Epigenetic mechanisms control gene activity and the development of an organism. The epigenome includes DNA methylation, histone modifications, and RNA-mediated processes, and disruption of this ...balance may cause several pathologies and contribute to obesity and type 2 diabetes (T2D). This Review summarizes epigenetic signatures obtained from human tissues of relevance for metabolism—i.e., adipose tissue, skeletal muscle, pancreatic islets, liver, and blood—in relation to obesity and T2D. Although this research field is still young, these comprehensive data support not only a role for epigenetics in disease development, but also epigenetic alterations as a response to disease. Genetic predisposition, as well as aging, contribute to epigenetic variability, and several environmental factors, including exercise and diet, further interact with the human epigenome. The reversible nature of epigenetic modifications holds promise for future therapeutic strategies in obesity and T2D.
Epigenetic factors are suggested to contribute to metabolic dysfunctions. In this Review, Ling and Rönn summarize evidence for altered DNA methylation, both as a cause and a consequence of human obesity and type 2 diabetes. As epigenetic alterations are dynamic in nature, they may also provide targets for drug development.
Type 2 diabetes (T2D) develops due to insulin resistance and impaired insulin secretion, predominantly in genetically predisposed subjects exposed to nongenetic risk factors like obesity, physical ...inactivity and ageing. Emerging data suggest that epigenetics also play a key role in the pathogenesis of T2D. Genome-wide studies have identified altered DNA methylation patterns in pancreatic islets, skeletal muscle and adipose tissue from subjects with T2D compared with nondiabetic controls. Environmental factors known to affect T2D, including obesity, exercise and diet, have also been found to alter the human epigenome. Additionally, ageing and the intrauterine environment are associated with differential DNA methylation. Together, these data highlight a key role for epigenetics and particularly DNA methylation in the growing incidence of T2D.
Type 2 diabetes (T2D) is a multifactorial, polygenic disease caused by impaired insulin secretion and insulin resistance. Genome-wide association studies (GWAS) were expected to resolve a large part ...of the genetic component of diabetes; yet, the single nucleotide polymorphisms identified by GWAS explain less than 20% of the estimated heritability for T2D. There was subsequently a need to look elsewhere to find disease-causing factors. Mechanisms mediating the interaction between environmental factors and the genome, such as epigenetics, may be of particular importance in the pathogenesis of T2D.
This review summarizes knowledge of the impact of epigenetics on the pathogenesis of T2D in humans. In particular, the review will focus on alterations in DNA methylation in four human tissues of importance for the disease; pancreatic islets, skeletal muscle, adipose tissue, and the liver. Case–control studies and studies examining the impact of non-genetic and genetic risk factors on DNA methylation in humans will be considered. These studies identified epigenetic changes in tissues from subjects with T2D versus non-diabetic controls. They also demonstrate that non-genetic factors associated with T2D such as age, obesity, energy rich diets, physical activity and the intrauterine environment impact the epigenome in humans. Additionally, interactions between genetics and epigenetics seem to influence the pathogenesis of T2D.
Overall, previous studies by our group and others support a key role for epigenetics in the growing incidence of T2D.
Abstract Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this ...defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1 , provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
Aging associates with impaired pancreatic islet function and increased type 2 diabetes (T2D) risk. Here we examine whether age-related epigenetic changes affect human islet function and if ...blood-based epigenetic biomarkers reflect these changes and associate with future T2D. We analyse DNA methylation genome-wide in islets from 87 non-diabetic donors, aged 26-74 years. Aging associates with increased DNA methylation of 241 sites. These sites cover loci previously associated with T2D, for example, KLF14. Blood-based epigenetic biomarkers reflect age-related methylation changes in 83 genes identified in human islets (for example, KLF14, FHL2, ZNF518B and FAM123C) and some associate with insulin secretion and T2D. DNA methylation correlates with islet expression of multiple genes, including FHL2, ZNF518B, GNPNAT1 and HLTF. Silencing these genes in β-cells alter insulin secretion. Together, we demonstrate that blood-based epigenetic biomarkers reflect age-related DNA methylation changes in human islets, and associate with insulin secretion in vivo and T2D.
Type 2 diabetes is a complex trait with both environmental and hereditary factors contributing to the overall pathogenesis. One link between genes, environment, and disease is epigenetics influencing ...gene transcription and, consequently, organ function. Genome-wide studies have shown altered DNA methylation in tissues important for glucose homeostasis including pancreas, liver, skeletal muscle, and adipose tissue from subjects with type 2 diabetes compared with nondiabetic controls. Factors predisposing for type 2 diabetes including an adverse intrauterine environment, increasing age, overweight, physical inactivity, a family history of the disease, and an unhealthy diet have all shown to affect the DNA methylation pattern in target tissues for insulin resistance in humans. Epigenetics including DNA methylation may therefore improve our understanding of the type 2 diabetes pathogenesis, contribute to development of novel treatments, and be a useful tool to identify individuals at risk for developing the disease.
