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diff --git a/gnqa/data/study1/datasets/gpt4o/dataset_domainexpert_diabetes_3.json b/gnqa/data/study1/datasets/gpt4o/dataset_domainexpert_diabetes_3.json new file mode 100644 index 0000000..eac65b9 --- /dev/null +++ b/gnqa/data/study1/datasets/gpt4o/dataset_domainexpert_diabetes_3.json @@ -0,0 +1,104 @@ +{ + "question": [ + "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?" + ], + "answer": [ + "Rare variants identified through whole-genome sequencing can contribute to the heritability of Type 2 diabetes by revealing genetic determinants that are not detected by Genome-Wide Association Studies (GWAS). These rare variants, especially those with significant effects, can increase our understanding of Type 2 diabetes heritability and help identify individuals at risk early on. However, current studies suggest that these rare variants are likely scattered across many genes, rather than being clustered in a small number of genes.", + "Recent findings suggest that non-coding RNAs, specifically long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play significant roles in the pathogenesis of diabetes and its complications. They are involved in various cellular processes such as inflammation, fibrosis, and hypertrophy in renal cells, which contribute to the progression of diabetic nephropathy. They can also affect DNA methylation and histone modifications, resulting in increased chromatin accessibility to transcription factors near pathological genes. Furthermore, lncRNAs and miRNAs can increase the expression of pathological genes via post-transcriptional mechanisms. Some specific lncRNAs like MALAT1, MEG3, ANRIL, PVT1, MIAT, MGC, Gm4419, and TUG1 have been implicated in complications like diabetic retinopathy and nephropathy. Similarly, miRNAs have been found to regulate important pathogenic responses and hold potential as diagnostic biomarkers and therapeutic targets.", + "The interaction between multiple polygenic risk scores (PRS) improves the prediction of Type 1 and Type 2 diabetes by aggregating the genetic risk of individual alleles across the genome. This provides a comprehensive view of an individual's genetic predisposition to diabetes. The PRS can capture information on individual patterns of disease predisposition, which can help predict diabetes risk, support differential diagnosis, and understand phenotypic and clinical heterogeneity. However, the effectiveness of PRS can vary across different ethnic groups and populations.", + "Recent single-cell RNA-sequencing studies have revealed that multiple monogenic diabetes genes are highly expressed in beta cells. However, other non-beta cell types also express genes mutated in monogenic diabetes. Dysregulated glucagon secretion in type 1 diabetic islets is accompanied by decreased expression of important islet transcription factors and increased expression of stress response factors, suggesting changes in alpha cell identity may lead to their dysfunction. Transcriptomic heterogeneity in normal and T2D islets is associated with variability in alpha cell electrophysiological measures. These studies implicate the dysfunction of both alpha and beta cells in diabetes pathogenesis.", + "In diabetic individuals, there are significant differential DNA methylation profiles in pancreatic islets compared to non-diabetic individuals. This includes 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets. These methylation changes were not present in blood cells from diabetic individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose. These changes can affect over 250 genes, some of which are also differentially expressed, and may be linked to b-cell functionality, cell death, and adaptation to metabolic stress. These epigenetic changes are not observed in other tissues, indicating tissue-specificity." + ], + "contexts": [ + [ + "\t\n\nIt should be noted that a great number of low frequency variants might not be identified by GWAS owing to the required genome-wide significance level.According to the existing studies, many important loci are also obscured as a result of borderline associations.The known variants account for only a small amount of the overall estimated genetic heritability; therefore, there is still a long way to go in terms of understanding the pathogenesis of type 2 diabetes.", + "\t\n\nIf common causal alleles explain a substantial component of T2D susceptibility, the contribution of rare and low-frequency risk variants may be less than is often assumed: resequencing studies will soon provide empirical data to address this hypothesis.In particular, it will be important to determine whether, as the number of susceptibility loci increases, there is evidence that the pathophysiological mechanisms implicated by human genetics coalesce around a limited set of core pathways and networks.Our data suggest that this may be the case, with a variety of analytical approaches pointing to cell cycle regulation, adipocytokine signaling and CREBBP-related transcription factor activity as key processes involved in T2D pathogenesis.", + "\tFuture perspective\n\nGiven the rapid pace of technological advancement in genetics, discovery of many more genetic determinants of T2D may be expected in future.At present, GWAS are limited in their ability to detect rare variants.Sequencing, which is expected to become much more economical, may benefit greatly in this respect by identifying rare genetic variants with significant effects on T2D risk in a given population.This would result in an increased understanding of T2D heritability so that at risk individuals may be detected early on.However, functional studies need to evolve at an equally rapid pace to be able to translate these discoveries into clinical practice.\tGenetics & genomics of T2D\n\n 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. Genetic variability is responsible for the difference in response to antidiabetic drugs seen across individuals.", + "\t\n\nOver the past two years, there has been a spectacular change in the capacity to identify common genetic variants that contribute to predisposition to complex multifactorial phenotypes such as type 2 diabetes (T2D).The principal advance has been the ability to undertake surveys of genome-wide association in large study samples.Through these and related efforts, $20 common variants are now robustly implicated in T2D susceptibility.Current developments, for example in high-throughput resequencing, should help to provide a more comprehensive view of T2D susceptibility in the near future.Although additional investigation is needed to define the causal variants within these novel T2Dsusceptibility regions, to understand disease mechanisms and to effect clinical translation, these findings are already highlighting the predominant contribution of defects in pancreatic b-cell function to the development of T2D.", + "\tGenetic variants\n\nThe 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 5 study participants, has helped us to better understand the genetic architecture of this disease.Single nucleotide polymorphisms (SNPs) in more than 60 regions throughout the genome (so-called susceptibility loci containing multiple genes) were found to be associated with the risk of type 2 diabetes [39, 41-44].Most of these SNPs are common, with minor allele frequencies of 10-90%.Interestingly, loci associated with diabetes risk show only a partial overlap with loci that determine levels of fasting glucose, 2 h glucose and HbA 1c .Thus, some loci influence both disease risk and glycaemic traits, whereas others seem to mainly regulate glucose levels within the physiological range without affecting the development of overt type 2 diabetes, and vice versa [45,46].", + "\t\n\nFigure 3 displays results for three representative models: a 'purifying selection' model in which low-frequency and rare variants explain approximately 75% of T2D heritability; an intermediate model in which both common and lower-frequency variants contribute substantially; and a 'neutral' model in which common variants explain about 75% of T2D heritability.The predictions of the first two models differ markedly from the empirical data with respect to the numbers of low-frequency and rare risk variants that are associated with T2D.Specifically, these two models predict a larger number and greater effect size of low-frequency variants should be found in our whole-genome sequencing study as compared to those observed in the empirical data.By contrast, the empirical data are consistent with predictions under the 'neutral' commonvariant model.\t\nThere is compelling evidence that the individual risk of type 2 diabetes (T2D) is strongly influenced by genetic factors 1 .Progress in characterizing the specific T2D-risk alleles responsible has been catalysed by the ability to perform genome-wide association studies (GWAS).Over the past decade, successive waves of T2D GWAS-featuring ever larger samples, progressively denser genotyping arrays supplemented by imputation against more complete reference panels, and richer ethnic diversity-have delivered more than 80 robust association signals 2-8 .However, in these studies, the alleles interrogated for association were predominantly common (minor allele frequency (MAF) >5%), and with limited exceptions 7,9 , the variants driving known association signals were also common, with individually modest impacts on T2D risk [2][3][4][5][6][7][8]10 . Varation at known loci explains only a minority of observed T2D heritability 2,3,11 .Residual genetic variance is partly explained by a long tail of common variant signals of lesser effect 2 .However, the contribution to T2D risk that is attributable to lower-frequency variants remains a matter of considerable debate, not least because of the relevance of disease architecture to clinical application 11 .Next-generation sequencing enables direct evaluation of the role of lower-frequency variants to disease risk 7,12,13 .This paper describes the efforts of the coordinated, complementary strategies pursued by the Genetics of Type 2 Diabetes (GoT2D) and Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) consortia.GoT2D collected comprehensive genomewide sequence data from 2,657 T2D cases and controls; T2D-GENES focused on exome sequence variation, assembling data (after inclusion of GoT2D exomes) from a multiethnic sample of 12,940 individuals.Both consortia used genotype data to expand the sample size available for association testing for a subset of the variants exposed by sequencing.", + "\t\n\nRecent data (67) and ongoing investigations indicate that other types of common genetic variation (e.g., copy number or structural variants, such as deletions and duplications) may contribute little to the observed familial clustering of type 1 diabetes risk.However, rare loss-offunction structural gene variants could still make an important contribution to type 1 diabetes risk, through identification of which particular gene in a region of association could harbor a causal variant.With further advances in array and sequencing technologies, it is anticipated that such loss-of-function variants will be identified that influence susceptibility to type 1 diabetes (68).Inferences from genetic studies.Each newly identified association of a candidate locus with type 1 diabetes presents new challenges.Finding the causal genes and the causal variants, understanding how they affect disease pathophysiology, and dissecting their contribution to type 1 diabetes risk remain the major undertakings.For some genes, the effect sizes of risk alleles are such that larger collections of patients will be needed to identify the causal genes and limit the number of potential causal variants.Genotype-phenotype fine-mapping studies, however, can be performed with much smaller sample sizes while still achieving convincing statistical evidence (e.g., 42).Each confirmed gene, based on both statistical and functional evidence, provides a key piece of the etiology of type 1 diabetes, regardless of the magnitude of the odds ratio as a measure of the population association.\t\n\nCombinations of many alleles, possibly hundreds, combine with effects of environmental factors (probably numerous and ubiquitous) to establish the risk profile for type 1 diabetes.Each common variant in isolation has a subtle effect on disease risk, but each may alter a key function in the immune system and its interaction with pancreatic -cells.Recent discussion of \"missing heritability\" for complex human traits has considered the source of this variation and appropriate research strategies to detect these genetic effects (61).Studies in populations that are distinct from Europeans or European ancestry, such as populations of recent African ancestry or from Asian countries, are likely to narrow the large chromosomal regions of association identified in current studies and to increase the yield of rare variants (69).Future studies examining rare variants, structural variation, and polymorphisms not well imputed should be helpful in uncovering the remaining missing heritability in type 1 diabetes.", + "\t\n\nUntil recently, genome-wide linkage and candidate studies have been the main genetic epidemiological approaches to identifying the precise genetic variants underlying T2D heritability.These efforts confirmed only a few susceptibility variants, including those in PPARG, KCNJ11, WFS1, HNF1A, HNF1B, HNF4A, TCF7L2, and ADIPOQ (1,6,27,56,81,102).Recent genome-wide association studies (GWAS) have unveiled over 50 novel loci associated with T2D and more than 40 associated with T2D-related traits including fasting insulin, glucose, and proinsulin (16,48,57,82,87,97,105) (Table 1).Clinical investigations of some of the T2D loci, thus far, suggest that the genetic components of T2D risk act preferentially through -cell function (20).This pattern may only be a function of case diagnostic criteria, which weigh heavily on parameters reflecting advanced stages of the disease.This notion is supported by the incomplete overlap of single-nucleotide polymorphisms (SNPs) contributing to variation in quantitative traits with those associated with overt T2D (20).With the exception of TCF7L2, most variants contribute modestly to T2D risk and together explain only a small proportion of the familial clustering of T2D, suggesting that many more loci await discovery (10,12,97).", + "\tDiscussion\n\nIt has been hypothesized that rare genetic variants with moderate effects on disease risk could account for much of the missing heritability of complex traits. 