Meta‐analysis of genome‐wide DNA methylation and integrative omics of age in human skeletal muscle

Abstract

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/).

Skeletal muscle methylome and transcriptome integration reveals profound sex differences related to muscle function and substrate metabolism

“…While FOXO3 skeletal muscle gene expression differs between males and females, it seems that the direction is opposite in young and old individuals, which emphasizes the caution that should be used when interpreting sex differences across a large age range of individuals. Interestingly, FOXO3 was hypomethylated in skeletal muscle with age in a recent study from our group [ 97 ]. Regions with differential DNA methylation in the three validated genes were located in enhancers (FOXO3 and ALDH1A1) and regions of strong transcription (GGT7), suggesting that subsequent transcription may be altered.…”

Aging, Skeletal Muscle, and Epigenetics

“…23 Since then, it has been confirmed that the methylation status of approximately 200 CpG sites can accurately predict chronological age in skeletal muscle tissue. 24,25 We extended these findings using technology allowing greater coverage of 850,000 CpG sites in skeletal muscle tissue, as well as isolated skeletal muscle-derived stem cells, from older (mean, 83 years) versus young adults (mean, 27 years). 26 Indeed, we also demonstrated an accumulation of methylation (hypermethylation) in both aged muscle tissue and isolated muscle-derived stem cells, with tissue displaying enriched hypermethylation in load-/growth-associated gene pathways such as focal adhesion and mTORC signaling and isolated cells displaying this enrichment in genes within the calcium signaling pathway.…”

Aerobic Exercise Training Rejuvenates the Human Skeletal Muscle Methylome Ten Years after Breast Cancer Treatment and Survival

“…Ageing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns [22]. Together with others we have recently helped developed a muscle-specific epigenetic clock (muscle-specific epigenetic age test, or ‘MEAT’) that predicts age with better accuracy than the pan-tissue epigenetic clock [31, 32]. Therefore, for the analysis of DNA methylation age (DNAm Age), we extracted the β-values from normalized data, as previously described.…”

“…This is because, with age, it is proposed that our cells (including SkM [27-29]) accumulate methylation [22, 30] and therefore become ‘hypermethylated’. Meta-analysis of the SkM methylome also identified that hypermethylation predominantly occurs in gene regulatory regions with advancing age [31, 32]. In contrast, both acute exercise and chronic training have been shown to decrease methylation (i.e., hypomethylation) in both human and mouse SkM [17, 18, 33-35].…”

“…2) Investigate the influence of 5 months aerobic exercise training on the DNA methylome in SkM of breast cancer survivors, with the hypothesis that aerobic training will ‘rejuvenate’ the epigenetic landscape back towards DNA methylation signatures observed in healthy age-matched controls. 3) Investigate whether cancer survival affects an individual’s epigenetic age and whether exercise can alter epigenetic ageing using a recently developed muscle-specific epigenetic clock [31, 32]. For this, we hypothesise that cancer survival will increase epigenetic age and that aerobic training could prevent this increase when compared to cancer survivors who do not undertake training.…”