Research Paper Volume 12, Issue 1 pp 740—755
Network analysis of human muscle adaptation to aging and contraction
- 1 Department of Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, Exeter EX1 2LU, UK
- 2 Biosciences, University of Exeter, Exeter EX4 4QD, UK
- 3 MRC-ARUK Centre for Musculoskeletal aging Research and National Institute of Health Research, Biomedical Research Centre, Royal Derby Hospital Centre, School of Medicine, University of Nottingham, Derby DE22 3DT, UK
- 4 Department of Surgery, Postgraduate Entry Medical School, Royal Derby Hospital Centre, School of Medicine, University of Nottingham, Derby DE22 3DT, UK
- 5 School of Health Sciences, Örebro University, Örebro 70182, Sweden
Received: August 29, 2019 Accepted: December 24, 2019 Published: January 7, 2020
https://doi.org/10.18632/aging.102653How to Cite
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.