Impaired insulin secretion is a hallmark of type 2 diabetes (T2D). Epigenetics may affect disease susceptibility. To describe the human methylome in pancreatic islets and determine the epigenetic ...basis of T2D, we analyzed DNA methylation of 479,927 CpG sites and the transcriptome in pancreatic islets from T2D and non-diabetic donors. We provide a detailed map of the global DNA methylation pattern in human islets, β- and α-cells. Genomic regions close to the transcription start site showed low degrees of methylation and regions further away from the transcription start site such as the gene body, 3'UTR and intergenic regions showed a higher degree of methylation. While CpG islands were hypomethylated, the surrounding 2 kb shores showed an intermediate degree of methylation, whereas regions further away (shelves and open sea) were hypermethylated in human islets, β- and α-cells. We identified 1,649 CpG sites and 853 genes, including TCF7L2, FTO and KCNQ1, with differential DNA methylation in T2D islets after correction for multiple testing. The majority of the differentially methylated CpG sites had an intermediate degree of methylation and were underrepresented in CpG islands (∼ 7%) and overrepresented in the open sea (∼ 60%). 102 of the differentially methylated genes, including CDKN1A, PDE7B, SEPT9 and EXOC3L2, were differentially expressed in T2D islets. Methylation of CDKN1A and PDE7B promoters in vitro suppressed their transcriptional activity. Functional analyses demonstrated that identified candidate genes affect pancreatic β- and α-cells as Exoc3l silencing reduced exocytosis and overexpression of Cdkn1a, Pde7b and Sept9 perturbed insulin and glucagon secretion in clonal β- and α-cells, respectively. Together, our data can serve as a reference methylome in human islets. We provide new target genes with altered DNA methylation and expression in human T2D islets that contribute to perturbed insulin and glucagon secretion. These results highlight the importance of epigenetics in the pathogenesis of T2D.
Current knowledge about the role of epigenetics in type 2 diabetes (T2D) remains limited. Only a few studies have investigated DNA methylation of selected candidate genes or a very small fraction of ...genomic CpG sites in human pancreatic islets, the tissue of primary pathogenic importance for diabetes. Our aim was to characterize the whole-genome DNA methylation landscape in human pancreatic islets, to identify differentially methylated regions (DMRs) in diabetic islets, and to investigate the function of DMRs in islet biology. Here, we performed whole-genome bisulfite sequencing, which is a comprehensive and unbiased method to study DNA methylation throughout the genome at a single nucleotide resolution, in pancreatic islets from donors with T2D and control subjects without diabetes. We identified 25,820 DMRs in islets from individuals with T2D. These DMRs cover loci with known islet function, e.g.,
,
, and
Importantly, binding sites previously identified by ChIP-seq for islet-specific transcription factors, enhancer regions, and different histone marks were enriched in the T2D-associated DMRs. We also identified 457 genes, including
,
,
,
, and
, that had both DMRs and significant expression changes in T2D islets. To mimic the situation in T2D islets, candidate genes were overexpressed or silenced in cultured β-cells. This resulted in impaired insulin secretion, thereby connecting differential methylation to islet dysfunction. We further explored the islet methylome and found a strong link between methylation levels and histone marks. Additionally, DNA methylation in different genomic regions and of different transcript types (i.e., protein coding, noncoding, and pseudogenes) was associated with islet expression levels. Our study provides a comprehensive picture of the islet DNA methylome in individuals with and without diabetes and highlights the importance of epigenetic dysregulation in pancreatic islets and T2D pathogenesis.
Little is known about the extent to which interactions between genetics and epigenetics may affect the risk of complex metabolic diseases and/or their intermediary phenotypes. We performed a ...genome-wide DNA methylation quantitative trait locus (mQTL) analysis in human adipose tissue of 119 men, where 592,794 single nucleotide polymorphisms (SNPs) were related to DNA methylation of 477,891 CpG sites, covering 99% of RefSeq genes. SNPs in significant mQTLs were further related to gene expression in adipose tissue and obesity related traits. We found 101,911 SNP-CpG pairs (mQTLs) in cis and 5,342 SNP-CpG pairs in trans showing significant associations between genotype and DNA methylation in adipose tissue after correction for multiple testing, where cis is defined as distance less than 500 kb between a SNP and CpG site. These mQTLs include reported obesity, lipid and type 2 diabetes loci, e.g. ADCY3/POMC, APOA5, CETP, FADS2, GCKR, SORT1 and LEPR. Significant mQTLs were overrepresented in intergenic regions meanwhile underrepresented in promoter regions and CpG islands. We further identified 635 SNPs in significant cis-mQTLs associated with expression of 86 genes in adipose tissue including CHRNA5, G6PC2, GPX7, RPL27A, THNSL2 and ZFP57. SNPs in significant mQTLs were also associated with body mass index (BMI), lipid traits and glucose and insulin levels in our study cohort and public available consortia data. Importantly, the Causal Inference Test (CIT) demonstrates how genetic variants mediate their effects on metabolic traits (e.g. BMI, cholesterol, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)) via altered DNA methylation in human adipose tissue. This study identifies genome-wide interactions between genetic and epigenetic variation in both cis and trans positions influencing gene expression in adipose tissue and in vivo (dys)metabolic traits associated with the development of obesity and diabetes.
PDF
