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diff --git a/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_diabetes.json b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_diabetes.json new file mode 100644 index 0000000..f95e6a6 --- /dev/null +++ b/gnqa/paper2_eval/data/dataset/gpt4o/gpt4o_de_diabetes.json @@ -0,0 +1,289 @@ +{ + "question": [ + "How do recent advancements in multi-omics approaches, including proteomics and metabolomics, contribute to our understanding of Type 2 diabetes pathogenesis?", + "What novel diabetic loci have been identified through the latest meta-analyses of large-scale genome-wide association studies (GWAS)?", + "How do epigenetic modifications, such as DNA methylation and histone modification, influence the expression of diabetes-related genes?", + "Can you elaborate on the role of the gut microbiome in modulating host genetic predispositions to diabetes?", + "How effective are machine learning algorithms in integrating genomic data to predict individual risk and progression of diabetes?", + "What are the implications of recent findings on the role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity?", + "How do post-translational modifications of proteins affect key signaling pathways involved in glucose homeostasis?", + "What insights have been gained from studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome and Alstr\u00f6m Syndrome?", + "How do genetic and epigenetic differences between monozygotic twins discordant for diabetes inform our understanding of its etiology?", + "What potential therapeutic targets have been identified through recent studies on the interaction between genetic variants and environmental factors in diabetes development?", + "How do rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes?", + "What are the latest findings on the role of non-coding RNAs in the pathogenesis of diabetes?", + "How does the interaction between multiple polygenic risk scores (PRS) improve the prediction of Type 1 and Type 2 diabetes?", + "What are the mechanistic insights into the beta-cell failure pathways gleaned from recent single-cell RNA-sequencing studies?", + "How does the epigenetic landscape of key metabolic tissues change in diabetic versus non-diabetic individuals?", + "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?", + "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?", + "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?,", + "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?", + "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + ], + "answer": [ + "Recent advancements in multi-omics approaches, including proteomics and metabolomics, have significantly enhanced our understanding of the pathogenesis of Type 2 diabetes (T2D). These approaches allow for a comprehensive analysis of the molecular components involved in T2D, providing insights into the disease's complexity and heterogeneity. 1. **Integration of Omics Data**: The integration of data from proteomics, genomics, and transcriptomics helps in understanding the pathophysiology of T2D and the varied responses to different glucose-lowering therapies. This holistic view is crucial for identifying the molecular underpinnings of T2D and tailoring personalized treatment strategies [1]. 2. **Metabolomics Insights**: Metabolomics studies have identified specific metabolites, such as branched-chain and aromatic amino acids, that are associated with obesity and T2D. These findings suggest that metabolic alterations play a significant role in the development of T2D, offering potential targets for therapeutic intervention [1]. 3. **Complex Interactions**: T2D is caused by complex interactions between genetic and environmental factors. Metabolomics has helped elucidate these interactions by identifying metabolic footprints associated with T2D, thus moving from mere information gathering to actionable knowledge [2], [3]. 4. **Biomarker Discovery**: Advances in omics technologies have led to the discovery of numerous candidate biomarkers for T2D. Although only a small proportion of these biomarkers have been thoroughly investigated, they hold promise for improving the prediction and management of T2D [9]. 5. **Beyond Traditional Models**: The use of metabolomics has expanded our understanding of T2D beyond traditional models focused on the pancreas, liver, and muscle. This broader perspective is essential for developing more effective prevention and treatment strategies [10]. Overall, these advancements in multi-omics approaches provide a more detailed and nuanced understanding of T2D pathogenesis, paving the way for improved diagnostic and therapeutic options.", + "The latest meta-analyses of large-scale genome-wide association studies (GWAS) have identified several novel loci associated with diabetes. Specifically, the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium, using genotype data from almost 150,000 individuals predominantly of European descent, identified 10 novel type 2 diabetes (T2D) susceptibility loci that reached genome-wide significance [8].", + "Epigenetic modifications, such as DNA methylation and histone modification, play significant roles in influencing the expression of diabetes-related genes. 1. **DNA Methylation**: This modification can lead to the silencing of key genes involved in diabetes. For example, increased DNA methylation of the promoter of the PPARGC1A gene, which is crucial for regulating mitochondrial genes, has been observed in diabetic islets. This methylation can modulate diabetes by altering gene expression [1]. Additionally, altered DNA methylation patterns can change the expression of inflammatory and pathological genes, contributing to the development of diabetic complications [2]. 2. **Histone Modifications**: These modifications can affect chromatin structure and gene expression. In the context of diabetes, histone post-translational modifications (PTMs) have been associated with the regulation of genes susceptible to type 1 diabetes [3]. Changes in chromatin histone modifications can regulate pathological gene expression in diabetes, influencing the disease's progression [2]. Overall, both DNA methylation and histone modifications can lead to the repression or activation of genes that are critical in the pathogenesis and progression of diabetes, affecting insulin production, inflammation, and other metabolic processes.", + "The gut microbiome plays a significant role in modulating host genetic predispositions to diabetes through various mechanisms. The interactions between host genetics, metabolism, and the immune system are crucial in shaping the microbiome and influencing the predisposition to diseases like diabetes [1]. Research suggests that the gut microbiota affects numerous biological functions throughout the body, and its characterization has become a major research area in biomedicine. Studies have shown that gut bacteria play a fundamental role in diseases such as obesity and diabetes [6]. Dysbiosis, or an imbalance in the gut microbiota, is associated with obesity and type 2 diabetes (T2D), indicating that the composition of the gut microbiome can influence metabolic diseases [6]. Moreover, genetic control of the gut microbiota composition has been observed, suggesting that host genetics can influence the microbiome, which in turn can modulate metabolic traits and potentially affect the risk of developing diabetes [10]. This implies a bidirectional relationship where genetics can shape the microbiome, and the microbiome can influence the expression of genetic predispositions to diabetes. In summary, the gut microbiome interacts with host genetics and metabolism, potentially modulating the risk of diabetes by influencing metabolic processes and immune responses [1], [6], [10].", + "Machine learning algorithms show promise in integrating genomic data to predict individual risk and progression of diabetes, but there are challenges and limitations to consider. 1. Genomic data is considered to yield better patient-centric outcomes than traditional tabular data for predicting diabetic illnesses [1]. This suggests that machine learning models that incorporate genomic data may provide more accurate predictions. 2. Machine learning has been applied to integrate various types of data, including genomic and epigenomic biomarkers, to determine type 2 diabetic status. This approach has revealed connections between diabetic classification and other biological functions, indicating the potential of machine learning in this area [5]. 3. The integration of physiological, biochemical, genetic, and epigenetic features with machine learning algorithms has shown potential for more informative diagnostics and personalized treatment approaches for diabetes [8]. 4. However, there are limitations, such as the need for larger sample sizes and extensive training to achieve considerable accuracy when using polygenic scores-based approaches with genomic data [4]. Overall, while machine learning algorithms have demonstrated potential in integrating genomic data for diabetes prediction, further research and development are needed to overcome current limitations and improve accuracy and applicability in clinical settings.", + "Recent findings highlight the significant role of long non-coding RNAs (lncRNAs) in the regulation of insulin secretion and sensitivity, with several implications for understanding and potentially treating diabetes. 1. **Regulation of Islet Function**: LncRNAs have been shown to regulate the development and function of pancreatic islets, which are crucial for insulin secretion. For instance, the lncRNA H19 is involved in this regulatory process [1]. This suggests that lncRNAs could be critical in maintaining normal insulin secretion and could be targets for therapeutic intervention in diabetes. 2. **Impact on Insulin Synthesis and Secretion**: Specific lncRNAs, such as Meg3, have been found to affect insulin synthesis and secretion in pancreatic beta cells [4]. This indicates that lncRNAs play a direct role in the cellular mechanisms that control insulin production, which is essential for maintaining glucose homeostasis. 3. **Therapeutic Potential**: Due to their specific functions in regulating cellular pathways, lncRNAs are considered promising therapeutic targets. Their expression patterns in tissues often correlate with the progression of diabetes, making them potential biomarkers for diagnosis and prognosis [3]. 4. **Association with Insulin Resistance and Diabetes**: Altered levels of lncRNAs are closely associated with the onset and progression of insulin resistance and diabetes [5]. This association underscores the potential of lncRNAs as targets for interventions aimed at improving insulin sensitivity and managing diabetes. 5. **Research and Clinical Implications**: The diverse roles of lncRNAs in insulin resistance and diabetes suggest their importance in future research for diagnosis, prognosis, and therapy of the disease [2]. This calls for further investigations and collaborations among researchers, clinicians, and patients to fully understand and harness the potential of lncRNAs in diabetes management. Overall, these findings imply that lncRNAs are integral to the regulation of insulin secretion and sensitivity, offering new avenues for research and treatment strategies in diabetes.", + "Post-translational modifications (PTMs) of proteins play a significant role in regulating key signaling pathways involved in glucose homeostasis. Here are some ways PTMs affect these pathways: 1. **Protein Kinases and Glucose Homeostasis**: The PI3K/AKT, MAPK, and AMPK signaling pathways are crucial for glucose homeostasis, and these pathways are regulated by protein kinases, which can be modulated by PTMs such as phosphorylation [1]. 2. **Histone Modifications**: Histone post-translational modifications are involved in the regulation of genes associated with diabetes pathogenesis, including those related to insulin and islet-specific transcription factors. These modifications can influence gene expression and thereby affect glucose metabolism [3]. 3. **N-glycosylation**: The glycosylation of glucose transporter 2 is an example of a PTM that promotes insulin secretion, which is vital for maintaining glucose levels and suppressing diabetes [6]. 4. **Sirtuins and Deacetylation**: The sirtuin family, particularly SIRT1, is involved in regulating factors related to metabolism and insulin secretion. Sirtuins are deacetylases, and their activity represents a form of PTM that can influence glucose homeostasis [10]. These examples illustrate how PTMs can modulate signaling pathways and protein functions, ultimately impacting glucose homeostasis and related metabolic processes.", + "Studying the genetic basis of syndromic forms of diabetes, such as Wolfram Syndrome, has provided several insights: 1. **Genotype-Phenotype Correlation**: Research has highlighted the importance of understanding genotype-phenotype correlations in Wolfram Syndrome. This involves identifying specific genetic mutations and understanding how they manifest in clinical symptoms, which can aid in more accurate diagnosis and management of the syndrome [1], [6]. 2. **Genetic Mutations**: Wolfram Syndrome is primarily caused by mutations in the WFS1 gene, which encodes the wolframin ER transmembrane glycoprotein. These mutations lead to a range of symptoms including diabetes mellitus, optic atrophy, and deafness. Some severe cases are associated with dominant heterozygous variants [4]. 3. **Early Onset and Symptoms**: The syndrome is characterized by early onset of diabetes mellitus, typically around the age of 6, followed by other symptoms such as optic atrophy and sensorineural deafness in later years [5]. 4. **Potential for Broader Implications**: Insights from studying Wolfram Syndrome have implications for basic science and clinical practice. They emphasize the need for accurate clinical descriptions and early recognition of symptoms, which can improve patient outcomes and inform treatment strategies [6]. 