Adaptive time course of the skeletal muscle proteome during programmed resistance training in rats
Bibliographic record
Abstract
Abstract Resistance training (RT) promotes muscle protein accretion and myofiber hypertrophy, driven by dynamic processes of protein synthesis and degradation. While molecular studies have focused on acute signalling or long-term hypertrophy and strength gains, a critical gap remains in understanding the intermediate processes of muscle adaptation. Acute signalling does not always correlate directly with long-term outcomes, highlighting the need for a time-course analysis of protein abundance and turnover rates. To address this, we utilised deuterium oxide labelling and peptide mass spectrometry to quantify absolute protein content and synthesis rates in skeletal muscle. A daily programmed resistance training regimen was applied to the rat tibialis anterior (TA) via electrical stimulation of the left hind limb for 10, 20, and 30 days (5 sets of 10 repetitions daily). Muscle samples from stimulated (Stim) and contralateral control (Ctrl) limbs were analysed, quantifying 658 protein abundances and 215 protein synthesis rates. Unsupervised temporal clustering of protein responses revealed distinct phases of muscle adaptation, with early (0-10 days) and mid (10-20 days) responses driven by differential protein accretion rates in ribosomal and mitochondrial networks, respectively. These findings suggest that subsets of proteins exhibit distinct adaptation timelines due to variations in translation and/or degradation rates. A deeper understanding of these temporal shifts could improve strategies for optimising muscle growth and functional adaptation to resistance training.
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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".