6,9,10,62We have taken a first step toward testing this hypothesis for type 2 diabetes.We did not detect any significant associations between rare coding variants and common forms of diabetes.Our study was underpowered to detect weak genetic effects, but if much of the heritability of type 2 diabetes is explained by variants in a modest number of genes, we should have detected at least one associated locus at our Bonferroni significance threshold.Thus, our empirical results, combined with the statistical power simulations, suggest that when clustered in fewer than 20 genes, coding variants of moderate effect do not account for much of the missing heritability of a common polygenic disorder such as type 2 diabetes.\t\n\nOne 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%. [33][34][35] Through GWASs, 63 loci have been reproducibly associated 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. 36ere, we seek to evaluate the role that rare coding variants play in the genetic basis of common forms of type 2 diabetes.We performed a deep whole-exome sequencing study of 2,000 Danish individuals.We applied both single-marker and gene-based association tests.Although we failed to detect any significant association after multiple test corrections, our simulations suggest that our results are informative about the genetic architecture of type 2 diabetes.In particular, our study suggests that when clustered in a small number of genes, rare coding variants of moderate to strong effect are unlikely to account for much of the missing heritability.Rather, if rare coding variants are an important factor in type 2 diabetes risk, they are most likely scattered across many genes.Our results have important implications for the design and interpretation of future medical resequencing studies.\t\n\nOur empirical and simulation results are compatible with a variety of different genetic architectures for type 2 diabetes.First, if rare coding variants are responsible for the majority of the heritability of the trait, the variants are most likely scattered across many (>20) different genes.Thus, genetic variants in no one gene can account for much of the heritability of the trait.Biologically, such a model would postulate that there are a large number of genes that can be mutated to cause type 2 diabetes in a given individual.Each individual would then carry a subset of genetic variants located in several of the many causal genes.Our finding that genes previously implicated in obesity risk through GWASs showed unusually low SKAT p values in our study supports a scenario in which low-frequency and rare variants in multiple genes could be responsible for risk of common metabolic diseases.It also suggests that genes carrying common variants associated with a trait could also carry additional low-frequency and rare coding variants that increase disease risk.\t\n\nAlthough our results argue that low-frequency and rare coding variants in a modest number of genes do not account for the majority of the heritability of common forms of type 2 diabetes, it is not clear how generalizable this result is to other complex traits.Several other exome sequencing studies have failed to detect any significant associations between low-frequency variants and schizophrenia, 77 epilepsy, 78 autism, 79 or autoimmune diseases. 80][83] Thus, the genetic architecture and the role of low-frequency and rare variants are likely to be trait dependent and will need to be addressed empirically.", + "\tType 2 Diabetes\n\nWhile a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44).Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (45)(46)(47).There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene-gene interactions, and epigenetics.Most type 2 variants are in noncoding genomic regions.Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48).It is possible that children of mothers carrying KCNQ1 are born with a reduced functional b-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49).Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes.", + "\t\n\nAt least three large exome and genome sequencing projects are ongoing to discover variants influencing type 2 diabetes and related traits.The Go-T2D study is performing lowcoverage whole-genome sequencing, deep exome sequencing, and 2.5 M SNP array genotyping of 1,425 type 2 diabetes cases and 1,425 controls from Northern Europe [41].The T2D-GENES Project 1 study is performing exome sequencing of 5,000 type 2 diabetes cases and 5,000 controls from five ancestral groups, and the T2D-GENES Project 2 study is performing deep whole-genome sequencing of >500 individuals from 20 large Mexican American pedigrees [42].These projects will detect many novel lowfrequency and rare variants that, when analyzed in sufficiently large numbers of subjects, can be expected to identify new insights into the genetic basis for disease.\tConclusions\n\nHow will sequencing genomes influence the health of people at risk for or affected with diabetes?The more complete understanding of the biological mechanisms underlying diabetes derived from these studies may lead to identification of novel drug targets.Individuals with variants in genes responsible for MODY or neonatal diabetes respond better to specific drugs [50,51], and sequencing may identify small numbers of individuals with combinations of rarer, more highly penetrant variants that respond better to specific therapeutic options.Although sets of known variants for type 2 diabetes do not add substantially to prediction of type 2 diabetes development in the overall population [52,53], identification of individuals at greater or lower genetic risk for diabetes within the overall population or in specific subgroups, such as younger onset or leaner individuals [54,55], could lead to better targeted health information and also allow identification of higher risk individuals leading to more efficient design of clinical trials for disease prevention.", + "\t\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes.\t\n\nGenome-wide association (GWAS) and sequencing studies are providing new insights into the genetic basis of type 2 diabetes (T2D) and the inter-individual variation in glycemic traits, including levels of glucose, insulin, proinsulin and hemoglobin A1c (HbA1c).At the end of 2011, established loci (P < 5 10 8 ) totaled 55 for T2D and 32 for glycemic traits.Since then, most new loci have been detected by analyzing common [minor allele frequency (MAF)>0.05]variants in increasingly large sample sizes from populations around the world, and in trans-ancestry studies that successfully combine data from diverse populations.Most recently, advances in sequencing have led to the discovery of four loci for T2D or glycemic traits based on low-frequency (0.005 < MAF 0.05) variants, and additional low-frequency, potentially functional variants have been identified at GWAS loci.Established published loci now total 88 for T2D and 83 for one or more glycemic traits, and many additional loci likely remain to be discovered.Future studies will build on these successes by identifying additional loci and by determining the pathogenic effects of the underlying variants and genes." + ], + [ + "\t\n\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop / mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression.\t\nIt is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication.Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression.Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-b1 (TGF-b1) or high glucose.Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP.Cluster microRNAs and lnc-MGC are decreased in diabetic Chop / mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress.A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice.Relevance to human DN is also demonstrated.These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression.", + "\t\n\nNumerous recent reports have demonstrated abnormal expression of various miRNAs in renal, vascular and retinal cells under diabetic conditions, and in vivo models of related diabetic complications [8,[87][88][89][90][91]. Notably, the functional relevance of these miRNAs has been highlighted by the fact they target key genes associated with the progression of, or protection against, these complications.In particular, the role of miRNAs in diabetic nephropathy has been extensively studied, including in the actions of TGF- related to fibrosis and other key renal outcomes in vitro and in vivo [8,[87][88][89][90].In diabetic retinopathy, several miRNAs have been reported to modulate the disease by targeting factors associated with angiogenesis, inflammation, and oxidant stress in RECs and in diabetic retinas [88,89].Reports have also implicated various miRNAs in the aberrant expression of genes associated with diabetic cardiomyopathy [88,91].In addition, effective in vivo targeting of miRNAs has now been demonstrated thanks to advances in nucleotide chemistry and the design of nuclease-resistant anti-miRNAs, which suggest future translational potential of miRNA-based therapies for human diabetic complications [8].Importantly, since miRNAs are stable in biological fluids such as urine and serum [8], they are being assessed in samples from various clinical cohorts as valuable biomarkers for the early detection of diabetic complications, for which there is a major unmet clinical need.It is clear that research in the field of miRNAs and diabetic complications will continue at a rapid pace.", + "\tIntroduction\n\nDiabetes-related complications represent one of the most important health problems worldwide with dire social and economic projections (Cooper, 2012).One of the most important medical concerns of the diabetes epidemic is diabetic nephropathy (DN).Diabetic nephropathy is regarded as a prototypical disease of gene and environmental interactions because not all diabetic subjects with traditional risk factors develop clinically evident nephropathy, indicating a role for individual susceptibility.The majority (>85%) of GWAS-identified single nucleotide polymorphisms (SNPs) are located in the non-coding regions of the genome and thus their functional implication lies in identifying the target genes, cell types, and the mode of dysregulation caused by these non-coding SNPs (Maurano et al., 2012).Recent studies indicate that complex trait-causing variants localize to cell-type-specific, functionally important gene regulatory regions where they can disrupt or create transcription factor binding sites to alter transcript levels only in disease-target cell types (Ko and Susztak, 2013;Susztak, 2014).Several elements of the immune system including cytokines and resident chemokines, macrophage recruitment, T lymphocytes, and immune complex deposition have recently been associated with DN (Navarro-Gonzlez and Mora-Fernndez, 2008;Gaballa and Farag, 2013).Since renal cells are also capable of synthesizing pro-inflammatory cytokines such as tumor necrotic factor-alpha (TNF-), interleukin-1 (IL-1) and interleukin-6 (IL-6), therefore, these cytokines acting in a paracrine or autocrine manner may induce significant effects leading to the development and progression of several renal disorders (Matoba et al., 2010;Pruijm et al., 2012;Shankar et al., 2011).The rationale of this study involved a concerted effort of genotyping, correlation and gene expression techniques involving three pro-inflammatory cytokine genes in the development and progression of DN as well as identification of high risk patients involving susceptibility or poor clinical outcome.", + "\t\n\nThese studies indicated limited detection of certain biological processes that are also relevant to the pathogenesis of diabetic nephropathy.These included genes pertinent to inflammation and angiogenesis.The limited detection was thought to be attributed to the apparent lack of sensitivity that was associated with the geneoriented averaging probe signals.This shortcoming was rectified by the use of ChipInspector, which is based on single probe analysis and de novo gene annotation that bypasses the probe set definition based on the out-of-date genomic data.In doing so, the single probe-based analysis yielded reduced background noise with enhanced sensitivity and fewer false positives.It also successfully identified the Wnt signaling pathway activated in diabetic nephropathy [63].\t\n\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3 isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies.\tNewly Identified Genes Relevant in the Progression of Diabetic Nephropathy\n\nThe cellular events such as increased flux of polyols and hexosamines; generation of AGEs; increased activity of PKC, transforming growth factor--Smad-MAPK (mitogen-activated protein kinase) pathway and GTP-binding proteins; G1 cell cycle arrest associated with altered expression of cyclin kinases and their inhibitors; and generation of ROS are responsible for a final outcome of increased synthesis and deposition of ECM.The ROS, whether mitochondrial or cell membrane-derived, are also responsible for the activation of the renin-angiotensin system that eventually contributes to glomerular hyperfiltration and subsequent renal fibrosis (fig. 1) [71].In addition to these macromolecules, newly identified genes, such as RSOR/MIOX, Tim44 and Rap1b, may also be an integral part of the hyperglycemia-induced cytosolic and mitochondrial processes that culminate in the development of diabetic nephropathy [48][49][50][51][52][53][54][55].\t\nOne of the major problems facing clinical nephrology currently throughout the world is an exponential increase in patients with end-stage renal disease (ESRD), which is largely related to a high incidence of diabetic nephropathy.The latter is characterized by a multitude of metabolic and signaling events following excessive channeling of glucose, which leads to an increased synthesis of extracellular matrix (ECM) glycoproteins resulting in glomerulosclerosis, interstitial fibrosis and ultimately ESRD.With the incidence of nephropathy at pandemic levels and a high rate of ESRD, physicians around the world must treat a disproportionately large number of diabetic patients with upto-date innovative measures.In this regard, identification of genes that are crucially involved in the progression of diabetic nephropathy would enhance the discovery of new biomarkers and could also promote the development of novel therapeutic strategies.Over the last decade, we focused on the recent methodologies of high-throughput and genome-wide screening for identification of relevant genes in various animal models, which included the following: (1) single nucleotide polymorphism-based genome-wide screening; (2) the transcriptome approach, such as differential display reverse transcription polymerase chain reaction (DDRT-PCR), representational difference analysis of cDNA (cDNA-RDA)/suppressive subtractive hybridization, SAGE (serial analysis of gene expression) and DNA Microarray; and (3) the proteomic approach and 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectroscopic analysis.Several genes, such as Tim44 (translocase of inner mito-chondrial membrane-44), RSOR/MIOX (renal specific oxidoreductase/myo-inositol oxygenase), UbA52, Rap1b (Ras-related GTPase), gremlin, osteopontin, hydroxysteroid dehydrogenase-3 isotype 4 and those of the Wnt signaling pathway, were identified as differentially expressed genes in kidneys of diabetic rodents.Functional analysis of these genes and the subsequent translational research in the clinical settings would be very valuable in the prevention and treatment of diabetic nephropathy.Future trends for identification of the biomarkers and therapeutic target genes should also include genome scale DNA/histonemethylation profiling, metabolomic approaches (e.g.metabolic phenotyping by 1H spectroscopy) and lectin microarray for glycan profiling along with the development of robust data-mining strategies.", + "\t\n\nThe current study takes an important first step towards this goal by identifying specific sets of genes whose expression accurately classifies patient samples with regard to diabetic neuropathy progression and by analysing their interactions within known cellular pathways.Identifying common elements in these complex networks will yield novel insights into disease pathogenesis, provide new therapeutic targets and identify potential diabetic neuropathy biomarkers.The genes identified in the current study confirm data gathered from experimental models of diabetes and provide a comprehensive picture of the expression of multiple targets in a single human tissue sample.", + "\tM A N U S C R I P T A C C E P T E D\n\nIn relation to the regulation of gene expression, the role of microRNAs (miRNAs) in diabetic retinopathy has been gaining more emphasis.miRNAs are non-coding small RNAs which modulate post-transcriptional control of gene expression through degradation or translational repression of key messenger RNAs.miRNAs can be detected in serum (free, associated with proteins or within membrane-bound particles) (Weiland et al., 2012), vitreous (Ragusa et al., 2013) and aqueous (Dunmire et al., 2013).As reviewed by Mastropasqua et al., miRNAs hold considerable interest for diabetic retinopathy since they can regulate important pathogenic responses such as angiogenesis, blood flow, neural cell dysfunction, tissue-specific inflammation and glucose metabolism (Mastropasqua et al., 2014).Although based on a small patient sample, it has been reported that three separate miRNAs (miR-21, miR-181c, and miR-1179) in serum of patients with diabetic retinopathy have potential to be used as biomarkers for early detection of disease (Li et al., 2014;Qing et al., 2014).While this is still a growing research area, miRNAs hold considerable clinical potential in the diabetic retinopathy field, both as possible drug-targets for regulation of dysfunctional cell responses and as diagnostic biomarkers.", + "\t\n\nAll these suggest towards important roles of various lncRNAs in complications associated with diabetes and, therefore, assume importance to be studied in detail.\tRoles of lncRNAs in diabetic complications\n\nApart from being involved in major metabolic tissues during diabetes as discussed above, lncRNAs are implicated in complications associated with diabetes.Diabetic retinopathy is one of the common complications in diabetic patients, which leads to impaired or loss of vision.Altered expression of lncRNAs, namely MALAT1 [82,83] and MEG3 [84], are reported to be associated with diabetic retinopathy.In STZ-induced diabetic rats, the expression of MALAT1 is elevated in the endothelial cells of the retina and knockdown of MALAT1 ameliorates retinopathy in STZ-induced rats [82].The lncRNA, MEG3, was also found to be downregulated in the retina of STZ-induced diabetic mice and its in vitro knockdown in retinal endothelial cells was found to regulate cell proliferation, viability, and migration [84].Hyperglycemia as in diabetes causes upregulation of ANRIL levels in endothelial cells [85,86], and this elevates the levels of the PRC2 subunit, EZH2 that consequently promotes the expression of VEGF, a key promoter of angiogenesis [85].Another major complication associated with diabetes is diabetic nephropathy, and this is considered a major cause of end-stage renal disease and disability in diabetic patients [87].Recent studies show that lncRNAs play important roles in the development of diabetic nephropathy and accumulation of extracellular matrix (ECM) proteins.There is higher expression of the lncRNA, PVT1, during diabetic nephropathy, and this increase leads to increased fibrosis due to accumulation of ECM proteins in renal cells [88]; downregulation of PVT1 reduces ECM accumulation [88].LncRNA PVT1 is also a host to miR-1207-5p and this miRNA is shown to regulate the expression of fibronectin1 (FN1), plasminogen activator inhibitor-1 (PAI1), and transforming growth factor beta 1 (TGF1) [89].In renal tube injury during diabetes, the lncRNA, MIAT, is under-expressed, and this negatively correlates with creatinine and BUN levels in the serum of these subjects.It has been shown to regulate cell viability of proximal convoluted renal tubules [90].In diabetic nephropathic mice, the lncRNA, MGC, is increased in renal mesangial cells.Interestingly, this lncRNA harbours a cluster of approximately 40 miRNAs, and is regulated by the ER stress marker C/EBP homologous protein (CHOP) [91].In CHOP -deficient mice, there is decreased expression of the lncRNA, MGC, and the clustered miRNAs, and these mice have shown an improvement in diabetic nephropathy [91].Diabetic nephropathy is also associated with increased levels of lincRNA, Gm4419, and this exerts its action by interacting with NF-.Knockdown of this lincRNA in renal mesangial cells lowers cellular proliferation and inhibits expression of NF- in hyperglycemic states [92].The lncRNA, TUG1, that is upregulated in diabetic nephropathy acts as sponge for miR-377 and regulates PPAR- expression which further modulates the expression of FN1, collagen type IV alpha 1 chain (COL4A1), PAI1, and TGF1 in renal mesangial cells [93].Diabetic cardiomyopathy is a critical end-stage complication associated with diabetes.Several such cardiovascular complications and myocardial dysfunction in diabetic patients lead to heart failure [94].Differential expression analysis in cardiac tissue from normal and diabetic rats shows that the lncRNA, MALAT1, is upregulated during cardiomyopathy and knockdown of this lncRNA improves left ventricular systolic function by reducing myocardial inflammation in diabetic rats [95,96].Decreased expression of the lncRNA, H19, is also reported during diabetes [68,70], and this often results in decreased expression of the exonic miRNA, miR-675 [97,98].mir-675 directly targets the voltage-dependent anion channel 1 (VDAC1) which is involved in mitochondria-mediated apoptosis in the cardiac tissue during diabetes.H19 overexpression in diabetic rats reduces oxidative stress, apoptosis, and inflammation, and improves ventricle function [98].LncRNAs NONRATT021972 and uc.48+ are reported to be associated with diabetic neuropathic pain [99,100], and inhibition of both have been shown to alleviate such neuropathic pain by activating the P2X3 receptor.Impaired wound closure is a notable complication associated with diabetes and a recent report shows decreased levels of the lncRNA, Lethe in such impaired dorsal wounds of diabetic mice.This was demonstrated to be associated with increased ROS production, possibly through regulation of NOX2 expression [101].", + "\t\n\nSkol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy.Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab.By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes.In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar.This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene.", + "\t\n\nUnderstanding how these various pathways translate to cognitive dysfunction in humans with T2DM needs further investigation.", + "\t\nInsight into the molecular mechanisms that underlie the origin and progression of diabetic nephropathy remains limited in part because conventional research tools have restricted investigators to focus on single genes or isolated pathways.Microarray technologies provide opportunities for evaluating genetic factors and environmental effects at a genomic scale during the pathogenesis of diabetic nephropathy.Despite", + "\t\n\nDR. HARRINGTON: You mentioned Liu's data from China [abstract; Liu Z-H et al J Am Soc Nephrol 14:400A, 2003], which overwhelmed me.Apparently there are 182 genes whose expression is up-or down-regulated significantly in patients with diabetes.If I asked you to pick the \"top three\" genes other than the ACE polymorphisms, which three would you choose and why?DR.ADLER: Well, actually I didn't see all of their results nor did they report all 182.But I guess my favorite ones would be some that relate to the ROS pathway because this is an all-purpose pathway of cell injury fueled by a hyperglycemic environment; some that relate to podocyte structure to explain the development of proteinuria; and TGF-b, which is a master regulator of sclerosis and fibrosis.", + "\tIncRNAs and microRNAs\n\nFigure 1 | Emerging molecular mechanisms of diabetic nephropathy.Diabetic conditions induce the expression of growth factors such as transforming growth factor 1 and angiotensin II, cytokines and AGEs to promote inflammation, fibrosis and hypertrophy, which contribute to the progression of diabetic nephropathy.These factors stimulate various signal transduction mechanisms that activate downstream transcription factors.They can also affect DNA methylation and histone modifications, which result in increased chromatin accessibility to transcription factors near pathological genes in renal cells.Coordinated interactions between transcription factors and epigenetic mechanisms can increase the expression of not only coding RNAs, but also noncoding RNAs such as microRNAs and lncRNAs.Furthermore, microRNAs and lncRNAs can also increase the expression of pathological genes via post-transcriptional mechanisms.Notably, the induction of key coding genes and proteins, lncRNAs and microRNAs can also 'lock' open chromatin states to create persistent expression of genes, which could be one mechanism of metabolic memory.Abbreviations: AGE, advanced glycation end-product; lncRNA, long noncoding RNA.\tReview criteria\n\nA search for original published articles focusing on \"diabetic nephropathy\", \"signal transduction\", \"noncoding RNAs\", \"microRNAs\", \"long noncoding RNAs\", \"genetics\" and \"epigenetics\" was performed in MEDLINE and PubMed.All articles identified were English-language, full-text papers.We also searched the reference lists of identified articles for further relevant papers.