5. **Risk Assessment**: There is ongoing research to determine if heterozygotes for Wolfram Syndrome are at risk for maturity-onset diabetes, which could have implications for understanding genetic risk factors in broader populations [2]. These insights not only enhance our understanding of Wolfram Syndrome but also contribute to the broader field of genetic research in diabetes, potentially informing personalized medicine approaches for more common forms of the disease.", + "The study of monozygotic twins discordant for diabetes provides valuable insights into the etiology of the disease by highlighting the roles of both genetic and epigenetic factors. Since monozygotic twins share identical genomes, any differences in disease manifestation between them can often be attributed to non-genetic factors, such as epigenetic modifications or environmental influences. 1. **Genetic Component**: The high concordance rates for diabetes in monozygotic twins, compared to dizygotic twins, underscore a significant genetic component to the disease [1], [6]. This suggests that genetic predispositions, particularly those affecting insulin sensitivity and secretion, play a crucial role in the development of diabetes [1]. 2. **Epigenetic Influences**: Despite identical genetic makeup, monozygotic twins can exhibit differences in disease susceptibility due to epigenetic variations. These variations can arise from environmental factors and gene-environment interactions, which are crucial in understanding the etiology of diabetes [4]. Epigenetic differences, such as DNA methylation patterns, have been observed in monozygotic twins and may contribute to discordance in disease states [9]. 3. **Environmental and Lifestyle Factors**: The discordance in diabetes among monozygotic twins also points to the influence of environmental factors and lifestyle choices, such as diet and physical activity, which can modify epigenetic marks and affect disease outcomes [6]. In summary, the study of monozygotic twins discordant for diabetes highlights that while genetic predispositions are significant, epigenetic modifications and environmental factors also play critical roles in the disease's etiology. This understanding can help in developing more targeted prevention and treatment strategies that consider both genetic and non-genetic factors.", + "Recent studies on the interaction between genetic variants and environmental factors in diabetes development have identified several potential therapeutic targets. These include: 1. **Primary Regulators of Insulin Secretion and Action**: Several type 2 diabetes (T2D) risk variants have been identified as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. This suggests that targeting these pathways could be a potential therapeutic strategy [6]. 2. **Specific Genetic Variants**: Newly discovered single nucleotide variants (SNVs) allow for better characterization of abnormalities in early insulin processing and secretion. Genes such as TCF7L2, SLC30A8, and C2CD4B have been highlighted as potential targets due to their roles in these processes [6]. 3. **Gene-Environment Interactions**: The interaction between genetic susceptibility and environmental factors such as physical activity and dietary fat has been shown to modify the risk of glucose homeostasis and T2D. This indicates that interventions targeting these environmental factors could potentially mitigate the genetic risk [7]. These findings underscore the importance of considering both genetic and environmental factors in developing therapeutic strategies for diabetes.", + "Rare variants identified through whole-genome sequencing contribute to the heritability of Type 2 diabetes by potentially explaining some of the \"missing heritability\" that common variants identified through genome-wide association studies (GWAS) do not account for. While GWAS have identified many common variants associated with Type 2 diabetes, these explain only a fraction of the heritability of the disease [4]. The missing heritability could be located in low-frequency and rare variants, particularly in noncoding regions of the genome [1]. However, studies have shown that rare coding variants, especially when clustered in a small number of genes, are unlikely to account for much of the missing heritability [10]. Instead, if rare coding variants are significant, they are likely scattered across many genes [10]. Therefore, while rare variants may contribute to the heritability of Type 2 diabetes, their exact role and impact remain to be fully elucidated, and larger multi-population studies are needed to reliably identify rare variants exclusively associated with Type 2 diabetes [6].", + "The latest findings on the role of non-coding RNAs in the pathogenesis of diabetes highlight several key aspects: 1. **Role of lncRNAs in Diabetes**: Long non-coding RNAs (lncRNAs) are implicated in mediating complex pathological mechanisms of diabetes. They are involved in post-transcriptional regulation and are associated with orchestrated networks that influence diabetes pathogenesis [5]. LncRNAs are considered better therapeutic targets due to their specific functions in regulating cellular pathways and their expression patterns that correlate with the progression of diabetes [7]. 2. **Epigenetic Influence**: Non-coding RNAs, including microRNAs and lncRNAs, can influence epigenetic mechanisms. They can promote the expression of pathological genes through post-transcriptional and post-translational mechanisms, contributing to metabolic memory and sustained gene expression in diabetic conditions [4]. 3. **Regulation of Islet Function**: LncRNAs have been shown to regulate pancreatic islet function, which is central to understanding diabetes pathophysiology. For instance, the lncRNA H19 has been implicated in islet development and function [8]. 4. **MicroRNAs in Disease**: MicroRNAs (miRs) play critical roles in various diseases, including diabetes, by influencing proliferation, differentiation, and development [2]. These findings underscore the importance of non-coding RNAs as regulatory players in diabetes and its complications, offering potential avenues for therapeutic intervention.", + "The interaction between multiple polygenic risk scores (PRS) can improve the prediction of Type 1 and Type 2 diabetes by combining information from various genetic loci associated with these diseases. This approach allows for a more comprehensive assessment of an individual's genetic risk. Specifically, combining information from common risk polymorphisms has been shown to improve disease prediction for Type 2 diabetes [3]. Additionally, partitioning polygenic scores according to factors of disease heterogeneity and mapping genetic loci to different immune-cell subtypes can enhance the predictive power of PRS, particularly for Type 2 diabetes [9]. These strategies leverage the aggregation of genetic risk from multiple sources, thereby capturing a larger proportion of the genetic variance underlying these traits and improving early diagnosis, intervention, and prevention efforts [4].", + "Recent single-cell RNA-sequencing studies have provided significant mechanistic insights into beta-cell failure pathways. These insights include: 1. **De-differentiation Signatures**: Single-cell analyses of human islet cells have revealed de-differentiation signatures, suggesting that beta cells may lose their specialized functions and revert to a more progenitor-like state, which contributes to their dysfunction in diabetes [1]. 2. **Transcriptional Regulation**: Advances in single-cell genomic profiling have enhanced our understanding of transcriptional regulation in non-beta cell types, which may play crucial roles in the hallmark features of beta-cell insufficiency and dysfunction in type 2 diabetes (T2D) [2]. 3. **ER Stress and Heterogeneity**: Single-cell transcriptomic analyses have identified subpopulations of beta cells experiencing endoplasmic reticulum (ER) stress. This stress is implicated in the dysfunction of both alpha and beta cells, contributing to diabetes pathogenesis [8]. These findings highlight the complexity of beta-cell failure and underscore the importance of single-cell technologies in unraveling the molecular mechanisms underlying diabetes.", + "The epigenetic landscape of key metabolic tissues shows several changes when comparing diabetic individuals to non-diabetic individuals: 1. **DNA Methylation Changes**: In diabetic individuals, increased DNA methylation has been observed in the promoter region of the PPARGC1A gene in both islets and skeletal muscle [3]. This suggests a potential mechanism by which gene expression related to metabolism is altered in diabetes. 2. **Histone Modifications**: There are disruptions in histone methylation patterns in diabetic states. While healthy individuals maintain stable histone methylation patterns, these can be disrupted in diabetes, indicating changes in the epigenome associated with inflammation and metabolic memory [2]. 3. **Impact on Gene Expression**: Epigenetic modifications, such as DNA methylation, have been linked to reduced expression of genes involved in diabetes and metabolism. Variations in DNA methylation have been noted near diabetes susceptibility genes and enhancers [6]. 4. **Tissue-Wide Epigenetic Changes**: Diabetes mellitus, characterized by high glucose stress, leads to epigenetic changes across most tissues impacted by the disease, including the cardiovascular system and immune system [7]. 5. **Adipose Tissue**: In subjects with type 2 diabetes, altered DNA methylation and differential expression of genes influencing metabolism and inflammation have been observed in adipose tissue [9]. These findings collectively suggest that diabetes is associated with specific epigenetic alterations across various metabolic tissues, which may contribute to the pathophysiology of the disease.", + "Recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo include the use of CRISPR-mediated homology-directed repair (HDR) to correct specific genetic mutations associated with diabetes. For instance, CRISPR technology has been used to correct point mutations in patient-derived induced pluripotent stem cells (iPSCs) targeting diabetes-related gene defects. The most efficient method employed in iPSCs is CRISPR/Cas9-based HDR, where a Cas9-mediated cut is generated adjacent to the site of interest, and a homologous donor template with the intended nucleotide change is recombined by HDR [9]. Additionally, there has been a successful correction of a variant in the Wolfram syndrome 1 (WFS1) gene using CRISPR-mediated HDR, which improved insulin secretion in iPSC-differentiated beta-like cells [3]. These advancements highlight the potential of CRISPR-based genome editing to correct monogenic forms of diabetes by targeting specific genetic mutations in vivo.", + "Genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes by combining genetic, epigenetic, transcriptomic, and phenotypic information. This integration helps identify genes and novel metabolic pathway targets that are crucial for understanding mechanistic relationships with insulin resistance and pancreatic islet failure [1]. Additionally, complementary systems-level data, such as protein-protein interactions and gene expression, provide insights into the mechanisms underlying the pathogenesis of complex traits like type 2 diabetes (T2D) [8]. This multi-omics approach allows for a more comprehensive understanding of the genome-to-phenome correlation in T2D, which is essential for examining the disease's complex genetic architecture [9].", + "Genomic imprinting has a significant impact on the susceptibility and progression of diabetes. Imprinting can influence the expression of genes involved in metabolic processes, which are crucial in the development of diabetes. For instance, changes in imprinting status at specific loci, such as the KCNQ1 locus, have been linked to type 2 diabetes susceptibility, indicating that temporal changes in imprinting can affect the function of pancreatic islets and contribute to diabetes risk [6]. Additionally, imprinting defects have been associated with specific forms of diabetes, such as transient neonatal diabetes, suggesting that imprinted genes play a role in the disease's onset and progression [4]. Furthermore, the effects of maternal diabetes on the offspring's epigenome, including alterations in DNA methylation profiles, highlight the role of imprinting in the intergenerational transmission of diabetes risk [3], [7]. These epigenetic changes can lead to a permanent programming of the developing offspring, increasing the risk of diabetes in subsequent generations [8]. Overall, genomic imprinting is a critical factor in understanding the genetic and epigenetic mechanisms underlying diabetes susceptibility and progression.", + "Longitudinal genomics studies are crucial for understanding gene-environment interactions in diabetes onset and management because they allow researchers to observe how genetic variations interact with environmental factors over time. This approach helps in identifying temporal patterns and causal relationships that are not possible to discern from cross-sectional studies [2]. By incorporating genotype measurements into longitudinal studies, researchers can gain significant insights into the genetic basis of diseases like diabetes, which can lead to better prediction, targeted prevention, and stratified treatment of type 2 diabetes (T2D) [3]. Additionally, these studies can help stratify T2D into subclasses that can be treated more effectively by understanding gene-lifestyle interactions [8]. Overall, longitudinal genomics studies provide a comprehensive framework to explore how genetic predispositions and environmental exposures collectively influence diabetes-related traits and disease progression.", + "Recent integrative genomics approaches, such as the combination of single-cell RNA sequencing and epigenomic profiling, have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. These