Background Knowledge of age‐related DNA methylation changes in skeletal muscle is limited, yet this tissue is severely affected by ageing in humans. Methods We conducted a large‐scale epigenome‐wide association study meta‐analysis of age in human skeletal muscle from 10 studies (total n = 908 muscle methylomes from men and women aged 18–89 years old). We explored the genomic context of age‐related DNA methylation changes in chromatin states, CpG islands, and transcription factor binding sites and performed gene set enrichment analysis. We then integrated the DNA methylation data with known transcriptomic and proteomic age‐related changes in skeletal muscle. Finally, we updated our recently developed muscle epigenetic clock (https://bioconductor.org/packages/release/bioc/html/MEAT.html). Results We identified 6710 differentially methylated regions at a stringent false discovery rate <0.005, spanning 6367 unique genes, many of which related to skeletal muscle structure and development. We found a strong increase in DNA methylation at Polycomb target genes and bivalent chromatin domains and a concomitant decrease in DNA methylation at enhancers. Most differentially methylated genes were not altered at the mRNA or protein level, but they were nonetheless strongly enriched for genes showing age‐related differential mRNA and protein expression. After adding a substantial number of samples from five datasets (+371), the updated version of the muscle clock (MEAT 2.0, total n = 1053 samples) performed similarly to the original version of the muscle clock (median of 4.4 vs. 4.6 years in age prediction error), suggesting that the original version of the muscle clock was very accurate. Conclusions We provide here the most comprehensive picture of DNA methylation ageing in human skeletal muscle and reveal widespread alterations of genes involved in skeletal muscle structure, development, and differentiation. We have made our results available as an open‐access, user‐friendly, web‐based tool called MetaMeth (https://sarah-voisin.shinyapps.io/MetaMeth/).

BackgroundEven if genetics play an important role, individual variation in stature remains unexplained at the molecular level. Indeed, genome-wide association study (GWAS) have revealed hundreds of variants that contribute to the variability of height but could explain only a limited part of it, and no single variant accounts for more than 0.3% of height variance. At the interface of genetics and environment, epigenetics contributes to phenotypic diversity. Quantifying the impact of epigenetic variation on quantitative traits, an emerging challenge in humans, has not been attempted for height. Since insulin-like growth factor 1 (IGF1) controls postnatal growth, we tested whether the CG methylation of the two promoters (P1 and P2) of the IGF1 gene is a potential epigenetic contributor to the individual variation in circulating IGF1 and stature in growing children.ResultsChild height was closely correlated with serum IGF1. The methylation of a cluster of six CGs located within the proximal part of the IGF1 P2 promoter showed a strong negative association with serum IGF1 and growth. The highest association was for CG-137 methylation, which contributed 13% to the variance of height and 10% to serum IGF1. CG methylation (studied in children undergoing surgery) was approximately 50% lower in liver and growth plates, indicating that the IGF1 promoters are tissue-differentially methylated regions (t-DMR). CG methylation was inversely correlated with the transcriptional activity of the P2 promoter in mononuclear blood cells and in transfection experiments, suggesting that the observed association of methylation with the studied traits reflects true biological causality.ConclusionsOur observations introduce epigenetics among the individual determinants of child growth and serum IGF1. The P2 promoter of the IGF1 gene is the first epigenetic quantitative trait locus (QTLepi) reported in humans. The CG methylation of the P2 promoter takes place among the multifactorial factors explaining the variation in human stature.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-015-0062-8) contains supplementary material, which is available to authorized users.

ObjectiveObesity and type 2 diabetes (T2D) arise from the interplay between genetic, epigenetic, and environmental factors. The aim of this study was to combine bioinformatics and functional studies to identify miRNAs that contribute to obesity and T2D.MethodsA computational framework (miR-QTL-Scan) was applied by combining QTL, miRNA prediction, and transcriptomics in order to enhance the power for the discovery of miRNAs as regulative elements. Expression of several miRNAs was analyzed in human adipose tissue and blood cells and miR-31 was manipulated in a human fat cell line.ResultsIn 17 partially overlapping QTL for obesity and T2D 170 miRNAs were identified. Four miRNAs (miR-15b, miR-30b, miR-31, miR-744) were recognized in gWAT (gonadal white adipose tissue) and six (miR-491, miR-455, miR-423-5p, miR-132-3p, miR-365-3p, miR-30b) in BAT (brown adipose tissue). To provide direct functional evidence for the achievement of the miR-QTL-Scan, miR-31 located in the obesity QTL Nob6 was experimentally analyzed. Its expression was higher in gWAT of obese and diabetic mice and humans than of lean controls. Accordingly, 10 potential target genes involved in insulin signaling and adipogenesis were suppressed. Manipulation of miR-31 in human SGBS adipocytes affected the expression of GLUT4, PPARγ, IRS1, and ACACA. In human peripheral blood mononuclear cells (PBMC) miR-15b levels were correlated to baseline blood glucose concentrations and might be an indicator for diabetes.ConclusionThus, miR-QTL-Scan allowed the identification of novel miRNAs relevant for obesity and T2D.

