Role of PGC-1α during acute exercise-induced autophagy and mitophagy in skeletal muscle
Bibliographic record
Abstract
Regular exercise leads to systemic metabolic benefits, which require remodeling of energy resources in skeletal muscle. During acute exercise, the increase in energy demands initiate mitochondrial biogenesis, orchestrated by the transcriptional coactivator peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α). Much less is known about the degradation of mitochondria following exercise, although new evidence implicates a cellular recycling mechanism, autophagy/mitophagy, in exercise-induced adaptations. How mitophagy is activated and what role PGC-1α plays in this process during exercise have yet to be evaluated. Thus we investigated autophagy/mitophagy in muscle immediately following an acute bout of exercise or 90 min following exercise in wild-type (WT) and PGC-1α knockout (KO) animals. Deletion of PGC-1α resulted in a 40% decrease in mitochondrial content, as well as a 25% decline in running performance, which was accompanied by severe acidosis in KO animals, indicating metabolic distress. Exercise induced significant increases in gene transcripts of various mitochondrial (e.g., cytochrome oxidase subunit IV and mitochondrial transcription factor A) and autophagy-related (e.g., p62 and light chain 3) genes in WT, but not KO, animals. Exercise also resulted in enhanced targeting of mitochondria for mitophagy, as well as increased autophagy and mitophagy flux, in WT animals. This effect was attenuated in the absence of PGC-1α. We also identified Niemann-Pick C1, a transmembrane protein involved in lysosomal lipid trafficking, as a target of PGC-1α that is induced with exercise. These results suggest that mitochondrial turnover is increased following exercise and that this effect is at least in part coordinated by PGC-1α.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".