approaches have allowed researchers to delve deeper into the transcriptional regulation of non-beta cell types, which may play crucial roles in the hallmark features of beta cell insufficiency and dysfunction associated with Type 2 Diabetes (T2D) [1]. Single-cell RNA sequencing has been particularly instrumental in high-throughput diabetes research by enabling the sequencing of individual cells from human pancreatic islets. This is important given the heterogeneity within the islets of Langerhans, which consist of various cell types. By tracking genetic changes in individual cells, researchers can better understand the complex cellular landscape and the specific contributions of different cell types to diabetes pathogenesis [2]. Furthermore, epigenomic profiling adds another layer of complexity by revealing how epigenetic changes can modulate gene expression without altering the DNA sequence. These changes are crucial for maintaining the secretory capacity, survival, and functional identity of pancreatic islets, as well as their response to insulin [8]. The integration of these genomic and epigenomic data helps identify regulatory elements and pathways that could be targeted for therapeutic interventions, moving from correlation to causation in understanding diabetes [10]." + ], + "contexts": [ + [ + "proteomics, genomics, and transcriptomics) are based on the study of constituents of the cell or body in a collective way. The ndings made with use of these approaches are being integrated to better understand the pathophysiology of type 2 diabetes and the heterogeneity of responses to di erent glucose-lowering therapies. Findings from studies that used metabolomics and lipidomics showed that increases in branched-chain and aromatic aminoacids were associated with obesity and type 2 diabetes.", + "Metabolomics Applied to Diabetes Research Moving From Information to Knowledge James R. Bain, Robert D. Stevens, Brett R. Wenner, Olga Ilkayeva, Deborah M. Muoio, and Christopher B. Newgard Type 2 diabetes is caused by a complex set of interactions between genetic and environmentalfactors. Recent work has shown that human type2 diabetes is a constellation of disorders associ- ated with polymorphisms in a wide array of genes, witheach individual gene accounting for /H110211% of disease risk", + "between protein signals and type 2 diabetes incidence. Acta Diabetol. doi: 10.1007/s00592-012-0376-3 82. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB (2009) Metabolomics applied to diabetes re-search: moving from information to knowledge. Diabetes 58: 2429 244383. Suhre K, Meisinger C, Dring A et al (2011) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 5:e13953", + "The future: genetics, epigenetics, and omics Although understanding of the genetics of type 2 diabetes has advanced rapidly, much remains unknown. How genes interact with the environment to cause progressive loss of -cell function is unclear. Environmental factors and hyperglycaemia could contribute to epigenetic changes in DNA and histones, thereby modifying gene expression in organs implicated in the pathogenesis and progression of type 2 diabetes, including in cells. 82,83", + "potential to make far-reaching contributions to our understanding of molecular basis of T2D and the development of novel strategies for patient care. 2.1 Introduction Type 2 diabetes (T2D) is a common, chronic disorder whose prevalence is increas-ing rapidly across the globe. Like other complex diseases, T2D represents achallenge for genetic studies aiming to uncover the underlying pathophysiological mechanisms. It is predicted that T2D will affect 592 million individuals by 2035", + "inthepathogenesisoftype2diabetesandmetabolism, Current Opinion in Clinical Nutrition and Metabolic Care ,vol.10,no .4, pp .420426,2007 . [110] M.C.Cornelis,E.J.T.Tchetgen,L.Liangetal.,Gene-environ- ment interactions in genome-wide association studies: a com- parative study of tests applied to empirical studies of type 2 diabetes, American Journal of Epidemiology ,v o l.17 5,no .3,p p . 191202,2012. [111] M.L.Metzker,Sequencingtechnologiesthenextgeneration, Nature Reviews Genetics ,vol.11,no.1,pp.3146,2010.", + "meta-ana lysis provides insight intothegenetic architecture oftype2diabetes susceptibility. NatGenet. 2014; 46:234 244. https://doi.or g/10.103 8/ng.2897 PMID: 24509480 26. Morris AP,Voight BF,Teslovich TM,Ferreira T,Segr A-V, Steinthorsdot tirV,etal.Large-sc aleassoci- ation analysis provide sinsights intothegenetic architecture andpathophysi ology oftype2diabetes. NatGenet. 2012; 44:981 990. https://doi.or g/10.103 8/ng.2383 PMID: 228859 22", + "monitoring and preventing progression to costly co-morbidities. The principal concept of metabolomics being able to find some metabolites differing in a control and a type 2 diabetic group is established. It is not our goal here to show this once again. The questions we ask are rather How well are different approaches suited to attain this goal? and What are optimal settings under which such studies can be successful?. Others have already investigated these questions before [16,17,18]. However, we", + "Owing to current advances in -omics technologies, such as genomics, transcriptomics, proteomics and metabolomics, the number of candidate biomarkers keeps growing; however, only a small proportion of these has been investigated withreference to their potential to improve the prediction of type 2 diabetes. Genetic variants The heritability of glycaemic traits and type 2 diabetes is high [40], and the large genome-wide association studies published to date since the first in 2007, based on up to >10 5study", + "have improved our understanding of the complexity of T2DM pathophysiology, beyond the classic triumvirate of -cell, skeletal muscle and liver87. However, the ability of these biomarkers to predict future risk of T2DM beyond anthropometric measures, lifestyle factors and fasting levels of glucose and lipids is still debatable87. Within the past 7years, a complementary, novel set of T2DM biomarkers has largely been generated by metabo- lomic studies, which systematically analyse metabolites" + ], + [ + "wide association study identi es novel risk loci for type 2 diabetes. Nature (2007) 445:881 5. doi: 10.1038/nature05616 27. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science (2007) 316:1341 5. doi: 10.1126/science.1142382 28. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature (2016) 536:41 7.", + "novel loci for type 1 diabetes. Diabetes 58:290295. DOI: https://doi.org/10.2337/db08-1022, PMID: 18840781 Huang J, Ellinghaus D, Franke A, Howie B, Li Y . 2012. 1000 Genomes- based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data. European Journal of Human Genetics 20:801805. DOI: https://doi.org/10.1038/ejhg.2012.3, PMID: 22293688 Hundhausen C, Roth A, Whalen E, Chen J, Schneider A, Long SA, Wei S, Rawlings R, Kinsman M, Evanko SP ,", + "general population, these loci show limited effect in DKD, especially in individuals with type 1 diabetes [ 6]. Genome- wide association studies (GWAS) have previously identified ahandful of genetic loci for DKD at the genome-wide signifi- cance level ( p<510 8)[711]. Recently, a meta-analysis of GWAS, including up to 19,406 individuals with type 1 diabetes from the Diabetic Nephropathy Collaborative Research", + "Table 2.1 Major published T2D GWAS and meta-analyses StudyEthnicity/ origin NcasesaN controlsaNovel loci identiedGWAS or meta-analysis discoveryapproach GWAS arrayReference panel forimputationT2D phenotype denition/otherspecs Diabetes Gene Discovery Group (Sladek et al. 2007 ), NatureEuropean 694 645 SLC30A8 ,HHEX /IDE GWA Illumina 300k + Family history of T2D, AAO <45 years, BMI <30 kg/m 2 FinlandUS Investi-gation of NIDDMGenetics (FUSION)(Scott et al. 2007a ), ScienceEuropean 1161 1174 CDKN2A/2B ,", + "scale gene-centric meta-analysis across 39 studies identifies type 2diabetes loci. Am J Hum Genet. 2012;90(3):410 25. 13. Haiman C, Fesinmeyer M, Spencer K, Buzkova P, V oruganti V , Wan P, et al. Consistent directions ofeffect for established type 2 diabetes risk variants across populations: the Population Architectureusing Genomics and Epidemiology (PAGE) Consortium. Diabetes. 2012;61(6):1642 7.In the most complete trans-ethnic T2D GWAS", + "9. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881885. 10. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. (2008) Meta- analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40: 638645.11. Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322: 881888.", + "scale ongoing efforts to localize and characterize T2D susceptibility genes using genome-wide association study (GWAS) approaches. To date, the GWAS method has achieved substantial success in localizing novel T2D susceptibility loci and loci for T2D-related glycemic traits (about 90 loci), obesity loci (~90), and loci for metabolic syndrome or its components (~50 loci), e.g. reviews: [4,20,28,29,41,47,51,64,65,67] . However, common variants identi ed by GWAS explain only about", + "T2D GWA meta-analysis performed by the DIAbetes Genet-ics Replication and Meta-analysis (DIAGRAM) Consortium [6]. Using genotype data from almost 150,000 individuals, predominantly of European descent, the consortium was ableto define 10 novel T2D-susceptibility loci to genome-wide significance, and to highlight several hundreds more that, whilst failing to reach the stringent criteria typically regardedas proof, are nonetheless highly likely to reflect genuine", + "18. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881-885. 19. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007; 316:1341-1345. 20. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT , Lund University, and Novartis Institutes of BioMedical", + "additive, dominant, and recessive) and did not adjust for mul - tiple comparisons. The third study is the largest GWAS con - ducted to date and is a meta-analysis of two GWASs, Genetics of Kidneys in Diabetes (GoKinD) and Epidemiology of Dia - betes Interventions and Complications (EDIC) studies [24]. This study by Grassi et al. [24] involved 2,829 European sub - jects with T1DM. The most significant variant was rs476141 located in a long non-coding RNA ( LOC339529 ) in chromo -" + ], + [ + "diabetes due to epigenetic silencing of Pdx1, a key transcription factor that regulates insulin gene 301 expression and beta cell differentiation. Both hi stone modifications a nd DNA methylation were 302 implicated (111). In another study, it was shown th at, in diabetic islets , there was increased DNA 303 methylation of the promoter of PPAR-gamma co-activator 1 gene ( PPARGC1A ), a factor that 304 plays a key role in regulating mitochondrial ge nes and in the modulation of diabetes (87). 305", + "altered DNA methylation (DNA-me) at various genes in target cells all of which over time can 1009 result in changes to the expr ession patterns of inflammatory, sclerotic and other pathological 1010 genes and the ultimate developm ent of diabetic complications. 1011 1012 Figure 2: Model for epigenetic regulation of pa thological gene expressi on in diabetes via 1013 changes in chromatin histone modifications. Post translational modifications on the N- 1014", + "Dependent Demethylation of Regulatory Elements Correlates with Chromatin State and Improved Cell Function. Cell Metab. 2015 ,22, 619632. [CrossRef] 228. Zhang, H.; Pollin, T.I. Epigenetics Variation and Pathogenesis in Diabetes. Curr. Diab. Rep. 2018 ,18, 121. [CrossRef] 229. Miao, F.; Chen, Z.; Zhang, L.; Liu, Z.; Wu, X.; Yuan, Y.-C.; Natarajan, R. Proles of epigenetic histone post-translational modications at type 1 diabetes susceptible genes. J. Biol. Chem. 2012 ,287, 1633516345. [CrossRef]", + "Epigenetic Mechanisms in Diabetic Complications 14 DNA methylation at prom oter CpG islands has been associ ated with gene repression and 292 is a well studied epigenetic mark in the c ontext of tumor suppressor genes and cancer (129). 293 However, much less is known a bout DNA methylation in diabetes . A recent report has shown 294 that the insulin promoter DNA was methylated in mouse embryonic stem cells and only becomes 295", + "Epigenetics: deciphering its role in diabetes and its chronic complications. Clin. Exp. Pharmacol. Physiol. 38, 401409 (2011). 61. Cooper, M.E. & El-Osta, A. Epigenetics: mechanisms and implications for diabetic complications. Circ. Res. 107, 14031413 (2010). 62. Miao, F. etal. Profiles of epigenetic histone post- translational modifications at type1 diabetes susceptible genes. J.Biol. Chem. 287, 1633516345 (2012). 63. Sapienza, C. etal. DNA methylation profiling", + "Emerging evidence shows that epigenetic mecha-nisms in chromatin including histone PTMs, DNAme, and miRNAs also might play key roles in the etiology of diabetes and DN. The persistence ofepigenetic modi cations triggered by diabetic stim- uli could be one of the key mechanisms underlying metabolic memory. A role for several HMTs and thecorresponding histone PTMs has been shown in the expression of brotic and in ammatory genes asso-", + "inflammation-related epigenetic modifications: focus on DNA methylation. Exerc Immunol Rev. 2015;21:26 41. 17. Milagro FI, Mansego ML, De Miguel C, Martinez JA. Dietary factors, epigenetic modifications and obesity outcomes: progresses and perspectives. Mol Aspects Med. 2013;34(4):782 812. 18. Caramori ML, Kim Y , Goldfine AB, et al. Differential gene expres- sion in diabetic nephropathy in individuals with type 1 diabetes. J Clin Endocrinol Metab. 2015;100(6):E876 82.", + "elevated glucose level is not the only factor that leads to mal- adaptive epigenetic modifications in diabetes. DNA methyla- tion can also be influenced by reactive oxygen species, both directly through oxidative m odification DNA preventing methylation and indirectly through its effects on methylation writing/erasing enzymes [ 15]. Many other factors including hypoxia, inflammation, cytokines and growth factors, drugs, nutrition and even physical activity can modify epigenetic", + "1306 1313. 