Recent studies suggest that insulin-like growth factor binding protein 2 (IGFBP-2) may protect against type 2 diabetes, but population-based human studies are scarce. We aimed to investigate the prospective association of circulating IGFBP-2 concentrations and of differential methylation in the IGFBP-2 gene with type 2 diabetes risk.

The mechanisms underlying improved insulin sensitivity after surgically-induced weight loss are still unclear. We monitored skeletal muscle metabolism in obese individuals before and over 52 weeks after metabolic surgery. Initial weight loss occurs in parallel with a decrease in muscle oxidative capacity and respiratory control ratio. Persistent elevation of intramyocellular lipid intermediates, likely resulting from unrestrained adipose tissue lipolysis, accompanies the lack of rapid changes in insulin sensitivity. Simultaneously, alterations in skeletal muscle expression of genes involved in calcium/lipid metabolism and mitochondrial function associate with subsequent distinct DNA methylation patterns at 52 weeks after surgery. Thus, initial unfavorable metabolic changes including insulin resistance of adipose tissue and skeletal muscle precede epigenetic modifications of genes involved in muscle energy metabolism and the long-term improvement of insulin sensitivity.

Short children using growth hormone (GH) to accelerate their growth respond to this treatment with a variable efficacy. The causes of this individual variability are multifactorial and could involve epigenetics. Quantifying the impact of epigenetic variation on response to treatments is an emerging challenge. Here we show that methylation of a cluster of CGs located within the P2 promoter of the insulin-like growth factor 1 (IGF1) gene, notably CG-137, is inversely closely correlated with the response of growth and circulating IGF1 to GH administration. For example, variability in CG-137 methylation contributes 25% to variance of growth response to GH. Methylation of CGs in the P2 promoter is negatively associated with the increased transcriptional activity of P2 promoter in patients' mononuclear blood cells following GH administration. Our observation indicates that epigenetics is a major determinant of GH signaling (physiology) and of individual responsiveness to GH treatment (pharmacoepigenetics).

Obesity is a worldwide epidemic and contributes to global morbidity and mortality mediated via the development of nonalcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D), cardiovascular (CVD) and other diseases. It is a consequence of an elevated caloric intake, a sedentary lifestyle and a genetic as well as an epigenetic predisposition. This review summarizes changes in DNA methylation and microRNAs identified in blood cells and different tissues in obese human and rodent models. It includes information on epigenetic alterations which occur in response to fat-enriched diets, exercise and metabolic surgery and discusses the potential of interventions to reverse epigenetic modifications.

The identification of individuals with a high risk of developing type 2 diabetes (T2D) is fundamental for prevention. Here, we used a translational approach and prediction criteria to identify changes in DNA methylation visible before the development of T2D. Islets of Langerhans were isolated from genetically identical 10-week-old female New Zealand Obese mice, which differ in their degree of hyperglycemia and in liver fat content. The application of a semiexplorative approach identified 497 differentially expressed and methylated genes ( P = 6.42e-09, hypergeometric test) enriched in pathways linked to insulin secretion and extracellular matrix-receptor interaction. The comparison of mouse data with DNA methylation levels of incident T2D cases from the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort, revealed 105 genes with altered DNA methylation at 605 cytosine-phosphate-guanine (CpG) sites, which were associated with future T2D. AKAP13 , TENM2 , CTDSPL , PTPRN2 , and PTPRS showed the strongest predictive potential (area under the receiver operating characteristic curve values 0.62–0.73). Among the new candidates identified in blood cells, 655 CpG sites, located in 99 genes, were differentially methylated in islets of humans with T2D. Using correction for multiple testing detected 236 genes with an altered DNA methylation in blood cells and 201 genes in diabetic islets. Thus, the introduced translational approach identified novel putative biomarkers for early pancreatic islet aberrations preceding T2D.