Autophagy is essential to support skeletal muscle plasticity in response to endurance exercise
Bibliographic record
Abstract
Physical exercise is a stress that can substantially modulate cellular signaling mechanisms to promote morphological and metabolic adaptations. Skeletal muscle protein and organelle turnover is dependent on two major cellular pathways: Forkhead box class O proteins (FOXO) transcription factors that regulate two main proteolytic systems, the ubiquitin-proteasome, and the autophagy-lysosome systems, including mitochondrial autophagy, and the MTORC1 signaling associated with protein translation and autophagy inhibition. In recent years, it has been well documented that both acute and chronic endurance exercise can affect the autophagy pathway. Importantly, substantial efforts have been made to better understand discrepancies in the literature on its modulation during exercise. A single bout of endurance exercise increases autophagic flux when the duration is long enough, and this response is dependent on nutritional status, since autophagic flux markers and mRNA coding for actors involved in mitophagy are more abundant in the fasted state. In contrast, strength and resistance exercises preferentially raise ubiquitin-proteasome system activity and involve several protein synthesis factors, such as the recently characterized DAGK for mechanistic target of rapamycin activation. In this review, we discuss recent progress on the impact of acute and chronic exercise on cell component turnover systems, with particular focus on autophagy, which until now has been relatively overlooked in skeletal muscle. We especially highlight the most recent studies on the factors that can impact its modulation, including the mode of exercise and the nutritional status, and also discuss the current limitations in the literature to encourage further works on this topic.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".