31. Miao F, et al.; DCCT/EDIC Research Group (2014) Evaluating the role of epigenetic histone modifications in the metabolic memory of type 1 diabetes. Diabetes 63(5): 1748 1762. 32. Reddy MA, Tak Park J, Natarajan R (2013) Epigenetic modifications in the patho- genesis of diabetic nephropathy. Semin Nephrol 33(4):341 353. 33. Bell CG, et al. (2010) Genome-wide DNA methylation analysis for diabetic nephrop- athy in type 1 diabetes mellitus. BMC Med Genomics 3:33.", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had" + ], + [ + "diabetes? Is altered gut epithelial function and integrity important in the pathoge nesis of type 1 diabetes, and if so, what is the mechanism(s) and relation to dysbiosis and how do we demonstrate impaired function in humans? How important are the interactions between host genetics, metab olism and the immune system in shaping the microbiome and predilection to disease?", + "the gut, which might trigger an inflammatory response and play arole in the development of diabetes. In conclusion, our data suggest that the levels of glucose tolerance or severity of diabetes should be considered while linking microbiota with obesity and other metabolic diseases in humans. It is especially important for developing the strategies to modify the gut microbiota inorder to control metabolic diseases, since obesity and diabetes mightbe associated with different bacterial populations. Methods", + "2011;342:d35. [68] Hara N, Alkanani AK, Ir D, Robertson CE, Wagner BD, Frank DN, et al. The role of the intestinal microbiota in type 1 diabetes. Clin Immunol 2013;146:1129. [69] Beyan H, Wen L, Leslie RD. Guts, germs, and meals: the origin of type 1 diabetes. Curr Diab Rep 2012;12:45662. [70] Atkinson MA, Chervonsky A. Does the gut microbiota have a role in type 1 diabetes? Early evidence from humans and", + "diabetes. ISME J. 5,8291 (2011). 30. Brown, C. T. et al. Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.PLoS ONE 6,e25792 (2011). 31. Endesfelder, D. et al. Compromised gut microbiota networks in children with anti-islet cell autoimmunity. Diabetes 63,2006 2014 (2014). 32. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260273 (2015).", + "661678 (2007). 4. Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 13411345 (2007). 5. Musso, G., Gambino, R. & Cassader, M. Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu. Rev. Med. 62, 361380 (2011). 6. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 16351638 (2005).", + "The gut microbiota affects numerous biological functionsthroughout the body and its characterisation has becomea major research area in biomedicine. Recent studieshave suggested that gut bacteria play a fundamental rolein diseases such as obesity, diabetes and cardiovasculardisease. Data are accumulating in animal models andhumans suggesting that obesity and type 2 diabetes(T2D) are associated with a profound dysbiosis. Firsthuman metagenome-wide association studiesdemonstrated highly signi cant", + "18 Burcelin R. Regulation of metabolism: a cross talk between gut microbiota and its human host. Physiology (Bethesda) 2012;27:300 7. 19 Breen DM, Rasmussen BA, Cote CD, et al . Nutrient-sensing mechanisms in the gut as therapeutic targets for diabetes. Diabetes 2013;62:3005 13. 20 Karlsson F, Tremaroli V, Nielsen J, et al . Assessing the human gut microbiota in metabolic diseases. Diabetes 2013;62:3341 9. 21 Backhed F, Ding H, Wang T, et al . The gut microbiota as an environmental factor", + "interactions play a role in human obesity, insulin resistance and type 2 diabetes? Obes Rev 2011; 12: 27281. 47 Kootte RS, Vrieze A, Holleman F, et al. The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab 2012; 14: 11220. 48 Qin J, Li Y , Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490: 5560. 49 Karlsson FH, Tremaroli V, Nookaew I, et al. Gut metagenome in", + "Other factors Interest in the role of the gut microbiome in the devel - opment of T2DM has exploded in the past few years, and variation in the diversity and composition of the gut microbiota has been tied to T2DM100. For example, levels of butyrate-producing bacteria are decreased in the gut microbiota of patients with T2DM compared with that of healthy individuals101. In addition, evidence suggests that ambient air pollution is an emerging risk factor for", + "52. Parks, B.W., et al., Genetic control of obesity and gut microbiota composition in response to high -fat, high -sucrose diet in mice. Cell Metab, 2013. 17(1): p. 141 -52. 53. Org, E., et al., Genetic and environmental c ontrol of host -gut microbiota interactions. Genome Res, 2015. 25(10): p. 1558 -69. 54. McKnite, A.M., et al., Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLoS One, 2012. 7(6): p. e39191." + ], + [ + "All the mentioned models rely on tabular datasets such as PIMA and ECG signals [ 47] in classifying the records with possible diabetic illnesses. The current study considers that genomic data yields a better patient-centric outcome than tabular data. 2.3. Genomics for Type 2 Diabetes Many research studies have been carried out on genetic-based illness prediction. Incorporating machine learning approaches with genetic-based illness prediction could", + "- chondrially rich, provides a direct connection between physiological dysfunction observed in the heart and the impact of altered genomic profiles in the mitochondrion and nucleus. Machine-learning, which at current has been applied to very few genetic applications, may play a significant role in defining the epigenome of those with diabetes mellitus, likely unveiling genes and molecular pathways first impacted by the pathology. The challenges ofmachine learning intheclinical setting", + "15. Ali, M.M.; Paul, B.K.; Ahmed, K.; Bui, F.M.; Quinn, J.M.W.; Moni, M.A. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput. Biol. Med. 2021 ,136, 104672. [CrossRef] 16. Bell, C.G.; Teschendorff, A.E.; Rakyan, V .K.; Maxwell, A.P .; Beck, S.; Savage, D.A. Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus. BMC Med. Genom. 2010 ,3, 33. [CrossRef]", + "Diagnostics 2022 ,12, 3067 6 of 30 Table 1. Various existing models for diabetes prediction. Approach Type of Data Applicability Limitations polygenic scores-based approach [12]Genomic DataUsed in the evaluation of clinical trials and illness screening mechanismsThe polygenic score approach needs larger samples and tremendous training for considerable Accuracy. Singular Value Decomposition [13]Genomic Data Tabular Data The image they are usedThey are used in ranking the feature", + "In the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, bio - chemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status. In 50 patients, machine-learning algorithms revealed the interconnectedness between dia - betic classification, mitochondrial function, and methyla -", + "Diabetes mellitus is a multifaceted disease, consisting of systemic comorbidities which necessitate a variety of treatment modalities and stratify those affected with the disease [5]. Before the implementation of machine-learning algorithms in medicine, linear statistical models have highlighted measures, such as HbA1c, as diagnos - tic staples for the evaluation of diabetes mellitus onset and progression [6]. By exploring these previously pub -", + "tool that combines both genetic and clinical featur es in order to identify diabetic nephropathy in patients with T2D [81]. Leung et al . compared several machine learning methods that include partial least square regression, classification and regression tree, the C5.0 Decision Tree, Random For est, naive Bayes, neural networks and support vector machines [82]. The dataset used consists of both genetic (Single Nucleotide Polymorphisms - SNPs) and clinical data. Age, age of diagnosis, systolic", + "- ylation status and total nuclear methylation provided the best predictive measures for assessing type 2 diabetes mellitus. The incorporation of physiological, biochemical, genetic, and epigenetic features with machine-learning algorithms exemplifies the potential for more informa - tive diagnostics in the future, as well as personalized approaches to generalized treatment modalities (Fig.6). Discussion Machine-learning can be applied as a systems biol -", + "- tures is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient. Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled popu - lation grows. Conclusions Our work highlights the importance of identifying bio -", + "10 Meigs JB, Shrader P, Sullivan LM et al. Genotype score in addition to common risk factors for prediction of Type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 11 Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA 299, 13201334 (2008). \t Systematic\treview\tof\tearly\tresearch\tinto\tgenomic\tmedicine \t adoption\tin\tthe\tclinical\tcare\tof\tcommon\tchronic\tdiseases. \t Outlines\tboth\tphysician\tand\tpatient\tperspectives\ttowards" + ], + [ + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "this would require further investiga-tions, both invivo and invitro and critical networking among researchers, clinicians, and patients. Nevertheless, the implications of lncRNAs in diverse facets of insulin resistance and diabetes are indicative of their roles in the diagnosis, prognosis, and therapy of this disease in future.", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "58. You L, Wang N, Yin D etal (2016) Downregulation of long noncoding RNA Meg3 affects insulin synthesis and secretion in mouse pancreatic beta cells. J Cell Physiol 231:852862 59. Arnes L, Akerman I, Balderes DA, Ferrer J, Sussel L (2016) betalinc1 encodes a long noncoding RNA that regulates islet beta-cell formation and function. Genes Dev 30:502507 60. Akerman I, Tu Z, Beucher A etal (2017) Human pancreatic beta cell lncRNAs control cell-specific regulatory networks. Cell Metab 25:400411", + "of lncRNAs in the development and function of metabolic tissues, and therefore, their altered levels are closely asso-ciated with the onset and progression of insulin resistance and diabetes. Roles oflncRNAs indiabetic complications Apart from being involved in major metabolic tissues dur -", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "in transgenerational transmission of gestational diabetes mellitus which leads to impaired islet structure and func-tion [ 54]. To understand the roles of lncRNAs in regu- lating pancreatic function, several research groups have profiled lncRNA expression in mouse and human pancre-atic islets [55, 56]. Transcriptome analysis in pancreatic -cells of type 2 diabetes patients identified tissue-specific and dynamically regulated abnormally expressed lncR -", + "1831 Lnc-ing non- coding RNAs withmetabolism anddiabetes: roles oflncRNAs 1 3 endocrine hormones, insulin and glucagon, where insulin is the anabolic master regulator which controls periph -", + "Vol.:(0123456789)1 3Cellular and Molecular Life Sciences (2018) 75:18271837 https://doi.org/10.1007/s00018-018-2760-9 REVIEW Lncing noncoding RNAs withmetabolism anddiabetes: roles oflncRNAs NehaGoyal1,2 DeveshKesharwani1,2 MalabikaDatta1,2 Received: 18 September 2017 / Revised: 29 December 2017 / Accepted: 24 January 2018 / Published online: 31 January 2018 Springer International Publishing AG, part of Springer Nature 2018 Abstract", + "(2013). A novel mechanism regulating insulin secretion involving Herpud1 inmice. Diabetologia 56, 15691576 . Zhao, X.Y., and Lin, J.D. (2015). Long noncoding RNAs: a new regulatory code in metabolic control. Trends Biochem. Sci. 40, 586596 . 1806 Cell Reports 17, 17951806, November 8, 2016" + ], + [ + "regulates glucose-induced biological responses in pancreatic beta-cells. Diabetes. 2008;57:2708-17. 29. Schultze SM, Hemmings BA, Niessen M, Tschopp O. PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis. Expert Rev Mol Med. 2012;14:e1. 30. White MF. IRS proteins and the common path to diabetes. Am J Physiol Endocrinol Metab. 2002;283:E413-22. 