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diff --git a/gnqa/data/study1/datasets/gpt4o/dataset_domainexpert_diabetes_4.json b/gnqa/data/study1/datasets/gpt4o/dataset_domainexpert_diabetes_4.json new file mode 100644 index 0000000..fee1f31 --- /dev/null +++ b/gnqa/data/study1/datasets/gpt4o/dataset_domainexpert_diabetes_4.json @@ -0,0 +1,109 @@ +{ + "question": [ + "What recent advancements have been made in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo?", + "How do genome-wide association studies (GWAS) integrate with multi-omics data to elucidate the complex genetic architectures of diabetes?", + "What is the impact of genomic imprinting on the susceptibility and progression of diabetes?,", + "How do longitudinal genomics studies help in understanding gene-environment interactions in diabetes onset and management?", + "How have recent integrative genomics approaches, such as the use of single-cell RNA sequencing combined with epigenomic profiling, advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions?" + ], + "answer": [ + "The text does not provide specific recent advancements in leveraging CRISPR-based approaches to correct monogenic forms of diabetes in vivo. However, it mentions the potential of using CRISPR/Cas9 gene editing tools to understand the impact of identified variants on cell function, growth, and survival, which might ultimately provide a means of treating the disease, likely after the transplantation of genetically", + "Genome-wide association studies (GWAS) integrate with multi-omics data by identifying genetic variants associated with diabetes and then examining these variants in the context of other biological data. This includes protein-protein interactions, gene expression data, and epigenomic data. The integration of this data can help to identify key pathways and mechanisms involved in the disease, such as insulin secretion and signaling. Additionally, the integration can help to identify potential drug targets and provide insights into the role of environmental factors and gene-environment interactions in disease development.", + "Genomic imprinting can impact the susceptibility and progression of diabetes by permanently programming the developing offspring's response to certain environmental factors. This can increase the risk of diabetes in subsequent generations, suggesting that these changes also affect the germ cell line. The imprinting can be influenced by factors such as maternal undernutrition, infant's low birthweight, and rapid postnatal child growth. Additionally, certain specific sncRNAs changes associated with paternal diets can impair glucose tolerance in offspring, potentially leading to type II diabetes. However, the exact mechanisms of this imprinting and its effects can vary between populations and are not fully understood.", + "Longitudinal genomics studies help in understanding gene-environment interactions in diabetes by tracking changes over time and identifying how genetic variants and environmental factors interact to influence diabetes risk. They can provide insights into the function of novel diabetes loci and the pathways through which environmental exposures act, thereby improving our understanding of diabetes etiology. These studies can also help identify high-risk individuals for diet and lifestyle interventions, and potentially inform pharmacological interventions if certain genotypes are found to be more or less responsive to specific medications. Furthermore, they can contribute to the development of personalized medicine in diabetes by identifying genetic variants that influence an individual's response to antidiabetic drugs.", + "Recent integrative genomics approaches have significantly advanced our understanding of cellular heterogeneity and gene regulatory networks in pancreatic beta cells under diabetic conditions. Single-cell RNA sequencing has been used to discern cellular heterogeneity within the islets of Langerhans, particularly for beta cells. This has uncovered major gene expression differences between exocrine and endocrine cell types, including less abundant cell types. It has also shed light on the novel roles for each rare cell type based on their activated signalling pathways and receptor proteins. Epigenomic profiling, on the other hand, has provided detailed genome-wide patterns of methylation and chromatin accessibility in human islet material. This has led to a refined regulatory map which offers more granular annotation of functional impact. The combination of these techniques has helped to pinpoint likely causal variants in T2D pathogenesis and has accelerated the definition of causal mechanisms implicated in T2D pathogenesis." + ], + "contexts": [ + [ + "\t\n\nThe advancements in both differentiation protocols and genome-editing technologies make it now possible to study the effect of genetic perturbations on human -cell development.\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.\tGlucose\n\nPresently, there are several examples of patientderived hiPSC cell lines that have been used to determine the molecular mechanisms of diabetes mellitus.For example, hiPSC lines derived from patients with monogenic diabetes have been generated for several MODY genes, including GCK 79 , HNF1B 79 , HNF4A 79 , HNF1A 79,80 and CEL 79 .In one patient with NDM, an activating mutation located in the DNA-binding domain of STAT3 (Lys392Arg) was identified 81 .Of note, patient-derived STAT3 Lys392Arg hiPSC cells had premature endocrine cell differentiation owing to upregulation of NEUROG3 and INS that was rescued by correction of the mutation 82 , which is consistent with a pancreatic hypoplasia phenotype.In two other patients with NDM, heterozygous mutations in the INS gene (Cys96Arg and Cys109Tyr) were identified that were predicted to disrupt disulfide bonds in proinsulin 83 .Furthermore, hiPSC-derived -like cells derived from skin biopsies of affected individuals carrying these two mutations had increased endoplasmic reticulum stress and reduced proliferation 83 , suggesting a mechanistic cause of diabetes mellitus through decreased -cell mass.In addition, patient-specific hiPSCs carrying common heterozygous missense mutations found in the transactivation domain of the MODY gene PDX1 (Pro33Thr and Cys18Arg) had defects in the formation of pancreatic progenitors, which resulted in impaired glucose-stimulated insulin secretion 84 .", + "\tCONCLUSIONS\n\nhPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes.Studying monogenic forms of diabetes and syndromes of insulin resistance using these tools would be extremely useful given the lack of an autoimmune attack and confounding effects of insulin resistance and obesity.One caveat of this methodology at the moment is the \"low\" efficiency of deriving human beta cells in vitro [75,76], possibly due to our incomplete knowledge on human pancreatic development.Another explanation would be the lack of in vivo environmental cues emanating from proximal tissues such as the vasculature.Nonetheless, successful disease modeling of MODY2 [7] and Wolfram Syndrome [8] already suggests a high possibility of success.These technologies have the potential to elucidate the underlying pathophysiology that stem from defects in 1) beta cell development, metabolism or survival or 2) development of adipocyte.For instance in the case of MODY2, it is now clear that GCK mutation affects glucose-stimulated insulin secretion but not insulin synthesis or beta cell proliferation [7].With the latest advances in the derivation of mature and functional human pancreatic beta-like cells from hPSCs in vitro [75e77], eventually circumventing the requirement for in vivo maturation, disease modeling of diabetes is expected to progress exponentially.The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.Findings from these proposed studies could also offer clues to the pathophysiology of the \"garden variety\" of type 2 diabetes which is known to manifest defects in each of these tissues.hPSCs and genome editing tools may also provide an opportunity to better understand the relevance of gene variants identified from GWAS studies, in causing T1D, T2D, obesity and metabolic syndromes, given that they exhibit only modest effects and w85% of the variants map onto noncoding regions such as enhancers or regulatory elements [104].Investment into hPSCs and genome editing would allow a better mechanistic understanding of the pathophysiology of monogenic and complex diseases relevant for organismal homeostasis and therefore an improved approach to stratified personalized medicine.By identifying the impact of gene variants on disease predisposition, prophylactic measures in the form of lifestyle alterations or medication could be adopted early on in life to delay or even prevent the onset of diabetes and/or metabolic diseases.It is also likely that these hiPSCbased disease modeling studies would provide insights into approaches to predict the susceptibility of disease.Henceforth, the translational potential of studying human diabetes and metabolic syndrome disease mechanisms is huge, with opportunities for early prophylactic intervention that could have long-term implications for global health care and reduction of economic burden.While the derivation of hiPSCs from human tissues is relatively easier and gaining popularity compared to just a few years ago [2], it is likely that the modern technology of generating site-specific nucleases will also rapidly mature to make in vitro disease modeling a routine approach.