\t\n\n| microRNAs relevant to the pathogenesis of diabetic nephropathy\tKey points\n\n Diabetic conditions induce inflammation, fibrosis and hypertrophy in renal cells through various cytokines and growth factors such as transforming growth factor 1, angiotensin II and platelet-derived growth factor The engagement of cytokines and growth factors with their receptors triggers signal transduction cascades that result in the activation of transcription factors to increase expression of inflammatory and fibrotic genes These signalling mechanisms affect epigenetic states-such as DNA methylation and chromatin histone modifications-to augment the expression of profibrotic and inflammatory genes, as well as noncoding RNAs Noncoding RNAs that are induced by diabetic conditions can also promote the expression 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 as new therapeutic targets for diabetic nephropathy" + ], + [ + "\t\n\nGenetic risk scores (GRSs) that combine information from multiple genetic variants have been evaluated as a tool for the prediction of type 2 diabetes.Meigs et al. (23) found that a GRS with 18 variants was significantly associated with the risk of developing type 2 diabetes in the Framingham Heart Study (FHS) (odds ratio [OR] 1.12 per variant allele) and that persons in the highest out of three risk categories had an OR of 2.6 for developing type 2 diabetes compared with persons in the lowest risk category.However, this GRS did not improve the prediction of diabetes beyond traditional nongenetic risk factors (23), and the same was true for an updated GRS that included 65 variants (24).To put this into perspective, a prognostic marker with an OR of 3.0 that correctly identifies 80% of persons who will develop diabetes would incorrectly classify 60% of persons who will not develop diabetes (25); this degree of discrimination is not useful clinically (26).", + "\t\n\nDespite heterogeneity across populations in risk allele frequency or effect size in type 2 diabetes genes, the combined effects of multiple genetic variants using genetic scores based on the number of risk alleles appear to be similar across different ethnic groups.Typically, each risk allele increment is associated with a 10-20% increased risk of type 2 diabetes (41,42).These data suggest that the overall contribution of the identified genetic loci to type 2 diabetes is similar between Caucasians and other ethnic groups, and that these loci do not appear to explain ethnic differences in diabetes risk.In predicting future risk of diabetes, the clinical utility of these cumulative genetic risk scores appears to be limited in either high-or low-risk populations.", + "\t\n\nThe promise of genetic risk scoring for diabetes can be evaluated in the framework of three perspectives.First is the potential for robust prediction of diabetes risk.Second is the prospect of designing targeted preventive and therapeutic interventions (personalized medicine).Thirdly, increased knowledge could provide genomic clues to ethnic disparities in diabetes.Regarding robustness of prediction, results from the Framingham Offspring Study showed that clinical risk assessment (using age, sex, family history, BMI, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level) performed as well as cumulative genotype score at 18 loci in predicting incident type 2 diabetes during 28 years of follow-up of initially normoglycemic subjects (14).Also, cumulative genotype score at 34 loci did not add significantly to clinical risk factors in predicting progression from impaired glucose tolerance to type 2 diabetes among the multiethnic cohort enrolled in the Diabetes Prevention Program (15).One current limitation is the incomplete framework from which GRS is constructed.For example, the 17 SNPs studied in the present report (17) represent just about half of the .30diabe-toSNPs identified to date.Even the latter do not represent all possible risk loci, and important information on structural variants that might increase diabetes risk is often lacking.Thus, current experience renders the promise of robust genetic prediction and personalized diabetes intervention a distant hope.", + "\tDISCUSSION\n\nType 2 diabetes is a highly polygenic trait, and hundreds of loci associated with the disease have been identified, mostly via large GWAS meta-analyses conducted under additive genetic models (2,3).This prior work has produced useful results, identifying potential therapeutic targets and also enabling the creation of polygenic scores capable of quantifying one's genetic risk (34).A sizeable fraction of the heritability of type 2 diabetes, however, remains unexplained by loci identified using additive models.Recessive modeling offers a way to identify new associations, creating opportunities for discovery and improved genetic risk stratification.", + "\t\n\nTwo more recent population -based studies using a longitudinal design with prospectively investigated cohorts have examined the predictive value of a genotype score in addition to common risk factors for prediction of T2DM [194,195] .Meigs et al. [194] reported that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common clinical risk factors alone [195] .A similar conclusion was drawn in the paper by Lyssenko et al. [196] , along with an improved value of genetic factors with an increasing duration of follow -up, suggesting that assessment of genetic risk factors is clinically more meaningful the earlier in life they are measured.They also showed that -cell function adjusted for insulin resistance (using the disposition index) was the strongest predictor of future diabetes, although subjects in the prediabetic stage presented with many features of insulin resistance.It is also noteworthy that many of the variants that were genotyped appear to infl uence -cell function.The addition of DNA data to the clinical model improved not only the discriminatory power, but also the reclassifi cation of the subjects into different risk strategies.Identifying subgroups of the population at substantially different risk of disease is important to target these subgroups of individuals with more effective preventative measures.As more genetic variants are now identifi ed, tests with better predictive performance should become available with a valuable addition to clinical practice.", + "\t\n\nRecent large-scale genome-wide association studies (GWAS) in diverse populations have identified hundreds of genetic loci associated with T2D [7][8][9].Polygenic risk scores (PRS), which aggregate the genetic risk of individual alleles across the genome, are thus promising to predict future T2D occurrence and improve early diagnosis, intervention, and prevention of T2D [10][11][12][13][14][15].However, to date, T2D PRS were most widely developed and validated in individuals of European descent.Given that the predictive performance of PRS often attenuates in non-European populations [16], and communities of color are experiencing continuing increased rates of T2D [2][3][4][5], it is critically important to assess and optimize the transferability of T2D PRS in diverse populations before they can be deployed in clinical settings.\t\n\nRecent studies have demonstrated in European individuals that T2D PRS can provide predictive power for incident T2D above and beyond established risk factors such as age, body mass index (BMI), smoking, physical activity levels, and history of high glucose and hypertension and can identify high-risk individuals and stratify lifetime risk trajectories of T2D patients [42,43], suggesting potential for clinical translation.However, most existing T2D scores were developed and validated in individuals of European descent.As the interest in the clinical implementation of PRS for common diseases like T2D continues to grow, a major challenge is the uncertainty about how best to combine multi-ethnic GWAS and estimate polygenic risk in diverse populations.\t\n\nBackground: Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color.Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention 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.\t\nBackground: Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color.Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention 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. Methods:We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls).We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results:The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined.The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives.The post hoc ancestry adjustment", + "\t\n\nThe currently known risk variants have rather modest effect sizes; the presence of each risk variant or allele is only associated with increases in diabetes risk of between 5% and 40% (ORs 1.05-1.4).Therefore, these loci do not explain more than 10-15% of the estimated genetic heritability of type 2 diabetes [44,49].This estimate is in line with the observation that known risk variants explain only a small fraction of family history-associated diabetes risk [50].Combinations of up to 40 SNPs resulted in AROCs of 0.55-0.63,which is substantially lower than those achieved by age, sex and BMI alone.In some studies, the addition of genotype information to models based on established anthropometric and clinical It should be noted that the effect of genetic markers on risk prediction may be more pronounced in younger individuals, in leaner persons and in studies with long follow-up periods [53,54], but few studies on young populations, in which the assessment of future genetic risk may be most relevant, are currently available [55].The initial age of individuals is closely related to the time horizon for any model to predict type 2 diabetes.Several prospective studies have applied genetic risk scores for follow-up times of approximately 10 years.This time period corresponds to that in tools such as the Framingham Risk Score, which estimates an individual's 10-year risk for incident cardiovascular disease.It has been proposed that genetic risk scores might be more helpful in longer term prediction because, in contrast to variables used in clinical risk scores, genetic variants do not change over time [52,56].Eventually, the time horizon for risk models needs to correspond to the period before the onset of type 2 diabetes in which preventive efforts are most effective.", + "\t\n\nIn conclusion, the inclusion of common genetic variants that are associated with type 2 diabetes very slightly improved the prediction of future type 2 diabetes, as compared with the inclusion of clinical risk factors alone.Although this effect might be too small to allow for individual risk prediction, it could be useful in reducing the number of subjects who would need to be included in intervention studies aimed at the prevention of type 2 diabetes.Supported by grants from the Swedish Research Council (including Linn grant 31475113580), the Heart and Lung Foundation, the Swedish Diabetes Research Society, a Nordic Center of Excellence Grant in Disease Genetics, the Diabetes Program at the Lund University, the Finnish Diabetes Research Society, the Sigrid Juselius Foundation, the Phlsson Foundation, the Crafoord Foundation, the Folkhlsan Research Foundation, the Novo Nordisk Foundation, the European Network of Genomic and Genetic Epidemiology, the Wallenberg Foundation, and the European Foundation for the Study of Diabetes.", + "\t\n\nIdentification of individuals at increased genetic risk for T2D may enhance screening strategies and allow for targeted prevention.Previous attempts to deploy genetic data for disease prediction have shown limited utility 44,45 .We used a revised BMI-unadjusted meta-analysis, generated from all samples other than the UK Biobank samples, to develop genome-wide polygenic risk scores (PRSs) 46 , which we then applied to predict T2D status in the 18,197 cases and 423,697 controls from the UK Biobank (Europeans only; Methods) 46 .Maximal discrimination (area-under-the-curve C statistic of 66%, equivalent to that derived from BMI, age, and sex in the same sample) was obtained from a PRS of 136,795 variants (r 2 > 0.6, P < 0.076; Supplementary Fig. 10).Individuals in the top 2.5% of the PRS distribution were at 3.4-fold-increased risk (prevalence = 11.2%)compared with the median (prevalence = 3.3%), and at 9.4-fold-increased risk compared with the bottom 2.5% (prevalence = 1.2%).Low T2D prevalence in the UK Biobank reflected the age distribution of the cohort and preferential ascertainment of healthy individuals; however, similar prevalence ratios were observed in the subset of individuals > 55 years of age at recruitment (14.2% versus 1.6%).If applied to the general UK population, an equivalent performance would equate to lifetime T2D risks of ~59.7% and ~6.7% for individuals from those extremes, on the basis of current UK general-population prevalence rates for individuals > 55 years of age 47 .", + "\t\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\nDuring the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes.As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management.In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity.We also describe the challenges that will need to be overcome if this potential is to be fully realized.\t\n\n During the last decade, there have been major advances in our understanding of the genetic basis of the most common subtypes of type 1 (T1D) and type 2 diabetes (T2D), with .500robust associations identified Although individual variants typically have only a modest effect on risk, when combined into a polygenic score, they offer increasing power to capture information on individual patterns of disease predisposition with the potential to influence clinical management\tSummary and Further Discussion\n\nAfter many years of frustration at the slow progress that had been made in the translation of recent discoveries in human genetics-notably the many risk variants for common, multifactorial forms of diabetes identified through GWAS and sequencing-there is now growing optimism that the use of polygenic scores will offer substantial clinical benefit and contribute to efforts to forestall the growing morbidity and mortality associated with these conditions.Some early clinical applications have emerged, mostly related to positive identification of those who have developed, or are at the highest imminent risk of developing, TD (, -).