An epigenetic clock for human skeletal muscle
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Description
<p><b>Background</b>: Ageing is associated with DNA methylation changes in all human tissues, and epigenetic markers can estimate chronological age based on DNA methylation patterns across tissues. However, the construction of the original pan-tissue epigenetic clock did not include skeletal muscle samples and hence exhibited a strong deviation between DNA methylation and chronological age in this tissue. <br/></p><p><b>Methods</b>: To address this, we developed a more accurate, muscle-specific epigenetic clock based on the genome-wide DNA methylation data of 682 skeletal muscle samples from 12 independent datasets (18–89 years old, 22% women, 99% Caucasian), all generated with Illumina HumanMethylation (HM) arrays (HM27, HM450, or HMEPIC). We also took advantage of the large number of samples to conduct an epigenome-wide association study of age-associated DNA methylation patterns in skeletal muscle. <br/></p><p><b>Results</b>: The newly developed clock uses 200 cytosine-phosphate–guanine dinucleotides to estimate chronological age in skeletal muscle, 16 of which are in common with the 353 cytosine-phosphate–guanine dinucleotides of the pan-tissue clock. The muscle clock outperformed the pan-tissue clock, with a median error of only 4.6 years across datasets (vs. 13.1 years for the pan-tissue clock, P < 0.0001) and an average correlation of ρ = 0.62 between actual and predicted age across datasets (vs. ρ = 0.51 for the pan-tissue clock). Lastly, we identified 180 differentially methylated regions with age in skeletal muscle at a false discovery rate < 0.005. However, gene set enrichment analysis did not reveal any enrichment for gene ontologies. <br/></p><p><b>Conclusions</b>: We have developed a muscle-specific epigenetic clock that predicts age with better accuracy than the pan-tissue clock. We implemented the muscle clock in an r package called Muscle Epigenetic Age Test available on Bioconductor to estimate epigenetic age in skeletal muscle samples. This clock may prove valuable in assessing the impact of environmental factors, such as exercise and diet, on muscle-specific biological ageing processes.</p>
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ID Code: | 202281 | ||||
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Item Type: | Contribution to Journal (Journal Article) | ||||
Refereed: | Yes | ||||
ORCID iD: |
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Measurements or Duration: | 12 pages | ||||
Keywords: | Ageing, Biological age, DNA methylation, Epigenetic age, Epigenetic clock, Skeletal muscle | ||||
DOI: | 10.1002/jcsm.12556 | ||||
ISSN: | 2190-5991 | ||||
Pure ID: | 62837684 | ||||
Divisions: | Current > Research Centres > Centre for Genomics and Personalised Health Past > Institutes > Institute of Health and Biomedical Innovation Current > QUT Faculties and Divisions > Faculty of Health |
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Funding Information: | This work was supported by Sarah Voisin's National Health and Medical Research Council Early Career Research Fellowship (APP11577321) and by Nir Eynon's National Health and Medical Research Council Career Development Fellowship (APP1140644). The Gene Skeletal Muscle Adaptive Response to Training and LITER studies were both supported by the Collaborative Research Network for Advancing Exercise and Sports Science (201202) from the Department of Education and Training, Australia. Mr Nicholas Harvey was supported by a PhD stipend also provided by Bond University Collaborative Research Network for Advancing Exercise & Sports Science. This research was also supported by infrastructure purchased with Australian Government Education Investment Fund Super Science Funds as part of the Therapeutic Innovation Australia—Queensland Node project. We also greatly acknowledge Erika Guzman at the Australian Translational Genomics Centre/Institute for Health and Biomedical Innovation/Queensland University of Technology for performing the HMEPIC assays in the LITER study. We would also like to acknowledge Matthew McKenzie at Deakin University for coming up with the MEAT acronym. The authors adhere to the ethical guidelines for publishing in the . Journal of Cachexia, Sarcopenia and Muscle This work was supported by Sarah Voisin's National Health and Medical Research Council Early Career Research Fellowship (APP11577321) and by Nir Eynon's National Health and Medical Research Council Career Development Fellowship (APP1140644). The Gene Skeletal Muscle Adaptive Response to Training and LITER studies were both supported by the Collaborative Research Network for Advancing Exercise and Sports Science (201202) from the Department of Education and Training, Australia. Mr Nicholas Harvey was supported by a PhD stipend also provided by Bond University Collaborative Research Network for Advancing Exercise & Sports Science. This research was also supported by infrastructure purchased with Australian Government Education Investment Fund Super Science Funds as part of the Therapeutic Innovation Australia—Queensland Node project. We also greatly acknowledge Erika Guzman at the Australian Translational Genomics Centre/Institute for Health and Biomedical Innovation/Queensland University of Technology for performing the HMEPIC assays in the LITER study. We would also like to acknowledge Matthew McKenzie at Deakin University for coming up with the MEAT acronym. The authors adhere to the ethical guidelines for publishing in the Journal of Cachexia, Sarcopenia and Muscle. | ||||
Copyright Owner: | 2020 The Author(s) | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 21 Jul 2020 03:17 | ||||
Last Modified: | 22 Jul 2024 10:59 |
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