\tEmploying hPSCs and genome editing tools to study diabetes and metabolic syndromes\n\nIn general, the strategy to carry out in vitro disease modeling of diabetes and related metabolic syndromes with hPSCs and genome editing tools would be to 1) derive hiPSCs from patients with these conditions, 2) generate \"repaired/corrected\" isogenic controls [53] and then 3) differentiate them into pancreatic cells or target cells of relevance, such as immune cells in the case of T1D or myocytes, adipocytes and hepatocytes in the case of T2D (Figure 2).If patient material is inaccessible, one could introduce (naturally occurring) mutations or gene variants into hESCs and differentiate them accordingly to study disease mechanisms.Since excellent reviews have been published recently, we will provide a brief overview to familiarize the reader with the classification of diabetes and metabolic disorders.\t\n\nBackground: Diabetes and metabolic syndromes are chronic, devastating diseases with increasing prevalence.Human pluripotent stem cells are gaining popularity in their usage for human in vitro disease modeling.With recent rapid advances in genome editing tools, these cells can now be genetically manipulated with relative ease to study how genes and gene variants contribute to diabetes and metabolic syndromes.Scope of review: We highlight the diabetes and metabolic genes and gene variants, which could potentially be studied, using two powerful technologies e human pluripotent stem cells (hPSCs) and genome editing tools e to aid the elucidation of yet elusive mechanisms underlying these complex diseases.Major conclusions: hPSCs and the advancing genome editing tools appear to be a timely and potent combination for probing molecular mechanism(s) underlying diseases such as diabetes and metabolic syndromes.The knowledge gained from these hiPSC-based disease modeling studies can potentially be translated into the clinics by guiding clinicians on the appropriate type of medication to use for each condition based on the mechanism of action of the disease.\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).", + "\t\n\nMoving beyond cancer phenotypes, indirect in vivo screens are beginning to be used in other disease models.A genome-scale knockout screen in pancreatic beta-cells transplanted into a mouse model for Type 1 Diabetes identified genetic factors preventing autoimmune clearance of transplants.Inhibition of an identified gene hit, Rnls , with pargyline [101] prevented an autoimmune reaction and confirmed that the screen was able to identify candidates of therapeutic relevance [11] .", + "\t\n\nIn addition, knock-out and transgenic mice have become powerful tools in elucidating the influence of specific genes in glucose metabolism and the pathogenesis of diabetes.This includes understanding which transcription factors are involved in pancreas development (Habener et al., 2005) and elucidation of insulin signalling pathways (Kahn, 2003;Wang and Jin, 2009).Tissue-specific knockouts have proven to be particularly useful in studying insulin signalling (Neubauer and Kulkarni, 2006) as the global insulin receptor knock-out is non-viable (Accili et al., 1996).", + "\t\n\nThe use of gene-editing tools to create targeted monogenic animal models to explore the physio logical role of specific genes has burgeoned in the past 20 years.Although this endeavour has taught us a great deal about the cellular and molecular underpinnings of energy homeostasis, it is becoming increasingly clear that metabolic characterization of transgenic animal models might be less predictive of the physiological function of the gene of interest than is often assumed.For example, genetic manipulation could impose compensatory biological changes during development that in turn take over the function of an otherwise key gene.Exemplifying the predictive limitations of germline gene knockouts, mice deficient in glucagon-like peptide 1 receptor (GLP-1R) are protected from DIO and exhibit only mild defects in glucose tolerance.Taken at face value, these data imply that GLP-1R agonism would have little to no metabolic benefits 220,221 .Yet, GLP-1R agonists belong to the currently best-in-class therapeutics for treating obesity and T2DM, exemplifying the hazards of determining gene function on the basis of germline transgenic models and also illustrating the possibility of overlooking relevant therapeutic utility 222 .", + "\t\n\nIn the present study we devised a new strategy for predicting which proteins and biological pathways would be altered in vivo under such circumstances (Figure S1 in Additional file 1).Our approach was built on the in vitro molecular rules encompassed by the site-specific context score criteria, as these criteria can significantly enrich a gene list in genuine targets when a single miRNA is studied in a cell-based system [34].Using three to nine times the number of human subjects (n = 118) as previous studies [1][2][3][4] and a more comprehensive 'genomewide' RNA profiling strategy (>47,000 mRNA sequences, and >500 miRNA sequences), we aimed to identify the global molecular nature of skeletal muscle insulin resistance in human T2D and provide new bioinformatic and protein level validation for our conclusions.", + "\t\n\nIn this study, we set out to expand upon previous studies of the islet regulome in several ways.First, we explored the human islet methylome in unprecedented depth using Whole-Genome Bisulphite Sequencing (WGBS) applied to a set of 10 human islet preparations.Second, we explored both basal and genotype-dependent variation in chromatin accessibility through ATAC-seq in 17 human islet samples.Third, we integrated these genome-wide data with existing islet regulatory annotations to generate a high-resolution, epigenome map of this key tissue.Finally, we used this detailed map to interpret GWAS signals for T2D (and the related trait of fasting glucose) and deduce the molecular mechanisms through which some of these loci operate.", + "\t\n\nTargeted Allelic Expression Profiling in Human Islets Identifies cis-Regulatory Effects for Multiple Variants Identified by Type 2 Diabetes Genome-Wide Association Studies Diabetes 2015;64:1484-1491 | DOI: 10.2337/db14-0957", + "\t\n\nIt is increasingly clear that genetic variants may act through multiple tissues, e.g.liver, neurons and other islet cells, even though the major endpoint is often an alteration in circulating insulin levels [34,44,45]; thus, a purely 'b-cell-centric' approach to the unravelling of their actions is likely to have only partial success.Important challenges therefore remain to understand the underlying physiology and the tissue interactions through which variants may act.The deployment in animal models of new tools for genome editing optogenetics may help by providing a means to rapidly and selectively control gene expression in a single cell type, thus shining new light on the action of genes identified through genome-wide association study.", + "\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\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\nIn vivo delivery of CRISPR editing tools into pancreatic b cells in people with diabetes is likely to face enormous challenges for two main reasons: 1. b cells are postmitotic, thus disfavouring HDR-mediated CRISPR editing.2. Selective targeting to these cells will be required, likely involving cell type-tropic viruses (272), raising evident concerns over off-target effects and toxicity.Hence, the most likely and feasible way of CRISPR editing has to be an ex vivo system where b cells can first be engineered by CRISPR editing and then transplanted into patients (Figure 2).