Abstract
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.
Highlights
After the age of 30, skeletal muscle mass decreases at a rate of 3%–8% per decade, and the rate of loss accelerates after age 65 [1, 2]
Post-Resistance exercise training (RT) skeletal muscle biopsies were only available for 22 of the 31 participants assessed at baseline, computed tomography (CT) mid-thigh muscle cross-sectional area (CSA) and dual-energy X-ray absorptiometry (DXA) thigh muscle mass were measured on all 31 participants both pre- and post-RT
Using two computational approaches to complement and bolster one another, we have provided evidence that baseline expression of skeletal muscle gene networks may aid in prediction of the degree of hypertrophy
Summary
After the age of 30, skeletal muscle mass decreases at a rate of 3%–8% per decade, and the rate of loss accelerates after age 65 [1, 2]. This well-established reduction in skeletal muscle mass with advancing age, accompanied by more precipitous declines in strength and power [3], is known to heighten the risk of falls and injuries [4] and to compromise quality of life via reduced functional capacity, mobility, and independence. Aging muscle atrophy and accompanying changes in body composition are linked to increased risk of metabolic dysfunction (e.g., insulin resistance and diabetes) [5]. Resistance exercise training (RT) is the most effective treatment to date to counteract this aging-related muscle loss and weakness [6–8]. Expanding our understanding of factors that likely contribute to this heterogeneity should enable identification of prospective “low responders” that may benefit from alternative approaches to RT, perhaps in combination with adjunctive treatments, to mitigate age-related muscle decline and its consequences
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