\tPolygenic Scores in Action\n\nPredicting T2D onset The slow onset of TD, coupled to evidence that the damaging consequences often predate the clinical diagnosis by some years (), emphasizes the clinical value of early diagnosis.The capacity for drugs and lifestyle interventions to lead to substantial reductions in the progression to diabetes (, ) motivates efforts to identify those at the greatest future risk of developing TD.As discussed above, genetic predictors have the particular advantage of offering predictive information that is stable throughout life.\t\n\nIn this review, however, we focus on a different route from human genetics to translation, one that derives estimates of an individual's predisposition to diabetes and its subtypes (in the form of polygenic scores) from the patterns of individual geneticvariation at sites known to influence diabetes predisposition.\t\n\n The generation of polygenic scores based on overall T2D predisposition can identify individuals with a high future risk of diabetes who may benefit from targeted interventions", + "\t\n\nThe discriminatory capacity of genetic variants for T2D risk prediction and patient stratification has been assessed in longitudinal studies by examining whether inclusion of genetic risk scores (GRS) in predictive models increases the area under the receiver-operating-characteristic curve compared to predictive models including only clinical parameters.Early studies suggested that inclusion of GRS provided little improvement in T2D risk prediction compared to clinical risk factors and family history alone (Lyssenko et al. 2008;Meigs et al. 2008;Balkau et al. 2008;Talmud et al. 2010;de Miguel-Yanes et al. 2011).More recent studies, incorporating increasing numbers of T2D risk variants into the GRS, have also had mixed results (Hivert et al. 2011;Muhlenbruch et al. 2013;Vaxillaire et al. 2014).For example, while a recent study incorporating 43 T2D associated variants showed little improvement in T2D prediction, inclusion of the GRS in predictive models improved the receiver-operating-characteristic curve for subgroups of subjects at increased risk of T2D, including obese subjects, older participants, and those with a family history of diabetes (Muhlenbruch et al. 2013).Similarly, Hivert et al. have shown that a GRS with 34 variants was significantly associated with increased risk of progression to T2D in high-risk individuals, as well as a reduced effect of lifestyle interventions on genetic risk (Hivert et al. 2011)." + ], + [ + "\tA measure of -cell exocytosis based on electrical current. the scalability of such studies.Moreover, a genome-wide CRISPR loss-of-function screen performed in 2019 identified 373 potential regulators of insulin production in the mouse insulinoma-derived Min6 -cell line 178 .Extending genome-wide screens to human -cell models and increasing the diversity of cellular read-outs will provide orthogonal data sets for integration with existing genetic and genomic resources, in order to elucidate downstream biology.As the current protocols for hiPSC differentiation are expensive, are time-consuming and have variability in differentiation efficiency, continued advancements in differentiation protocols will enable similar approaches in these cell models.", + "\t\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\t\n\nRecent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell.MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis.We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility.We used Illumina small RNA sequencing to profile the miRNA fraction in three preparations each of primary human islets and of enriched beta-cells generated by fluorescenceactivated cell sorting.In total, 366 miRNAs were found to be expressed (i.e..100cumulative reads) in islets and 346 in betacells; of the total of 384 unique miRNAs, 328 were shared.A comparison of the islet-cell miRNA profile with those of 15 other human tissues identified 40 miRNAs predominantly expressed (i.e..50% of all reads seen across the tissues) in islets.Several highly-expressed islet miRNAs, such as miR-375, have established roles in the regulation of islet function, but others (e.g.miR-27b-3p, miR-192-5p) have not previously been described in the context of islet biology.As a first step towards exploring the role of islet-expressed miRNAs and their predicted mRNA targets in T2D pathogenesis, we looked at published T2D association signals across these sites.We found evidence that predicted mRNA targets of islet-expressed miRNAs were globally enriched for signals of T2D association (p-values ,0.01, q-values ,0.1).At six loci with genome-wide evidence for T2D association (AP3S2, KCNK16, NOTCH2, SCL30A8, VPS26A, and WFS1) predicted mRNA target sites for islet-expressed miRNAs overlapped potentially causal variants.In conclusion, we have described the miRNA profile of human islets and beta-cells and provide evidence linking islet miRNAs to T2D pathogenesis.\tDiscussion\n\nUsing next-generation sequencing, we have established the first catalog of miRNAs in human pancreatic islets and beta-cells, and explored the overlap between these miRNAs and T2D genetic susceptibility.Our catalog not only serves as a valuable resource for those interested in the roles of specific miRNAs in normal islet physiology and beta-cell function, it also provides a reference for the study of miRNA mediated abnormalities in islets from type 2 diabetic donors.The abundance of miR-375 in the miRNA profile provides valuable support for a critical role in human pancreatic beta-cells, mirroring the well-established role in rodent islet biology.miR-375 null mice are hyperglycaemic and exhibit reduced beta-cell mass [40].In a clonal rodent beta-cell line (MIN6), knockdown or over-expression of this miRNA influences glucose-stimulated insulin secretion [7].Furthermore, knockdown of miR-375 in obese ob/ ob mice results in a more profound effect on glycaemia leading to a severe diabetic phenotype in these mice [40].Our study establishes that miR-375 is also abundantly expressed in human islets and warrants further studies to define the contribution of miR-375 to the pathogenesis of T2D.", + "\t\n\nOne strategy to study these monogenic syndromes would be to derive hiPSCs from these patients, differentiate them into pancreatic progenitors and then transplant these progenitors into immunocompromised (SCID-Beige or NSG) mice for in vivo maturation (Figure 2).This methodology has been recently used to successfully model MODY2, demonstrating that beta cells derived from hiPSCs with GCK mutation are indeed less sensitive to glucose levels [7].Endoplasmic reticulum (ER) stress-related diabetes in patients with Wolfram syndrome has also been modeled using hiPSC-derived beta cells, demonstrating that WFS1 protein maintains ER function in beta cells by acting upstream of the unfolded protein response (UPR) pathways [8].phenotypes occurring in humans.Likewise, the stepwise analysis of human pancreatic development with this strategy would likely provide mechanistic insights into the ability of a single gene mutation (PDX1, PTF1A, HNF1B, GATA6 and GATA4) to promote pancreatic agenesis/ atrophy.Further, studying mutations in KCNJ11 and ABCC8 using hiPSC-derived beta cells may elucidate the mechanistic differences between permanent and transient neonatal diabetes [64].Overall, insulin production and secretion could be compared between diseased and gene-corrected pancreatic cells to understand the underlying cause of each type of monogenic diabetes (Figure 2).", + "\tPRECISE CELLULAR GENOMICS\n\nElucidating 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 profiling 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 insufficiency and failure linked to T2D genetic risk and pathophysiology.Single cell transcriptome analysis of human islet cells indicate that multiple monogenic diabetes genes are highly expressed in beta cells (e.g., PDX1, PAX4, INS, HNF1A, and GCK) [27].However, other non-beta cell types express genes mutated in monogenic diabetes (such as PAX6 and RFX6), congenital hyperinsulinemia (HADH, UCP2) and those implicated as T2D GWAS target/effector genes [28].Recent study of type 1 diabetic (T1D) human islets has provided surprising insights into alpha cell biology.In T1D islets, the alpha cell proportions remain relatively unchanged despite abnormal glucagon secretion [29].This dysregulated glucagon secretion is instead accompanied by decreased expression of important islet transcription factors including ARX, MAFB, and RFX6 and increased expression of stress response factors such as ATF4, ERN1, and HSPA5 [29] suggesting that changes in alpha cell identity may ultimately lead to their dysfunction.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 stress response genes (e.g., DDIT3, XBP1, PPP1R15A) [30].Interestingly, this transcriptomic heterogeneity was consistent in normal and T2D islets and associated with variability in alpha cell electrophysiological measures; ER stressed alpha cells had lower cellular size and Na peak current.Prior single cell transcriptomic analyses have also noted subpopulations 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 be dependent on each other, as under hypoglycemic conditions, T2D islets show reduced insulin, C-peptide, and glucagon secretion [33].Additionally, during a glycemic clamp experiment, an increase in glucagon secretion was positively correlated with beta cell function suggesting that signaling between the two islet cell types is crucial for maintaining glucose homeostasis.Studies of delta cells in Sst-Cre transgenic mouse models [34e36] reveal that timely regulation of insulin secretion is controlled by various delta-cell specific pathways.Induction of the ghrelin receptor (Ghsr) in delta cells was correlated with enhanced somatostatin release and ultimately reduced insulin and glucagon secretion [35,36].Furthermore, the peptide hormone Ucn3 was shown to be co-released with insulin from beta cells to activate type 2 corticotropin-releasing hormone receptor (Crhr2) on delta cells in an alternate pathway that promotes somatostatin release and negatively regulates insulin levels [34].Delta cells are also notably enriched for G protein-coupled receptors (e.g., GLP1R, GIPR, GPR120) which exert careful control over metabolism [37].These receptors are also common therapeutic targets of T2D, suggesting that treatment and management of the disease should not neglect delta cell (dys)function and/or survival.Efforts to characterize the epigenomes of each islet cell type are emerging and revealing new insights of cellular fate and differentiation.Two groups have performed open chromatin profiling of purified beta and alpha cell fractions [10,12] and identified between 1850 and 3999 beta and 5316-27,000 alpha-specific peaks.These cell-specific regions were enriched for transcription factor motifs implicated in cell development and were enriched for diabetes-associated SNPs.Arda and colleagues also suggest that the beta cell epigenome is plastic and capable of being derived from other endocrine and exocrine precursor cells.Discrepancies in the numbers of cell-specific peaks determined by both groups are likely due to the cell surface markers used to enrich for each.CD26/DPP4, used by Arda et al., is a strong positive selector for alpha cells, which then enables negative selection for beta and other minor cell populations.However, this method of enrichment for beta cells will not remove contaminating delta and PP/gamma cells.Continued development of new tools and markers for islet cell enrichment, such as NTPDase3 [38] should continue to help us to understand changes elicited by genetic and environmental factors in each distinct cell type.Iterative proteomic screens in human islets are also proving useful for identifying putative cell-specific surface markers for isolation [39], wherein beta and delta cell populations were obtained by co-enrichment for CD9 and CD56.Challenges currently remain to exclusively enrich for the minor islet cell types (delta, gamma/PP), thus strategies that negatively select for these cells may be needed.Study of the rarer gamma/PP cells, which constitute roughly <1e5% of the total islet volume, remain limited due to the lack of known cell-surface markers for enrichment and purification (Figure 2).Whole islet analyses are unable to capture cell type-specific changes and therefore preclude analysis of their potential roles in T2D genetics and pathophysiology.Given the clear and extensive genotype effects on cis-RE usage [13,15] and gene expression [11,16,17] in islets, more extensive analysis of sorted cell types from multiple individuals is warranted to define a representative set of islet cell-specific REs and distinguish condition-specific from genotype-driven effects on their use and activity.\t\n\nunderstand each cell type's genomic architecture and better characterize their roles in islet resilience and failure.Experimental manipulation of the regulatory elements and/or the target genes identified by (epi)genomic approaches described above and modeling the putative pathways 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 \"the\" mouse (C57BL/6) to multiple mouse models for insights into the effects of naturally occurring genetic variation on islet function and physiology [61] and for manipulation of key genomic elements should also help characterize the dynamic range of islet behavior and response.T2D is a heterogeneous, complex, and progressive disorder, as multiple subtypes have been identified and associated with different genetic risk and clinical outcome profiles.Future islet genomics studies that focus on identifying the distinct subgroups of individuals with distinct genes/pathways that are disrupted and/or contributing to islet (dys)function at basal and/or responsive states are needed.Furthermore, priority should be given to profiling more islets from pre-diabetic and T2D individuals to characterize the transition between basal to stressed to T2D state and determine if there are intermediate signatures for islet failure and T2D onset.Together, this multi-pronged approach toward studying T2D genetics and islet pathophysiology will help identify additional targets and opportunities for intervention that can be exploited for more precise and effective preventative, treatment, and management options for T2D.