Dynamic proteome profiling uncovers age-related impairments in proteostasis and the protective effects of resistance exercise in human skeletal muscle
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».