Network analysis of human muscle adaptation to aging and contraction

Etheridge, Timothy; Willis, CRG; Atherton, Philip; Ames, Ryan; Szewczyk, Nathaniel; Deane, Colleen; Kadi, Fawzi; Phillips, Bethan; Smith, Kenneth; Boereboom, Catherine; Wilkinson, Daniel; Abdulla, Haitham; Williams, John; Bukhari, Syed; Lund, Jonathan

Abstract

Resistance exercise (RE) remains a primary approach for minimising aging muscle decline. Understanding muscle adaptation to individual contractile components of RE (eccentric, concentric) might optimise RE-based intervention strategies. Herein, we employed a network-driven pipeline to identify putative molecular drivers of muscle aging and contraction mode responses. RNA-sequencing data was generated from young (21±1 y) and older (70±1 y) human skeletal muscle before and following acute unilateral concentric and contralateral eccentric contractions. Application of weighted gene co-expression network analysis identified 33 distinct gene clusters (‘modules’) with an expression profile regulated by aging, contraction and/or linked to muscle strength. These included two contraction ‘responsive’ modules (related to ‘cell adhesion’ and ‘transcription factor’ processes) that also correlated with the magnitude of post-exercise muscle strength decline. Module searches for ‘hub’ genes and enriched transcription factor binding sites established a refined set of candidate module-regulatory molecules (536 hub genes and 60 transcription factors) as possible contributors to muscle aging and/or contraction responses. Thus, network-driven analysis can identify new molecular candidates of functional relevance to muscle aging and contraction mode adaptations.

Citation

Etheridge, T., Willis, C., Atherton, P., Ames, R., Szewczyk, N., Deane, C., Kadi, F., Phillips, B., Smith, K., Boereboom, C., Wilkinson, D., Abdulla, H., Williams, J., Bukhari, S., & Lund, J. (2020). Network analysis of human muscle adaptation to aging and contraction. Aging, 12(1), 740-755. https://doi.org/10.18632/aging.102653

• Journal Article Type: Article
• Acceptance Date: Dec 24, 2019
• Online Publication Date: Jan 7, 2020
• Publication Date: Jan 15, 2020
• Deposit Date: Jan 12, 2020
• Publicly Available Date: Jan 13, 2020
• Journal: Aging
• Electronic ISSN: 1945-4589
• Publisher: Impact Journals
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 1
• Pages: 740-755
• DOI: https://doi.org/10.18632/aging.102653
• Public URL: https://nottingham-repository.worktribe.com/output/3714217
• Publisher URL: https://www.aging-us.com/article/102653/text