\t\n\nFigure2: Moving towards a more precise understanding of islet cellular genomics and responses.Proper elucidation of islet (dys)function and its association with T2D pathogenesis is confounded by individual genetic variation as well as islet cellular heterogeneity.To obtain a better understanding of both, future studies must prioritize strategies to obtain purified islet cell type populations (e.g., beta, alpha, delta, gamma/PP) via sorting with specific cell surface markers.Characterization of each cell type-specific genomic profile at baseline, stimulated, and diseased conditions will provide clearer understanding of key cellular and molecular processes that are altered and important in T2D development.Additionally, by sampling islets from multiple individuals and leveraging genotypes, it will be possible to identify cis-regulatory elements and genes that are influenced by genetics rather than disease state.SNP single nucleotide polymorphism; QTL quantitative trait locus; ER endoplasmic reticulum.\t\n\nFigure3: Challenges with identifying gene expression alterations in type 2 diabetes.Gene expression measurements from RNA-seq data typically represent only a snapshot of tissues' or cell types' transcriptome at a given point in time.In recent comparative analyses of islet intact and single cell transcriptomes from T2D and ND individuals, relatively few genes are significantly altered despite the clear phenotypic differences between them.This may suggest that the mechanisms that precede islet failure and T2D pathogenesis are post-transcriptional and cannot be detected in conventional RNA-seq analyses.However, it is also possible that the putative paths of these genes' alterations over the course of islet physiological decline and T2D development are simply being missed.Genes that are important for islet function and resilience (e.g., Gene A) and those whose expression directly induces or is the consequence of islet failure (e.g., Gene C) may be detected in a comparative analysis between islets at healthy and decompensated states.However, response genes that are temporarily induced by islet stress (e.g., Gene B) would not be detected in this comparison.", + "\tModels of beta cell function\n\nThe beta cell plays a central role in the development of both type 1 and type 2 diabetes as well as playing a key role in less common classifications of diabetes such as maturity onset diabetes of the young (MODY), gestational diabetes, neonatal diabetes and other beta cell syndromes such as hyperinsulinism.Therefore, models of beta cell function are highly relevant in understanding pathways that can lead to the inability of beta cells to secrete appropriate amounts of insulin.Such models are often genetically manipulated, such as mutations of Kir6.2 to study KATP channel function (Girard et al., 2009) or mutations in glucose kinase to understand the function of the glucose sensor in beta cells (Fenner et al., 2011).A role for serotonin in the expansion of islets in pregnancy has recently been elucidated by studying the islets of mice lacking the serotonin receptor Htr2b (Kim et al., 2010).Studies such as these can increase our knowledge of beta cell function and its role in a variety of conditions.However, it should be pointed out that the same mutation in humans can lead to different symptoms in mice as recently shown by Hugill et al., where a mutation in Kcnj11 (encoding a subunit of the KATP channel) caused hypersecretion of insulin and hypoglycaemia in their patient, but glucose intolerance and reduced insulin secretion in mice (Hugill et al., 2010).However, this may prove useful in understanding the transition from hyperinsulinism of infancy (HI) to diabetes in some patients (Hugill et al., 2010).", + "\t\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.\t\n\nHuman genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D).However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS).We characterised patterns of chromatin accessibility (ATAC-seq, n = 17) and DNA methylation (whole-genome bisulphite sequencing, n = 10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks.We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant.At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants.The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.", + "\t\n\nA number of mechanisms could contribute to the reduced insulin secretion in vivo that has been associated with several T2D susceptibility variants.Dissection of the underlying cellular pathology requires 1) access to relevant human tissues from nonrisk and risk genotype carriers, which facilitates the correct translation of association signals compared with studying genetically modified animals, and 2) characterization of the effect of genotype on detailed cellular phenotypes.There are fundamental electrophysiological and secretory differences between human and rodent b-cells, making the study of human islets essential to investigate the influence of T2D susceptibility variants on b-cell function.The biophysical and ultrastructural examination of human b-cells in the current study identified four T2D variants that were associated with reduced exocytosis and enabled characterization of the mechanisms for the exocytotic impairment.The results shed new light on the pathophysiology linked with these risk variants, near TCF7L2, ADRA2A, KCNJ11, and KCNQ1, and demonstrate that defective b-cell exocytosis can be an important pathogenic mechanism in genetic subgroups of T2D.The data suggest that there may be considerable heterogeneity in the cellular pathways that lead to reduced insulin secretion, which may explain why the reduction of exocytosis is evident only in genetic subgroups and not in the entire T2D cohort.Stratification based on genetic variants may therefore be useful to better resolve the disease mechanisms.Similar approaches may therefore be valuable to study the T2D susceptibility variants that were not associated with defective b-cell exocytosis in the current study (Table 1) and may instead impair systemic insulin release through effects on b-cell mass and/or glucose sensing or indirectly via incretins and innervation.", + "\t\n\nNevertheless, for diseases such as diabetes and obesity, limited access to the tissues most obviously implicated in disease pathogenesis-the pancreatic b cell and hypothalamus, respectively-represents a serious obstacle to such studies.Advances in stem cell science offer the exciting prospect of overcoming this limitation through re-differentiation of patient-derived induced pluripotent stem (iPS) cells to generate authentic cellular models of key tissues.In parallel, ongoing large-scale sequencing studies are likely to reveal novel low frequency and rare risk alleles in coding sequence, some with larger effects than those encountered by existing GWAS.The expectation is that these will be inherently more amenable to experimental follow-up, accelerating the pace of functional discovery and delivering biological insights that will underpin the development of novel diagnostic and therapeutic options.", + "\t\n\nIt is worth mentioning that in [132], a meta-analysis study was conducted, where a collection of gene expression datasets of pancreatic beta-cells, conditioned in an environment resembling T1D induced apoptosis, such as exposure to proinflammatory cytokines, in order to identify relevant and differentially expressed genes.The specific genes were then characterized according to their function and prior literature-based information to build temporal regulatory networks.Moreover, biological experiments were carried out revealing that inhibition of two of the most relevant genes (RIPK2 and ELF3), previously unknown in T1D literature, have a certain impact on apoptosis.", + "\t\n\nNotably, single-cell transcriptome profiling has been utilized in the past few years to discern cellular heterogeneity within the islets of Langerhans (Fischer et al. 2019;Tritschler et al. 2019Tritschler et al. , 2017)), particularly for beta cells (Baron et al. 2016;Lawlor et al. 2017a;Segerstolpe et al. 2016;Teo et al. 2018;Xin et al. 2016).Segerstolpe et al. ( 2016) investigated cell-type specific gene expression in the pancreas of healthy and type 2 diabetic individuals and uncovered major gene expression differences (transcriptional signatures) between exocrine and endocrine cell types, including the less abundant cell types such as human delta, gamma and epsilon cells.Previously, these cells had been difficult to observe due to bulk characterization methods (Lawlor et al. 2017a), however, single-cell RNA sequencing has shed light on the novel roles for each rare cell type based on their activated signalling pathways and receptor proteins (Lawlor et al. 2017a;Segerstolpe et al. 2016).For example, insight into the transcriptome of the minority cell type, epsilon cells and its ghrelin-producing capability was provided (Segerstolpe et al. 2016), as well as the expression of the rare delta and gamma cell types that are prompted by hormonal cues from leptin, ghrelin and dopamine signalling pathways to facilitate metabolic signalling in the pancreas (Lawlor et al. 2017a).Further single-cell RNA investigations by Xin et al. (2016) showed a total of 245 genes to be affected by type 2 diabetes when compared to non-diabetic single-cell transcriptomes.Among the common transcript expression profiles found between the human islet cells, only 20 genes (for example, RBP4, DLK1, ADCYAP1, RGS16, SOX4, BMP5, TIMP2, TSPAN1, MAFB and TFF3) were specific to a certain cell type (Xin et al. 2016).Lastly, a few recent reviews have tracked the progress of genes linked to specific endocrine cell types in these studies (see Chiou et al. 2019;Tritschler et al. 2017), with some going as far as to re-analyse the single-cell transcriptome datasets using a machine learning approach (Ma and Zheng 2018).The in-depth analyses reported on oxidative stress being the perpetrator to enhance beta-cell dysfunction as a final result, together with the potential activation of pathways linked to beta-cell apoptosis that may be the resulting cause of an insulin gene expression deficit in type 2 diabetes (Ma and Zheng 2018).", + "\t\nThe inheritance of variants that lead to coding changes in, or the mis-expression of, genes critical to pancreatic beta cell function can lead to alterations in insulin secretion and increase the risk of both type 1 and type 2 diabetes.Recently developed clustered regularly interspaced short palindromic repeats (CRISPR/Cas9) gene editing tools provide a powerful means of understanding the impact of identified variants on cell function, growth, and survival and might ultimately provide a means, most likely after the transplantation of genetically \"corrected\" cells, of treating the disease.Here, we review some of the disease-associated genes and variants whose roles have been probed up to now.Next, we survey recent exciting developments in CRISPR/Cas9 technology and their possible exploitation for b cell functional genomics.Finally, we will provide a perspective as to how CRISPR/Cas9 technology may find clinical application in patients with diabetes.", + "\t\n\nOur primary intent was to employ an integrative genomics approach to identify mitogenic mechanisms with potential application for human beta cell expansion (Supplementary Fig. 1).This approach entails integrating whole-exome and RNAsequencing data into network analysis to computationally model insulinoma molecular events relative to normal adult and juvenile human beta cells.We reasoned that although some molecular events in insulinoma are likely relevant to the mechanisms of tumor formation, some may serve to uncover the genetic mechanisms that enforce beta cell quiescence, and are bypassed in such benign tumors.We further validated combinations of lead candidate genes derived from this approach as beta cell mitogenic mediators.Notably, we focused on insulinomas from subjects not known to be members of multiple endocrine neoplasia type 1 (MEN1) kindreds, as the MEN1 gene has been previously reported as one of the most frequently mutated genes in hereditary pancreatic neuroendocrine tumors (PNETs), although MEN1 mutations are uncommon in sporadic insulinomas [5][6][7] .Despite attempting to exclude MEN1 subjects, we nevertheless find widespread abnormalities in genes functionally related to MEN1, revealing a previously unsuspected unifying mechanism underlying insulinoma.", + "\t\n\nIn summary, we established an isogenic hESC platform to systematically evaluate the role of disease-associated loci in the survival and function of human pancreatic beta-like cells in vitro and in vivo.The platform can be used to study other disease-associated loci/variants with respect to beta-like cell function.It is worth noting that the glucose-responding cells derived using the current reported protocols are not equivalent to primary human beta cells.Ca 2+ flux assays suggested that approximately 30%-40% of the insulin-GFP + cells show increased cytosolic Ca 2+ concentrations in response to glucose stimulation (Figure S7Q), whereas robust glucose-induced signaling was observed in more than 70% of human beta cells based on the previous report (Rezania et al., 2014).The restricted functionality of pancreatic beta-like cells derived using current protocols might limit their application for evaluating subtle contributions of genes to glucose metabolism and Ca 2+ signaling.Thus, additional work is needed to further improve the protocol to derive mature pancreatic beta-like cells.In addition, the platform established here can also be applied to study the role of disease-associated loci/variants in other diabetes-related cell types, such as hepatocytes, adipocytes, muscles, and/or intestinal neuroendocrine cells.Finally, the system may be used as a highthroughput/content chemical screening platform to identify candidate drugs correcting allele-specific defects for precision therapy of metabolic diseases.