p53 Improves Aerobic Exercise Capacity and Augments Skeletal Muscle Mitochondrial DNA Content
Bibliographic record
Abstract
RATIONALE: Exercise capacity is a physiological characteristic associated with protection from both cardiovascular and all-cause mortality. p53 regulates mitochondrial function and its deletion markedly diminishes exercise capacity, but the underlying genetic mechanism orchestrating this is unclear. Understanding the biology of how p53 improves exercise capacity may provide useful insights for improving both cardiovascular as well as general health. OBJECTIVE: The purpose of this study was to understand the genetic mechanism by which p53 regulates aerobic exercise capacity. METHODS AND RESULTS: Using a variety of physiological, metabolic, and molecular techniques, we further characterized maximum exercise capacity and the effects of training, measured various nonmitochondrial and mitochondrial determinants of exercise capacity, and examined putative regulators of mitochondrial biogenesis. As p53 did not affect baseline cardiac function or inotropic reserve, we focused on the involvement of skeletal muscle and now report a wider role for p53 in modulating skeletal muscle mitochondrial function. p53 interacts with Mitochondrial Transcription Factor A (TFAM), a nuclear-encoded gene important for mitochondrial DNA (mtDNA) transcription and maintenance, and regulates mtDNA content. The increased mtDNA in p53(+/+) compared to p53(-/-) mice was more marked in aerobic versus glycolytic skeletal muscle groups with no significant changes in cardiac tissue. These in vivo observations were further supported by in vitro studies showing overexpression of p53 in mouse myoblasts increases both TFAM and mtDNA levels whereas depletion of TFAM by shRNA decreases mtDNA content. CONCLUSIONS: Our current findings indicate that p53 promotes aerobic metabolism and exercise capacity by using different mitochondrial genes and mechanisms in a tissue-specific manner.
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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) | 0.003 | 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".