Reinventing the Wheel: Voluntary Running Promotes Mitochondrial Adaptations in mtDNA Mutator Mouse Model of Aging
Bibliographic record
Abstract
The role of mitochondrial abnormalities and mitochondrial DNA (mtDNA) mutagenesis has been extensively characterized in aging. A causal role for mtDNA mutagenesis in aging is supported by recent studies demonstrating that the polymerase gamma (PolG) mutator mouse, harbouring a proofreading‐deficient copy of PolG, exhibits an accelerated aging phenotype, systemic mitochondrial dysfunction, multisystem failure, and reduced lifespan. Longitudinal studies in humans demonstrate that a physically active lifestyle reduces the risk of chronic diseases and increases life expectancy. We aimed to delineate if voluntary wheel running (VOL) can prevent mitochondrial respiratory chain dysfunction and attenuate mitochondrial ultrastructural abnormalities in skeletal muscle of PolG mice. At 3 months of age, 24 PolG mice (♀ = ♂) were randomly assigned to a sedentary (SED) or VOL group. VOL training attenuated the loss of muscle mass (19%) and prevented cardiac hypertrophy (20%) in PolG‐VOL vs. PolG‐SED (P<0.05). VOL training dramatically increased skeletal muscle mtDNA copy number (1.5‐fold), COX activity (75%), and complex‐IV subunit I (2‐fold) and subunit‐IV (1.7‐fold) protein content vs. PolG‐SED (P<0.05). Electron microscopic analysis revealed an atypical accumulation of swollen pleomorphic mitochondria with fragmented cristae and disrupted membranes in the skeletal muscle of PolG‐SED. VOL training reduced these mitochondrial morphological abnormalities in PolG mice. We propose that VOL training promotes mitochondrial capacity and offers a valuable therapeutic intervention for attenuating aging and related morbidity and mortality. (Funded by CIHR)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".