Muscle transcriptional networks linked to resistance exercise training hypertrophic response heterogeneity.

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. Code/Data: https://github.com/kallavin/MASTERS_manuscript/tree/master.