\t\n\ninsulin secretion.We begin by providing examples of genes and loci associated with altered T2D risk.Finally, we review the CRISPR tools that may offer the potential to correct these variants in the human b cell.\tIn Vitro and In Vivo b Cell Models for Studying Genetic Variants\n\nIn order to understand the pathogenic role of diabetes-associated genetic variants, tractable b cell models are essential.Mouse models, either transgenic or knock-out, are valuable for examining the roles of single genes, but their use is more limited in studies of intergenic regions given more substantial inter-species (mouse versus human) differences in these regions.As sources of human b cells, there are currently three possibilities.Firstly, primary islets isolated from organ donors: This source is, however, limited in terms of the availability and quality of islets (226).Secondly, clonal human b cells.Immortalized human EndoC-bH1 cells were developed in recent years after infection of foetal islets with large T antigen and further inoculation of islets in immunocompromised mice (227).Later generation EndoC-bH2 (228) and EndoC-bH3 (229) cell lines were subsequently established with more advanced features including regulated deletion of the immortalizing gene.The limitation of these cell lines, however, is their extremely slow growth rate which hampers their use.Given this slow growth rate -and the fact that these lines poorly tolerate expansion from a single cell-it is virtually impossible to modify them by HDR via CRISPR editing.A third possibility are therefore islet-like cells differentiated from human embryonic stem cells (hESC) or patient-derived induced pluripotent stem cells (iPSC).In light of the limitations of the above cellular models, laboratories are now focusing on hESC or iPSC in studies of gene function throughout b cell development by differentiating hESC/iPSC cells into mature b cells (230,231).Such directed differentiation protocols have recently been improved (21,159).", + "\t\n\ntreatment of T2DM.T5224 is able to strikingly rescue CDKAL1mutation-mediated pancreatic beta cell dysfunction in vivo, which is a proof of concept for a T2DM drug candidate rescuing a gene-specific defect in vivo." + ], + [ + "\t\n\nInsights from integration of genetic and epigenomic data.Using genome-wide association studies (GWAS) data for related glycaemic (for example, fasting glucose, 2-hour glucose), lipid (for example, triglycerides) and anthropometric traits (for example, BMI), a 2018 study proposed distinct clusters of T2DM GWAS loci, three of which showed clear evidence for islet cell dysfunction 21 (Fig. 1).Another study used soft clustering to group T2DM genetic loci on the basis of their associations with related traits, and similarly found loci that are associated with islet cell dysfunction 22 .Although for many of these loci neither the causal variants nor the effector transcripts are known, the most probable mechanism for modulation of the risk of T2DM is through islet cell dysfunction 21 .\tKey points\n\n Genome-wide association studies (GWAS) have identified >400 signals associated with the risk of type 2 diabetes mellitus (T2DM). The pancreatic islet has been identified as a key tissue involved in mediating GWAS signals in T2DM risk. Integrating genetic, epigenomic and cellular data can unlock the biology behind GWAS signals.", + "\t\n\nGenes identified in GWASs when evaluated in the context of complementary systems level data such as that related to proteinprotein interactions and to and gene expression can provide insights into the mechanisms underlying pathogenesis of complex traits [22][23][24].Here, we have combined these approaches toward deciphering genome to phenome correlation in T2D (Figure 1).Given that T2D GWAS genes do not directly relate to disease pathophysiology, our main aim was to examine if this genome to phenome correlation gap can be abridged by considering GWAS genes in conjunction with physical and genetic interaction, and gene expression data.\t\nGenome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic b cells and insulin signaling in target tissues.However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion.Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology.We used a systems biology approach to unravel genome to phenome correlation in T2D.We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance.Genes at lower significance threshold showed enrichment of insulin secretion related pathway.Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion.The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs.The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways.Next, we generated genomewide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients.Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology.Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling.Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance.This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed b cell dysfunction to precipitate diabetes.\t\n\nGenome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic b cells and insulin signaling in target tissues.However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion.Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology.We used a systems biology approach to unravel genome to phenome correlation in T2D.We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance.Genes at lower significance threshold showed enrichment of insulin secretion related pathway.Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion.The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs.The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways.Next, we generated genomewide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients.Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology.Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling.Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance.This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed b cell dysfunction to precipitate diabetes.", + "\t\nBackground: Genome-wide association studies (GWAS) have recently identified many new genetic variants associated with the development of type 2 diabetes.Many of these variants are in introns of known genes or between known genes, suggesting they affect the expression of these genes.The regulation of gene expression is often tissue and context dependent, for example occurring in response to dietary changes, hormone levels, or many other factors.Thus, to understand how these new genetic variants associated with diabetes risk may act, it is necessary to understand the regulation of their cognate genes.Results: We identified fourteen type 2 diabetes-associated genes discovered by the first waves of GWAS for which there was little prior evidence of their potential role in diabetes (Adam30,", + "\t\n\nGenomic variations and DNA profiling of those at risk for type 2 diabetes Despite many candidate gene studies and genome-wide linkage studies, very few susceptibility loci for type 2 diabetes have been identified until the recent emergence of genomic-wide association (GWA) data and large-scale replication studies (Table 2).Meta-analysis of GWA studies provides the unique opportunity to investigate the heterogeneity or consistency of genomic associations across diverse datasets and study populations.Recently, Voight et al. (32), using large-scale association analyses combining the data from eight GWA studies, identified 12 new susceptibility loci for type 2 diabetes.", + "\t\n\nGenome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).", + "\tIntroduction\n\nGenome-wide association studies (GWAS) have identified approximately 80 loci robustly associated with predisposition to type 2 diabetes (T2D) [1][2][3] and a further 70 influencing a range of continuous glycemic traits [4][5][6][7][8][9][10] in non-diabetic subjects.There is substantial, though far from complete, overlap between these two sets of loci.Physiological studies in non-diabetic individuals indicate that most of these loci primarily influence insulin secretion rather than insulin sensitivity, highlighting a key role for the pancreatic islets of Langerhans in the mechanistic underpinnings of these association signals [11,12].These findings have motivated efforts to catalogue the epigenomic and transcriptional landscape of human islets and to apply these findings to deliver biological insights into disease pathogenesis.Recently, it has been shown, for example, that GWAS signals for T2D and fasting glucose show significant co-localization with islet enhancers [13,14].", + "\tIntroduction\n\nGenome wide association studies (GWAS) of type 2 diabetes mellitus and relevant endophenotypes have shed new light on the complex etiology of the disease and underscored the multiple molecular mechanisms involved in the pathogenic processes leading to hyperglycemia [1].Even though these studies have successfully mapped many diabetes risk genetic loci that could not be detected by linkage analysis, the risk single nucleotide polymorphisms (SNP) have small effect sizes and generally explain little of disease heritability estimates [2].The poor contribution of risk loci to diabetes inheritance suggests a prominent role of environmental factors (eg.diet, physical activity, lifestyle), gene environment interactions and epigenetic mechanisms in the pathological processes leading to the deterioration of glycemic control [3,4].", + "\t\n\nGenome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.