File(s) stored somewhere else
Please note: Linked content is NOT stored on CQUniversity and we can't guarantee its availability, quality, security or accept any liability.
Meta‐analysis of genome‐wide DNA methylation and integrative omics of age in human skeletal muscle
journal contributionposted on 2022-02-15, 00:59 authored by Sarah Voisin, Macsue Jacques, Shanie Landen, Nicholas R Harvey, Larisa M Haupt, Lyn R Griffiths, Sofiya Gancheva, Meriem Ouni, Markus Jähnert, Kevin J Ashton, Vernon G Coffey, Jamie‐Lee M Thompson, Thomas DoeringThomas Doering, Anne Gabory, Claudine Junien, Robert Caiazzo, Hélène Verkindt, Violetta Raverdy, François Pattou, Philippe Froguel, Jeffrey M Craig, Sara Blocquiaux, Martine Thomis, Adam P Sharples, Annette Schürmann, Michael Roden, Steve Horvath, Nir Eynon
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/).
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Number of Pages15
Publisher LicenseCC BY
Additional RightsCC BY 4.0
External Author AffiliationsUniversity of California Los Angeles,; Norwegian School of Sport Sciences; KULeuven, Belgium; Deakin University; Univ Lille, Université Paris-Saclay, France; German Center for Diabetes Research (DZD); Victoria University; Bond University; Bond University; Queensland University of Technology