rapidly develop hyperglycaemia as their β -cells are unable to maintain the high levels of insulin secretion required throughout life.Investigation of these different animal models may help explain why some humans with morbid obesity never develop Type 2 diabetes whilst others become hyperglycaemic at relatively modest levels of insulin resistance and obesity." + }, + { + "document_id": "b954224b-333b-4d82-bb9a-6e5b3837849e", + "text": "Introduction\n\nAnimal experimentation has a long history in the field of diabetes research.The aim of this article is to review the commonly used animal models and discuss the recent technological advances that are being employed in the discipline.The review is based on an extensive literature search using the terms rodent, mouse, rat, animal model, transgenics, knockout, diabetes and pathogenesis, in scientific journal databases such as MEDLINE ®.In addition, abstracts presented at meetings of Diabetes UK, the European Association for the Study of Diabetes and the American Diabetes Association over the last 5 years were examined in order to gain an appreciation of recent and ongoing research projects." + } + ], + "ed1a5572-124a-4824-8b9c-5a540e5d6092": [ + { + "document_id": "ed1a5572-124a-4824-8b9c-5a540e5d6092", + "text": "Assessment of Diabetes\n\nMice were monitored for the development of diabetes as described previously (Wicker et al. 1994)." + } + ] + }, + "data_source": [], + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "diabetes", + "mouse", + "insulin", + "db/db", + "streptozotocin", + "AKITA", + "transgenic", + "knockout", + "glucose", + "tolerance" + ], + "metadata": [ + { + "object": "Hyperglycemia and blood pressure were similar between Trpc6 knockout and wild-type Akita mice, but knockout mice were more insulin resistant. In cultured podocytes, knockout of Trpc6 inhibited expression of the Irs2 and decreased insulin responsiveness. Data suggest that knockout of Trpc6 in Akita mice promotes insulin resistance and exacerbates glomerular disease independent of hyperglycemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab367197" + }, + { + "object": "High levels of IP6K3 mRNA were found in myotubes and muscle tissues. Expression was elevated under diabetic, fasting, and disuse conditions in mouse skeletal muscles. Ip6k3-/- mice had lower blood glucose, less insulin, decreased fat, lower weight, increased plasma lactate, enhanced glucose tolerance, lower glucose during an insulin tolerance test, and reduced muscle Pdk4 expression. Ip6k3 deletion extended lifespan.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab348326" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "Mice overexpressing protein S showed significant improvements in blood glucose level, glucose tolerance, insulin sensitivity, and insulin secretion compared with wild-type counterparts. diabetic protein S transgenic mice developed significantly less severe diabetic glomerulosclerosis than controls.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab482040" + }, + { + "object": "Sequence difference between C57BL/6J and C57BL/6N strains of mice. Pmch knockout mice display decreased circulating glucose, abnormal glucose tolerance and increased oxygen consumption. N carries a private missense variant in this gene isoleucine to threonine. N mice display increased oxygen consumption, but higher circulating glucose levels and normal glucose tolerance compared to J.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5150" + }, + { + "object": "Ghrl-/- and Ghsr-/- male mice studied after either 6 or 16 h of fasting had blood glucose concentrations comparable with those of controls following intraperitoneal glucose, or insulin tolerance tests, or after mixed nutrient meals. Collectively, our data provide strong evidence against a paracrine ghrelin-GHSR axis mediating insulin secretion or glucose tolerance in lean, chow-fed adult mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab322269" + }, + { + "object": "Patients with type 2 diabetes have significantly higher concentrations of plasma fetuin-B compared with normal glucose tolerance subjects and plasma fetuin-B is strongly associated with glucose and lipid metabolism, chronic inflammation and first-phase glucose-stimulated insulin secretion and insulin resistance.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab584502" + }, + { + "object": "In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab406411" + }, + { + "object": "Gestational glucose tolerance in WT mice, but not Ffar2-/- mice improved while on antibiotics. Gestational glucose tolerance worsened in Ffar2-/- mice during a second pregnancy. Maternal Ffar2 expression had no effect on the growth rates and glucose and glucose tolerance in the offspring.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab321474" + }, + { + "object": "ApoA-I is inversely associated with insulin resistance in patients with impaired glucose tolerance, and low apoA-I is an independent risk factor for impaired glucose tolerance IGT. These results indicate that apoA-I plays an important role in regulating insulin sensitivity and glucose metabolism in patients with IGT.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab76907" + } + ], + "question": "List mouse studies that involve insulin.", + "subquestions": null, + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "usage": { + "chatgpt": 5772, + "gpt-4": 4159, + "gpt-4-turbo-preview": 3223 + }, + "user_id": 2 + }, + "document_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32", + "task_id": "FFE5C939E5793BBDDC6D95D8AA6FAA32" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response06.json b/gnqa/paper1_eval/src/data/responses/test/response06.json new file mode 100644 index 0000000..bb965b6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response06.json @@ -0,0 +1,201 @@ +{ + "created_at": "2024-03-25T14:09:27.751236Z", + "data": { + "amplify": false, + "answer": "The typical weight of a mouse liver can vary, but in the context provided, it can weigh up to 4.5 grams in a 40 gram mouse and 1.2 grams in a 20 gram normal mouse.", + "context": { + "5c8c5649-dbaa-43cb-a8ce-7d3bc6458a38": [ + { + "document_id": "5c8c5649-dbaa-43cb-a8ce-7d3bc6458a38", + "text": "\n\nFIG. 6. Hepatic steatosis during DIO is associated with loss of eAT mass.A: Liver weight (adjusted for body weight) of mice fed a HF diet for 1, 4, 8, 12, 16, and 20 weeks.B: Inverse association of eAT mass and liver weight (as in A) between DIO weeks 12 and 20.C: Representative micrographs of hematoxylin and eosin-stained liver sections demonstrating that hepatic macrosteatosis in HF-fed mice is initially evident at DIO week 12 and increases through week 20." + }, + { + "document_id": "5c8c5649-dbaa-43cb-a8ce-7d3bc6458a38", + "text": "\n\nRESEARCH DESIGN AND METHODS-Male C57BL/6 mice were fed a high-fat diet for 20 weeks to induce obesity.Every 4 weeks, insulin resistance was assessed by intraperitoneal insulin tolerance tests, and epididymal (eAT) and inguinal subcutaneous AT (iAT) and livers were harvested for histological, immunohistochemical, and gene expression analyses." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "BXD and HMDP mouse strains, as well as HXB/BXH rat strains, with\nhigher Cd36 expression had increased fat mass and body weight, as well as decreased VO 2 and liver acid\nbeta−glucosidase activity (Figure S2.4B-C), confirming the involvement of Cd36 in metabolism [126] and\nsuggesting a potential role in Gaucher's disease, which results from the deficiency of acid beta−glucosidase\n[127]. An association between Abca8a liver transcripts and triglyceride levels was also revealed (Figure\nS2.4D)." + } + ], + "65d16255-3edd-46fb-a100-2ab8ba6abcdd": [ + { + "document_id": "65d16255-3edd-46fb-a100-2ab8ba6abcdd", + "text": "The mice were sacrificed at 9 am after a 4-hour fast. (A-E)\nPARPi reduced body weight (A; *, #, and $ indicates significant differences between\n\n27\nHFHS and CD, HFHS and PAPRi-Prev, and HFHS and PARPi-Ther, respectively),\nliver weight (B), epididymal fat pad (C), liver triglyceride content (D), and cholesterol\n(E) in both preventive and therapeutic cohorts (n=8-10). (F,G) Representative images\nof livers (F) and liver sections stained with H&E and Oil Red O (lipid content appears\nin red) (G), (n= 4-5)." + }, + { + "document_id": "65d16255-3edd-46fb-a100-2ab8ba6abcdd", + "text": "CD45 positive cells\nappear brown. (n=4). * P <0.05; ** P < 0.001; *** P< 0.0001. Data are expressed as\nthe mean ± SEM. One-way ANOVA with a post-hoc Bonferroni test was used for all\nstatistical analyses. Male mice were used in these experiments. Fig. 5. Liver damage in MCD diet-induced NAFLD was reversed by NAD+\nrepletion. C57BL/6J mice were fed with CD, MCD, or MCD+PARPi (PARPi, 50\nmg/kg/day). The mice were sacrificed at 9 am after a 4-hour fast. (A) PARPi reduces\nglobal protein PARylation and (B) recovers NAD+ levels in liver tissue (n=6)." + }, + { + "document_id": "65d16255-3edd-46fb-a100-2ab8ba6abcdd", + "text": "At\n10 weeks of age, male C57BL/6J mice were challenged with an MCD diet for 5\nweeks. Similar to the effects seen in mice on a HFHS diet, MCD-fed mice treated\nwith PARPi in a preventive manner exhibited reduced PARylation and increased\nhepatic NAD+ levels (Fig. 5A and B). Mice fed with a MCD diet for 5 weeks showed classical pathophysiological\ncharacteristics of NAFLD, including hepatic steatosis, inflammation and fibrosis. MCD\ndiet increased AST and ALT levels compared to a control diet, while PARPi treatment\nreduced their levels (Fig. 5C and D)." + } + ], + "8e92b2e3-b525-4c17-a0cb-5ca740a74c66": [ + { + "document_id": "8e92b2e3-b525-4c17-a0cb-5ca740a74c66", + "text": "\n\nThe left inguinal, gonadal, and retroperitoneal fat pads were dissected and weighed individually. (Prior data showed that weights of left and right fat pads are highly correlated. )The mesenteric fat pad was also dissected and weighed.An adiposity index (AI) was computed for each mouse as follows: the left inguinal, gonadal, and retroperitoneal fat pad weights were summed, doubled, added to mesenteric fat pad weight, divided by body weight, and multiplied by 100.The ratios of the individual fat pad weights divided by body weight and expressed as a percentage (for example, 200× left gonadal fat pad weight/body weight) were analyzed as separate traits, as were blood glucose level, plasma leptin level (log 10 transformed), body weight, and body length." + } + ], + "a5e25b91-4846-4a42-b9b4-838031ec19b7": [ + { + "document_id": "a5e25b91-4846-4a42-b9b4-838031ec19b7", + "text": "Metabolic phenotypes were compared between\nmice in the upper (Lonp1-high) and lower (Lonp1-low) quartiles with respect to WAT Lonp1 expression (n=9–10 mice per\nCopyright © 2021 Korean Endocrine Society\n\nVAT mRNA levels of OXPHOS-complex and UPRmt genes\nin relation to BMI\nAmong 48 patients, 11 were obese (≥25 kg/m2), 11 were overweight (23 to 24.9 kg/m2), and 26 were of normal or underweight (<22.9 kg/m2), according to the World Health Organization Asia-Pacific Obesity Classification [16]. Clinical characteristics of the participants stratified by BMI (<23 kg/m2 vs. ≥23\nkg/m2) are summarized in Table 1." + } + ], + "acfbb3e9-6eeb-4541-bd1f-9f460de09958": [ + { + "document_id": "acfbb3e9-6eeb-4541-bd1f-9f460de09958", + "text": "In an F2 cohort derived from these parental strains, we have\nshown that the range of blood glucose, insulin levels, and body\nweight exceeds that of either the C57BL/6 (B6) leptinob/ob or BTBR\nleptinob/ob parental strains. We went on to identify several diabetesrelated QTL in this F2 sample [21,22]. In the current study, we\nfocused on a subset of 60 F2 mice that have previously been\nevaluated in detail with regard to liver gene expression profiles\n[24] to ask if the abundances of hepatic metabolic intermediates\nwould show sufficient heritability to enable us to map metabolic\nQTL (mQTL)." + } + ], + "af4c6e19-fafe-4178-a9eb-213991f344d6": [ + { + "document_id": "af4c6e19-fafe-4178-a9eb-213991f344d6", + "text": "(E–G) Data from CTB6F2 (E) and HMDP (F) mouse cohorts, and the HXB/BXH rat cohort (G) indicate significant negative correlations between liver Rpl26 levels\nand body weight, and other metabolic traits. adipose tissue (subWAT) mass (Figure 2D), suggesting pleiotropic effects of Pten. The links between Pten and neurobiological and metabolic phenotypes have been confirmed by independent studies (Kwon et al. , 2006; Ortega-Molina et al. ,\n2012). Overall, PheWAS showed that 4,230 out of 11,548 genes\nwere associated with at least one phenotypic trait and all genes\nhad significant associated molecular traits after phenome-wide\ncorrection (Figures 2E; Table S3)." + } + ], + "b1a1282d-421f-494a-b9df-5c3c9e1e2540": [ + { + "document_id": "b1a1282d-421f-494a-b9df-5c3c9e1e2540", + "text": "Curves of weight ( • ... • ) and blood sugar concentration\nwith age in a less typical diabetic mouse\n\nDiabetologia\n\n(I\n\n--I\n\n)\n\nAside from the large accumulations of fat, subcutaneously in axillary and inguinal regions and intraabdominally in mescnteric and gonadal fat pads, the\nmost striking anatomical deviation is the size of the\nliver. The liver m a y weigh up to 4.5 grams in a 40\ngram mouse, compared with 1.2 grams in a 20 gram\nnormal mouse." + } + ], + "b71befbe-2a20-434e-907e-0ae581373243": [ + { + "document_id": "b71befbe-2a20-434e-907e-0ae581373243", + "text": "In mice, within hours after the last meal, the organs\nrespond with changes in gene expression mainly in general metabolism (70). The role of the liver is\nto provide energy for glucose-dependent tissues, by glycogenolysis, gluconeogenesis, ketogenesis,\nand fatty-acid β-oxidation (71). The basic architecture of the lobules and the zonation are not\naffected, but the cell size declines in prolonged fasting, when murine liver restores partly its glycogen\ndeposits, and much of gene expression returns to control values (72). In Abcb4-/- mice, collagens,\nfibronectin and vimentin, responsible for the structural integrity of the ECM, were strongly affected\nby fasting." + }, + { + "document_id": "b71befbe-2a20-434e-907e-0ae581373243", + "text": "James SJ, Muskhelishvili L. Rates of apoptosis and proliferation vary with caloric intake and may influence\nincidence of spontaneous hepatoma in C57BL/6 x C3H F1 mice. Cancer Res 1994 Nov 1;54(21):5508-5510. 50. Hakvoort TB, Moerland PD, Frijters R, Sokolovic A, Labruyere WT, Vermeulen JL, et al. Interorgan\ncoordination of the murine adaptive response to fasting. J Biol Chem 2011 May 6;286(18):16332-16343. 51. Lin S, Saxena NK, Ding X, Stein LL, Anania FA. Leptin increases tissue inhibitor of metalloproteinase I\n(TIMP-1) gene expression by a specificity protein 1/signal transducer and activator of transcription 3\nmechanism. Mol Endocrinol 2006 Dec;20(12):3376-3388. 52." + } + ], + "b942c082-a734-47d7-8494-8457ce995ce2": [ + { + "document_id": "b942c082-a734-47d7-8494-8457ce995ce2", + "text": "\n\nCharacterization of lean and obese control and mGHRKO mice" + } + ], + "c2df1cd8-c962-4fac-88c9-cad52f7753b0": [ + { + "document_id": "c2df1cd8-c962-4fac-88c9-cad52f7753b0", + "text": "\n\nConsistent with the broad up-regulation of genes associated with fatty acid synthesis (Table 1), Oil Red O staining of liver sections from 15-d-old pups and naturally aged mice revealed enhanced accumulation of triacylglycerides in both compared to control littermates and 8-wk-old mice (Figure 7C), indicating hepatic steatosis.This and the absence of adipose tissue suggest that Csb m/m /Xpa À/À mice display generalized lipodystrophy (loss and abnormal redistribution of body fat) [31]., and Csb m/m /Xpa À/À mice (n ¼ 6).The levels of IGF1 (ng/ml) and glucose (mmol/l) in the serum of Csb m/m /Xpa À/À mice are significantly lower than that of control littermates (p , 0.0004 and p , 0.04, respectively). (C) PAS staining for glycogen and Oil Red O staining for triglycerides in livers of 15-d-old wt and Csb m/m /Xpa À/À mice and 96-wk-old wt mice.Pictures were taken at 1003 magnification.Note the large polyploid nuclei in the 96-wk-old wt mouse liver and the reduced glycogen levels in the Csb m/m /Xpa À/À liver after overnight fasting.doi:10.1371/journal.pbio.0050002.g007" + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "Association between lifespan and metabolic organ weights\nWe measured weight of certain metabolic organs and tissues of a subsample of cases on\nboth diets at ~500 days of age. HFD mice (n = 63) had 84% greater fat mass, 25% greater\nheart mass, 19% greater liver mass, and 18% greater kidney mass at ~500 days compared\nto controls (n = 71). However, HFD did not influence brain mass (Supplemental Table)." + } + ], + "ddd79d05-8140-48d7-a7fe-5685bb6b50f8": [ + { + "document_id": "ddd79d05-8140-48d7-a7fe-5685bb6b50f8", + "text": "\n\nYoung adult dwarf mice have more body fat than normal mice.But, with age, normal mice from this line accumulate fat at a higher rate, and the percent body fat in old DF mice does not differ from that of normal mice, as measured by dual energy X-ray absorptiometry (DEXA) (29).Downregulation of lipid biosynthetic genes and upregulation of -oxidation-related genes in the liver of DF mice may explain this slower rate of fat deposition." + } + ], + "dfebf2a5-8553-41f9-af2d-f781778d1342": [ + { + "document_id": "dfebf2a5-8553-41f9-af2d-f781778d1342", + "text": "(b) Serum levels of liver injury markers, triglyceride, and cholesterol profiles of 20-month-old WT (n = 6) and Gdf15 KO (n = 6)\nmice. (c) Serum levels of pro-inflammatory cytokines of 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. (d) H&E staining for liver tissues\nof 20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice. Scale bar, 200 μm. Arrows indicate fat accumulation. (e) Fixed adipose tissue from\n20-month-old WT (n = 6) and Gdf15 KO (n = 6) mice was stained for F4/80 antibodies. Scale bar, 200 μm." + } + ], + "e7a99e2b-a89f-4091-b6e0-c445fd4948bb": [ + { + "document_id": "e7a99e2b-a89f-4091-b6e0-c445fd4948bb", + "text": "(12) studied liver\ngene expression changes in Stat5b knockout and wild-type\nmice, finding 1,603 differentially regulated genes, with 850\nbeing male- and 753 female biased (P ⬍ 0.05 and FC ⬎ 1.5). A large study consisting of 344 mice comprising an F2 cross\nbetween C57B/6J.apoE⫺/⫺ and C3H/HeJ.apoE⫺/⫺ strains\n(⬃50% from each sex) produced two reports (57, 61) that\nexamined sexually dimorphic gene expression in adipose tissue, brain, liver, and muscle. It was reported that 9,250 genes\nare dimorphic in the liver (P ⬍ 0.01 and FC ⬎ 1)." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "2006) studied liver gene expression\nchanges in Stat5b knockout and wild type mice, finding 1,603 differentially regulated genes,\nwith 850 being male- and 753 female-biased (p<0.05 and FC>1.5). A large study consisting\nof 344 mice comprising an F2 cross between C57B/6J.apoE-/- and C3H/HeJ.apoE-/- strains\n(~50% from each sex) produced two reports (Wang et al. 2006; Yang et al. 2006) which\nexamined sexually dimorphic gene expression in adipose tissue, brain, liver and muscle. It\nwas reported that 9,250 genes are dimorphic in the liver (p<0.01 and FC>1)." + } + ] + }, + "data_source": [], + "document_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "mouse", + "liver", + "weight", + "grams", + "diabetic", + "fat", + "metabolic", + "diet", + "NAFLD", + "PARPi" + ], + "metadata": [ + { + "object": "our design showed an association between the rs9939609 DNA variant and weight loss after a high polyunsaturated fat hypocaloric diet. Also, an interaction with the type of the hypocaloric diets and metabolic changes secondary to weight loss was observed. Metabolic improvement was better in A carriers with a weight loss secondary to a P hypocaloric diet.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab554681" + }, + { + "object": "Sustained, elevated levels of SAA1 were correlated with metabolic parameters and local cytokine expression in the liver following 16 weeks on the high-fat diet. We suggest that SAA1-derived amyloid deposition under long-term high-fat diet exposure may be associated with the complications of high-fat diet-induced obesity and metabolic disorders.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab759501" + }, + { + "object": "Aging, metabolism: DEPRECATED, Lifespan, longevity difference low fat minus high fat of females at UTHSC on either a normal low fat chow diet or a high fat diet 60% calories from fat, 12 hr light cycle only computed if more than 4 cases per diet [difference, days]", + "predicate": "http://purl.org/dc/terms/description", + "subject": "http://genenetwork.org/id/traitBxd_17469" + }, + { + "object": "interactions of fat intake with the genetic rs11150675 and transcriptional ILMN_1725441 variations at the NFATC2IP locus on 2-year weight change. cis-DNA methylation at cg26663590 of the NFATC2IP locus showed an opposite impact on weight-loss in response to high-fat vs low-fat diet. baseline methylation at cg26663590 causally mediated 52.8% of the effect of rs11150675 on 2-year weight-loss in the high-fat diet group", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab422351" + }, + { + "object": "Aging, metabolism: Mean life span, longevity of females, combined data both diets, on either a standard chow diet Harlan Teklad 7912 chow diet, 6.2% fat or on a high fat diet Harlan Teklad 06414, 18.4% protein, 60.3% calories from fat, 5.1 kcal/g at UTHSC on a 12 hr light cycle in polypropylene cages 145 in2 with up to 10 animals/cage, Harlan Teklad 7087 soft cob bedding unweighted average, updated Feb 2023 [days]", + "predicate": "http://purl.org/dc/terms/description", + "subject": "http://genenetwork.org/id/traitBxd_21450" + }, + { + "object": "an initial accelerated increase in body weight and fat mass of Bmal1-/- mice on high-fat diet may have been offset by the effect of premature ageing on organ weight, resulting in comparable weights after 15 weeks of high-fat diet.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab65355" + }, + { + "object": "Expression of Sirt1, Cox2i2, Parg, Pank3, Rhoa, Mrs2, Arhgap5, Igfbp3, Derl1, and Immp1l are all controlled by a QTL that peaks at Igf1r in BXD liver. LRS peak for Sirt1 of 19.5 when data are combined; LRS of 15.6 on high fat diet and LRS of 6 on low fat diet. Used dataset: EPFL/LISP BXD CD+HFD Liver Affy Mouse Gene 1.0 ST Apr13 RMA", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1805" + }, + { + "object": "Uqcrg expression maps to Chr 1 near Numts LRS ~17, B high, old genotypes using high fat diet data set EPFL/LISP BXD HFD Liver Affy Mouse Gene 1.0 ST Apr13 RMA. Possible link to longevity on high fat diet. No eQTL to Chr 1 on chow diet using probe set 10385818.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6482" + }, + { + "object": "The absence of UCP1 augmented obesity weight gain, body fat mass, %body fat, fat depot size in high-fat diet- and cafeteria-fed 129S mice, with a similar or lower food intake, indicating that, when present, UCP1 indeed decreases metabolic efficiency. The increased obesity was due to a decrease in energy expenditure.