31. Erener S, Marwaha A, Tan R, Panagiotopoulos C, Kieffer TJ. Profiling of circulating microRNAs in children with", + "pathological processes involved in glucose metabolism by post transcriptional regulation of gene expression. Particular microRNAs can regulate cell function271, exposing key regulatory signalling pathways involved in restoration of cell mass, and provide a promising strat egy for improving insulin secretion and cell health in T2DM. Identification of novel insulin secretagogues that act directly on cells and enteroendocrine Kcells and Lcells in the intestine are under investigation, and", + "can result in diabetes and its complications including DN. Several studies show that key histone post- translational modifications are involved in the regulation of genes associated with the pathogenesis of diabetes, such as insulin and islet-specific transcription factors.48,60 Inaddi - tion, several groups are examining the role of histone post-translational modifications in adipocytes related to type2 diabetes, obesity and the metabolic syndrome.48,60", + "cascade of protein kinases and regulatory proteins of which IRS-1 and IRS-2 are most important. This causes suppression of glucose release from liver and kidney/ translocation of glucose transporters in muscle and adipose tissue to increase their glucose uptake, and inhibition of release of FF A into the circulation due to suppression of the activity of hormone-sensitive lipase and a simultaneous increase in their clearance from the circulation. Although", + "Magnan C, Postic C, Prip-Buus C, Vasseur-Cognet M (2008) The transcription factor COUP-TFII is negatively regulated by insulin and glucose via Foxo1- and ChREBP-controlled pathways. Mol Cell Biol 28: 65686579Rodgers JT, Lerin C, Haas W, Gygi SP, Spiegelman BM, Puigserver P (2005) Nutrient control of glucose homeostasis through a complex ofPGC-1alpha and SIRT1. Nature 434: 113118 Schwer B, Verdin E (2008) Conserved metabolic regulatory functions of sirtuins. Cell Metab 7:104112", + "of glucose transporter 2 glycosylation promotes insulin secretion in suppressing diabetes. Cell 123:1307 1321. PMID: 16377570 47. Whitaker GM, Lynn FC, McIntosh CH, Accili EA (2012) Regulation of GIP and GLP1 receptor cell sur- face expression by N-glycosylation and receptor heteromerization. PLoS One 7: e32675. doi: 10.1371/ journal.pone.0032675 PMID: 22412906 48. Johswich A, Longuet C, Pawling J, Abdel Rahman A, Ryczko M, et al. (2014) N-glycan remodeling on", + "strate 1), Pde3b (phosphodiesterase 3B), Hk2 (hexokinase 2), Foxo1 (forkhead box O1), Socs6 (suppressor of cytokine signaling 6), and Ogt (O-linked N-acetylglucosamine (GlcNAc) transferase). Impaired insulinsignaling is well known to negatively in uence glucose and lipid metabolism [62]. In adipose tissue, insulin stimulates glucose uptake by inducing translocation of GLUT4 to the cell surface, it increasesglycolysis rate by stimulating hexokinases ( Hk2) and suppresses lipolysis ( Acaca and Prkaa1 )[63].", + "signalling pathways by reducing insulin induced tyro sine phosphorylation of IRS1 and IRS2 (REF. 161) and by increasing degradation of IRS1 (REF. 162). Recent studies have demonstrated that the p85 regulatory subunit of PI3K interacts with XBP1s (the spliced, transcription ally active isoform of XBP1) and promotes the trans location of XBP1s into the nucleus to initiate the ER stress response163.Diabetic complications Diabetic microvascular complications are closely related", + "activated protein kinase. J Biol Chem. 2007;282:9777 -88. [44] Chakrabarti S, Davidge ST. High glucose -induced oxidative stress alters estrogen effects on ERalpha and ERbeta in human endothelial cells: reversal by AMPK activator. J Steroid Biochem Mol Biol. 2009;117:99 -106. [45] Mortuza R, Chen S, Feng B, Sen S, Chakrabarti S. High glucose induced alteration of SIRTs in endothelial cells causes ra pid aging in a p300 and FOXO regulated pathway. PLoS One. 2013;8:e54514.", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362" + ], + [ + "WFS1 and genotype-phenotype correlation in Wolfram syndrome. Am J Med Genet A. 2007;143A(14):1605 12. 61. McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60(5):793 9. 62. Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41 7. 63. Patch AM, Flanagan SE, Boustred C, Hattersley AT, Ellard S. Mutations in the ABCC8 gene encoding the SUR1 subunit of the KATP channel cause", + "enable physicians to ameliorate some of the complications that so devastate the lives of these patients. Three questions need answers from further studies: is there really a lack of diabetic complications in Wolfram syndrome patients compared with other diabetics? What is the nature of the neurodegeneration and its relation to diabetes mellitus? Are heterozygotes for Wolfram syndrome at risk of maturity-onset diabetes? This paper is dedicated to the memory of Robin Smith, a Wolfram", + "Monogenic and syndromic forms account for only a small,though highly informative, proportion of cases of nonau-toimmune diabetes. The challenge for medical science liesin bringing equivalent mechanistic insights and transla-tional benets to the hundreds of millions of peoplealready affected by, or at risk of, more common, typicalforms of diabetes. For type 2 diabetes, there is abundantevidence that individual susceptibility is inuenced byboth the combination of genetic variation at multiple sitesand a", + "responding to two causative genes have been identified to date. Wolfram syndrome 1 (WS1), characterized by diabetes insipidus, DM, optic atrophy, and deafness, is a rare autosomal recessive disease caused by variants in wolframin ER transmembrane gly- coprotein (WFS1). Severe cases with dominant heterozygous vari- ants are also reported (92). Often, patients first manifestation is DM at an average age of 6 years. Though most WS1 patients", + "finding study to describe the natural history, complications, prevalence, and inheritance of the syndrome. We identified 45 patients with Wolfram syndrome—a prevalence of one per 770000. Non-autoimmune, insulin- deficient diabetes mellitus presented at a median age of 6 years, followed by optic atrophy (11 years). Cranial diabetes insipidus occurred in 33 patients (73%) with sensorineural deafness (28, 62%) in the second decade; renal-tract abnormalities (26, 58%) presented in the third", + "Wolfram patients have a mitochondrial genome abnormality, but this has not yet been shown. The differential diagnosis indicates the importance of accurate clinical descriptions when presenting cases of the syndrome. Our study has implications for basic science and practice: more accurate characterisation of the syndrome will allow assessment of genotype/phenotype correlations; and earlier recognition of diabetes insipidus, gastrointestinal dysfunction, and central apnoeas should", + "onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.Monogenic diabetes: a gateway to precision medicine in diabetes Haichen Zhang,1 Kevin Colclough,2 Anna L. Gloyn,3,4 and Toni I. Pollin1", + "WFS1 mutations underlie a genetic syndrome of neonatal/infancy-onset diabetes, congenital sensorineural deafness, and congenital cataracts. Diabetes . 2017;66(7):20442053. 93. Rigoli L, Di Bella C. Wolfram syndrome 1 and Wolfram syndrome 2. Curr Opin Pediatr. 2012;24(4):512517 . 94. Bansal V, et al. Identification of a missense vari- ant in the WFS1 gene that causes a mild form of Wolfram syndrome and is associated with risk for type 2 diabetes in Ashkenazi Jewish individuals.", + "established. It has been corroborated by a series of obser-vations that include ethnic differences, familial aggrega-tion, twin studies, admixture studies, linkage studies, monogenic cases (e.g., MODY), mitochondrial cases of diabetes, and a constantly growing number of molecular markers [5] . On the other hand, the genetics of the meta- bolic syndrome remains complex [6] . It is highly unlikely that a single gene will account for a substantial portion", + "diabetes (0.5% carrier frequency) compared to controls (0.035%). One individual with early onset diabetes was homozygous for a rare pathogenic missense variant in the WFS1 gene but did not have the additional phenotypes associated with Wolfram syndrome. Conclusion: Targeted sequencing of genes linked with monogenic diabetes can identify disease-relevant mutations in individuals diagnosed with type 2 diabetes not suspected of having monogenic forms of the disease. Our data suggests" + ], + [ + "Studies of twins also provide compelling evidence for a genetic component to T2D. Estimates for concordance rates range from 0.29 to 1.00 in monozygotic (MZ) twins, while in dizygotic (DZ) twins the range is 0.100.43 [57, 58, 6164]. The high levels of heritability observed for insulin sensitivity and insulin secretion [6567] further reinforce the role of genetics in diabetes and indicate the primary genetic lesions for diabetes are likely to localize to genes in beta-cell-centric pathways.", + "It is therefore intriguing that A1C levels are signicantly correlated in monozygotic twins whether they are concor- dant for type 1 diabetes or not (4): in a discordant twin pairone twin is treated with insulin, whereas the other oneisnt, and thus this degree of correlation suggests thatgenetic contributors to A1C may be detectable despite thesuperimposition of a strong environmental modier. Rig-orous estimates of heritability of treated A1C, however, are not available.", + "Concordance rate for type II diabetes mellitus in monozy-gotic twins: actuarial analysis. Diabetologia 42:146150 3. Lehtovirta M, Kaprio J, Forsblom C, Eriksson J, Tuomilehto J, Groop L (2000) Insulin sensitivity and insulin secretionin monozygotic and dizygotic twins. Diabetologia43:285293 4. Florez JC, Hirschhorn J, Altshuler D (2003) The inherited basis of diabetes mellitus: implications for the genetic anal-ysis of complex traits. Annu Rev Genomics Hum Genet4:257291", + "disease susceptibility is not explained by genetics alone; environ- mental factors, gene by environment interactions, and epigenetic inuences are likely to play important roles in the etiology of T1D [5,6] . Monozygotic (MZ) twin pairs, discordant for T1D, represent an ideal system to test susceptibility factors not attributable to genetic variation, especially epigenetic variation, since the ge- nomes of the twins are identical. The ascertainment of disease-", + "epigenetic differences among monozygotic twins. A critical question is whether epigenetic marks are transmitted intactfrom parent to offspring and whether DNAm is allele- specific and covaries with allele-specific gene expression. For example, can we develop an epigenetic transmissiontest comparable to the transmission disequilibrium test used in genetic epidemiology? Finally, and most excitingly, we", + "their dietary and physical activity habits (Maes et al, 1997 ). There is also ample evidence that diabetes has a substantial genetic component. The con- cordance of type 2 diabetes in monozygotictwins ranges between 50 and 70% compared to 2037% in dizygotic twins (Kaprio et al, 1992 ; Newman et al, 1987 ; Poulsen et al 1999). Further evidence comes from studies that compare therisk in offspring with a family history of type 2 diabetes with offspring without such a fam-", + "monozygotic and dizygotic Danish twin pairs withinsulin dependent diabetes mellitus. Bmj 1997: 314:1575 1579. 30. R EDONDO MJ, R EWERS M, Y UL et al. Genetic deter- mination of islet cell autoimmunity in monozygotictwin, dizygotic twin, and non-twin siblings of patientswith type 1 diabetes: prospective twin study. Bmj 1999:318: 698 702. 31. L EVY-M ARCHAL C, P ATTERSON C, G REEN A. Variation", + "Studies in twins have demonstrated that 5070 % in the body mass index (BMI) variance may be explained by genetics ( Allison et al., 1996 ), and T2DM concordance was reported ranging from 1737 % in dizygotic to 5070 % in monozygotic twins ( Kaprio et al., 1992 ; Medici et al., 1999 ; Poulsen et al., 1999 ). In addition, family and adoption studies have reported heritability ranging from 2060 % for obesity ( Rice et al., 1999 ; Stunkard et al., 1986 ) and 3070 % for T2DM ( Meigs", + "Monozygotic twins exhibit numerous epigenetic differences: clues to twindiscordance? Schizophr Bull 29: 169178. 8. Oates NA, van Vliet J, Duffy DL, Kroes HY, Martin NG, et al. (2006) Increased DNA methylation at the AXIN1 gene in a monozygotic twin from a pair discordant for a caudal duplication anomaly. Am J Hum Genet 79: 155162. 9. Kuratomi G, Iwamoto K, Bundo M, Kusumi I, Kato N, et al. (2008) Aberrant DNA methylation associated with bipolar disorder identified from discordant", + "5 E/C128orts to estimate the heritability of T2D by a comparison of the concordance rates in mono- and dizygotic twins have varied greatly as a result of di/C128erences in ascertainment scheme, diagnostic criteria and follow-up duration.69Concordance for diabetes is generally higher in identical twins (supporting a genetic basis for disease), although the extremely high concordance rates in some early studies6were undoubtedly inated by ascertainment bias. Evidence from population studies" + ], + [ + "that genetic studies will ultimately identify key genetic elements that help determine susceptibility to diabetes,disease progression, and responsiveness to specific therapies, as well as help identify novel targets for futureintervention. A substantial number of genetic loci, gene polymorphisms, and mutations have already beenreported as having variable degrees of association with one or other type of diabetes (type 1, type 2, maturityonset diabetes of the young [MODY]), while others appear to be involved", + "ponse to thiazolidinedione therapy and candidate genes [100103]. Results from pharmacogenetic studies could potentially provide physicians with a powerful tool to adjust therapy appropriately for those individuals carry ing variants known to affect a given medication. Distefano and Watanabe have recently reviewed the pharmaco genetics of diabetes [104]. Genegene and geneenvironment interactions are also likely to be helpful to the clinician in making therapeutic", + "Genomics of T2D Diet, lifestyle, environment, and even genetic variation influence an individuals response to disease therapy. Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for iden - tifying genetic variants responsible for patient differ -", + "ease caused by interactions between multiple genetic and environmental factors. Significant progress has been made in understanding the genetic architecture of T2D over the past 10 years [1]. A number of genome-wide as- sociation studies in diverse human populations have identified more than 60 common variants and loci asso- ciated with risk for T2D [2]. These studies have also revealed a significant overlap between traits and pheno- types of monogenic diabetes with related common", + "21582171 (2014). 29. Wood, A. R. et al. A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants. Diabetes 66, 22962309 (2017). 30. Pickrell, J. K. Joint analysis of functional genomic data and genome- wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559573 (2014). 31. Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets", + "by GWASs [ 16,28,29]. A wide variety of network-based approaches have been applied to investigate the extent to which the genetics of T2D predisposition converge on a restricted set of biological pathways. Several T2D risk variants have been identied as primary regulators of insulin secretion, insulin action, and pancreatic islet transcription factors. [ 10,16]. The newly discovered SNVs allow the better characterization of abnormalities in early insulin processing and secretion. TCF7L2 ,SLC30A8 ,C2CD4B ,", + "[10] , many environmental factors [11] , and the interac- tions among those genetic and environmental factors. Physical activity and dietary fat have been reported to be important modifiers of the associations between glucose homeostasis and well-known candidate genes for T2DM [12] and there is reason to believe that a significant pro- portion of the susceptibility genes identified by GWASs will interact with these environmental factors to influ-ence the disease risk. Florez et al.", + "interactions suggest a way by which genetic risk may beameliorated, these environmental factors are of great relevanceto public health, and are the focus of a growing number of studies [7]. Environmental factors, such as diet and lifestyle, are important in the onset, development and progression of T2D and its related phenotypes [8,9]. The interactions of environmental factors with", + "cases. J Am Med Assoc. 