Acute Exercise‐Induced Mitophagy is Mediated in Part by PGC‐1α
Bibliographic record
Abstract
Chronic exercise results in systemic metabolic benefits in the major metabolic centers of the body. Skeletal muscle is a pillar of whole body metabolism and this tissue undergoes metabolic remodelling with repeated bouts of contractile activity. This is accomplished by increased mitochondrial turnover with a concurrent increase in both organelle biogenesis and removal. While biogenesis is largely orchestrated by the transcriptional co-activator PGC-1α, the regulation of mitochondrial degradation (i.e mitophagy) is less well established. Coincidentally, both mitochondrial biogenesis and autophagy are activated by an acute bout of exercise. However, the induction of mitophagy following exercise, and the interplay between autophagy and PGC-1α following acute exercise, have not been thoroughly examined. To address this, we subjected wild type (WT) and PGC-1α knockout animals (KO) to an acute bout of exhaustive exercise. Lack of PGC-1α resulted in a 40% drop in mitochondrial content and a 25% decline in running performance. KO mice also depicted a greater increase and a slower recovery of blood lactate following exercise, indicating metabolic distress. Indeed, exercise-induced activation of AMPK and p38 was greater in KO as compared to WT animals. Exercise also induced a 2-3-fold increase in gene transcripts of various mitochondrial (e.g COXIV, Tfam) and autophagy-related genes (e.g. p62, LC3) in WT animals only. Moreover, both autophagy and mitophagy flux were induced by exercise, but this increase was attenuated in KO animals. These results suggest that an acute bout of exercise is sufficient to induce mitochondrial turnover through increased biogenesis and degradation. Moreover, these two processes appear to be, at least in part, regulated by PGC-1α.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".