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab776097" + }, + { + "object": "Through phenotype analysis of high-fat diet-induced obesity and metabolic syndrome mice with muscle-, fat-, or liver-specific knockout of Ube2o, study identified Ube2o as an essential regulator of glucose and lipid metabolism programs in skeletal muscle, but not in adipose or liver tissue. Mechanistically, Ube2o acted as a ubiquitin ligase and targeted AMPKalpha2 for ubiquitin-dependent degradation in skeletal muscles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab955251" + } + ], + "question": "What is the typical weight of a mouse liver?", + "subquestions": null, + "task_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9", + "usage": { + "chatgpt": 6192, + "gpt-4": 4109, + "gpt-4-turbo-preview": 3136 + }, + "user_id": 2 + }, + "document_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9", + "task_id": "6BFBCECDC0E8EC5D39EAE8D98049FDD9" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response07.json b/gnqa/paper1_eval/src/data/responses/test/response07.json new file mode 100644 index 0000000..a1d2f27 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response07.json @@ -0,0 +1,203 @@ +{ + "created_at": "2024-03-25T14:14:08.479589Z", + "data": { + "amplify": false, + "answer": "C57BL/6 and DBA/2 mice differ in several ways. C57BL/6 mice are more accurate in tasks with greater demands and make fewer impulsive responses. They also have larger brain weights and are more resistant to radiation-induced genomic instability. DBA/2 mice, on the other hand, make fewer omission errors, respond more vigorously to certain influenza viruses, and perform better in less spatially demanding tasks. They also have larger cortical areas when adjusted for brain weight and are more sensitive to acute nephrotoxicity. Genetic differences between the two strains affect various responses including activity, memory, learning tasks, and responses to alcohol and opioids.", + "context": { + "069247f3-7209-4dbb-85e3-e9f11e273812": [ + { + "document_id": "069247f3-7209-4dbb-85e3-e9f11e273812", + "text": "However, when the data were adjusted\nfor brain weight, there was a significant (p = 0.008) difference between DBA/2J and C57BL/6J (2.14 ± 0.06 mm2\nand 1.96 ± 0.03 mm2, respectively) making the DBA/2J\nlarger by 8.50%. Total brain weight of DBA/2J animals\nwas significantly (p < 0.0001) smaller than that of C57BL/\n6J animals (0.35 ± 0.01 g, 0.42 ± 0.01 g respectively)." + } + ], + "08a6ce71-133e-426d-adfe-600ff52802a2": [ + { + "document_id": "08a6ce71-133e-426d-adfe-600ff52802a2", + "text": "Phenotypes are often very different between mouse strains with\ndiverse genetic backgrounds and the strain characteristics of DBA/\n2J are often contrasted with other genetically distinct inbred strains\nsuch as C57BL/6J. These defined genetic backgrounds provide an\nexcellent system for mapping modifier genes [20,21,22]. To study\nthese differences a number of DBA/2J-relevant resources have\nbeen generated. For instance, a genome-wide panel of congenic\nstrains has been created that contain portions of DBA/2J\nchromosomes on a C57BL/6J background [23]. These 65 strains\ncontain more than 95% of the DBA/2J genome." + } + ], + "0de2ad4a-b7e3-484e-8778-5ea47e42a4e4": [ + { + "document_id": "0de2ad4a-b7e3-484e-8778-5ea47e42a4e4", + "text": "Well-documented behavioral differences between C57 and\nDBA, including enhanced closed-arm preference and deficits in conditional fear, were\nobserved. This suggests at a minimum that the influence of previous testing in the two\nparental strains was comparable. The use of DBA/2J donor segments for the GTM panel may have implications for loci\nidentified in tests involving auditory stimuli, as this strain is known to undergo progressive\nhearing loss with age. While no rigorous examination of hearing capacity in the GTM has\nbeen conducted, inspection of time course data for individual mice in both the general\n\nMol Psychiatry." + } + ], + "1d3f76c8-87f6-402c-a488-4f6266bb7c9c": [ + { + "document_id": "1d3f76c8-87f6-402c-a488-4f6266bb7c9c", + "text": "Particularly\nstriking is the difference in their locomotor response:\nthe C57BL/6J strain shows a marked locomotor activation following an acute opiate administration,\nwhich is virtually absent in DBA/2 mice [6, 25, 29]. After chronic morphine treatment, either tolerance or\nsensitization of the locomotor response was evidenced in C57BL/6J mice, depending on the treatment paradigm, whereas no altered responses were\nobserved in the DBA/2J strain [1, 22, 29, 31]. Other\ninter-strain differences in reactions to opioids have\nalso been reported, including a greater sensitivity to\nopioid reward and stronger withdrawal symptoms in\nthe C57BL/6J strain [2, 6, 17, 30, 35]." + } + ], + "27e062d0-d5ed-4ee9-8783-f22882284865": [ + { + "document_id": "27e062d0-d5ed-4ee9-8783-f22882284865", + "text": "Although\nno differences in attentional performance were detected\nbetween C57BL/6J and DBA/2J, in line with previous reports\nin the 5-CSRTT and five-choice CPT (Loos et al . 2010;\nYoung et al . 2009), we observed significant differences\namong BXD recombinant inbred strains that transgressed\nbeyond the phenotypes of the founders. This suggested the\ncontribution of multiple genetic loci to these phenotypes,\nof which we detected a significant one on chromosome 16\nfor response variability." + } + ], + "2a92d7b5-946c-4a22-a4b9-26e950b0f757": [ + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "text": "Given the large differences that we found\npreviously (Crusio 2013) between C57BL/6 and DBA/2, this is\nunexpected. One possible explanation for the lower than expected\nperformance of the C57BL/6 and (at least some) BXD strains\nlies in the housing conditions. Our animal facility was built to\nhouse about 500 cages in one large breeding room. However,\nthe cage-washing installation (and the available personnel)\ncould not handle that many cages at a time. As a result,\nevery day one or two racks of cages were changed. C57BL/6\nmice are sensitive to such disruptions and, indeed, breeding\nresults were only mediocre." + } + ], + "581f83bc-3521-4cb3-ad3c-d905a90ecc29": [ + { + "document_id": "581f83bc-3521-4cb3-ad3c-d905a90ecc29", + "text": "C57BL/6 and DBA/2 mice is not yet fully understood but\ninvolves multiple genetic differences between the two mouse\nlineages, affecting several pathways and processes (1). Certain\ninfluenza viruses grow to higher titers in DBA/2 mice (A/Hong\nKong/213/2003 [H5N1] or A/Memphis/33/2008 [H1N1]) (data\nnot shown) while others do not (H7N3 and H10N5) (this\nstudy). Irrespective of the difference in viral loads, DBA/2 mice\nrespond more vigorously, producing larger quantities of certain proinflammatory molecules like TNF-␣, which was shown\nto correlate with increased morbidity and mortality in humans\n(5)." + } + ], + "5e5b18da-984c-415e-b2ce-e33b3c44b731": [ + { + "document_id": "5e5b18da-984c-415e-b2ce-e33b3c44b731", + "text": "Additionally, in this protocol the strains DBA/2J, A/J, NOD/ShiLt/J,\nC57BL/10J, SM/J, and C57BR/cdJ are AA sensitive; the strains\nCAST/EiJ and BTBR T⫹ tf/J are resistant; and the strains\nNZW/LacJ, KK,HIJ, and SWR/J have intermediate resistance to\nAA-induced acute nephrotoxicity (supplementary data; all supplementary material for this article is available online at the\njournal web site.). For this QTL study, C57BL/6J and DBA/2J mice were used\nas resistant and sensitive strains, respectively. Each strain has\na complete genomic sequence available, and the genetic basis\nof differences in their ability to respond to xenobiotics is\nextensively studied (reviewed in Ref. 8)." + } + ], + "66baf01d-e081-4034-b7ec-03592eac90a7": [ + { + "document_id": "66baf01d-e081-4034-b7ec-03592eac90a7", + "text": "The C57BL/6J X DBA/2J (BXD) recombinant inbred (RI)\nmouse strains, which are unique mosaic of alleles derived from\nthe parental C57BL/6J (B6) and DBA/2J (D2) strains have been\nconstructed as a high precision genetic reference population\nfor systems genetics in unraveling the genetic architecture\nof polygenic traits (Ashbrook et al. , 2019). The BXD family\nconsists of more than 150 BXD fully inbred strains that\nsegregate for ∼6 million genetic variants and thus can be\nused as an informative murine genetic reference panel." + } + ], + "810a8c3f-5ec5-4ce8-9ad1-149ce98a573d": [ + { + "document_id": "810a8c3f-5ec5-4ce8-9ad1-149ce98a573d", + "text": "Because\nwe have now shown that the parental strains C57BL/6J\nand DBA/2J markedly differ in both quantitative measures\nof cortex area size [6] and shape, this assures variation in\nthe derivative BXD lines, and provides an empirical basis\nfor using the BXD panel to study cortical development. Conclusion\nC57BL/6J and DBA/2J have markedly different cortical\narea maps, in both size and shape. These differences suggest polymorphism in genetic factors underlying cortical\nspecification, even between common isogenic strains. Comparing cortical phenotypes between normally varying inbred mice or between genetically modified mice can\nidentify genetic contributions to cortical specification." + } + ], + "8df298ea-4052-4a4a-bcd3-2e36818844f4": [ + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "The\nC57BL/6 mice were more accurate than DBA/2 mice at the\nshorter SD where the task demands were greater, and they also\nmade anticipatory (impulsive) responses at a lower rate. In contrast, the DBA/2 mice made fewer omission errors than the\nC57BL/6 but this effect was not seen until the final stages of\nthe experimental procedures. These findings are in agreement\nwith those of Greco et al. [18]. Although they used different breeders as well as different test chambers, training protocols and reinforcers, the results were similar: DBA/2 males\nwere less accurate and made more anticipatory responses than\nC57BL/6 males." + }, + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "DBA/2 mice perform poorly in other\nspatial tasks as well as in the 5-CSRTT (see Section 1) but\nthis is by no means true for paradigms that are less spatially\ndemanding. For instance, in the four-arm baited and cued versions of the radial maze, as well as in auditory fear conditioning,\nC57BL/6 and DBA/2 do not differ [1,30]; DBA/2 mice even\nperform better than C57BL/6 with regard to two-way active\navoidance learning [37]." + }, + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "While the factorial structure\nof C57BL/6 mice remained the same as under low attentional\ndemands (two factors), there was only one factor for DBA2\nmice. This factor was characterised by high positive loadings\n(>0.78) from the percent of correct responses and omission\nerrors, and a high negative loading (0.87) from anticipatory\nresponses. 4. Discussion\nThe results indicated that both C57BL/6 and DBA/2 mice\nwere able to learn the complex 5-CSRTT task but there were\nconsiderable quantitative differences in their performance." + }, + { + "document_id": "8df298ea-4052-4a4a-bcd3-2e36818844f4", + "text": "It can be seen that at all SD, accuracy was\ngreater for C57BL/6 than for DBA/2 mice. The clearest difference was at 1 s SD where C57BL/6 mice were responding at\na mean accuracy of 80% compared with the DBA/2 group for\nwhich the mean was 59% (Fig. 1(A)). With a SD of 5 s there was\nno significant main effect for group (F1,28 = 3.13), whereas at 2\nand 1 s SD significant group effects were achieved (F1,28 = 5.44\nand 25.1; P < 0.05 and 0.001, respectively)." + } + ], + "a67372ac-02b7-41c4-bb55-5152444c5479": [ + { + "document_id": "a67372ac-02b7-41c4-bb55-5152444c5479", + "text": "In marked contrast, the C57BL/6J strain\nwas found to have the highest level of oral morphine consumption [6]. However, sensitivity to the reinforcing\neffects of morphine in conditioned place preference and\nintravenous self-administration paradigms was higher in\nDBA mice than in C57BL [10]. The two frequently used\nlaboratory strains of mice C57BL/6J and DBA/2J show\nremarkable differences in analgesic response to morphine. Moreover, several studies have reported profound\ndifferences in morphine induced locomotor activity\nbetween the sensitive C57BL/6 and insensitive DBA/2\nmice [3,7]." + } + ], + "b73879de-43a6-48b0-ad69-98afadbfb997": [ + { + "document_id": "b73879de-43a6-48b0-ad69-98afadbfb997", + "text": ", increased exploration of the open\nareas) in both tests. One explanation is that DBA/2J is “susceptible”\nto this stressor, whereas C57BL/6J is “resilient.” However, a more\ncircumscribed but potentially more accurate interpretation is that\nboth strains react strongly to this particular stress regime, but\ndiffer in the manner in which the response manifests behaviorally. Thus, DBA/2J may develop a classic “passive” anxiety-like\nsuppression of approach behavior, whereas C57BL/6J may exhibit more of an “active” response to stress. This could reflect an\nincreased panic-like escape drive or manic-like reaction to stress\nin C57BL/6J, rather than a decrease in anxiety-like behavior." + } + ], + "d608e1a6-2bf1-4ad6-993d-453a328896a0": [ + { + "document_id": "d608e1a6-2bf1-4ad6-993d-453a328896a0", + "text": "Differences in radiation sensitivity between the BXD parental strains were first described\nby Roderick more than 45 years ago, with DBA/2J succumbing more quickly than\nC57BL/6J to a lethal dose of radiation (26). At more modest doses, C57BL/6J mice\nwere shown to be more resistant to radiation-induced genomic instability than DBA/2J\n(38, 84, 85)." + } + ], + "dbe5a781-3561-48cb-9f63-cfb4f3246434": [ + { + "document_id": "dbe5a781-3561-48cb-9f63-cfb4f3246434", + "text": "Genetic differences between C57 and DBA mice have been shown to translate into a broad spectrum\nof CNS related functional and molecular correlates, for example, differences in activity, impulsive\naction, hippocampal related memory and learning tasks, post- and pre-synaptic protein expression,\nand synaptic transmission and plasticity [27–40]. Through genetic linkage analyses, the genetic and\nphenotypic differences in the BXD panel of RI strains have resulted in identification of genes and loci\ninvolved in complex CNS functions, such as impulsivity [41], reversal learning [42], attention [43],\nneuronal oscillations [44], hearing loss [45], and fear and spatial learning [39,40]." + } + ], + "f4e26cf0-d214-41bf-b392-9c63a903b0b8": [ + { + "document_id": "f4e26cf0-d214-41bf-b392-9c63a903b0b8", + "text": "For example, the\nC57BL/6J (B6) and DBA2/J (D2)\ninbred mice frequently are used in\nalcohol research because they clearly\ndiffer in various responses to alcohol,\nincluding development of functional\ntolerance (Grieve and Littleton 1979),\nlocomotor activation (Phillips et al. 1998), and sensitivity to withdrawal\nsymptoms (Metten and Crabbe 1994). Because the environmental conditions\nin these experiments can be controlled,\nany differences observed between the\nmouse strains in these phenotypes most\nlikely can be attributed to genetic differences." + } + ], + "f6abed2a-3182-46be-aae6-97d99f08e73e": [ + { + "document_id": "f6abed2a-3182-46be-aae6-97d99f08e73e", + "text": "For example, when subjected to HFD, DBA/2J had 12.5% more body fat compared\nto C57BL/6J (P < 0.0001, Fig 1A). Additionally, the F1 offspring generated by DBA/2J dams\n(DBA/2J x C57BL/6J) had 10.6% more body fat (P < 0.001) compared to the F1 from C57BL/\n2J dams (C57BL/6J x DBA/2J). While the source of these latter effects appears to be maternal,\nfurther studies are needed to identify the molecular basis of these differences. In general,\ngenetic differences between strains impacted body weight variation throughout the experiment\n(P < 0.05) (Fig 1B)." + } + ] + }, + "data_source": [], + "document_id": "393CA44A783B1B62AE4A38BDC3BA656E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL/6", + "DBA/2", + "accuracy", + "anticipatory&responses", + "omission&errors", + "genetic&differences", + "cortical&area", + "alcohol&research", + "CNS&functions", + "AA-induced´&nephrotoxicity" + ], + "metadata": [ + { + "object": "A 2.8-kb cDNA encoding an 80-kDa melanoma Ag defined by a syngeneic anti-B16 melanoma mAb able to block anti-melanoma cytotoxic T cell responses. Mela transfectants are brightly stained with the antibody. Northern blot showed that this transcript was detected in mouse melanoma cells of C57BL/6 and DBA/2 origin, C1300 A/J neuroblastoma, L cell C3H and EL-4 T lymphoma C57BL/6, but not in other tumors, such as S913 fibrosarcoma C57BL/10, NIH3T3, 70 Z/3 pre-B lymphoma, and P3U1 plasmacytoma.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab89" + }, + { + "object": "findings indicate that hippocampal pCREB is closely tied to this form of associative conditioning only in C57BL/6 mice and that different neural substrates may support trace conditioning in C57BL/6 and DBA/2 strains", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab289786" + }, + { + "object": "KLK6 protein from 129 mice showed reduced SDS-PAGE mobility compared with that from C57BL/6 mice; recombinant KLK6 protein from 129 mice had a higher optimum pH and >15 times higher hydrolytic enzymatic activity for several substrates than that from C57BL/6 mice. These results suggest that KLKs may contribute to the genetic basis of the differences between mouse strains.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab439223" + }, + { + "object": "genetic differences in the VDR gene may be involved in the development of AITD and the activity of GD, whereas the genetic differences in the GC and CYP2R1 genes may be involved with the intractability of GD.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab316964" + }, + { + "object": "Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 DRD2, rs5744256 IL-18, rs2306022 ITGA11 with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab989251" + }, + { + "object": "studied time course of TLR9 and BD-2 gene expression by corneal epithelial cells in herpetic keratitis in C57Bl/6 and BALB/c mice; reduced TLR9 gene expression in the cornea of C57Bl/6 mice was associated with high sensitivity to infection caused by HSV-1", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab733376" + }, + { + "object": "S100P achieved 96.4% sensitivity, 93.3% specificity, 98.2% PPV, 87.5% NPV and 95.8% total accuracy, while IMP3 achieved 91.2% sensitivity, 86.7% specificity, 96.2% PPV, 72.2% NPV and 90.3% total accuracy for pancreatic ductal adenocarcinoma PDA.Both markers were sensitive and specific for diagnosis of PDA.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab690502" + }, + { + "object": "The expression intensity for the aldehyde dehydrogenase 7A1 ALDH7A1 mRNA and protein was significantly higher in C57BL/6 mice than DBA/2 mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab807658" + }, + { + "object": "The capability to express IL-4 and other Th2 cytokines is greater in DBA/2 splenocytes and CD4+ T cells than in C57BL/6 cells, a difference that correlates with altered IL-4 mRNA stability.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab788438" + }, + { + "object": "Comparison of behavioral studies in wild-type C57BL/6 mice and hemizygous Drd2 mice backcrossed into C57BL/6 background finds open-field locomotion, conditioned place preference, and avoidance learning are indistinguishable in the transgenic lines.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab151446" + } + ], + "question": "How are C57BL/6 and DBA/2 different?", + "subquestions": null, + "task_id": "393CA44A783B1B62AE4A38BDC3BA656E", + "usage": { + "chatgpt": 5792, + "gpt-4": 3986, + "gpt-4-turbo-preview": 3013 + }, + "user_id": 2 + }, + "document_id": "393CA44A783B1B62AE4A38BDC3BA656E", + "task_id": "393CA44A783B1B62AE4A38BDC3BA656E" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response08.json b/gnqa/paper1_eval/src/data/responses/test/response08.json new file mode 100644 index 0000000..dc7117a --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response08.json @@ -0,0 +1,197 @@ +{ + "created_at": "2024-03-25T14:18:12.274204Z", + "data": { + "amplify": false, + "answer": "Several behavioral QTLs have been found in mice. These include QTLs affecting aggressive behaviors, which have been identified in strains such as the Turku aggressive (TA) and non-aggressive (TNS) strains, the NC900 and NC100 strains, and the short attack latency (SAL) and long attack latency (LAL) strains. Other QTLs have been associated with traits like anxiety-related behavior, seizure, hippocampal volume, and alcohol preference. Additionally, QTLs related to alcohol, morphine or other drugs, painful arthritis, emotionality/anxiety, and seizure susceptibility have been mapped. There are also QTLs that influence exploratory behavior in mice.", + "context": { + "1a041a89-4da8-4ad5-b241-da36df917930": [ + { + "document_id": "1a041a89-4da8-4ad5-b241-da36df917930", + "text": "\n\nQuantitative trait locus (QTL) mapping has been carried out in numerous species to associate regions of the genome to phenotypes even before the structure of the genome was well understood (e.g., [3]).Rodents, especially mice, have been the species most prominently used for biomedically relevant traits.Amongst these, the BXD family of recombinant inbred (RI) strains derived from crossing two inbred strains-C57BL/6J and DBA/2J mice-have been extensively used for almost 50 years in fields such as neuropharmacology [4][5][6], immunology [7][8][9][10][11][12][13], behaviour [13][14][15][16][17][18][19][20][21], aging [21][22][23][24][25][26][27][28][29], neurodegeneration [30][31][32][33], and gut microbiome-host interactions [34]." + } + ], + "27e14ff3-b5a5-4f60-80a2-eaa2ab53e991": [ + { + "document_id": "27e14ff3-b5a5-4f60-80a2-eaa2ab53e991", + "text": "Milhaud JM, Halley H, Lassalle JM (2002) Two QTLs located on\nchromosomes 1 and 5 modulate different aspects of the performance of mice\nof the B6D Ty RI strain series in the Morris navigation task. Behav Genet 32:\n69–78. 16. Buck KJ, Rademacher BS, Metten P, Crabbe JC (2002) Mapping murine loci\nfor physical dependence on ethanol. Psychopharmacology (Berl) 160: 398–407. 17. Ferraro TN, Golden GT, Smith GG, Schork NJ, St Jean P, et al. (1997)\nMapping murine loci for seizure response to kainic acid. Mamm Genome 8:\n200–208. 18." + } + ], + "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce": [ + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Other aggression QTLs\nSeveral lines of mice have been selectively bred for high or low levels of o¡ensive\naggression, which con¢rms that a propensity for aggressive behaviours is partially\nheritable. These lines include the Turku aggressive (TA) and non-aggressive\n(TNS) strains bred in Finland, the NC900 and NC100 strains bred in North\nCarolina, and the short attack latency (SAL) and long attack latency (LAL)\nstrains bred in the Netherlands (Miczek et al 2001). In wild mice, there is evidence for a QTL a¡ecting aggressive behaviours in a\nregion of chromosome 17, the t region." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n65\n\nProgress towards identifying QTLs that a¡ect\naggressive behaviours in mice\nAn example of aggression QTLs identi¢ed as part of a whole genome scan\nOne of the few studies to identify intermale aggression QTLs as part of a whole\ngenome scan was published recently (Brodkin et al 2002). This study used NZB/\nB1NJ (extremely aggressive) and A/J (extremely unaggressive) inbred mice as\nparental strains. The methods chosen for housing and aggression testing were\ndesigned to control the e¡ect of non-genetic factors on the phenotype." