RETRACTED ARTICLE:Exercise-induced mitochondrial p53 repairs mtDNA mutations in mutator mice
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
BACKGROUND: Human genetic disorders and transgenic mouse models have shown that mitochondrial DNA (mtDNA) mutations and telomere dysfunction instigate the aging process. Epidemiologically, exercise is associated with greater life expectancy and reduced risk of chronic diseases. While the beneficial effects of exercise are well established, the molecular mechanisms instigating these observations remain unclear. RESULTS: Endurance exercise reduces mtDNA mutation burden, alleviates multisystem pathology, and increases lifespan of the mutator mice, with proofreading deficient mitochondrial polymerase gamma (POLG1). We report evidence for a POLG1-independent mtDNA repair pathway mediated by exercise, a surprising notion as POLG1 is canonically considered to be the sole mtDNA repair enzyme. Here, we show that the tumor suppressor protein p53 translocates to mitochondria and facilitates mtDNA mutation repair and mitochondrial biogenesis in response to endurance exercise. Indeed, in mutator mice with muscle-specific deletion of p53, exercise failed to prevent mtDNA mutations, induce mitochondrial biogenesis, preserve mitochondrial morphology, reverse sarcopenia, or mitigate premature mortality. CONCLUSIONS: Our data establish a new role for p53 in exercise-mediated maintenance of the mtDNA genome and present mitochondrially targeted p53 as a novel therapeutic modality for diseases of mitochondrial etiology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".