The Role of p53 in Determining Mitochondrial Adaptations to Endurance Training in Skeletal Muscle
Bibliographic record
Abstract
Abstract p53 plays an important role in regulating mitochondrial homeostasis. However, it is unknown whether p53 is required for the physiological and mitochondrial adaptations with exercise training. Furthermore, it is also unknown whether impairments in the absence of p53 are a result of its loss in skeletal muscle, or a secondary effect due to its deletion in alternative tissues. Thus, we investigated the role of p53 in regulating mitochondria both basally, and under the influence of exercise, by subjecting C57Bl/6J whole-body (WB) and muscle-specific p53 knockout (mKO) mice to a 6-week training program. Our results confirm that p53 is important for regulating mitochondrial content and function, as well as proteins within the autophagy and apoptosis pathways. Despite an increased proportion of phosphorylated p53 (Ser15) in the mitochondria, p53 is not required for training-induced adaptations in exercise capacity or mitochondrial content and function. In comparing mouse models, similar directional alterations were observed in basal and exercise-induced signaling modifications in WB and mKO mice, however the magnitude of change was less pronounced in the mKO mice. Our data suggest that p53 is required for basal mitochondrial maintenance in skeletal muscle, but is not required for the adaptive responses to exercise training.
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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".