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Neuroscientist 4:317^323\nBrodkin ES, Goforth SA, Keene AH, Fossella JA, Silver LM 2002 Identi¢cation of quantitative\ntrait loci that a¡ect aggressive behavior in mice. J Neurosci 22:1165^1170\nChesler EJ, Lu L, Wang J, Williams RW, Manly KF 2004 WebQTL: rapid exploratory analysis\nof gene expression and genetic networks for brain and behavior. Nat Neurosci 7:485^486\nDarvasi A 1997 Interval-speci¢c congenic strains (ISCS): an experimental design for mapping a\nQTL into a 1-centimorgan interval. Mamm Genome 8:163^167\nDarvasi A 1998 Experimental strategies for the genetic dissection of complex traits in animal\nmodels." + }, + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Brodkin: Such a course mapping study with only about 400 mice would be\nunlikely to detect a QTL that accounts for only 2.5% of the phenotypic variance,\nQTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n73\n\nbut it should detect a QTL that accounts for approximately 10% of the variance\n(Lynch & Walsh 1998, Darvasi 1998). QTLs of this magnitude of e¡ect on\nneurobiological or behavioural traits have been found fairly commonly in crosses\nbetween inbred mouse strains (see e.g. Wehner et al 1997)." + } + ], + "4de669b7-da76-42ef-a88a-afebf1e86734": [ + { + "document_id": "4de669b7-da76-42ef-a88a-afebf1e86734", + "text": "By correlating genotypes with phenotypes in quantitative trait locus (QTL)\nanalysis, a large number of polymorphic regions harboring\ntrait relevant allelic variation have been defined for a wide\nrange of behavioral phenotypes [17]. At present, there are\n340\n\n549 QTLs for behavioral phenotypes in the Mouse Genome\nInformatics database, which are largely derived from crosses\nof 2 inbred strains of mice [18]." + } + ], + "53a0a196-385a-47ba-9509-0d4f4b157cbf": [ + { + "document_id": "53a0a196-385a-47ba-9509-0d4f4b157cbf", + "text": "A search of the Mouse Genome\nInformatics database (www.informatics.jax.org, March 16,2006) revealed 34\nneurobehavioral- and/or pain-related QTLs mapped to >75 cM; these inc1ude seven traits\nrelated to alcohol, six to morphine or other drugs, two to painful arthritis, five to\nemotionality/anxiety, and one to seizure susceptibility. Several ofthese QTLs have been\nfinely mapped near the peak of linkage of our analgesia QTL." + } + ], + "60e08224-f0e8-409c-b00a-b9e7358d3548": [ + { + "document_id": "60e08224-f0e8-409c-b00a-b9e7358d3548", + "text": "The behavioral QTLs were determined from the MGI\ndatabase as of October 1, 2004. Alcrsp2 (Erwin et al. , 1997); Ap3q (Bachmanov et al. , 2002); Alcp12 (Gill et al. , 1998). Behavioral QTLs have been mapped using other\nmouse strains, and their validity in the ILS and ISS strains has not been tested. Mb, megabases. Table 4." + } + ], + "835a094d-9c2b-4686-8725-d3c4123175b0": [ + { + "document_id": "835a094d-9c2b-4686-8725-d3c4123175b0", + "text": "In the fourth step, we sought to identify DNA sequence variants that influence\nboth molecular phenotypes as well as phenotypes at the structural and behavioral level. A\nremarkable region located on the distal end of mouse Chr 1 (172–178 Mb) was the ideal\nsubject for such an integrative study. This region, which we have named as Qrr1 (QTL\nrich region on distal Chr 1), is known for its unusually high density of QTLs for neural\nand behavioral traits, e.g. , traits like anxiety-related behavior, seizure, hippocampal\nvolume, and alcohol preference consistently map to this region." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": "Overall, these studies reveal the existence of an extensive\npolygenic system influencing the exploratory behavior of\nmice similar to the kind of genetic architecture shown to\ninfluence behavior in tests of fear and anxiety (Caldarone\net al. 1997; Flint et al. 1995; Gill & Boyle 2005; Henderson\net al. 2004; Laarakker et al. 2008; Singer et al. 2005; Turri\net al. 2001a,b). The significance of the QTL, and also of the\npolygenic system, is heightened by the finding that roughly\nthe same set of genes has the potential to influence some\nbehaviors from early adulthood to old age." + } + ], + "bd221ae3-3994-4fe2-b22d-b050b0d62bbf": [ + { + "document_id": "bd221ae3-3994-4fe2-b22d-b050b0d62bbf", + "text": "The behavioral phenotypes with QTLs on distal\nChr 17 are (1) prepulse inhibition, assayed by McCaughran\net al.41 in a panel of 21 BXD strains (trait ID on Genenetwork\nis 10396), (2) anxiety trait measure by time spent in open\nquadrant of zero-maze, assayed in a larger panel of 57 BXD\nstrains42 (trait ID 11696) and (3) handling induced convulsion\nas an index of ethanol withdrawal severity, measured in 25\nBXD strains43 (trait ID 10065). Gene–gene interaction analysis." + } + ], + "d0deb53b-7286-4fd0-9188-b7b9f366fd76": [ + { + "document_id": "d0deb53b-7286-4fd0-9188-b7b9f366fd76", + "text": "Quantitative 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 biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) strains derived\nfrom crossing two inbred strains—C57BL/6J and DBA/2J mice—have been extensively used for\nalmost 50 years in fields such as neuropharmacology [4–6], immunology [7–13], behaviour [13–21],\naging [21–29], neurodegeneration [30–33], and gut microbiome–host interactions [34]." + } + ], + "d2f9c5cf-835c-450a-bb42-a2454a99e058": [ + { + "document_id": "d2f9c5cf-835c-450a-bb42-a2454a99e058", + "text": "Two QTLs located on chromosomes 1 and 5 modulate different\naspects of the performance of mice of the BXD Ty RI strain series in the Morris navigation task. Behav Genet. 2002; 32:69–78. [PubMed: 11958544]\nMozhui RT, Ciobanu DC, Schikorski T, Wang XS, Lu L, Williams RW. Dissection of a QTL hotspot\non mouse distal chromosome 1 that modulates neurobehavioral phenotypes and gene expression. PLoS Genetics. 2008; 4:e1000260. [PubMed: 19008955]\nMulligan MK, Wang X, Adler AL, Mozhui K, Lu L, Williams RW. Complex control of GABA(A)\nreceptor subunit mRNA expression: variation, covariation, and genetic regulation. PLoS One. 2012; 7(4):e34586." + } + ], + "d6085c3a-6ade-499e-9fde-4c8ea682f20e": [ + { + "document_id": "d6085c3a-6ade-499e-9fde-4c8ea682f20e", + "text": "Type\nI and type II error rates for quantitative trait loci (QTL) mapping studies using\nrecombinant inbred mouse strains. Behav Genet, 26(2): 149-160. Bidwell, L. C., Willcutt, E. G., Defries, J. C., & Pennington, B. F. 2007. Testing for\nneuropsychological endophenotypes in siblings discordant for attentiondeficit/hyperactivity disorder. Biol Psychiatry, 62(9): 991-998. Bitanihirwe, B. K., Dubroqua, S., Singer, P., Feldon, J., & Yee, B. K. 2011. Sensorimotor\ngating and vigilance-dependent choice accuracy: a within-subject correlative\nanalysis in wild-type C57BL/6 mice. Behav Brain Res, 217(1): 178-187. 151\nReferences\nBitsios, P., & Giakoumaki, S. G. 2005." + } + ], + "d8993417-3a27-4000-b693-6cb4662b9f80": [ + { + "document_id": "d8993417-3a27-4000-b693-6cb4662b9f80", + "text": "Quantitative 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 biomedically relevant traits. Amongst these, the BXD family of recombinant inbred (RI) strains derived\nfrom crossing two inbred strains—C57BL/6J and DBA/2J mice—have been extensively used for\nalmost 50 years in fields such as neuropharmacology [4–6], immunology [7–13], behaviour [13–21],\naging [21–29], neurodegeneration [30–33], and gut microbiome–host interactions [34]." + } + ], + "f253e087-e030-40a8-8400-3b6bf50c1fd6": [ + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Other aggression QTLs\nSeveral lines of mice have been selectively bred for high or low levels of o¡ensive\naggression, which con¢rms that a propensity for aggressive behaviours is partially\nheritable. These lines include the Turku aggressive (TA) and non-aggressive\n(TNS) strains bred in Finland, the NC900 and NC100 strains bred in North\nCarolina, and the short attack latency (SAL) and long attack latency (LAL)\nstrains bred in the Netherlands (Miczek et al 2001). In wild mice, there is evidence for a QTL a¡ecting aggressive behaviours in a\nregion of chromosome 17, the t region." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "QTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n65\n\nProgress towards identifying QTLs that a¡ect\naggressive behaviours in mice\nAn example of aggression QTLs identi¢ed as part of a whole genome scan\nOne of the few studies to identify intermale aggression QTLs as part of a whole\ngenome scan was published recently (Brodkin et al 2002). This study used NZB/\nB1NJ (extremely aggressive) and A/J (extremely unaggressive) inbred mice as\nparental strains. The methods chosen for housing and aggression testing were\ndesigned to control the e¡ect of non-genetic factors on the phenotype." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Neuroscientist 4:317^323\nBrodkin ES, Goforth SA, Keene AH, Fossella JA, Silver LM 2002 Identi¢cation of quantitative\ntrait loci that a¡ect aggressive behavior in mice. J Neurosci 22:1165^1170\nChesler EJ, Lu L, Wang J, Williams RW, Manly KF 2004 WebQTL: rapid exploratory analysis\nof gene expression and genetic networks for brain and behavior. Nat Neurosci 7:485^486\nDarvasi A 1997 Interval-speci¢c congenic strains (ISCS): an experimental design for mapping a\nQTL into a 1-centimorgan interval. Mamm Genome 8:163^167\nDarvasi A 1998 Experimental strategies for the genetic dissection of complex traits in animal\nmodels." + }, + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Brodkin: Such a course mapping study with only about 400 mice would be\nunlikely to detect a QTL that accounts for only 2.5% of the phenotypic variance,\nQTL ANALYSIS OF AGGRESSIVE BEHAVIOURS IN MICE\n\n73\n\nbut it should detect a QTL that accounts for approximately 10% of the variance\n(Lynch & Walsh 1998, Darvasi 1998). QTLs of this magnitude of e¡ect on\nneurobiological or behavioural traits have been found fairly commonly in crosses\nbetween inbred mouse strains (see e.g. Wehner et al 1997)." + } + ] + }, + "data_source": [], + "document_id": "7C515AA69FEB13DBCB2B87E949390D2A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL", + "behavioral", + "mice", + "aggression", + "chromosome", + "Morris&navigation&task", + "neurobehavioral", + "ethanol&withdrawal", + "prepulse&inhibition", + "anxiety" + ], + "metadata": [ + { + "object": "Mice exposed to aggressive confrontations exhibited a similar pattern of species-typical aggressive and non-aggressive behaviors on the first and the last session. Repeated aggressive confrontations promoted an increase in plasma corticosterone. After 10 aggressive confrontation sessions, mice presented a non-significant trend toward reducing hippocampal levels of CRF, which inversely correlated with plasma corticosterone", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab481563" + }, + { + "object": "Overexpression of RGS2 in explicitly serotonergic neurons augments male aggression in control mice and rescues male aggression in Rgs2 -/- mice, while anxiety is not affected. Findings specifically identify that RGS2 expression in serotonergic neurons is sufficient to drive male aggression in mice and as a potential therapeutic target for treating aggression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab572353" + }, + { + "object": "Dopamine and DOPAC were not changed in 3-mo-old mice but were decreased at 8 mo in the striatum of PIKE-KO mice compared with wild-type mice. DA and DOPAC in hippocampus and substantia nigra were significantly decreased in 3-mo-old and 8-mo-old PIKE-KO mice as compared with wild-type mice. More severe motor defects in PIKE-KO and Fyn-KO mice than in wild-type mice exposed to alpha synuclein and MPTP.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab237945" + }, + { + "object": "We found a significant reduction of testosterone levels in mGluR7 knockout KO mice. Social investigating behaviour of intact mGluR7 KO mice also differed from that of wild-type mice; e.g. the KO mice showing less frequent anogenital sniffing and more frequent grooming behaviour. Further, castrated mGluR7 KO mice have smaller seminal vesicles than those of castrated wild-type mice, although intact mice were no different.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1004015" + }, + { + "object": "Mice exposed to aggressive confrontations exhibited a similar pattern of species-typical aggressive and non-aggressive behaviors on the first and the last session. Repeated aggressive confrontations promoted an increase in plasma corticosterone. Repeated sessions of social instigation or aggressive confrontation did not alter BDNF concentrations at the prefrontal cortex and hippocampus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab481564" + }, + { + "object": "Recognition memory improved with exercise in WT mice, was impaired in TNFR1-/- exercise mice, showed non-significant impairment with exercise in TNF-/- mice, and no changes in TNFR2-/- mice. In spatial learning there were exercise related improvements in WT mice, non-significant but meaningful impairments evident in TNFR1-/- exercise mice, modest improvement in TNF-/- exercise mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab632171" + }, + { + "object": "No difference between wild-type WT and v1a and v1b double knockout dKO mice was found in olfactory preferences for estrous female odor to male odor. Over all four mating tests, the number of mounts and pursuits after receptive females was significantly greater in dKO mice than in WT mice. In the elevated plus maze and the open field test, dKO mice showed lower anxiety-like behavior than WT mice.