p53 regulates skeletal muscle mitophagy and mitochondrial quality control following denervation-induced muscle disuse
Bibliographic record
Abstract
Persistent inactivity promotes skeletal muscle atrophy, marked by mitochondrial aberrations that affect strength, mobility, and metabolic health leading to the advancement of disease. Mitochondrial quality control (MQC) pathways include biogenesis (synthesis), mitophagy/lysosomal turnover, and the mitochondrial unfolded protein response, which serve to maintain an optimal organelle network. Tumor suppressor p53 has been implicated in regulating muscle mitochondria in response to cellular stress; however, its role in the context of muscle disuse has yet to be explored, and whether p53 is necessary for MQC remains unclear. To address this, we subjected p53 muscle-specific KO (mKO) and WT mice to unilateral denervation. Transcriptomic and pathway analyses revealed dysregulation of pathways pertaining to mitochondrial function, and especially turnover, in mKO muscle following denervation. Protein and mRNA data of the MQC pathways indicated activation of the mitochondrial unfolded protein response and mitophagy-lysosome systems along with reductions in mitochondrial biogenesis and content in WT and mKO tissue following chronic denervation. However, p53 ablation also attenuated the expression of autophagy-mitophagy machinery, reduced autophagic flux, and enhanced lysosomal dysfunction. While similar reductions in mitochondrial biogenesis and content were observed between genotypes, MQC dysregulation exacerbated mitochondrial dysfunction in mKO fibers, evidenced by elevated reactive oxygen species. Moreover, acute experiments indicate that p53 mediates the expression of transcriptional regulators of MQC pathways as early as 1 day following denervation. Together, our data illustrate exacerbated mitochondrial dysregulation with denervation stress in p53 mKO tissue, thus indicating that p53 contributes to organellar maintenance via regulation of MQC pathways during muscle atrophy.
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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".