\t\n\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.\t\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\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\nIn the past decade, genome-wide association (GWAS) and sequencing studies have identified genetic loci that help explain the inherited basis of T2D and glycemic traits.These studies are providing insights into the genetic architecture of T2D, including the number, frequency and effect sizes of risk variants in populations around the world.The polygenic nature of T2D is now well established, and multiple risk variants are being identified at some loci, suggesting allelic heterogeneity.Concurrently, increasing numbers of genes and variants have been implicated in monogenic forms of diabetes, including maturity onset diabetes of the young (MODY) and neonatal diabetes (7), and at least five genes have been implicated in both monogenic and polygenic diabetes (8).A recent simulation study evaluated genetic architectures for consistency with results from T2D genetic studies and found that many different disease models were still possible with respect to the number of loci, allele frequencies and level of selective pressure (9).Ongoing studies should more substantially narrow the bounds on feasible architectures (9).", + "\tIntroduction\n\nType 2 diabetes is a complex metabolic disease with a substantial heritable component [1].Over the past seven years, genome-wide association studies (GWAS) have successfully identified over 70 common risk variants associated with type 2 diabetes [2][3][4][5].Association signals at many of these loci localize to nonprotein-coding intronic and intergenic regions and likely harbor regulatory variants altering gene transcription.In recent years great advances have facilitated identification of regulatory elements genome-wide using techniques including DNase-seq and FAIRE-seq (formaldehyde-assisted isolation of regulatory elements), which identify regions of nucleosome depleted open chromatin, and ChIP-seq (chromatin immunoprecipitation), which identify histone modifications to nucleosomes and transcription factor binding sites.Several studies have successfully integrated trait-associated variants at GWAS loci with publicly available regulatory element datasets in disease-relevant cell types to guide identification of regulatory variants underlying disease susceptibility [6][7][8][9][10].", + "\tINTRODUCTION\n\nMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004).", + "\t\n\nBackground: Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D).One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes.However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.Methods: Here, we describe implementation of an analytical pipeline to address this question.First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals.Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks.Third, we use GWAS data to test the T2D association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network." + ], + [ + "\tA. Genetic Screening\n\nWe have discussed above the genetic component of T1D.The genetic susceptibility to T1D is determined by genes related to immune function with the potential exception of the insulin gene (434).The genetic susceptibility component of T1D allows some targeting of primary preventive care to family members of diagnosed T1D patients, but there is no complete inheritance of the disease.Nevertheless, the risk for developing T1D compared with people with no family history is 10 -15 times greater.Although 70% of individuals with T1D carry defined risk-associated genotypes at the HLA locus, only 3-7% of the carriers of such genetic risk markers develop diabetes (3).", + "\tIntrauterine environment and imprinting\n\nIntrauterine and postnatal environment can aff ect future risk of diabetes and cardiovascular disease via fetal programming. 117The thrifty genotype and thrifty phenotype hypotheses seem to apply to Asian populations.Maternal undernutrition, infant's low birthweight, and rapid postnatal child growth are all associated with increased risk of diabetes in off spring, and these factors might be especially relevant to developing countries such as India 88 and China. 1188][119] In view of the increase in childhood obesity and increasing number of women with young-onset diabetes in Asia, this link will further exacerbate the situation by creating a vicious cycle of diabetes begetting diabetes.", + "\tIf an environmental contributor is near ubiquitous and the genetic\npredisposition common as well, interventions are most sensibly weighted towards\nenvironmental risk factor modification. Even here, though, there is room for further research, since the etiopathogenesis\nof type 2 diabetes may not be as well understood as some suggest. Specifically,\nChaufan implies that dietary intervention to prevent prenatal programming\nleading to susceptibility to develop type 2 diabetes (the fetal origins of adult onset\ndisease hypothesis) is as evidence-based as dietary management of the adult diabetic state. However, many questions remain in this area.", + "\t\nIn 1976, the noted human geneticist James Neel titled a book chapter \"Diabetes Mellitus: A Geneticist's Nightmare.\" 1 Over the past 30 years, however, the phenotypic and genetic heterogeneity of diabetes has been painstakingly teased apart to reveal a family of disorders that are all characterized by the disruption of glucose homeostasis but that have fundamentally different causes.Recently, the availability of detailed information on the structure and variation of the human genome and of new high-throughput techniques for exploiting these data has geneticists dreaming of unraveling the genetic complexity that underlies these disorders.This review focuses on type 1 diabetes mellitus and includes an update on recent progress in understanding genetic factors that contribute to the disease and how this information may contribute to new approaches for prediction and therapeutic intervention.Type 1 diabetes becomes clinically apparent after a preclinical period of varying length, during which autoimmune destruction reduces the mass of beta cells in the pancreatic islets to a level at which blood glucose levels can no longer be maintained in a physiologic range.The disease has two subtypes: 1A, which includes the common, immune-mediated forms of the disease; and 1B, which includes nonimmune forms.In this review, we focus on subtype 1A, which for simplicity will be referred to as type 1 diabetes.Although there are rare monogenic, immune-mediated forms of type 1 diabetes, 2,3 the common form is thought to be determined by the actions, and possible interactions, of multiple genetic and environmental factors.The concordance for type 1 diabetes in monozygotic twins is less than 100%, and although type 1 diabetes aggregates in some families, it does not segregate with any clear mode of inheritance. 