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab529453" + }, + { + "object": "Neonatal maternal separation MS lead to increased anxiety-like behavior in Cdh13-/- mice compared to the other two MS groups. Cdh13-/- mice showed a context-dependent effect on stress- & anxiety-related behavior, impaired extinction learning following contextual fear conditioning & decreased impulsivity, & a mild decrease in errors in the Barnes maze & reduced risk-taking in the light-dark transition test after MS.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab520848" + }, + { + "object": "We identified low activity and high anxiety-like behaviors in Il18r1-/- mice, whereas Il18-/- mice displayed little anxiety-like behavior.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1007843" + }, + { + "object": "significantly higher binding of POLR2A to the Tmem132d promoter region of high anxiety behavior mice than in low anxiety behavior mice", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab707366" + } + ], + "question": "What behavioural QTLs have been found in mice?", + "subquestions": null, + "task_id": "7C515AA69FEB13DBCB2B87E949390D2A", + "usage": { + "chatgpt": 6048, + "gpt-4": 4191, + "gpt-4-turbo-preview": 3207 + }, + "user_id": 2 + }, + "document_id": "7C515AA69FEB13DBCB2B87E949390D2A", + "task_id": "7C515AA69FEB13DBCB2B87E949390D2A" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response09.json b/gnqa/paper1_eval/src/data/responses/test/response09.json new file mode 100644 index 0000000..22fa49e --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response09.json @@ -0,0 +1,195 @@ +{ + "created_at": "2024-03-25T14:30:20.651749Z", + "data": { + "amplify": false, + "answer": "You can look for genes that regulate hippocampal neural stem cell proliferation in the results of gene expression profiling studies, such as the one mentioned in the text. Specifically, the study identified LRP6 as a novel regulator of adult hippocampal neurogenesis. Other genes like Lef1, Mtdh, and Chchd8 may also be involved.", + "context": { + "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84": [ + { + "document_id": "2dfc2b82-b8eb-4e73-957a-0ea8a4401a84", + "text": "Other cell cyclerelated genes, such as p21, p18 and p27, were also reported to be involved in\nregulating different types of hematopoietic cells (Cheng 2004; Steinman 2002). For example, p21 and p18 specifically control HSC proliferation, whereas p27\nonly affects hematopoietic progenitor cells. Further study of the chromosome 3\nQTL interval in the congenic mouse model may provide a platform leading to the\ndiscovery of novel cycle-active gene and/or functions of already known genes. The apoptotic analyses shown in Table 3.2 are novel." + } + ], + "3278febd-171a-485d-bd6e-0cbb523d73ec": [ + { + "document_id": "3278febd-171a-485d-bd6e-0cbb523d73ec", + "text": "Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI,\nVellenga E, Wang J, Manly KF, Lu L, Chesler EJ, Alberts R, Jansen RC,\nWilliams RW, Cooke MP, de Haan G: Uncovering regulatory pathways that\naffect hematopoietic stem cell function using ‘genetical genomics’. Nat\nGenet 2005, 37(3):225-32. 29. Overall RW, Kempermann G, Peirce J, Lu L, Goldowitz D, Gage FH,\nGoodwin S, Smit AB, Airey DC, Rosen GD, Schalkwyk LC, Sutter TR,\nNowakowski RS, Whatley S, Williams RW: Genetics of the hippocampal\ntranscriptome in mouse: a systematic survey and online neurogenomics\nresource." + } + ], + "489539fd-f7c5-44eb-bb58-5fc19d50a7cf": [ + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "text": "In summary, I have identified p107 and Snx5 as quantitative trait genes that\nregulate the number of HSCs in B6 and congenic mice. CAFC assays confirmed that\nincreased expression of both genes increases HSC number in an in vitro setting. Although the increased expression of both Snx5 and p107 resulted in small increases in\nHSC number, the changes are biologically significant given the extensive proliferative\npotential of primitive stem cells." + } + ], + "66fc5ee9-0126-431f-add0-819957499810": [ + { + "document_id": "66fc5ee9-0126-431f-add0-819957499810", + "text": "The molecular mechanisms that regulate progenitor cell division and\ndifferentiation in the RMS remain largely unknown. Here, we surveyed the mouse genome in an\nunbiased manner to identify candidate gene loci that regulate proliferation in the adult RMS. We\nquantified neurogenesis in adult C57BL/6J and A/J mice and 27 recombinant inbred lines derived\nfrom those parental strains. We showed that the A/J RMS had greater numbers of\nbromodeoxyuridine-labeled cells than that of C57BL/6J mice with similar cell cycle parameters,\nindicating that the differences in the number of bromodeoxyuridine-positive cells reflected the\nnumber of proliferating cells between the strains." + }, + { + "document_id": "66fc5ee9-0126-431f-add0-819957499810", + "text": "Page 10\n\nNIH-PA Author Manuscript\n\nSeptin 9 (Sept9) and cyclin-dependent kinase 3 (cdk3) and are two other genes that are\nworth mentioning because even though they are not directly linked to neurogenesis, they are\nboth cell cycle regulatory genes. Sept9 is involved in the progression through G1 of the cell\ncycle and it is highly expressed throughout the adult mouse brain (Gonzalez et al. , 2009). Whereas, cdk3 is expressed at low levels throughout the adult mouse brain and it is required\nfor G1-S transition (Braun et al. , 1998)." + } + ], + "835a094d-9c2b-4686-8725-d3c4123175b0": [ + { + "document_id": "835a094d-9c2b-4686-8725-d3c4123175b0", + "text": "Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T et al. (2005). Uncovering regulatory pathways that effect hematopoietic stem cell function using\n‘genetical genomics’. Nat Genet 37:225–232. Cai L, Morrow EM, Cepko CL (2000). Misexpression of basic helix-loop-helix genes in\nthe murine cerebral cortex affects cell fate choices and neuronal survival. Development\n127:3021–3030. Caldarone B, Saavedra C, Tartaglia K, Wehner JM, Dudek BC, Flaherty L (1997). Quantitative trait loci analysis affecting contextual conditioning in mice. Nat Genet\n17:335–337. Calder AJ, Lawrence AD, Young AW (2001). Neuropsychology of fear and loathing. Nature Rev Neurosci 2:352–363." + } + ], + "8fb56fda-e1a2-4407-acb2-9a5983861202": [ + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "As further step, this\nfinding opens the door to study the molecular networks via which LRP6 acts to\nregulate proliferation. ! '*! ! +&(/. ((&-*)\n\n5.2. Redox regulation of Adult Hippocampal Precursor Cells\n\n5.2.1. Hypoxia increases AHPCs proliferation and neuronal differentiation\nOxygen concentration plays an important role in cellular development and\ntissue homeostasis. In the brain, depending on the tissue, the oxygen\nconcentration varies from 0.1 to 5% and in the rat hippocampus it is around\n3.2% (Studer et al. , 2000)." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "While this study covers only one part in\nthe several conceptual levels of regulation we are confident that this work will\nlead to finding a central regulatory pathway that regulates adult hippocampal\nprecursor cell proliferation. ! &*! ! +&(/. ((&-*)\n\n5.1.1. Establishment of AHPCs\nIsolating the precursor cells has become extremely important in order to study\nthem in detail away from the influence of their in vivo niche. Once the cells are\nin culture they express their autonomous, intrinsic properties without the niche\ninfluences such as cell-cell contacts, blood vessels, known and unknown\ngrowth factors and network activities." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "Gene expression profiling\nusing RNA samples from proliferating cultures of the 20 BXD mice strains\nyielded two cis eQTL candidates that directly regulated proliferation, LRP6\nand Chchd8. LRP6 is well known as a co-receptor of Wnt signaling, but the\nfunction of Chchd8 is not known. Further experimentation, using over-\n\n! I! ! SUMMARY\n\nexpression and gene silencing demonstrated that LRP6 negatively regulates\nAHPCs proliferation. Thus, from this study using a system genetics approach,\nwe were able to identify, LRP6 as a novel regulator of adult hippocampal\nneurogenesis. ! V! ! INTRODUCTION\n\n2. INTRODUCTION\n2.1." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "Gene expression profiling ...............................................................68\n4.1.8. LRP6 is a novel regulator of AHPCs proliferation .........................73\n4.2. Redox regulation of Adult Hippocampal Precursor Cells................78\n4.2.1. AHPCs yield increased under hypoxic conditions..........................78\n\n! T! ! TABLE OF CONTENTS\n\n4.2.2. More neuronal differentiation under hypoxic conditions................79\n5. DISCUSSION ..............................................................................................81\n5.1. Systems genetic approach to identify genes regulating AHPCs\nproliferation .................................................................................................81\n5.1.1. Establishment of AHPCs................................................................82\n5.1.2. Variation in proliferative and differentiative properties of AHPCs83\n5.1.3. QTL analysis ...................................................................................86\n5.1.4. Candidate genes from gene expression profiling ............................87\n5.1.5. Lrp6 as negative regulator of AHPCs proliferation ........................89\n5.2. Redox regulation of Adult Hippocampal Precursor Cells................92\n5.2.1." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "Mapping determinants of human gene expression by\nregional and genome-wide association. Nature 437, 1365-1369. Chiasson, B.J. , Tropepe, V., Morshead, C.M. , and van der Kooy, D. (1999). Adult mammalian forebrain ependymal and subependymal cells\ndemonstrate proliferative potential, but only subependymal cells have neural\nstem cell characteristics. Journal of Neuroscience 19, 4462-4471. Cipolleschi, M.G. , Dello Sbarba, P., and Olivotto, M. (1993). The role of\nhypoxia in the maintenance of hematopoietic stem cells. Blood 82, 20312037. Clarke, D.L. , Johansson, C.B. , Wilbertz, J., Veress, B., Nilsson, E., Karlstrom,\nH., Lendahl, U., and Frisen, J. (2000)." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "List of BXD AHPC lines stored\n\nTable 3. List of eQTls in 0.6 threshold range\n\nTable 4. Cis acting genes regulating proliferation trait\n\n! U#! ! PUBLICATIONS\n\nPublications\n\nA protocol for isolation and enriched monolayer cultivation of neural precursor\ncells from mouse dentate gyrus. Harish Babu*, Jan-Hendrik Claasen*, Suresh\nKannan, Annette E. Rünker, Theo Palmer, Gerd Kempermann. Front. Neurosci. 5:89. doi: 10.3389/fnins.2011.00089\n\nSystem genetics approach yields candidate genes regulating adult hippocampal\nprecursor cells proliferation, Manuscript in preparation (first author paper)\n\n! U##! ! SUMMARY\n\n1. SUMMARY\nAdult hippocampal neurogenesis is regulated at various levels and by various\nfactors." + }, + { + "document_id": "8fb56fda-e1a2-4407-acb2-9a5983861202", + "text": "A recent study suggesting the role of mitochondria and\n\n! &&! ! +&(/. ((&-*)\n\ncytochrome\n\noxidase\n\nin\n\nenhancing\n\nhippocampal\n\nneurogenesis\n\nduring\n\ninflammation (Voloboueva et al. , 2010) may reveal the link for Chchd8 gene in\nadult neurogenesis. 5.1.5. Lrp6 as negative regulator of AHPCs proliferation\nThe results from our gene expression profiling suggest that high expression\nlevel of Lrp6 is associated with slow proliferating AHPCs and vice versa. We\nconfirmed this result by over expressing LRP6 in AHPCs. This revealed that\nLRP6 over expression reduced the proliferation of AHPCs by more than 2fold." + } + ], + "9497cd3a-8b36-46d3-be18-d9a6f4c36a27": [ + { + "document_id": "9497cd3a-8b36-46d3-be18-d9a6f4c36a27", + "text": "Two types of collagen and N-Cadherin were also in this pathway. The top upstream regulators of this gene set were Huntingtin (HTT) which regulates 32 of\nthe 193 genes analyzed (p = 1.22 × 10−15), and β-estradiol which may regulate 39 out of 193\ngenes in the set (p = 4.06 × 10−10). 