Dynamic proteome profiling uncovers age-related impairments in proteostasis and the protective effects of resistance exercise in human skeletal muscle
Bibliographic record
Abstract
Abstract A loss of proteostasis is a primary hallmark of ageing that has emerged from mechanistic studies in model organisms, but little is currently known about changes to proteostasis in the muscle of older humans. We used stable isotope labelling (deuterium oxide; D2O) in vivo, and peptide mass spectrometry of muscle samples to investigate differences in proteome dynamics between the muscle of younger (28 ± 5 y; n=4) and older (69 ± 3 y; n=4) men during either habitual activity or resistance exercise training. We quantified the abundance of 1787 proteins and the turnover rate of 1046 proteins in bi-lateral samples of vastus lateralis (n=32 samples total) taken before and after a 15-day program including 5 sessions of unilateral leg-press exercise (3 sets of 10 repetitions at 90% of 10 RM). Our protein abundance profiling revealed a stoichiometric imbalance within the proteostasis network in aged skeletal muscle, including subunits of eIF3, subunits of 40S and 60S ribosomal proteins. The rate of bulk, mixed-protein synthesis was not different between younger and older men, but most ribosomal proteins were less abundant in the muscle of older participants, suggesting ribosomes in older muscle may exhibit increased translational efficiency to maintain similar levels of protein turnover compared to ribosomes in younger muscle. Resistance exercise partially restored age-related disruptions to the proteostasis network. In older skeletal muscle, resistance exercise specifically increased the absolute turnover rate (ATR) of mixed mitochondrial proteins, with increased fractional turnover rate (FTR) of prohibitin 1 (PHB1) and profilin-1 (PROF1), and increased abundance of prohibitin 2 (PHB2). These adaptations may suggest resistance exercise promotes mitochondrial proteostasis by facilitating the synthesis and maintenance of key mitochondrial proteins. Thus, our Dynamic Proteome Profiling data provide an impetus for further exploration of the role of proteostasis in maintaining skeletal muscle quality and supports resistance exercise as a potential therapeutic strategy to promote healthy skeletal muscle ageing in humans. In Brief Nishimura et al. used Dynamic Proteome Profiling to uncover whether the distorted proteomic landscape of ageing skeletal muscle is associated with altered turnover of specific proteins. Basal muscle from older men exhibits a divergence in protein abundance between subunits of eIF3 and subunits of 40S and 60S ribosomal proteins, whereas resistance exercise partially restored age-related disruptions in the muscle proteome. In older muscle, protein-specific turnover generally increases after resistance exercise, independent of changes in protein abundance, suggesting improved protein quality and renewal. Created in BioRender. Nishimura, Y. (2025) https://BioRender.com/p2a1aio Highlights Dynamic Proteome Profiling in human skeletal muscle ageing Ageing alters muscle proteostasis Mixed-muscle protein synthesis does not differ between younger and older men Resistance exercise increased mitochondrial protein turnover specifically in older muscle Protein-specific responses to resistance exercise differed between age groups
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".