4-7Despite these complexities, knowledge of genetic factors that modify the risk of type 1 diabetes offers the potential for improved prediction, stratification of patients according to risk, and selection of possible therapeutic targets.As germ-line factors, genetic risk variants are present and amenable to study at all times -before, during, and after the development of diabetes.Thus, genetic information can serve as a potential predictive tool and provide insights into pathogenetic factors occurring during the preclinical phase of the disease, when preventive measures might be applied. Gene tic S t udiesBecause of the uncertainty regarding the number and action of genes involved in type 1 diabetes, genetic studies have tended to focus on approaches that require few assumptions about the underlying model of disease risk.The two primary approaches have been linkage studies (using pairs of affected relatives, typically siblings) and association studies (using either case-control or family-based designs).Linkage studies using affected sibling pairs seek to identify regions of the genome that are shared", + "\t\n\nGenetic susceptibility to type 1 diabetes (T1D) is well supported by epidemiologic evidence; however, disease risk cannot be entirely explained by established genetic variants identified so far.This study addresses the question of whether epigenetic modification of the inherited DNA sequence may contribute to T1D susceptibility.Using the Infinium HumanMethylation450 BeadChip array (450k), a total of seven long-term disease-discordant monozygotic (MZ) twin pairs and five pairs of HLA-identical, disease-discordant non-twin siblings (NTS) were examined for associations between DNA methylation (DNAm) and T1D.Strong evidence for global hypomethylation of CpG sites within promoter regions in MZ twins with TID compared to twins without T1D was observed.DNA methylation data were then grouped into three categories of CpG sites for further analysis, including those within: 1) the major histocompatibility complex (MHC) region, 2) non-MHC genes with reported T1D association through genome wide association studies (GWAS), and 3) the epigenome, or remainder of sites that did not include MHC and T1D associated genes.Initial results showed modest methylation differences between discordant MZ twins for the MHC region and T1D-associated CpG sites, BACH2, INS-IGF2, and CLEC16A (DNAm difference range: 2.2%e5.0%).In the epigenome CpG set, the greatest methylation differences were observed in MAGI2, FANCC, and PCDHB16, (DNAm difference range: 6.9%e16.1%).These findings were not observed in the HLA-identical NTS pairs.Targeted pyrosequencing of five candidate CpG loci identified using the 450k array in the original discordant MZ twins produced similar results using control DNA samples, indicating strong agreement between the two DNA methylation profiling platforms.However, findings for the top five candidate CpG loci were not replicated in six additional T1Ddiscordant MZ twin pairs.Our results indicate global DNA hypomethylation within gene promoter regions may contribute to T1D; however, findings do not support the involvement of large DNAm differences at single CpG sites alone in T1D.", + "\t\n\nIt has been suggested that maternal imprinting of IDDM2 could contribute to the observation that type 1 diabetes appears to be transmitted less frequently to the offspring of diabetic mothers than to those of diabetic fathers [18,27].This, however, cannot be the explanation because there is no evidence for maternal imprinting in families from the UK, and yet in the 332 UK type 1 diabetes multiplex families studied here, there are 26 fathers with type 1 diabetes and only 7 affected mothers.The preferential transmission of predisposing IDDM2-VNTR alleles from fathers is consistent with a maternal imprinting effect in families from France [18], the USA [10,18,21] (Figure 2; Table 3) and Canada [27].However, in a large family data set from the UK, and in smaller data sets from Denmark and Sardinia, the transmission of VNTR susceptibility alleles is more pronounced from mothers than from fathers, and now significantly so in UK families (Figure 2; Table 3).Comparison of the results from the USA with those from the UK suggest that unexplained inter-population differences in this parent-of-origin effect may exist (P<0.025).However, it would appear that in some populations, and in particular the UK, maternal imprinting cannot provide a simple explanation for parent-of-origin effects in type 1 diabetes.Expression of insulin in the adult pancreas appears to be related to the length of the VNTR, with class III alleles associated with lower levels of INS mRNA.Our results are consistent with those of Lucassen et al. [24] and Owerbach and Gabbay [33] who have found a similar 1.5-to 3-fold lower expression associated with class III alleles in transiently transfected rodent pancreatic -cell lines in vitro.Also, in this issue, Vafiadis et al. [1] report that in 10 human foetal pancreas samples, the class III VNTR alleles are associated with significantly lower levels of INS mRNA than class I alleles.Kennedy et al. [25], on the other hand, also using transfected pancreatic -cell lines, found that the INS mRNA levels were higher in a class III VNTR compared with a class I VNTR transfectant.One explanation for this apparent discrepancy might lie in the type of VNTR alleles Kennedy et al. transfected into the cell line.We have found that there are over twenty class I VNTR alleles defined by length [10], and that alleles of a given length can vary in sequence composition [26] (S.T.B. & J.A.T., unpublished data).The transmission of some class I VNTR alleles (e.g.allele 814 mobility units; corresponding to 42 repeat units) from class I/III heterozygous parents to diabetic offspring does not reflect the overall significant positive transmission of the class I as a whole [10].", + "\t\nFamily and twin studies indicate that a substantial fraction of susceptibility to type 1 diabetes is attributable to genetic factors.These and other epidemiologic studies also implicate environmental factors as important triggers.Although the specific environmental factors that contribute to immune-mediated diabetes remain unknown, several of the relevant genetic factors have been identified using two main approaches: genome-wide linkage analysis and candidate gene association studies.This article reviews the epidemiology of type 1 diabetes, the relative merits of linkage and association studies, and the results achieved so far using these two approaches.Prospects for the future of type 1 diabetes genetics research are considered.", + "\t\n\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\nGenome-wide search for genes affecting the age at diagnosis of type 1 diabetes.\t\n\nGenes affecting type 1 diabetes diagnosis age / A. Syreeni et al.", + "\tIntroduction\n\nGenetic susceptibility to the major autoimmune disor- ders, including insulin-dependent diabetes mellitus (IDDM), is complex.Much evidence suggests that IDDM is polygenic, explaining the paucity of ex- tended pedigrees and the high frequency of nonfamil- ial disease (Risch 1987; Thomson et al. 1988).To date, only a single genetic region, the major histocom- patibility complex (MHC) on chromosome 6, has Received May 29, 1990; revision received September 19, 1990.Address for correspondence and reprints: J. I. Bell, Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DU, England.", + "\t\n\nThus, the most likely scenario is that these genes are more poised for activation in the case group compared with the control group, contributing to various diabetes complications in the long term.This could be a consequence of the early exposure to hyperglycemia (measured by HbA 1c level), which is known to be associated with increased rates of long-term diabetes complications.", + "\tType 1 Diabetes\n\nThe higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (22)(23)(24)(25)(26). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50-60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28).Approximately 50 additional genes individually contribute smaller effects (25,29).These contributors include gene variants that modulate immune regulation and tolerance (30)(31)(32)(33), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic b-cells (37).Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39).Together, these gene variants explain ;80% of type 1 diabetes heritability.Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.", + "\tPerspectives\n\nThe studies described in this review systematically indicate an association between paternal diets, quantitative changes in specific sncRNAs, and impaired glucose tolerance in firstgeneration offspring.The potential importance of these findings for humans is obvious, considering that type II diabetes is one of the fastest growing health problems of our time and that impaired glucose tolerance defines a pre-diabetic state.If the described results are indeed applicable to humans, measures of specific sncRNAs (e.g.miRNA-let-7, tRF-Gly-GCC, and tRF modifications m 5 C and m 2 G) could potentially work as assayable quantitative biomarkers.Obesity biomarkers have significant potential to improve characterization of an obesity phenotype, which may prove relevant in assessing the risk of type II diabetes, thereby helping to counteract the global rise of obesity and diabetes.Identification of these epigenetic biomarkers could help identify younger individuals who are predisposed to the development of obesity and type II diabetes, allowing for personalized intervention.", + "\t\n\nThe diabetogenic effects of manipulating the intrauterine environment are probably mediated by a permanent programming of the developing offspring, e.g. by the mechanism of imprinting.Of interest, the increased risk of diabetes continues into subsequent generations, suggesting the changes also affect the germ cell line [143].", + "\tDiabetes 2004, 53:1905-1910. Page 8 of 9\n(page number not for citation purposes)\nBMC Medical Genetics 2006, 7:85\n\n17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. Schulze MB, Hu FB: Primary prevention of diabetes: what can\nbe done and how much can be prevented? Annu Rev Public\nHealth 2005, 26:445-467. King H, Aubert RE, Herman WH: Global burden of diabetes,\n19952025: prevalence, numerical estimates, and projections. Diabetes Care 1998, 21:1414-1431. Permutt MA, Wasson J, Cox N: Genetic epidemiology of diabetes. J Clin Invest 2005, 115:1431-1439.", + "\t\nExtensive epidemiological and experimental evidence have shown that exposure to an adverse intrauterine environment as observed in offspring of pregnancies complicated by obesity or diabetes, can program susceptibility to metabolic, endocrine and cardiovascular disorders later in life.Although most studies have concentrated on the maternal environment, it is also becoming evident that paternal exposure to obesity or diabetes can result in the later development of metabolic disorders in the offspring.Such programmed effects might not be limited to the first directly exposed generation, but could be transmitted to subsequent generations.This suggests the existence of mechanisms by which metabolic changes in parental phenotype are transmissible to offspring.The mechanisms which underpin the transmission of the programmed effects across generations are still unclear.However, epigenetic regulation of transcription has emerged as a strong candidate for mediating the heritability of metabolic diseases.Here, we review the most relevant evidence from human and animal studies showing transmission of programming effects of obesity or diabetes across generations, and the current mechanisms underlying either maternal or paternal influences on the metabolic status of offspring.\t\n\nAlthough most studies have concentrated on the maternal environment, it is also becoming evident that paternal exposure to obesity or diabetes can result in the later development of metabolic disorders in the offspring.Such programmed effects might not be limited to the first directly exposed generation, but could be transmitted to subsequent generations.This suggests the existence of mechanisms by which metabolic changes in parental phenotype are transmissible to offspring.The mechanisms which underpin the transmission of the programmed effects across generations are still unclear.However, epigenetic regulation of transcription has emerged as a strong candidate for mediating the heritability of metabolic diseases.Here, we review the most relevant evidence from human and animal studies showing transmission of programming effects of obesity or diabetes across generations, and the current mechanisms underlying either maternal or paternal influences on the metabolic status of offspring.", + "\t\n\nPresently, 48 other genomic regions, referred to as susceptibility regions, have been found to also confer susceptibility to T1D (Burren et al., 2011;Steck and Rewers, 2011;Yang et al., 2011;Bluestone et al. 2010;Poicot et al., 2010;Todd et al., 2010;Todd et al., 2007).But their contribution is minimal in comparison to the HLA locus (Gillespie, 2014).Also, research has shown that less than 10% of individuals with HLA-conferred diabetes susceptibility actually progress to clinical disease (Knip andSiljandera, 2008, Wenzlau et al., 2008).This implies that additional factors are needed to trigger and drive -cell destruction in genetically predisposed persons (Knip and Siljandera, 2008).Environmental factors are believed to influence the expression of T1D.The reason being that in the case of identical twins, if one twin has T1D, the other twin only has it 30%-50% of the time, despite having the same genome.This means that other factors contribute to the prevalence or onset of this disease (Knip et al., 2005).\tCONTRIBUTION TO KNOWLEDGE\n\nThrough this research, the following facts have been discovered about the genomics of T1D:" + ], + [ + "\t\n\nThe fact that all these technologies are relatively high throughput also means that large scale studies can be performed.This is particularly appropriate to type 2 diabetes, obesity, and their related disorders, which are on the whole polygenic disorders.This necessitates either the cross comparison of a number of monogenic animal models of the disease to produce an understanding of the extremes of the metabolic interactions associated with the diseases or the investigation of a disease process in a large population to investigate how a specific gene mutation or disease process interacts with the genetic diversity found in a normal population.", + "\t\n\nIn conclusion, genome-wide studies have added valuable scientific data to our repertoire of diabetes knowledge.However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.The latter realization ought to temper enthusiasm for the indiscriminate use of genetic testing for diabetes.", + "\t\n\nSuccessful integration of genotyping data with longitudinal phenotypic information produced from several national health registries has provided strong support for 10 loci showing a genome-wide significance for the association with T2DM complications, some of them with already known importance to the comorbid conditions analyzed.We believe that these findings provide deeper insight into the pathogenesis of T2DM complications and suggest novel candidate genes for further functional studies, while our targeted approach highlights several susceptibility loci showing a directionally consistent impact on phenotype in multiple populations.", + "\tGenomics of T2D\n\nDiet, lifestyle, environment, and even genetic variation influence an individual's response to disease therapy.Like GWAS which identify genetic variants conferring risk for a disease, studies have been carried out for identifying genetic variants responsible for patient differences in drug response.Pharmacogenomics in diabetes focuses on the study of gene polymorphisms which influence an individual's response to antidiabetic drugs.Such genetic variants influence the pharmacodynamics and/or pharmacokinetics of the drug, thus affecting its efficacy or toxicity in an individual.The difference in response to treatments and therapies across individuals on account of these factors strengthens the case for personalized medicine in diabetes.", + "\t\n\nThe availability of detailed information on gene environment interactions may enhance our understanding of the molecular basis of T2D, elucidate the mechanisms through which lifestyle exposures influence diabetes risk, and possibly help to refine strategies for diabetes prevention or treatment.The ultimate hope is genetics might one day be used in primary care to inform the targeting of interventions that comprise exercise regimes and other lifestyle therapies for individuals most likely to respond well to them.", + "\t\n\nIn the Framingham Offspring Cohort, knowledge of common genetic variation aptly reclassifies younger people for T2D risk beyond clinical risk factors, but not older people (12).In the Whitehall II Cohort, common genetic variants for 2-h glucose contributed to the agerelated rise of 2-h glucose levels, whereas associations of variants for fasting glucose were constant over time (40).Although these two studies do not include G E interactions, they support the notion that genetic variants that modify T2D susceptibility to a particular exposure may do so only when the exposure occurs during specific developmental stages (34).It is therefore important to consider the dynamic or timedependent nature of G E interactions (2).