PGC-1α modulates denervation-induced mitophagy in skeletal muscle
Bibliographic record
Abstract
BACKGROUND: Alterations in skeletal muscle contractile activity necessitate an efficient remodeling mechanism. In particular, mitochondrial turnover is essential for tissue homeostasis during muscle adaptations to chronic use and disuse. While mitochondrial biogenesis appears to be largely governed by the transcriptional co-activator peroxisome proliferator co-activator 1 alpha (PGC-1α), selective mitochondrial autophagy (mitophagy) is thought to mediate organelle degradation. However, whether PGC-1α plays a direct role in autophagy is currently unclear. METHODS: To investigate the role of the co-activator in autophagy and mitophagy during skeletal muscle remodeling, PGC-1α knockout (KO) and overexpressing (Tg) animals were unilaterally denervated, a common model of chronic muscle disuse. RESULTS: Animals lacking PGC-1α exhibited diminished mitochondrial density alongside myopathic characteristics reminiscent of autophagy-deficient muscle. Denervation promoted an induction in autophagy and lysosomal protein expression in wild-type (WT) animals, which was partially attenuated in KO animals, resulting in reduced autophagy and mitophagy flux. PGC-1α overexpression led to an increase in lysosomal capacity as well as indicators of autophagy flux but exhibited reduced localization of LC3II and p62 to mitochondria, compared to WT animals. A correlation was observed between the levels of the autophagy-lysosome master regulator transcription factor EB (TFEB) and PGC-1α in muscle, supporting their coordinated regulation. CONCLUSIONS: Our investigation has uncovered a regulatory role for PGC-1α in mitochondrial turnover, not only through biogenesis but also via degradation using the autophagy-lysosome machinery. This implies a PGC-1α-mediated cross-talk between these two opposing processes, working to ensure mitochondrial homeostasis during muscle adaptation to chronic disuse.
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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".