1956;161:1628 30. 3. Duncan LE, Keller MC. A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am J Psychiatry. 2011;168:1041 9. 4. Brito EC et al. Previously associated type 2 diabetes variants may interact with physical activity to modify the risk of impaired glu- cose regulation and type 2 diabetes: a study of 16,003 Swedish adults. Diabetes. 2009;58:1411 8.", + "this occurs. Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.We have seen considerable progress in our understanding of the role that both environ- ment and genetics play in the development of T2D. Recent work suggests that the adverse effect of some established T2D-associated loci may be greatly attenuated by appropriate" + ], + [ + "and rare coding variants do not account for much of theheritability of type 2 diabetes. Under this scenario, themissing heritability could be located in common orlow-frequency and rare variants in noncoding regionsof the genome. Recent studies that jointly modeled dia-betes or obesity risk as a function of genetic relatednessacross all of the GWAS SNPs have suggested that much of the heritability of these traits can be explained by", + "T2D heritability. 3. Uncovering the Signicance of Rare-Coding and Non-Coding Genetic Variants in the Etiology of Type 2 Diabetes As previously stated, GWASs have uncovered many new genetic associations that are relevant to T2D, but GWAS ndings represent common and mid-frequency genetic variations, thus excluding rare frequency variants and also cumulative effect of many variants with small effect sizes. Missing heritability refers to the portion of genetic variance that cannot be explained by all signicant", + "could be accounted for by low-frequency and rare variants of moderate effect in a small number of genes. Our whole-exome sequencing study has explicitly addressed thisquestion. Additionally, we did not examine whether thereare fewer than 20 genes involved in type 2 diabetes butrather looked at whether rare coding variants in fewerthan 20 genes account for much of the heritability. In such a model, any number of other genes that do not", + "contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome", + "One common disease that has been subjected to intense genetic study is type 2 diabetes. 32The heritability of type 2 diabetes has been estimated to be around 30%.3335 Through GWASs, 63 loci have been reproducibly associ-ated with type 2 diabetes. 36However, as for other complex traits, the associated SNPs can only account for <20% of the heritability estimated from family studies.36 Here, we seek to evaluate the role that rare coding vari-", + "prevalence of T2D. These authors found rare variants that were not detected previously in population studies, but none of them were associated with T2D [ 49]. Larger multi-population studies and more advanced study methods are needed to reliably identify rare variants that are exclusively associated with T2D to eventually uncover missing T2D heritability. 3.2. Genetic Variants in Familial Studies of Type 2 Diabetes The development of T2D is driven by the combined effect of environmental factors and a", + "variance in disease risk that can be accounted for bythe 63 previously identied associations with commonvariants. Our empirical and simulation results are compatible with a variety of different genetic architectures for type2 diabetes. First, if rare coding variants are responsiblefor the majority of the heritability of the trait, the variants are most likely scattered across many ( >20) different", + "Genome-wide association studies (GWAS) have been helpful in identifying a large number of genetic variants conferring risk to T2D. However, only close to 10% heritability is explained by these variants. Other genetic variants, particularly those which are rare but with significant effects need to be identified.", + "and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. Nat. Genet. 46, 294298 (2014). 168. Lek, M. etal. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285291 (2016).169. Xue, A. etal. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018). 170. Huyghe, J. R. etal. Exome array analysis identifies", + "diabetes. In particular, our study suggests that when clus-tered in a small number of genes, rare coding variants ofmoderate to strong effect are unlikely to account formuch of the missing heritability. Rather, if rare coding var-iants are an important factor in type 2 diabetes risk, theyare most likely scattered across many genes. Our resultshave important implications for the design and interpreta- tion of future medical resequencing studies. Subjects and Methods Study Populations" + ], + [ + "13 De Rosa et al. Type 2 Diabetes and CVD Frontiers in Endocrinology | www.frontiersin.org January 2018 | Volume 9 | Article 2176. Fatica A, Bozzoni I. Long non-coding RNAs: new players in cell differentia- tion and development. Nat Rev Genet (2014) 15:721. doi:10.1038/nrg3606 177. Wang KC, Chang HY . Molecular mechanisms of long noncoding RNAs. Mol Cell (2011) 43:90414. doi:10.1016/j.molcel.2011.08.018 178. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet (2011) 12:86174. doi:10.1038/nrg3074", + "Epigenetic Mechanisms in Diabetic Complications 16 other non-coding RNAs can also in teract with transcriptional co -regulators and thereby further 337 influence epigenetics and tran scriptional regulation (82, 104). 338 Recent findings have demonstrated a critical role for miRs in various diseases. They have 339 been found to play key roles in proliferation, di fferentiation, development, and in cancer, where 340", + "Beltrami, C., Angelini, T.G., Emanueli, C., 2015. Noncoding RNAs in diabetes vascular complications. J. Mol. Cell. Cardiol. 89, 42 50.https://doi.org/10.1016/j.yjmcc. 2014.12.014 . Brookheart, R.T., Michel, C.I., Listenberger, L.L., et al., 2009. The non-coding RNA gadd7 is a regulator of lipid-induced oxidative and endoplasmic reticulum stress. J. Biol.Chem. 284, 7446 7454. https://doi.org/10.1074/jbc.M806209200 . Carter, G., Miladinovic, B., Patel, A.A., et al., 2015. Circulating long noncoding RNA", + "Noncoding RNAs that are induced by diabetic conditions can also promote theexpression of pathological genes via various post-transcriptional and post-translational mechanisms These epigenetic mechanisms and noncoding RNAs can lead to persistently open chromatin structures at pathological genes and sustained gene expression, which can also be a mechanism for metabolic memory Key epigenetic regulators, microRNAs and long noncoding RNAs could serve", + "tion among researchers ( Knoll et al., 2015 ). As an important post-transcriptional pathogenesis of diabetes, lncRNAs and their associated orchestrated networks are implicated in mediating complex pathological mechanisms of diabetes ( Kato et al., 2016; Liu et al., 2014 ). To delineate the inuence of lncRNAs and 172 iScience 19, 162176, September 27, 2019", + "coding RNAs [18]. A number of indirect lines of evi-dence point to the involvement of epigenetic changes indiabetic nephropathy. Murine models of disease progres-sion displaying temporal variation in gene expressionhave indicated these supra-sequence devices may beinvolved in the pathogenesis [19]. Gene expressionchanges reflect dynamic alterations in gene transcription and also messenger RNA stabi lity, which may be influ-", + "To conclude, it would be apt to state that lncRNAs are widely implicated in diverse domains of cell metabolism and their altered expression is associated with diabetes and its complications. Although originally thought to be non-functional, lncRNA genes transcribe into lncRNAs that exert important and specific functions in regulating cellular pathways. Due to this specificity, lncRNAs are considered better therapeutic targets. In addition, their expression patterns in tissues quite follow the progress of", + "NAs to be mapped to diabetic susceptible loci [49 52], all suggesting towards critical roles of lncRNAs in insulin resistance, diabetes, and its associated complications. LncRNAs asregulators ofislet function The pancreatic islet is an important central node to researchers to understand the pathophysiology of diabe-tes [53]. The possible regulation of islet development and function by lncRNAs was first demonstrated by Ding etal., where the lncRNA, H19 (Fig. 4), was shown to be involved", + "expected to rise due to the increasing incidence of diabetes, which necessitates the need for exploration of new molecular aspects of DR to expand the current scope of therapy. In the last two decades, the rapid advent of high-throughput genomic technology has made it evident that more than 97% of the human genome is comprised of non-protein-coding elements, such as non-coding RNAs (ncRNAs) 6. Although significant research has been conducted in annotating the transcripts that arise from these", + "regulation, control of mRNA decay, and sequestration of transcription factors. Although the underlying causes that define the diabetic phenotype are extremely intricate, most of the studies in the last decades were mostly centered on protein-coding genes. However, current opinion in the recent past has authenticated the contributions of diverse lncRNAs as critical regula - tory players during the manifestation of diabetes. The current review will highlight the importance of lncRNAs in regulating" + ], + [ + "review of polygenic risk scores for type 1 and type 2 diabetes. Int J Mol Sci. 2020;21(5):1703. 48. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:121924. 49. Ding Y, Hou K, Burch KS, Lapinska S, Priv F, Vilhjalmsson B, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS", + "(GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and inter vention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.", + "prediction of type 2 diabetes. N. Engl. J. Med. 359, 22082219 (2008). 45. Weedon, M. N. et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS. Med. 3, e374 (2006). 46. Euesden, J., Lewis, C. M. & OReilly, P . F. PRSice: Polygenic Risk Score software. Bioinformatics 31, 14661468 (2015). 47. Gatineau, M. et al. Adult obesity and type 2 diabetes (Public Health England,", + "(GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [79]. Polygenic risk scores (PRS), which aggregate the genetic risk of individ - ual alleles across the genome, are thus promising to pre - dict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [1015]. However, to date, T2D PRS were most widely developed and vali - dated in individuals of European descent. Given that the predictive performance of PRS often attenuates in non-", + "in advance. Polygenic Risk Scores (PRS) were proposed by Duncan L. et al. [ 8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence. These weights are assessed from the regression coefcient associated with each locus. These combined genetics features and correlation matrices would signicantly assist the entire eld of genomics study [ 9]. These studies on", + "performance. Conclusions: By integrating T2D GWAS from multiple populations, we developed and validated a transancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare. Keywords: Polygenic risk score, Type 2 diabetes, Diverse populations, Clinical implementation", + "Owing to their small effect sizes, SNP associations have very little clinical applicability for risk prediction. A polygenic risk score (PRS) attempts to estimate the combined risk from multiple SNPs that have been associated with a certain trait with genome-wide sig-nificance. By accounting for a large proportion of the genetic variance underlying a trait, the overall effect size", + "8.Padilla-Mart nez, F., Collin, F., Kwasniewski, M., and Kretow- ski, A. (2020). Systematic review of polygenic risk scores for type 1 and type 2 diabetes. Int. J. Mol. Sci. 21, 1703 . 9.Rao, A., and Knowles, J. (2019). Polygenic risk scores in coro- nary artery disease. Curr. Opin. Cardiol. 34, 435440 . 10.Dikilitas, O., Schaid, D.J., Kosel, M.L., Carroll, R.J., Chute, C.G., Denny, J.A., Fedotov, A., Feng, Q., Hakonarson, H., Jar-vik, G.P., et al. (2020). Predictive utility of polygenic risk scores", + "partitioned polygenic scores according to factors of disease heteroge- neity, as successfully demonstrated for type 2 diabetes (32). Another strategy could be the mapping of statistically associated genetic loci to different immune-cell subtypes according to gene expression patterns derived from single-cell RNA sequencing (33). Autoimmune PRS, possibly in combination with other genetic and nongenetic predictors, may be of importance to manage the risk of", + "genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated ( n= 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents ( n= 620), carotid intima-media thickness (cIMT) in adult women ( n= 781), dyslipidemia ( n= 7723), and coronary heart disease (CHD) ( n= 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. (Continued on next page)" + ], + [ + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse", + "therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome andRNA-sequencing data was employed to extensively characterize the genomic and molecularlandscape of insulinomas relative to normal beta cells. Here, we show at the pathway levelthat the majority of the insulinomas display mutations, copy number variants and/or dys-regulation of epigenetic modifying genes, most prominently in the polycomb and trithoraxfamilies. Importantly, these processes are coupled to co-expression", + "gesting that changes in alpha cell identity may ultimately lead to theirdysfunction. Analysis of normal and T2D islet single cells with simultaneous RNA-seq and patch clamping (patch-seq) also revealed subpopulations of alpha cells with varying enrichment for ER stressresponse genes (e.g., DDIT3, XBP1, PPP1R15A )[30]. Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets", + "RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. https ://doi.org/10.15252 /msb.20188 746 Ma L, Zheng J (2018) Single-cell gene expression analysis reveals -cell dysfunction and deficit mechanisms in type 2 diabe-tes. BMC Bioinform 19:515. https ://doi.org/10.1186/s1285 9-018-2519-1 Macaulay IC, Ponting CP, Voet T (2017) Single-cell multiom- ics: multiple measurements from single cells. Trends Genet 33:155168. https ://doi.org/10.1016/j.tig.2016.12.003", + "peak current. Prior single cell transcriptomic analyses have also notedsubpopulations of ER-stressed beta cells [31,32] which implicates the dysfunction of both alpha and beta cells in diabetes pathogenesis.Similarly, the integrity of beta and alpha cell functions seem to beReview S18MOLECULAR METABOLISM 27 (2019) S15 eS24/C2112019 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ). www.molecularmetabolism.com", + "to understanding human development using single-cell tran-scriptomics. Development 144:1584. https ://doi.org/10.1242/dev.15045 8 Camp JG, Wollny D, Treutlein B (2018) Single-cell genomics to guide human stem cell and tissue engineering. Nat Methods 15:661667. https ://doi.org/10.1038/s4159 2-018-0113-0 Carrano AC, Mulas F, Zeng C, Sander M (2017) Interrogating islets in health and disease with single-cell technologies. Mol Metab 6:9911001. https ://doi.org/10.1016/j.molme t.2017.04.012", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been" + ], + [ + "To date, the overwhelming majority of studies including and assessing genetic variation have pro led the steady state patterns of epigeneticmodi cations and gene expression in islets or their constituent cell types. Others have compared how these steady state measures differ between T2D and non-diabetic (ND) individuals [13,16,40 e44]. Sur- prisingly, these studies, especially transcriptome analyses, haveidenti ed only modest alterations despite clear phenotypic differences", + "T1D and resulting complications (99). These epig enomic profiling studies suggest that, while a 415 reasonably stable histone methylation pattern is maintained in healthy individuals over time in a 416 cell-type specific setting, this pa ttern can be disrupted in a dis ease state. Moreover, they also 417 provide a glimpse of the inflammatory cell epig enome under the diabetic state and suggest that 418 new information about diabetes, its complicatio ns and metabolic memory can be obtained by 419", + "hyperglycaemia, epigenetic changes have also been noted in other experimental settings of hyperglycaemia. For example, increased DNA methylation has been described for the promoter region of the peroxisome proliferator-activated receptor- g(PPAR g) coactivator-1 agene (PPARGC1A) in diabetic islets ( Ling et al., 2008 ). Similar hypermethylation in the promoter region of the PPARGC1A gene has been noted in the skeletal muscle from diabetic patients,", + "and correlated with mitochondrial content ( Barr /C18es et al., 2009 ). Epigenetic changes have also been suggested to be responsible forthe legacy effect of reduced risk of vascular complications after a period of sustained tight glucose control, or metabolic memory of transient hyperglycaemia and increased risk of diabetic vascular injury ( Pirola et al., 2010 ). Histone methylation variations have been noted in monocytes cultured in high glucose, as well as blood", + "Epigenetic Mechanisms in Diabetic Complications 17 Interestingly, the sirtuin (SIRT) family of deacetylases, specifically SIRT1, has been found to 360 regulate several factors involved in metabolism, adipogenesis a nd insulin secretion (86). HATs 361 and HDACs can also modulate NF- B transcriptional activity (4, 44) resulting in changes in 362", + "ing that environment and diet may influence epigenetic mod-ifications that predispose individuals to diabetes [ 46]. Aber- rant DNAme has also been reported in the reduced expression of genes involved in diabetes and metabolism, and DNAme variations have also been noted near diabetes susceptibility genes and enhancers [ 15,47]. Genomic DNA from diabetic patients with nephropa- thy relative to those without displayed differential meth- ylation at several genes, including UNC13B , which had", + "of diabetes mellitus on the body is a high glucose stressed condition, altering substrate metabolism and causing systemic inflammation [60]. Due to this environmental change, researchers have shown how epigenetic changes occur across most, if not all, tissues that are impacted by diabetes mellitus [49, 61]. In the cardiovascular system, the heart, circulatory system, and regulating immune system are all tran -", + "nephropathy. Exp. Physiol. 98, 934945 (2013). 48. Reddy, M.A., Tak Park, J. & Natarajan, R. Epigenetic modifications in the pathogenesis ofdiabetic nephropathy. Semin. Nephrol. 33, 341353 (2013). 49. Li, S.L. etal. Enhanced proatherogenic responses in macrophages and vascular smooth muscle cells derived from diabetic db/db mice. Diabetes 55, 26112619 (2006). 50. El-Osta, A. etal. Transient high glucose causes persistent epigenetic changes and altered gene", + "exhibit decreased plasticity of genome-wide muscle DNA methylation by high-fatoverfeeding. Diabetologia 2014;57:1154-1158. 53. Nilsson E, Jansson PA, Perfilyev A, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes 2014;63:2962-2976. 54. Aslibekyan S, Demerath EW, Mendelson M, et al. Epigenome-wide study identifies", + "etal. Hyperglycemia induces a dynamic cooperativity of histone methylase and demethylase enzymes associated with gene-activating epigenetic marks that coexist on the lysine tail. Diabetes (2009) 58:122936. doi:10.2337/ db08-1666 111. Keating S, Plutzky J, El-Osta A. Epigenetic changes in diabetic and cardio-vascular risk. Circ Res (2016) 118:170622. doi:10.1161/CIRCRESAHA. 116.306819 112. Paneni F, Volpe M, Lscher TF, Cosentino F. SIRT1, p66(Shc), and Set7/9 in" + ], + [ + "A variety of cellular and animal models have been developed and applied over the past few years to experimentally manipulate cis-regulatory elements and their target gene function as it related to beta cell/isletfunction, glucose homeostasis, and T2D pathogenesis. CRISPR/Cas9 hasrevolutionized our ability to modify genomes and epigenomes almost at will. Unsurprisingly, CRISPR (epi)genome editing tools can and have been used to target putative T2D target genes [54] orcis-REs[55] in beta", + "(276279). Through CRISPR-mediated HDR and base editing, it is possible to correct the vast majority of genetic variants, if notall. Conversion of GWAS-identi ed non-coding variants has not been conducted/documented in the diabetes eld, but it seems inevitable that such work will be carried out in the near futureHu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 11", + "Cas9 editing to restore insulin production in differentiated iPSCcells that mimicked neonatal diabetes ( 251,252). Likewise, Shi et al. converted a patient-speci c mutation in GATA6 gene and showed that the mutation involved (GATA6 R456C) has a similar effect to GATA6 knockout ( 21). Most recently, correction of a variant in the Wolfram syndrome 1 ( WFS1 ) gene by CRISPR- mediated HDR improved insulin secretion in iPSC-differentiatedb-like cells ( 253). Studies on GWAS identi ed genetic variants", + "in response to various stimuli including glucose aftertransplantation in an immunocompromised mouse model (230,231). However, the use of iPSC is controversial and there are some concerns over genetic and epigenetic variations iniPSCs which might affect cell function after differentiation ( 275). Manipulation of hESC/iPSC cells via CRISPR-Cas9 technology provides a platform for the correction of genomic mutations not only in diabetes but in other disease elds as well", + "hPSCs [48,49] for correcting the COL7A1 [50] anda1-antitrypsin genes [51]. Given the superior cutting ef ciency, CRISPR/Cas9 is increasingly becoming the favored choice for genome editing inhPSCs [16,52] . 3.2. Employing hPSCs and genome editing tools to study diabetes and metabolic syndromes In general, the strategy to carry out in vitro disease modeling of dia-", + "Due to its simplicity and adaptability, CRISPR has rapidly become the most popular genome editing tool available for the mammalian genome ( 50,63). Because NHEJ DNA repair often introduces unwanted indels at the Cas9 cutting site, CRISPR hasbeen used to knock-out genes by introducing frameshiftmutations, resulting in protein depletion ( 156,157). In the diabetes eld, CRISPR has also been adopted to study several genes in bcell lines and in human ES-derived bcells ( 21,151,", + "RNP and single strand edDNA (ssDNA) donor which carriesdesired changes such as insertion of loxP site ( 255,259265). Using CRISPR-Cas9, leptin and leptin receptor knockout mice have been established as tools in diabetes and obesity research ( 160,255,256). Knock-in mouse models have also been established via HDR to achieve cell-speci c deletion of the gene ( 266). Genome Editing: Clinical Application in Diabetes An important goal in genetic research is to identify the genetic", + "to how CRISPR/Cas9 technology may nd clinical application in patients with diabetes. Keywords: genome editing, beta cell, genome-wide association studies, maturity onset of diabetes of the young, stem cells, mouse models INTRODUCTION Type 2 diabetes (T2D) affects an estimated 425 million people worldwide, a number predicted to rise to 629 million by 2045 ( 1). The disease usually involves insulin resistance but is ultimately the result", + "samples ( 236). CRISPR technology has been used recently to correct point mutations in patient-derived iPSCs to target diabetes-relatedgene defects. To date, the most ef cient method used in iPSC is CRISPR/Cas9-based homology-directed repair (HDR). Here, a Cas9-mediated cut is generated adjacent to the site of interest. A homologous donor template with the intended nucleotidechange containing silent mutations in the gRNA sequence(167) can then be recombined by HDR. This approach has", + "free IPSCs from Human Pancreatic Cells Using the CRISPR-Cas9 System. J Vis Exp JoVE (2017). doi: 10.3791/56260 277. Millette K, Georgia S. Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. Curr Diabetes Rep (2017) 17:116. doi: 10.1007/s11892-017-0947-3Hu et al. Genome Editing of Pancreatic Beta Cells Frontiers in Endocrinology | www.frontiersin.org October 2020 | Volume 11 | Article 576632 19" + ], + [ + "The integration of genetic, epigenetic, transcriptomic and phenotypic information allows to identify genes and novel metabolic pathway targets that deserve further attention to elucidate mechanistic relationships with insulin resistance and pancreatic islet failure. Although the GWASs and EWASs shed light onto (epi)genomic landscape of T2D to a great extent, these methods have still explicit limitations to conquer, such as sample size, small effect size, low allele frequency, genetic heterogeneity", + "map of the human genome, spurred larger multi-institutional programs (e.g., 1000 Genomes Projects, Encyclopedia of DNA Elements [ENCODE], and Roadmap Epigenomics), that have the goal of tracking genomic and epigenomic changes across multiple populations [ 8]. Aforementioned studies enabled GWASs for complex diseases such as T2D. DNA amplication, Sanger sequencing, and microarray studies have shed light on the genetics of diabetes but have only provided a limited amount of data. An", + "Abstract While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under- surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait", + "The advancement that has taken place in Genome-Wide Association Studies (GWAS) holds tremendous information related to various gene patterns associated with divergent illnesses that are complex and challenging to perform reductive analysis from a single locus, as stated by Cho Ys [6] and Coron [7]. The evolution of GWAS has focused on integrating data related to multi-locus across the gene that would assist in predicting complex illnesses", + "1. Genome-wide association studies (GW AS) have made considerable progress in identifying genetic risk factors and in providing evidence for more in-depth understanding of the biological and pathological pathways underlying T2D. A recent study performed a meta-analysis of T2D across 32 GW AS of European ancestry par - ticipants and identified 243 genome-wide significant loci (403 distinct genetic variants) associated with T2D risk", + "1. Introduction Genome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1]. Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide poly-", + "how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes. Keywords Genotyping .Genome-wide association . Sequencing .Imputation .Exome .Genome . Fine-mapping .Diabetes .Quantitative traits .Metabochip . Single nucleotide polymorphism Introduction GWA studies have made progress toward understanding the inherited basis of type 1 and type 2 diabetes by detecting disease-associated DNA variants, usually with allele fre-", + "complementary systems level data such as that related to protein- protein interactions and to and gene expression can provideinsights into the mechanisms underlying pathogenesis of complextraits [2224]. Here, we have combined these approaches towarddeciphering genome to phenome correlation in T2D ( Figure 1 ). Given that T2D GWAS genes do not directly relate to disease", + "phenotypes [2,6]. The recently accomplished deep sequencing of human exomes has indeed suggested that rare variations contribute substantially to human phenotypic variation and disease susceptibility [73]. Availability of post-GWASs era data for T2D will be crucial in examining genome to phenomecorrelation in greater details. Emerging methods in pathway-wide analysis and integrative network based analysis of genetic association data in complex disorders will further help accelerate", + "Abstract Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic bcells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidatesperturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories" + ], + [ + "maternal diabetes reduces the precision of gene regulation in exposed individuals. Loss of precision in embry-onic gene regulation may include changes to the epigenome via deregulated expression of chromatin-modify-ing factors. Unraveling the mechanisms underlying such epigenetic modications in diabetic pregnancies willhelp to understand how teratogenic insults compromise embryonic development and possibly provide ave-nues for therapeutic intervention. Birth Defects Research (Part A) 88:601611, 2010.", + "and metabolic imprinting: the ongoing effects of maternal hyper-glycemia. Diabetes Care 30:2287 2292 9. Clausen TD, Mathiesen ER, Hansen T et al (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women withgestational diabetes mellitus or type 1 diabetes: the role of intrauter- ine hyperglycemia. Diabetes Care 31:340 346 10. Solomon CG, Willett WC, Carey VJ et al (1997) A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA 278:1078 1083", + "M. Gestational diabetes alters offspring DNA methylation profiles in human and rat: Identification of key pathways involved in endocrine system disorders, insulin signaling, diabetes signaling, and ILK signaling. Endocriniology 2015;156:2222 -38. [33] Murphy SK, Huang Z, Hoyo C. Differentially methylated regions of imprinted genes in prenatal, perinatal and postnatal human tissues. PLOS ONE 2012;7:e40924.", + "12. Kim JK, Samaranayake M, Pradhan S. Epigenetic mechanisms in mammals. Cell Mol Life Sci. 2009;66:596-612. 13. Horsthemke B, Buiting K. Genomic imprinting and imprinting defects in humans. Adv Genet. 2008;61:225-246. 14. Iacobuzio-Donahue CA. Epigenetic Changes in Cancer. Annu Rev Pathol. 2009;4:229-249. 15. Temple IK. Imprinting in human disease with special reference to transient neonatal diabetes and Beckwith-Wiedemann syn- drome. Endocr Dev. 2007;12:113-123.", + "and Knowler W C. Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: A study of discordant sibships. Diabetes 2000;49:2208 -11. [11] Feil R and Fraga MF. Epigenetics and the environment: Emerging patterns and implications. Nature Reviews Genetics 2012;13:97 -109. [12] Recillas -Targa F. DNA Methylation, Chromatin boundaries, and mechanisms of genomic imprinting. Archives of Medical Research 2002;33:428 -38.", + "53. T ravers,M.E. etal. Insights into the molecular mechanism for type2 diabetes susceptibility at the KCNQ1 locus from temporal changes in imprinting status in human islets. Diabetes 62, 987992 (2013). 54. Gulli,G., Ferrannini,E., Stern,M., Haffner,S. &DeFronzo,R.A. The metabolic profile of NIDDM isfully established in glucose-tolerant offspring of twoMexican-American NIDDM parents. Diabetes 41, 15751586 (1992). PRIMER NATURE REVIEWS | DISEASE PRIMERS VOLUME 1 | 2015 | 17", + "Gaudet, D., Hivert, M.F., Brisson, D., Bouchard, L., 2013 Sep. Gestational diabetesmellitus epigenetically affects genes predominantly involved in metabolic dis- eases. Epigenetics 8 (9), 935 e943. Salbaum, J.M., Kappen, C., 2012 Oct. Responses of the embryonic epigenome to maternal diabetes. Birth Defects Res. A Clin. Mol. Teratol. 94 (10), 770 e781. Salbe, A.D., Lindsay, R.S., Collins, C.B., Tataranni, P.A., Krakoff, J., Bunt, J.C., 2007 Feb.", + "environment are probably mediated by a permanent program-ming of the developing offspring, e.g. by the mechanism ofimprinting. Of interest, the increased risk of diabetes continuesinto subsequent generations, suggesting the changes also affectthe germ cell line [143]. Conclusions There is little doubt that some animal models of diabetes have", + "tal diabetes and later onset diabetes: a case of inher - ited insulin resistance. Arch. Dis. Child. 72:5657. 6. Temple, I.K., et al. 1995. An imprinted gene(s) for diabetes? Nat. Genet. 9:110112. 7. Temple, I.K., et al. 1996. Further evidence for an imprinted gene for neonatal diabetes localised to chro -", + "1994; Martinez-Frias et al., 1998). The underlying mecha-nisms are not well understood, but are thought to involve various responses of the embryonic genome to the adverse intrauterine environment (Greene, 2001;Loeken, 2008). To explore how conditions of maternal diabetes affect gene expression in the embryo, we recently conducted expression proling experiments on embryos from dia-betic dams compared to embryos from normal dams(Pavlinkova et al., 2009). We were able to demonstrate" + ], + [ + "genome-wide association scans on type 2 dia-betes (Lango et al, 2008 ; van Hoek et al, 2008 ). Both studies found a similar predictive value showing only a marginal improvement in the prediction of type 2 diabetes beyond classicalclinical characteristics. Thus, despite overwhelming signicances and repeated replications, the explained variance andpredictive value of the currently identied sus- ceptibility loci is too low to be clinically useful. 5 GeneEnvironment Interactions in Obesity and Diabetes", + "actions between genetic variation and environmental exposures and medical therapies has important implications for the predic- tion, targeted prevention, and s tratified treatment of T2D and many other diseases. The literature on gene-e nvironment interactions in diabetes-related traits is extensive, but few studies are accom- panied by adequate replication data or compelling mechanistic explanations. Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be", + "ined for a range of disorders, from diabetes, cancer and in ammatory bowel disease to depression. We refute the contention that incorporating the measurement of genotype into longitudinal-epidemiological studies is wasteful or unlikely to yield signi cant bene ts. 2008 Genetic effects on environmental vulnerability to disease. Wiley, Chichester (Novartis Foundation Symposium) p 128142 Slow progress understanding the genetic basis of many common diseases has been", + "In principle, each of these loci provides an opportunity to define the genetic architecture and pathophysiology of these traits. The earliest successes for genetic discovery in diabetes and obesity arose from the study of monogenic and syndromic forms of disease, for which the segregation of rare, but highly penetrant, alleles could be tracked using family-based linkage approaches that are well suited to that setting. Maturity-onset diabetes of the young, for example, accounts for ~12% of cases", + "wide GxE interactions in explaining the variance of diabetes-related traits. Citation: Zheng J-S, Arnett DK, Lee Y-C, Shen J, Parnell LD, et al. (2013) Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits. PLoS ONE 8(10): e77442. doi:10.1371/journal.pone.0077442 Editor: Maria Eugenia Saez, CAEBi, Spain Received April 10, 2013; Accepted September 3, 2013; Published October 28, 2013", + "data sharing to advance complex disease research. Nat. Rev. Genet. 17, 535549 (2016). 82. Franks,P .W., Pearson,E. & Florez,J.C. Gene- environment and gene-treatment interactions in type2 diabetes: progress, pitfalls, and prospects. Diabetes Care 36, 14131421 (2013). 83. Hagberg,J.M., Jenkins,N.T . & Spangenburg,E. Exercise training, genetics and type2 diabetes- related phenotypes. Acta Physiol. 205, 456471 (2012). 84. Langenberg,C. etal. Gene-lifestyle interaction and", + "Genomics and geneenvironment interactions Even though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mel - litus are more susceptible to T2DM than others, which suggests that individual differences in response to life - style interventions exist76. Substantial evidence from twin and family studies has suggested a genetic basis of T2DM77. Over the past decade, successive waves of", + "DNA variation with disease processes in a range of settings, from cell lines to human populations, and major advances have been made in coupling these complex datasets with information about extrinsic environmental exposures including drug prescription in ways that allowthe logical interrogation of gene-drug and gene-lifestyle interactions. Doing so may teach us about disease etiology and help stratify type 2 diabetes (T2D) into subclasses that can be treated more effectively, with", + "fuel subsequent functional and clinical translation studies. This is important, because diabetes medicine may rely increas- ingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative. How Are Gene-Environment Interactions Defined? The term gene-environment interaction has different meanings to different biomedical re searchers (see Supplement 1for glossary of terms used). However, here, we focus on the", + "Nutrients 2014, 6 5362 48. Cornelis, M.C.; Hu, F.B. Gene -enviroment interactions in the development of type 2 diabetes: Recent progress and continuing challenges. Annu. Rev. Nutr. 2012, 32, 245259. 49. Lee, Y.C.; Lai, C.Q.; Ordovas, J.M.; Parnell, L.D. A database of gene -enviroment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes. J. Data Mining Genomics Proteomics 2011, 2, 106, doi:10.4172/2153- 0602.1000106." + ], + [ + "4. PRECISE CELLULAR GENOMICS Elucidating the molecular mechanisms that lead to beta cell dysfunction and T2D pathogenesis has been a major focus of diabetes research for decades. However, advances in single cell genomic proling techniques have led to greater understanding of non-beta cell type transcriptional regulation and suggest that they may play important roles in hallmark features of beta cell insuf ciency and", + "Genes 2018 ,9, 374 7 of 19 4. Single-Cell RNA-seq as a Novel Approach in High-Throughput Type 2 Diabetes Research Islets of Langerhans are heterogeneous structures that consist of different cell types. Further research is needed to track genetic changes in individual pancreatic islet cells and in sorted cell populations. The massive development of NGS allowed the sequencing of single cells from human pancreatic islets. Considering the cell-type heterogeneity within Langerhans islets, such an approach", + "Advances ofsingle -cell genomics andepigenomics inhuman disease: whereare we now? 1 3 Brissova etal. 2018; Tritschler etal. 2017). Moreover, an increase in hyperglycaemia has been associated with a loss of beta-cell mass, function and organization and is the cell type most frequently studied for insulin resistance (Carrano etal. 2017; Lawlor etal. 2017b; Segerstolpe etal. 2016; Theis and Lickert 2019; Tritschler etal. 2017). Notably, single-cell transcriptome profiling has been", + "Tang X, Huang Y, Lei J, Luo H, Zhu X (2019) The single-cell sequenc- ing: new developments and medical applications. Cell Biosci 9:53. https ://doi.org/10.1186/s1357 8-019-0314-y Teo AKK etal (2018) Single-cell analyses of human islet cells reveal de-differentiation signatures. Cell Death Discov 4:14. https ://doi. org/10.1038/s4142 0-017-0014-5 Theis FJ, Lickert H (2019) A map of beta-cell differentiation pathways supports cell therapies for diabetes. Nature 569:342343. https ://", + "53. Eliasson L, Esguerra JL (2014) Role of non-coding RNAs in pancreatic beta-cell development and physiology. Acta Physiol (Oxf) 211:273284 54. Ding GL, Wang FF, Shu J etal (2012) Transgenerational glucose intolerance with Igf2/H19 epigenetic alterations in mouse islet induced by intrauterine hyperglycemia. Diabetes 61:11331142 55. Ku GM, Kim H, Vaughn IW etal (2012) Research resource: RNA-Seq reveals unique features of the pancreatic beta-cell tran-scriptome. Mol Endocrinol 26:17831792", + "24. Nica, A. C. et al. Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome. Genome Res. 23, 1554 1562 (2013). 25. Takane, K. K., Bender, A. & Stewart, A. F. Speci c targeting and sorting of puried human beta cells: de ning the human beta cell transcriptome. ADA Scienti c Sessions, San Francisco (2014). 26. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).", + "5. Genome-Wide Proling of Epigenetic Changes in Pancreatic Islets and Peripheral Tissues Epigenetic data added another layer of complexity to our understanding of the genomic bases of T2D. Given that a variable epigenetic pattern can modulate the link between the SNP and trait, consideration of this interplay is critically important. Molecular epigenetics involves changes in gene function that occur without a change in the nucleotide sequence via DNA methylation, histone", + "and model organisms. The combination of data from high-throughput approaches and association studies has provided compelling evidence that some epigenetic markers contribute to the risk of T2D [ 57,58]. Epigenetic alterations have been shown to affect the expression of genes that are crucial for maintaining pancreatic islet secretory capacity, survival, and functional identity and the proper response to insulin in peripheral tissues [ 59,60]. Furthermore, several epigenetic signatures, such", + "Epigenomic approaches: applications in diabetic complications research Epigenetic studies in human disease have been greatly accel- erated as a result of advances in whole-genome and epige- nome profiling technologies as well as bioinformatics andgenomic data analysis platforms [ 99,100]. DNAme is analysed using bisulfite conversion of genomic DNA, immu- noprecipitation of methylated DNA, followed byhybridisation to arrays or next-generation sequencing to ob-", + "understand each cell type s genomic architecture and better charac- terize their roles in islet resilience and failure. Experimental manipu- lation of the regulatory elements and/or the target genes identi ed by (epi)genomic approaches described above and modeling the putativepathways and processes they implicate in human islet cell lines (e.g., EndoC- bH1-H3) is essential to progress from correlation to causation. Similarly, transitioning from themouse (C57BL/6) to multiple mouse" + ] + ], + "task_id": [1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10] +}
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