The skeletal muscle hypertrophic response to resistance exercise training (RT) is highly variable across individuals. The molecular underpinnings of this heterogeneity are unclear. This study investigated transcriptional networks linked to RT-induced muscle hypertrophy, classified as 1) predictive of hypertrophy, 2) responsive to RT independent of muscle hypertrophy, or 3) plastic with hypertrophy. Older adults (n = 31, 18 F/13 M, 70 ± 4 yr) underwent 14-wk RT (3 days/wk, alternating high-low-high intensity). Muscle hypertrophy was assessed by pre- to post-RT change in mid-thigh muscle cross-sectional area (CSA) [computed tomography (CT), primary outcome] and thigh lean mass [dual-energy X-ray absorptiometry (DXA), secondary outcome]. Transcriptome-wide poly-A RNA-seq was performed on vastus lateralis tissue collected pre- (n = 31) and post-RT (n = 22). Prediction networks (using only baseline RNA-seq) were identified by weighted gene correlation network analysis (WGCNA). To identify Plasticity networks, WGCNA change indices for paired samples were calculated and correlated to changes in muscle size outcomes. Pathway-level information extractor (PLIER) was applied to identify Response networks and link genes to biological annotation. Prediction networks (n = 6) confirmed transcripts previously connected to resistance/aerobic training adaptations in the MetaMEx database while revealing novel member genes that should fuel future research to understand the influence of baseline muscle gene expression on hypertrophy. Response networks (n = 6) indicated RT-induced increase in aerobic metabolism and reduced expression of genes associated with spliceosome biology and type-I myofibers. A single exploratory Plasticity network was identified. Findings support that interindividual differences in baseline gene expression may contribute more than RT-induced changes in gene networks to muscle hypertrophic response heterogeneity.
|Number of pages||16|
|State||Published - 2021|
Bibliographical noteFunding Information:
The authors thank all study participants as well as clinical and laboratory staff involved in data collection. In addition, the authors thank UAB High-Performance Computing, supported by the National Science Foundation Grant OAC-1541310, the University of Alabama at Birmingham, and the Alabama Innovation Fund.
K. M. Lavin was supported by NIA F32AG062048; M. B. Bell was supported by NICHD T32HD071866; and parent clinical trial was supported by R01AG046920. Additional personnel effort supported by DARPA contract FA8650-19-C-7944, NIH grant P2CHD086851, NIH cooperative agreement U01AR071133.
© 2021 the American Physiological Society.
- Muscle growth
- Network biology
- Resistance exercise
- Skeletal muscle
ASJC Scopus subject areas