\t\n\nWe built on recent work deriving glucose-responsive pancreatic beta-like cells from hESCs/iPSCs (Pagliuca et al., 2014;Rezania et al., 2014) and used isogenic hESC-derived glucose-responding cells to systematically examine the role of several GWAS-identified genes in pancreatic beta cell function and survival.Whereas the mutations do not affect the generation of insulin + cells, they impaired insulin secretion both in vitro and in vivo, coinciding with defective glucose homeostasis.CDKAL1 / insulin + cells also displayed hypersensitivity to glucolipotoxicity.A high-content chemical screen identified a candidate drug that rescued CDKAL1 / -specific defects by inhibiting the FOS/JUN pathway.These studies represent a proof of principle for the use of isogenic hESC-derived cells to define the precise role of genes associated with disease though GWASs in human pancreatic beta cells, as well as the leadcompound identification for pharmacological intervention of T2DM." + ], + [ + "\t\n\nAlthough these proof-of-concept studies provide exciting insights into possible epigenetic mechanisms that may underpin the developmental origins of obesity and metabolic disorders later in life, one has to bear in mind their limitations.The early studies in general investigated only a small sample, lacked independent replication, and the methylation changes detected through the hypothesis-free genome-wide approach often do not reach biological levels of significance.Additional considerations include the use of tissues that are not embryonic in origin (e.g.placental tissue), tissues that contain a mixture of different cell types (e.g.umbilical cord or cord blood) as well as tissue of mixed maternal or fetal origin (placenta again).Therefore, epigenetic changes in the tissues studied thus far may not represent the full spectrum, or the most relevant epigenetic changes associated with maternal hyperglycaemia and its metabolic consequences, given the difficulty of investigating relevant metabolic tissues such as the pancreatic islet, muscle, liver, adipose tissue and brain.It is expected that some of the changes present in accessible tissue such as cord blood may also be present in other tissues, though the relationship between epigenetic markers in different tissues remains to be clarified because epigenetic marks are likely to be tissue-and context-specific.Recent studies suggest there are some consistent changes in methylation that are observed in blood and other tissues such as brain, signifying that peripheral blood may be useful for identifying functionally relevant epigenetic pathways in disease-relevant tissues (Davies et al., 2012).Another important issue is the need for prospective studies to eliminate the effect of reverse causality.This has been more of a problem in epigenetic studies in other disciplines, but less so in the field of developmental origins of health and disease, where there are large numbers of well-characterized longitudinal birth cohorts with longterm follow-up and a variety of biological specimens collected.We recently conducted a genome-wide analysis of GDM methylation changes by comparing offspring of mothers with GDM or controls from our longitudinal follow-up study (Tam et al., 2008(Tam et al., , 2010)).We found several consistent differentially methylated regions between GDM-offspring and non-exposed offspring at 8 and 15 years, suggesting that, at least for some of these markers, once the epigenetic changes are set they may persist through adolescence and beyond (Luan et al., 2014).\t\n\nIn addition to changes following exposure to intra-uterine 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 (PPARg) coactivator-1a gene (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 es et al., 2009).Epigenetic changes have also been suggested to be responsible for the \"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 monocytes of diabetic patients (Miao et al., 2007).In a series of landmark experiments, it was shown that endothelial cells exposed to short-term hyperglycaemia had persistently increased expression of the NF-kB active subunit p65, and was associated with increased promoter H3K4me1 and occupancy by the histone monomethyltransferase SET7/9.In addition, transient hyperglycaemia was also associated with sustained reduction of H3K9 methylation on the NF-kB p65 promoter, as well as recruitment of lysine-specific demethylase (LSD1) (El-Osta et al., 2008;Brasacchio et al., 2009).LSD1 has also been found to regulate H3K4 methylation in vascular smooth muscle cells in hyperglycaemic conditions, and may mediate the vascular inflammation (Reddy et al., 2008).Other epigenetic mechanisms including microRNAs and long noncoding RNAs have also been implicated in the pathogenesis of diabetic complications (Kato et al., 2014).", + "\tEpigenetic Mechanisms in Diabetic Complications 22\n\nsupportive animal studies demonstrated that mice exposed to short-term hyperglycemia followed by glucose normalization displayed sustained increases in promoter H3K4me1 and p65 expression in aortic endothelial cells (35).It is likely that similar epigenetic changes also occur in cells such as retinal pericytes and endothelial cells, or renal mesangial cells, tubules and podoctyes that are involved in common diabetic complications, retinopathy and nephropathy.\t\n\nOverall, these results indicate that prior exposure to hyperglycemia and even periods of transient high glucose or metabolic control can lead to epigenetic changes in target cells altering chromatin structure and resulting in long lasting repercussions for gene expression levels associated with the pathology of diabetic micro-and macro-vascular complications (Figure 2).", + "\tSummary\n\nIncreasing evidence shows that, besides the well-described biochemical mechanisms, epigenetic mechanisms might also participate by fine-tuning gene expression to modulate the aetiology of diabetic complications.Persistence of epigenetic modifications triggered by diabetic stimuli could be one of the key mechanisms underlying metabolic memory.However, the involvement of many epigenetic factors and mechanisms involved in the regulation of the modifications by upstream signal transduction pathways remains unknown.However, this is a rapidly expanding and dynamic field and it is likely that other epigenetic factors related to diabetic complications will soon be uncovered.Epigenomics may also aid in determining the functional roles of complications-associated genetic variants.It would be worthwhile to assess whether lifestyle modifications such as exercise and healthy diets can reduce diabetic complications by altering epigenetic marks.A recent study showed the beneficial effects of exercise on epigenetic marks related to diabetes [106].Because epigenetic changes are potentially reversible in nature, combination therapies with epigenetic drugs (epidrugs) [38] and antagomirs (miRNA inhibitors) [8] could be considered to complement the current treatments for complications.However, there are also key challenges.Since epigenetic patterns are cell specific, data from heterogeneous tissue samples and biopsies could be difficult to interpret.Furthermore, apart from hyperglycaemia, other factors associated with diabetes, including insulin resistance, obesity, dyslipidaemia, environment, lifestyles and genetics, can work independently or co-operatively to also promote epigenetic changes in various affected target tissues.\tEpigenetics and the epigenome: rationale for study in diabetic complications\n\nEpigenetic control of gene regulation plays an important role in development, cell identity, stable inheritance of gene expression patterns in differentiated cells, genomic imprinting, X chromosome inactivation, stem cell plasticity, differential disease susceptibility between monozygotic twins, and cellular responses to environmental signals [34,35].", + "\t\nIn addition to genetic predisposition, environmental and lifestyle factors contribute to the pathogenesis of type 2 diabetes (T2D).Epigenetic changes may provide the link for translating environmental exposures into pathological mechanisms.In this study, we performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors.We uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets.These methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose.For a subgroup of the differentially methylated genes, concordant transcriptional changes were present.Functional annotation of the aberrantly methylated genes and RNAi experiments highlighted pathways implicated in b-cell survival and function; some are implicated in cellular dysfunction while others facilitate adaptation to stressors.Together, our findings offer new insights into the intricate mechanisms of T2D pathogenesis, underscore the important involvement of epigenetic dysregulation in diabetic islets and may advance our understanding of T2D aetiology.\t\n\nIn addition to genetic predisposition, environmental and lifestyle factors contribute to the pathogenesis of type 2 diabetes (T2D).Epigenetic changes may provide the link for translating environmental exposures into pathological mechanisms.In this study, we performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors.We uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets.These methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose.For a subgroup of the differentially methylated genes, concordant transcriptional changes were present.Functional annotation of the aberrantly methylated genes and RNAi experiments highlighted pathways implicated in b-cell survival and function; some are implicated in cellular dysfunction while others facilitate adaptation to stressors.Together, our findings offer new insights into the intricate mechanisms of T2D pathogenesis, underscore the important involvement of epigenetic dysregulation in diabetic islets and may advance our understanding of T2D aetiology.\t\n\nThe goal of the present work was to clarify the hitherto poorly understood connection between DNA methylation and T2D pathogenesis and to determine whether identified epigenetic changes translate into functional effects that impinge on pancreatic b-cell function.For this, we have explored DNA methylation landscapes in islets isolated from T2D patients and non-diabetic individuals.\t\n\nIn conclusion, we report the first comprehensive and detailed analysis of epigenetic changes in T2D, specifically an altered DNA methylation profile in the pancreatic islets of T2D patients with a major preponderance of hypomethylation in sequences outside CGIs.These aberrant methylation events affect over 250 genes, a subset of which is also differentially expressed.The dysregulation of these genes in T2D may notably be linked to b-cell functionality, cell death and adaptation to metabolic stress.Examination of two genes identified by methylation profiling, NIBAN and CHAC1, revealed their biological functions in distinct processes of the ER stress response.Furthermore, our data highlight genes belonging to biological processes whose involvement in T2D\t\n\nAn important question with regard to epigenetic changes is: are the observed DNA methylation changes reflected in gene activity?By comparing the obtained DNA methylation profiles with microarray gene expression data, we were able to determine that a high proportion of genes in whose promoter T2D-related differential DNA methylation occurs are actively transcribed in pancreatic islets.A comparison with expression data of islet cell types (Dorrell et al, 2011) showed that most of the differentially methylated genes are expressed in b-cells.This allowed us to conclude that T2Drelated aberrant DNA methylation partially happens in the promoters of active genes.One has to keep in mind though that the expression studies in islets as well as in the b-cells analysed non-diabetic material.We observed mostly DNA hypomethylation in diabetic islets, not infrequently accompanied by elevated gene expression.Therefore, it can be assumed that the T2D-related hypomethylation leads, in part, to the induction of formerly silent genes.", + "\t\n\nEmerging evidence suggests an epigenomic link to T2D development.Reversible epigenetic changes such as histone modifications and DNA methylation may occur during intrauterine development and are believed to have long-term effects on offspring health and survival, including manifestation of disease phenotypes such as obesity or diabetes later in life [59,60].Environmental and nutritional stimuli influence future science group Genetics, genomics & personalized medicine in Type 2 diabetes: a perspective on the Arab region Review [61].Epigenetic regulation of genes may be responsible for the observed difference in T2D risk and drug response between individuals [62,63].Epigenomics may not only shed light on the environmental (including diet and