Distinct Roles of Zmynd17 and PGC1α in Mitochondrial Quality Control and Biogenesis in Skeletal Muscle
Bibliographic record
Abstract
Maintaining skeletal muscle mitochondrial quality is important not only for muscle activity but also for systemic metabolism. Exercise has long been recognized to have a positive impact on muscle mitochondrial quality. Although exercise triggers various changes in the mitochondrial dynamics, its molecular basis remains to be elucidated. We have previously reported that inactivation of the muscle-specific protein, zinc finger MYND domain-containing protein 17 (Zmynd17), results in mitochondrial abnormalities. To investigate the link between Zmynd17 activity and exercise-induced mitochondrial maintenance, we observed the effect of consecutive exercise on the mitochondrial quality in Zmynd17-deficient muscles. Zmynd17-deficient mice displayed abnormal mitochondrial morphology in limb muscles, which remarkably improved upon voluntary exercise. Interestingly, morphological abnormalities in mitochondria were even more apparent when PGC1α, a regulator of exercise-induced mitochondrial biogenesis, was overexpressed in Zmynd17-KO limb muscle. These abnormalities were also ameliorated by voluntary exercise. Our results show that neither the effect of consecutive exercise on mitochondrial quality nor PGC1α-induced mitochondrial biogenesis are mediated through Zmynd17 activity, thereby suggesting the existence of a complex mechanism of mitochondrial quality control in muscles.
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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 | 0.001 |
| 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".