3.2.2. Genes regulated by ethanol in the NAC following CIE—Three hundred\nseventy-eight probesets were exclusively altered by ethanol in the NAC only following CIE\n(Supplemental Fig. 2 and Table 5)." + } + ], + "9b3b1f72-2b99-45ce-b61b-b861fcf84604": [ + { + "document_id": "9b3b1f72-2b99-45ce-b61b-b861fcf84604", + "text": "Expression of a\nsubset of these neurogenesis-associated transcripts was controlled\nin cis across the BXD set. These self-modulating genes are particularly interesting candidates to control neurogenesis. Among\nthese were musashi (Msi1h) and prominin1兾CD133 (Prom1), both\nof which are linked to stem-cell maintenance and division. Twelve\nneurogenesis-associated transcripts had significant cis-acting\nquantitative trait loci, and, of these, six had plausible biological\nassociation with adult neurogenesis (Prom1, Ssbp2, Kcnq2, Ndufs2,\nCamk4, and Kcnj9). Only one cis-acting candidate was linked to\nboth neurogenesis and gliogenesis, Rapgef6, a downstream target\nof ras signaling." + } + ], + "9c266a06-68f9-4e25-8de4-87d8ee02d929": [ + { + "document_id": "9c266a06-68f9-4e25-8de4-87d8ee02d929", + "text": "Other cell cyclerelated genes, such as p21, p18 and p27, were also reported to be involved in\nregulating different types of hematopoietic cells (Cheng 2004; Steinman 2002). For example, p21 and p18 specifically control HSC proliferation, whereas p27\nonly affects hematopoietic progenitor cells. Further study of the chromosome 3\nQTL interval in the congenic mouse model may provide a platform leading to the\ndiscovery of novel cycle-active gene and/or functions of already known genes. The apoptotic analyses shown in Table 3.2 are novel." + } + ], + "b7f409c2-5328-4bd5-94f5-cc7456252ef6": [ + { + "document_id": "b7f409c2-5328-4bd5-94f5-cc7456252ef6", + "text": "\n\nand Tgfbr3 (transforming growth factor beta receptor 3).Of the significant genes correlated with the hippocampal cell death phenotype, there were 107 genes that were significant for a strain × treatment interaction.Four of these genes also showed an FC > 1.5: Gadd45g (growth arrest and DNA-damage-inducible, gamma), Kcnj13 (potassium inwardly rectifying channel, subfamily J, member 13), Plekhg1 (pleckstrin homology domain containing, family G (with RhoGef domain) member 1), and Sgms2 (sphingomyelin synthase 2)." + } + ], + "db0459f8-6602-48d7-be9b-14863a88bbe1": [ + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "text": "111\nBystrykh, L., E. Weersing, et al. (2005). \"Uncovering regulatory pathways that\naffect hematopoietic stem cell function using 'genetical genomics'. \"Nat\nGenet 37(3): 225-32. Cashman, J., A. C. Eaves, et al. (1985). \"Regulated proliferation of primitive\nhematopoietic progenitor cells in long-term human marrow cultures. \"Blood\n66: 1002-1005. Celeste, A., O. Fernandez-Capetillo, et al. (2003). \"Histone H2AX phosphorylation\nis dispensable for the initial recognition of DNA breaks. \"Nat Cell Biol 5(7):\n675-9. Chen, J., B. A. Astle, et al. (1999). \"Development and aging of primitive\nhematopoietic stem cells in BALB/cBy mice.\"Exp. Hematol. 27: 928-935. Cheng, T., N. Rodrigues, et al." + } + ], + "ee850069-4957-4159-97b9-38253ef00b18": [ + { + "document_id": "ee850069-4957-4159-97b9-38253ef00b18", + "text": "\n\nThe next category was Cellular Growth and Proliferation, which includes growth, proliferation, expansion and differentiation of cells and is also pertinent to the possible formation of new cells in this area of the hippocampus.37 genes were associated with this function.Not surprisingly, in the Cell Cycle function (Supplementary Table 2) we found thirty genes involved in cell cycle progression indicating the activity of dividing cells in this region." + } + ], + "f92e167e-0375-45b7-9d91-f8a4d0e1fbba": [ + { + "document_id": "f92e167e-0375-45b7-9d91-f8a4d0e1fbba", + "text": "Lef1 is expressed in cultured hippocampal\nneural stem cells in response to activation of the Wnt signaling\npathway (Cui et al. , 2011). Our evidence and the literature both\nsuggest that genes known to be involved in hippocampal adult\nneurogenesis are targets of Lef1, an important factor in generating\ngranule cells in the dentate gyrus during development (Galceran\net al. , 2000). The only two genes not targeted by Lef1 can be\nclosely associated with it: Mtdh regulates the expression of Lef1\n(Hu et al. , 2009; Yoo et al." + } + ] + }, + "data_source": [], + "document_id": "225D40F9ABA26046B89A427FAA204F2A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "LRP6", + "Chchd8", + "Lef1", + "Mtdh", + "AHPCs", + "hippocampal&neural&stem&cells", + "Wnt&signaling&pathway", + "cell&cycle", + "neurogenesis", + "proliferation" + ], + "metadata": [ + { + "object": "Results show that MbTrxC-AhpC forms an NADPH-dependent peroxidase ensemble for efficient reduction of H2O2 inside the mycobacterial antioxidant defense system and identify the amino acids involved in TrxC and AhpC interaction. AhpC undergoes a redox-modulated dimer to dodecamer formation, in which the unique mycobacterial N-terminal stretch of AhpC place a fundamental role. [AhpC, TrxC]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab662541" + }, + { + "object": "Functional studies demonstrated that miR-27 overexpression promoted multiple myeloma cell proliferation, facilitated cell cycle progression, and expedited cell migration and invasion; whereas miR-27 knockdown inhibited cell proliferation, induced cell cycle arrest, and slowed down cell motility.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab307388" + }, + { + "object": "Cell cycle profiling and proliferation assays revealed that the proximal alternative polyadenylation sites of CCND1 accelerated the cell cycle and promoted cell proliferation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab132405" + }, + { + "object": "Loss-of-function assays demonstrated that silenced FAM83H-AS1 obviously suppressed cell proliferation via regulating the cell-cycle distribution and cell apoptosis rate, and mechanistic experiments revealed that FAM83H-AS1 could epidemically silence CDKN1A expression through recruiting EZH2 to the promoter of CDKN1A, thereby influencing the cell cycle and proliferation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459131" + }, + { + "object": "This study showed that miR-30b-5p repressed cell proliferation and cell cycle of HCC cell lines and that miR-30b-5p mediated DNMT3A to repress proliferation, meanwhile it targeted USP37 for decelerating cell cycle.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab947658" + }, + { + "object": "Loss-of-function assays demonstrated that silenced FAM83H-AS1 obviously suppressed cell proliferation via regulating the cell-cycle distribution and cell apoptosis rate, and mechanistic experiments revealed that FAM83H-AS1 could epidemically silence CDKN1A expression through recruiting EZH2 to the promoter of CDKN1A, thereby influencing the cell cycle and proliferation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459132" + }, + { + "object": "eIF3a expression oscillated with cell cycle and peaked in S phase. Reducing eIF3a expression also reduced cell proliferation rate by elongating cell cycle but did not change the cell cycle distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab251875" + }, + { + "object": "eIF3a expression oscillated with cell cycle and peaked in S phase. Reducing eIF3a expression also reduced cell proliferation rate by elongating cell cycle but did not change the cell cycle distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab251876" + }, + { + "object": "MiR-152-3p was highly expressed in the bone marrow of chronic myeloid leukemia patients and cell lines and promoted the proliferation and cell cycle progression of K562 cells. Bioinformatics analysis, luciferase reporter assay, and Western blot revealed p27Kip1 p27 is a direct target of miR-152-3p. Up-regulation of p27 in K562 cells attenuated the effect of miR-152-3p on cell proliferation and cell cycle progression.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab487214" + }, + { + "object": "the HOX protein LIN-39 regulates the vulval precursor cell cycle at two levels. First, LIN-39 acts as a permissive factor by maintaining the expression of the cell cycle machinery in the vulval precursor cells, keeping them competent to proliferate. Second, LIN-39 triggers the lateral NOTCH signaling pathway in the adjacent 2 degrees vulval precursor cells, which over comes the cell cycle inhibition by LIN-31 Forkhead.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab821517" + } + ], + "question": "Where do I look for genes that regulate hippocampal neural stem cell proliferation?", + "subquestions": null, + "task_id": "225D40F9ABA26046B89A427FAA204F2A", + "usage": { + "chatgpt": 6607, + "gpt-4": 4369, + "gpt-4-turbo-preview": 3300 + }, + "user_id": 2 + }, + "document_id": "225D40F9ABA26046B89A427FAA204F2A", + "task_id": "225D40F9ABA26046B89A427FAA204F2A" +} diff --git a/gnqa/paper1_eval/src/data/responses/test/response10.json b/gnqa/paper1_eval/src/data/responses/test/response10.json new file mode 100644 index 0000000..bf4dab7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/test/response10.json @@ -0,0 +1,158 @@ +{ + "created_at": "2024-03-25T14:33:03.505102Z", + "data": { + "amplify": false, + "answer": "QTL stands for Quantitative Trait Locus. It is a region of the genome that is linked to a trait and contains a gene or genes that regulate a portion of the genetic variation for a particular phenotype. QTLs are associated with quantitative traits, which are traits that have measurable phenotypic variation due to genetic and/or environmental influences.", + "context": { + "0666777d-a2c5-40f5-b506-e78888b6b121": [ + { + "document_id": "0666777d-a2c5-40f5-b506-e78888b6b121", + "text": "QTL Mapping and Identification of Candidate Genes\nA QTL is a region of the genome shown to be linked to a trait. The purpose of mapping\nthis region is to identify a region of a genome that has a higher probability of harbouring the\ngenetic variations controlling variability in trait values." + } + ], + "07d409f4-91f9-4701-bb39-1421845a6321": [ + { + "document_id": "07d409f4-91f9-4701-bb39-1421845a6321", + "text": "Often a local eQTL will be caused by allelic variation in the regulatory region of\nthe gene or within the gene itself. mQTL\nA metabolite Quantitative Trait Locus is a region in the genome at which allelic variation\ncorrelates with the abundance variation of a certain metabolite. pQTL\nA protein Quantitative Trait Locus is a region in the genome at which allelic variation correlates with the abundance variation of a certain protein. Just like eQTL, pQTL can be\nlocal or distant according to the genomic position of the gene encoding for the protein relative to the QTL." + } + ], + "29f5af5f-8dc7-4e53-b0fa-66d37317a3f4": [ + { + "document_id": "29f5af5f-8dc7-4e53-b0fa-66d37317a3f4", + "text": "QTLs are regions within the\ngenome whose genetic variation modulates quantitatively a phenotype characteristic of\nthe particular trait under study (Lynch and Walsh, 1998). Determining the association\nbetween variations in specific disease phenotypes or a trait, with variations in genotypes\nof a reference population can be used to locate a QTL. One of the methods used for\nmapping QTLs associated with complex traits is genetic markers-trait association. Genetic markers associated with certain loci can be inherited in linkage disequilibrium. Generating populations with linked loci in disequilibrium is achieved though either\ncrosses between inbred lines, or use of the out-bred populations." + } + ], + "2a92d7b5-946c-4a22-a4b9-26e950b0f757": [ + { + "document_id": "2a92d7b5-946c-4a22-a4b9-26e950b0f757", + "text": "Quantitative trait locus-mapping is a statistical method\nused to map chromosomal intervals (loci) that contribute to\nheritable variance in phenotypes. The method simply compares the inheritance of allelic variants (B or D genotypes\nin our case) with differences in phenotypes. A QTL will\ngenerally cover a region that includes 10–100 genes, and\nthese positional