lifestyle) effect on T2D susceptibility in individuals but epigenetic markers may also help identify those at risk well before disease manifestation.Gene-expression analysis or transcriptomics is used for studying the expression profile of genes.A comparative analysis of expression states of genes between healthy and diseased cells can identify those implicated in disease pathology.The changes in expression of disease susceptibility genes can be monitored during different stages of a disease and help in disease prognosis.Similarly, a comparative expression profile for treated and untreated samples can help identify changes in gene-expression upon treatment with a particular drug.This makes geneexpression analysis an important tool for elucidating the role of genes in different biological states, for identifying potential targets for drug intervention and for biomarker selection to be used in disease diagnosis.In diabetes, gene-expression profiling has been used for establishing differential expression of inflammatory genes [64], for studying the effects of insulin treatment in skeletal muscle [65] and more recently for correlating insulin resistance and an altered lipid profile in peripheral blood [66].", + "\t\n\nWhether epigenetic changes pre-exist or are a consequence of T1D can only be established by long-term longitudinal studies of DNA methylation in subjects at risk for the disease.Since it will a priori remain almost impossible to investigate cells and mTEC in T1D patients, the question of tissue-specific methylation changes should have to be solved in animal models of T1D, like the NOD mouse.It is possible that the observed pattern of CpG methylation at the insulin locus may vary in other T1D and control populations as a reflect of gene-environment interactions proper to these populations.Until larger studies can be performed in such populations, the observed variations in DNA methylation should be considered restricted to the European people studied here.", + "\tISLET RESPONSES; MOVING BEYOND STEADY STATE MEASUREMENTS\n\nTo date, the overwhelming majority of studies including and assessing genetic variation have profiled the steady state patterns of epigenetic modifications 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 [13,16,40e44].Surprisingly, these studies, especially transcriptome analyses, have identified only modest alterations despite clear phenotypic differences in HbA1c and other metabolic traits in T2D vs. ND donors.This suggests that alterations in transcriptional regulation may not contribute to T2D pathogenesis, or that these (epi)genomic comparative studies are not effectively capturing the alterations associated with islet (patho) physiologic decline or T2D onset.Genomic assays such as RNA-seq provide only a snapshot of tissues' or cell types' transcriptomes at a given point in time.Genes that are important for islet function and resilience (e.g., Gene A) and genes whose expression induces islet failure (e.g., Gene C) would be detected in a comparative analysis between islets at healthy and T2D states (Figure 3).In contrast, genes that are temporarily induced by the initiation of islet stress or in the compensation or pre-diabetic stages (e.g., Gene B) before decline towards disease state would be missed.Furthermore, T2D is a complex disease with dynamic ranges of severity and secondary health complications across individuals.Thus, comparing single snapshots of gene expression in T2D individuals at different stages of islet health and disease progression may simply lead to obfuscation.Longitudinal studies of in vivo epigenetic and gene expression changes in islets of severe, early onset (db/db) or polygenic, late-onset (Tallyho, NZO) [45e47] diabetic mouse models may be the only practical solution to identify the temporal nature of these changes and identify the molecular features of islet dysfunction, compensation, and failure in T2D pathogenesis.Indeed, longitudinal analyses of aging islets in mice identified DNA methylation changes in key genomic regions associated with beta cell proliferation and metabolism [48].These findings suggest that changes in the islet (epi)genome and transcriptome may also be dynamic during the course of T2D development and progression.Alternatively, in vitro, it may be possible to subject human islets to diabetic-like conditions through the use of inflammatory cytokines and/ or oxidative and ER stress.Already, studies from a few groups have demonstrated clear differences in islet gene expression, including the modulation of putative T2D target genes, during stimulatory or stress responses, and certain epigenetic and gene expression features in islets are only revealed upon these in vitro or in vivo exposures, such as glucose-stimulated insulin secretion, palmitate, inflammatory cytokines or other response defects [49e53].Examining the transcriptomic and (epi)genomic changes of human islets under these various stressors over time may provide greater knowledge of the epigenetic and gene expression changes preceding islet stress, failure, and ultimately diabetes onset.", + "\t\n\nInteractions between environmental factors and genetic predisposition leading to epigenetic changes could provide a powerful risk association to diabetic complications, especially in relation to the metabolic memory phenomenon (Reddy et al., 2015).", + "\t\n\nRecent studies using vascular and inflammatory cells treated in vitro with high glucose (HG), or target cells and tissues derived from models of diabetes complications, provide strong evidence that alterations in epigenetic histone PTMs play key roles in diabetes-induced inflammation and vascular complications, and potentially in the metabolic memory phenomenon (17)(18)(19)(20)(21)(22)(23)(24)(25)(33)(34)(35)(36)(37)(38)(39)(40)(41).However, studies have not yet been performed directly in humans with diabetes and metabolic memory.To examine whether epigenetic mechanisms are related to glycemic history, the progression of complications and metabolic memory in human diabetes, we explored variations in the profiles of key histone PTMs at promoter regions in peripheral blood lymphocytes and monocytes obtained from selected EDIC cohort subjects.\t\n\nIn conclusion, we conducted comprehensive epigenomic profiling using cells from two selected subsets of DCCT/EDIC participants who experienced different rates of complications following a period with different levels of hyperglycemia to explore an epigenetic mechanism for metabolic memory in individuals with type 1 diabetes.Our results suggest that this metabolic memory phenomenon can in part be explained by increased epigenetic differences at key complication-related genes among individuals with higher HbA 1c levels that may contribute to further progression of complications during EDIC.", + "\tFuture research prospects\n\nalthough some of the fundamental mechanisms involved in generegulating epigenetic changes associ ated with hyperglycemia have now been identified, a number of funda mental challenges in this area remain to be addressed, such that the contribution of epigenetic changes to the etiology of diabetes mellitus can be under stood.From a clinical perspective, the continued follow up of participants in the DCCt-eDiC and uKPDs studies will enable investigators to determine the clinical effect of exposure to hyperglycemia, and whether tight glycemic control will appreciably lower the incidence of diabetic complications, further supporting the concept of metabolic memory.From a basic research perspective, the transfer of knowledge of epigenetic changes that drive gene expression will be critical to improved understand ing of the epigenome using highthroughput sequencing technologies.the development of more sensitive and sophisticated methodologies than those currently avail able and the advent of affordable, largescale, genome wide profiling and new bioinformatics tools will provide the means to determine the extent of specific epigenetic events that drive gene responses in patients with dia betes mellitus.Defining the molecular events that confer metabolic memory and its association with diabetic cell reviewS dysfunction will provide critical insights into the inter pretation of persistent epigenetic geneactivating events associated with Dna methylation and other histone modifications, as well as mirna expression patterns.\tCharting the epigenetic landscape\n\nthe studies discussed in this review have described important discoveries that mark the emergence of the epi genome and the tremendous influence of epi genetics on the etiology of diabetes mellitus.the identification of gene activating epigenetic changes mediated by hyper glycemia is of particular importance.the immunopurifica tion of chromatin and its associated protein determinants has profoundly influenced the investigation of chromatin structure and function. 79this investigation has resulted in a fundamental shift in our understanding of transcrip tional regulation and, specifically, the importance of struc tural and chemical variations of the chroma tinized Dna template in primary cellular models of hyper glycemia.the application of chromatin immuno purification can chart and distinguish gene sequences associated with histone modifications, transcription al coregulators and chromatin accessibility. 80,81he distinct patterns of gene expression associated with oxidative stress and the geneactivating changes in models of hyperglycemic variability have highlighted the contribution of cellular memory to the etiology of diabetes mellitus and inflammation of the vasculature.30 although the risk of persistent complications after return to normo glycemia is beginning to be appreciated, the molecular determinants that drive critical nuclear processes associ ated with metabolic memory are still not completely understood.82 emerging evidence suggests the patho genesis of diabetic complications could be influenced by gene-environment inter actions.although the nature of the epigenetic changes in models of glycemic vari ability have not been precisely mapped, regionalization of histone modifications is probably involved.24 to what extent does glucose regulate the transcriptional control afforded by structural and chemical modification of the chromatin template?Charting the epigenetic land scape is a major challenge and will probably reveal some surprising and unanticipated results.indeed, genomewide approaches to studying epigenetic determinants will add new levels of information that will help to establish an atlas of generegulatory events me diated by hyperglycemia.a profile of hyperacetylation events associated with geneactivating epigenetic changes has been developed to enable detailed study of the effects of hyperglycemia.this study used a novel approach to immunopurify the H3 acetylation moiety coupled with massive parallel sequencing approaches. 83Genomewide studies indi cate that human aortic cells are highly enriched with H3 acetylation in response to hyperglycemia and that such acetylation demonstrates specific regionalization in pro moter regions that often extend into transcribed areas of the gene sequence.Critical primary experiments to determine the hyperacetylation signature conferred by hyperglycemia will show the importance of genomewide epigenomic changes, such as those on human chromo somes 4q28.3,6q25.1, 12q23.3 and 22q12.3(Figure 5). intead of focusing on epigenetic changes at single loci, 28 which are often difficult to determine empirically, this discoverybased screening approach is unambiguous and indicates that histone acetylation has a widespread regu latory role that is correlated with geneactivating events.surprisingly, these studies distinguished major changes in genomewide hyperacetylation profiles as a result of physiologically relevant hyperglycemia that regulate specific histone code signatures precisely annotated to human gene expression patterns.with the identification of epigenetic changes, which will undoubtedly include other posttranslational modifications to histone and nonhistone proteins, we anticipate that understanding the genomic profiles will identify molecular pathways and signaling networks critical to the development of diabetes mellitus and its associated complications.", + "\t\n\nTo summarize: although there is ample evidence that epigenetic effects are likely to play a role in the pathogenesis of T2D and obesity, as well as to contribute to the observed familial aggregation, experimental studies that conclusively define the key loci responsible in disease-relevant tissues have yet to be performed (Table 1).It is also worth noting that existing studies have focused almost exclusively on DNA methylation, leaving other components of the epigenetic machinery (such as chromatin state) largely unexplored.Given limited access to diseaserelevant human tissues, progress will almost certainly depend on centralized community efforts such as the International Human Epigenome Consortium, which is generating reference epigenomes (including detailed methylome maps) for a panel state art state art of human tissues and cell types. 69This epigenetic counterpart of the Human HapMap effort will provide tissue-specific maps of methylation correlation structures, document the positions of variably methylated sites, and empower future epigenome-wide association study efforts." + ] + ], + "task_id": [ + { + "task_id": "251083E08BA7D70253243E7B75002E47" + }, + { + "task_id": "A6AC9E9314DDA1708A83D3B8F9D07EF6" + }, + { + "task_id": "DB518D0E92266848B81A1447F03D302C" + }, + { + "task_id": "8736099EC8F109703C73C7B7AE2046CA" + }, + { + "task_id": "E357FAAAD185EB55A4FB3E802EC9A57B" + } + ] +}
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