\tFUTURE PERSPECTIVES\n\nContinued investment in studies of G E interactions for T2D holds promise on several grounds.First, such studies may provide insight into the function of novel T2D loci and pathways by which environmental exposures act and, therefore, yield a better understanding of T2D etiology (66).They could also channel experimental studies in a productive direction.Second, knowledge of G E interactions may help identify high-risk individuals for diet and lifestyle interventions.This may also apply to pharmacological interventions if individuals carrying certain genotypes are more or less responsive to specific medications.The finding that patients with rare forms of neonatal diabetes resulting from KCNJ11 mutations respond better to sulfonylurea than to insulin therapy is just one example demonstrating the potential for this application of G E interaction research (69).Third, we are fast approaching an era when individuals can feasibly obtain their complete genetic profile and thus a snapshot of their genetic predisposition to disease.It will therefore be the responsibility of health professionals to ensure that their patients have an accurate interpretation of this information and a means to curb their genetic risk.A long-held goal of genetic research has been to tailor diet and lifestyle advice to an individual's genetic profile, which will, in turn, motivate him or her to adopt and maintain a protective lifestyle.There is currently no evidence that this occurs.Findings to date, however, indicate that behavioral changes can substantially mitigate diabetogenic and obesogenic effects of individual or multiple risk alleles, which has much broader clinical and public health implications.\t\n\nCross-sectional studies of G E interactions for quantitative traits such as measures of insulin sensitivity, glucose tolerance, and adiposity have also been informative.Focusing on continuously distributed traits may produce insight to some causal pathway underlying disease that is not adequately captured by studies of overt T2D (24).", + "\t\nThe genome is often the conduit through which environmental exposures convey their effects on health and disease.Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined.Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes.It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered.As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.\t\n\nThe literature on gene-environment interactions in diabetes-related traits is extensive, but few studies are accompanied by adequate replication data or compelling mechanistic explanations.Moreover, most studies are cross-sectional, from which temporal patterns and causal effects cannot be confidently ascertained.This has undermined confidence in many published reports of gene-environment interactions across many diseases; although interaction studies in psychiatry have been especially heavily criticized [3], many of the points made in that area relate to other diseases, not least to T2D, where the diagnostic phenotype (elevated blood glucose or HbA1c) is a consequence of underlying and usually unmeasured physiological defects (e.g., at the level of the pancreatic beta-cell, peripheral tissue, liver, and gut), and the major environmental risk factors are difficult to measure well.Nevertheless, several promising examples of geneenvironment interactions relating to cardiometabolic disease exist, as discussed below and described in Table 1, and interaction studies with deep genomic coverage in large cohorts are now conceivable; the hope is that these studies will highlight novel disease mechanisms and biological pathways that will fuel subsequent functional and clinical translation studies.This is important, because diabetes medicine may rely increasingly on genomic stratification of patient populations and disease phenotype, for which gene-environment interaction studies might prove highly informative.\tThe Rationale for Studying Gene-Environment Interactions\n\nIt is often said that T2D is the consequence of geneenvironment interactions [17].Indeed, both the environment and the genome are involved in diabetes etiology, and there are many genetic and environmental risk factors for which very robust evidence of association exists.But when epidemiologists and statisticians discuss gene-environment interactions, they are usually referring to the synergistic relationship between the two exposures, and there is limited empirical evidence for such effects in the etiology of cardiometabolic disease.Indeed, in non-monogenic human obesity, a condition widely believed to result from a genetic predisposition triggered by exposure to adverse lifestyle factors, of the >200 human gene-lifestyle interaction studies reported since 1995, only a few examples of gene-environment interactions have been adequately replicated [18], and because these results are derived primarily from cross-sectional studies with little or no experimental validation, even those that have been robustly replicated may not represent causal interaction effects.The evidence base for T2D is thinner still.Nevertheless, other data support the existence of gene-environment interactions in complex disease, thus motivating the search for empirically defined interactions in T2D.", + "\t\n\nGenome-wide interaction studies have potential to identify gene variants that influence diabetes risk that might not be detected using hypothesis-driven approaches.However, the statistical power limitations of such studies when applying conventional tests of interaction, combined with the challenges of identifying large cohort collections with appropriately characterized environmental, genetic, and phenotypic data, pose challenges that conventional genetic association studies do not face.Several methods have been developed to mitigate these challenges; among the most promising is the joint meta-analysis approach, which is derived from the model with two degrees of freedom popularized by Kraft et al. (45) and developed further by Manning et al. (46).Manning et al. (47) went on to apply the joint meta-analysis approach in a genome-wide study of 52 cohorts in which they tested for SNP main effects and interactions (with BMI) on fasting glucose and insulin levels.The analysis yielded novel experiment-wide association signals for main effects, but none was discovered for interactions.", + "\t\nGenome wide association studies (GWAS) have transformed the study of heritable factors influencing complex diseases such as type 2 diabetes (T2D), with the current tally of established risk loci approaching 70.Each of these loci has the potential to offer novel insights into the biology of this disease, and opportunities for clinical exploitation.However, the complexity of this condition has often frustrated efforts to achieve these functional and translational advances.This review describes progress made over the past year to expand genome wide association studies, to characterize the mechanisms through which diabetes risk loci operate, and to define the processes involved in diabetes predisposition.", + "\t\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.\t\n\nA new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.", + "\t\n\nIn summary, the present findings demonstrate that analysis of physiologically defined genome-wide interactions with variants strongly associated with insulin secretion is a potentially powerful approach for discovery of novel T2D loci and for expanding the knowledge base of disease etiology.A similar approach examining interactions with variants associated with key biomarkers may be of wider relevance in other complex human diseases.Results highlight the need for further study of genetic variation underlying T2D risk in African Americans as a means to improve our overall understanding of this disease.", + "\t\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.\t\n\nIn this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes.Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies.We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease.We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.", + "\tGenomics and gene-environment interactions\n\nEven though many cases of T2DM could be prevented by maintaining a healthy body weight and adhering to a healthy lifestyle, some individuals with prediabetes mellitus are more susceptible to T2DM than others, which suggests that individual differences in response to lifestyle interventions exist 76 .Substantial evidence from twin and family studies has suggested a genetic basis of T2DM 77 .Over the past decade, successive waves of T2DM genome-wide association studies have identified >100 robust association signals, demonstrating the complex polygenic nature of T2DM 5 .Most of these loci affect T2DM risk through primary effects on insulin secretion, and a minority act through reducing insulin action 78 .Individually, the common variants (minor allele frequency >5%) identified in these studies have only a modest effect on T2DM risk and collectively explain only a small portion (~20%) of observed T2DM heritability 5 .It has been hypothesized that lower-frequency variants could explain much of the remaining heritability 79 .However, results of a large-scale sequencing study from the GoT2D and T2D-GENES consortia, published in 2016, do not support such a hypothesis 5 .Genetic variants might help reveal possible aetiological mechanisms underlying T2DM development; however, the variants identified thus far have not enabled clinical prediction beyond that achieved with common clinical measurements, including age, BMI, fasting levels of glucose and dyslipidaemia.A study published in 2014 linked susceptibility variants to quantitative glycaemic traits and grouped these variants on the basis of their potential intermediate mechanisms in T2DM pathophysiology: four variants fitted a clear insulin resistance pattern; two reduced insulin secretion with fasting hyperglycaemia; nine reduced insulin secretion with normal fasting glycaemia; and one altered insulin processing 80 .Considering such evidence, the genetic architecture of T2DM is highly polygenic, and thus, substantially larger association studies are needed to identify most T2DM loci, which typically have small to modest effect sizes 81 .", + "\t\n\nThe public health genomics approach to type 2 diabetes.So, while exciting gene discoveries are being made, what can we do?The answer may lie in the relatively new field of public health genomics, \"a multidisciplinary field concerned with the effective and responsible translation of genome-based knowledge and technologies to improve population health\" (12).Researchers, policymakers, and practitioners in public health genomics use populationbased data on genetic variation and gene-environment interactions to develop, implement, and evaluate evidencebased tools for improving health and preventing disease.They also apply systematic evidence-based knowledge synthesis and appraisal of the clinical validity and utility of genomic applications in health practice.Validated genomic information is then integrated into disease control and prevention programs (13)." + ], + [ + "Recent 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.", + "PRECISE 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.", + "Genetic and physiological studies have highlighted the singular importance of pancreatic islet dysfunction in type 2 diabetes, but epigenomic characterisation of this tissue has been limited in large-scale community projects such as ENCODE and GTEx.The present study seeks to address this deficit by describing, in unprecedented detail, genome-wide patterns of methylation and chromatin accessibility in human islet material.We have combined these data with existing islet epigenomic marks to generate a refined regulatory map which, based on the evidence of improved enrichment for T2D association signals, offers more granular annotation of functional impact.\t\n\nIn this study, we set out to expand upon previous studies of the islet regulome in several ways.First, we explored the human islet methylome in unprecedented depth using Whole-Genome Bisulphite Sequencing (WGBS) applied to a set of 10 human islet preparations.Second, we explored both basal and genotype-dependent variation in chromatin accessibility through ATAC-seq in 17 human islet samples.Third, we integrated these genome-wide data with existing islet regulatory annotations to generate a high-resolution, epigenome map of this key tissue.Finally, we used this detailed map to interpret GWAS signals for T2D (and the related trait of fasting glucose) and deduce the molecular mechanisms through which some of these loci operate.\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\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.", + "It 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.", + "Against this background, a genome-wide analysis to identify stable and well-expressed genes in human islets and beta cells represents an essential tool for accurate normalization.To achieve this goal, we used high-depth RNA-sequencing data from the human beta cell line EndoC-H1 and human islets exposed to pro-inflammatory cytokines or palmitate.Genes were validated as putative reference genes by qPCR in EndoC-H1 cells, human islets and induced pluripotent stem cell (iPSC)-derived islets.", + "Notably, 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).", + "The 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\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.", + "Our 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\nAn integrative approach suggests proliferation drivers.Our integrative genomics approach led to the identification of ten coexpression modules (Fig. 2e) that may underlie insulinoma pathogenesis, many of which were linked to potential epigenetic dysregulation (Fig. 3b).Given our interest in beta cell regeneration, and seeking the mechanisms that may permit escape from beta cell quiescence in benign insulinomas, we elected to focus on the bisque4 co-expression module, the module in insulinomas that contained DEGs enriched for cell proliferation.Furthermore, the bisque4 cell cycle module was particularly enriched for betacell specific histone mark signature, including genes with bivalent marks, both H3K27me3 and H3K4me3 (Kaestner beta 28 : foldenrichment = 5.2, FDR = 4.0 10 6 ) (Fig. 3a,b).\t\nAlthough diabetes results in part from a deficiency of normal pancreatic beta cells, inducing human beta cells to regenerate is difficult.Reasoning that insulinomas hold the \"genomic recipe\" for beta cell expansion, we surveyed 38 human insulinomas to obtain insights into therapeutic pathways for beta cell regeneration.An integrative analysis of whole-exome and RNA-sequencing data was employed to extensively characterize the genomic and molecular landscape of insulinomas relative to normal beta cells.Here, we show at the pathway level that the majority of the insulinomas display mutations, copy number variants and/or dysregulation of epigenetic modifying genes, most prominently in the polycomb and trithorax families.Importantly, these processes are coupled to co-expression network modules associated with cell proliferation, revealing candidates for inducing beta cell regeneration.Validation of key computational predictions supports the concept that understanding the molecular complexity of insulinoma may be a valuable approach to diabetes drug discovery.", + "We 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\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.\tDISCUSSION\n\nWith more than 80 loci associated with T2DM identified by GWASs, a robust platform to evaluate the role of these loci using disease-relevant cells is urgently needed.Here, we report proof of principle for using isogenic hESC-derived glucose-responding cells to evaluate the role of these loci in the function and survival of human pancreatic beta cells under conditions mimicking both health and disease.The derived glucose-responding cells share the same genetic background, providing a unique resource to determine the precise role of genes or loci in human pancreatic beta cells independent of complications from genetic heterogeneity implied by other approaches, such as patient-derived iPSCs." + ] + ], + "task_id": [ + { + "task_id": "5683950D08F6BDAB60BEA8BAC610A10B" + }, + { + "task_id": "4261C4238D9943A6D54F8F140746169F" + }, + { + "task_id": "2F912BE55930B6A50194B230FDF18693" + }, + { + "task_id": "CC682B18ACF7B69EFDC2BE439BB2E059" + }, + { + "task_id": "662BE235286FA528E8B3E9610248FB71" + } + ] +}
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