candidates can then be ranked roughly on\nthe basis of criteria such as the types of DNA variants, patterns of mRNA expression, data from complementary human\ngenetic cohorts (GWAS and linkage) and relevant literature\nabout gene effects on central nervous system structure and\nfunction." + } + ], + "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce": [ + { + "document_id": "3f8db22e-d5f9-44ba-8f78-fc77ccf024ce", + "text": "Chromosomal\nregions containing a gene (or genes) that a¡ect the level of a quantitative trait are\ncalled quantitative trait loci (QTLs). The relevant genes in these regions have been\ncalled quantitative trait genes (QTGs) (Hitzemann et al 2003). Quantitative trait\nlocus (QTL) analysis is an experimental strategy for identifying QTLs, and\nultimately QTGs, that a¡ect quantitative traits. Because of the complexity of\nthese traits, progress in identifying QTGs has been slow compared to that in\ncloning genes underlying Mendelian traits (Glazier et al 2002)." + } + ], + "4049da4d-c7cf-4e30-9a21-c77609fad23d": [ + { + "document_id": "4049da4d-c7cf-4e30-9a21-c77609fad23d", + "text": "Expression QTL\nNext, we will examine expression quantitative trait loci (eQTLs). These are QTLs for gene\nexpression traits, a subset of the molecular phenotypes mentioned above. Much like classical\nphenotypes, expression of transcripts can be influenced by variants within the genome. However, because we know the location of the gene, we can split these eQTL into two\ncategories, trans- (or distal) or cis- (or local) eQTL. A trans-eQTL (or distal-eQTL) describes when the expression of a gene is influenced by a locus\nfar away from that gene, and therefore indicates that the gene of interest is downstream of\nanother gene." + } + ], + "40ebee6a-ba5a-4f21-86d1-78d421288687": [ + { + "document_id": "40ebee6a-ba5a-4f21-86d1-78d421288687", + "text": "These loci\nwhich are associated with changes in transcript expression are often termed\nexpression QTL (eQTL): a variant (or variants) within the locus alters the\nexpression of the gene of interest. An eQTL found near to the location (~ ≤\n1Mbp) of the transcript is described as a local eQTL, and are often called ciseQTL. This is in contrast to trans-eQTL which are found more distally. Cis-eQTL\nare interesting when they are found for a gene within a QTL for another\nphenotype (e.g." + } + ], + "621d8b0a-821b-45f8-ae91-aba0cdcdda10": [ + { + "document_id": "621d8b0a-821b-45f8-ae91-aba0cdcdda10", + "text": "The location of these genotypes are quantitative trait loci (QTLs) [Abiola et al. , 2003]. Detected via statistical methods [Doerge, 2002], QTLs are stretches of DNA highly associated with a specific phenotype, analogous to genetic landmarks which roughly indicate\nthe position of the active gene. QTLs are not defined at very fine granularity; they usually\ncorrespond to areas large enough to hold several genes. The genetic polymorphism (genotypes) in neighboring areas of a set of loci, as a group, influence structure and function on\nboth molecular and organismic scales." + } + ], + "6d850ba3-9219-4250-b17f-7cf4867ca354": [ + { + "document_id": "6d850ba3-9219-4250-b17f-7cf4867ca354", + "text": "Quantitative trait loci (QTL)\n\n132\n\nanalysis is a means to query the entire genome for DNA variants (markers) that show significant\n\n133\n\nassociations with the phenotype (quantitative trait) under investigation. This is the first step to\n\n134\n\nidentify candidate genes whose variants (alleles) affect the value of the phenotype. QTL analysis\n\n135\n\nwas performed using WebQTL (http://www.genenetwork.org) for each PCA factor. WebQTL\n\n136\n\nperforms 2,000 or more permutations of the strain data and significant QTL are defined by the\n\n137\n\nlikelihood ratio statistic (LRS) score of correctly ordered data exceeding all other permutations\n\n138\n\n95% of the time, i.e." + } + ], + "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d": [ + { + "document_id": "7b1cecf5-a2b9-4bd9-b92b-9bd6b96ed93d", + "text": "Expression QTL\nNext, we will examine expression quantitative trait loci (eQTLs). These are QTLs for gene\nexpression traits, a subset of the molecular phenotypes mentioned above. Much like classical\nphenotypes, expression of transcripts can be influenced by variants within the genome. However, because we know the location of the gene, we can split these eQTL into two\ncategories, trans- (or distal) or cis- (or local) eQTL. A trans-eQTL (or distal-eQTL) describes when the expression of a gene is influenced by a locus\nfar away from that gene, and therefore indicates that the gene of interest is downstream of\nanother gene." + } + ], + "95b99c09-c336-44fd-b378-f41991edb3aa": [ + { + "document_id": "95b99c09-c336-44fd-b378-f41991edb3aa", + "text": "These are referred to as expression QTLs, or\neQTLs (Schadt et al. , 2003), which control a portion of\nexpression variation of particular genes in a population. eQTLs result from genetic differences in regulatory elements close to or within the gene (apparent cis-acting\neQTLs) as well as those that map elsewhere in the genome\nfrom the gene whose expression is modulated (trans-acting\neQTLs). By combining microarray and QTL analysis on the\nsame mice, much can be learned about the genetic underpinnings of particular alcohol traits (Hitzemann et al. , 2004;\nTabakoff et al. , 2003)." + } + ], + "a8e16a9a-242b-492f-95f6-9e80a10e77cc": [ + { + "document_id": "a8e16a9a-242b-492f-95f6-9e80a10e77cc", + "text": "Working with complex traits that\ntypically vary in their manifestation across a continuous distribution, in contrast to the\nbinary nature of monogenic traits, QTLs are discovered by simply identifying loci with\nalleles that consistently covary with a phenotype across a population. Genomic regions that\nshow a sufficiently strong association with a phenotype are considered QTLs. The simplest,\nor most hopeful, interpretation of a mapped QTL is that the implicated region harbors a\nsingle gene affecting manifestation of the associated phenotype." + } + ], + "b078162f-a48d-405b-b2cf-3559fc3338c8": [ + { + "document_id": "b078162f-a48d-405b-b2cf-3559fc3338c8", + "text": "By definition, a\nquantitative trait locus is a chromosomal region that contains a gene, or genes, that\nregulate a portion of the genetic variation for a particular phenotype (Wehner et al. 2001). The goal of QTL mapping is to identify regions of the genome that harbour\ngenes relevant to a specified trait. QTL map locations are commonly determined by\ninitial screening of mice with specific genetic characteristics, such as recombinant\ninbred strains, the F2 of two inbred strains, or recombinant congenic strains (Flint\n2003)." + } + ], + "b103d0bf-16ab-4e53-bb3b-7c2af3cfd9f6": [ + { + "document_id": "b103d0bf-16ab-4e53-bb3b-7c2af3cfd9f6", + "text": "(2003)\nand others defined the expression QTLs (eQTLs) as either cis\n(mapping near the gene locus) or trans (mapping elsewhere in\nthe genome). When behavioral QTLs (bQTLs) and cis-eQTLs\noverlap, the cis-eQTL genes are inferred as strong quantitative\ntrait gene (QTG) candidates (see e.g. Farris et al. 2010). The\nsituation for trans-eQTLs is more complicated since the QTL\nconfidence interval is generally larger and any gene within the\nQTL interval could have a regulatory role. The application of genetical genomics to mouse has\ngenerally focused on segregating populations involving\nR. Hitzemann et al." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Page 2\n\nDefinition of a QTL\nNIH-PA Author Manuscript\n\nA quantitative trait is one that has measurable phenotypic variation owing to genetic and/or\nenvironmental influences. This variation can consist of discrete values, such as the number of\nseparate tumours in the intestine of a cancer-prone mouse, or can be continuous, such as\nmeasurements of height, weight and blood pressure. Sometimes a threshold must be crossed\nfor the quantitative trait to be expressed; this is common among complex diseases. A QTL is a genetic locus, the alleles of which affect this variation." + } + ], + "d09e59f1-14d1-4391-8419-90c6d6bc2fde": [ + { + "document_id": "d09e59f1-14d1-4391-8419-90c6d6bc2fde", + "text": "When the phenotype of interest is a quantitative trait, such as blood pressure or cholesterol levels, the underlying genetic locus is\nreferred to as a “QTL”. A common strategy investigates the\nassociation between quantitative traits of transcriptional responses and their underlying DNA loci called “response\nQTLs” (reQTLs) (Albert and Kruglyak 2015). Studies have\nprovided clear evidence for the colocalization of reQTLs\nand disease-related loci (Caliskan et al. 2015)." + } + ], + "e7bc9d83-6c3b-405c-a552-29874b927860": [ + { + "document_id": "e7bc9d83-6c3b-405c-a552-29874b927860", + "text": "81\nGene Expression Quantitative Trait Locus Analysis\nQuantitative trait locus (QTL) mapping is a statistical technique that finds\nassociations between phenotype and genotype in a genetically segregating population\n(Lander and Botstein 1989). Here, we performed eQTL mapping on the male and female\ndata separately. There were 1,137 significant (q≤0.5 and p≤0.025) male and 1,232\n\nfemale eQTLs. First, we explored differences in patterns of eQTL locations between sexes by\nplotting the genomic locations of each eQTL versus the transcript location (Figure 4.3a, b)." + } + ], + "f253e087-e030-40a8-8400-3b6bf50c1fd6": [ + { + "document_id": "f253e087-e030-40a8-8400-3b6bf50c1fd6", + "text": "Chromosomal\nregions containing a gene (or genes) that a¡ect the level of a quantitative trait are\ncalled quantitative trait loci (QTLs). The relevant genes in these regions have been\ncalled quantitative trait genes (QTGs) (Hitzemann et al 2003). Quantitative trait\nlocus (QTL) analysis is an experimental strategy for identifying QTLs, and\nultimately QTGs, that a¡ect quantitative traits. Because of the complexity of\nthese traits, progress in identifying QTGs has been slow compared to that in\ncloning genes underlying Mendelian traits (Glazier et al 2002)." + } + ], + "f67f291b-2ea5-4d78-9595-2cbbc35dc415": [ + { + "document_id": "f67f291b-2ea5-4d78-9595-2cbbc35dc415", + "text": "1.4\n\nQ u a n tita tiv e T rait L ocu s M a p p in g\n\nQ uantitative tra it loci (QTLs) are genetic regions on a chromosome th a t control\ncertain quantitative traits, such as crop yield or body fat. QTL m apping involves con\nstruction of genomic m aps and testing for association between tra its and polymorphic\nmarkers. A significant association provides evidence th a t a QTL is near th e m arker." + } + ], + "f8184d24-6bd9-4450-a13e-d656aa2afb02": [ + { + "document_id": "f8184d24-6bd9-4450-a13e-d656aa2afb02", + "text": "\n\nCurrent data processing capabilities have also made it possible to search genome-wide for QTL (quantitative trait loci) [109].QTL mapping seeks to identify the relationship between various genomic locations and a set of quantitative traits, leading to a chromosomal location and ultimately to identification of gene(s) with the final goal of looking at gene expression.Among other things, this will lead to a better understanding of genetic mechanisms of variation and adaptation [121].Results can then be applied to adjust conservation measures in response to rapid change, for example, by identifying the genetic adaptability potential of individuals to be used in assisted migration or reintroduction [122,123]." + } + ] + }, + "data_source": [], + "document_id": "C346DA54E54A8AE66035F3BA22439DC0", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "QTL&mapping", + "quantitative&trait&locus", + "gene&expression", + "genetic&variation", + "phenotype", + "eQTL", + "genomic®ions", + "allelic&variation", + "DNA&loci", + "statistical&methods" + ], + "metadata": [], + "question": "What does QTL mean?", + "subquestions": null, + "task_id": "C346DA54E54A8AE66035F3BA22439DC0", + "usage": { + "chatgpt": 5348, + "gpt-4": 3693, + "gpt-4-turbo-preview": 2691 + }, + "user_id": 2 + }, + "document_id": "C346DA54E54A8AE66035F3BA22439DC0", + "task_id": "C346DA54E54A8AE66035F3BA22439DC0" +} |