Endurance Exercise and Systemic Mitochondrial Rejuvenescence: Run for Your Life!
Bibliographic record
Abstract
A causal role for mitochondrial DNA (mtDNA) mutagenesis in mammalian 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. Studies in primary cells from mitochondrial myopathy patients indicate that inducing mitochondrial biogenesis via over‐expression of PGC‐1α has therapeutic potential. We aimed to delineate if endurance exercise‐mediated induction of PGC‐1α metabolic network can prevent premature aging and systemic decline in PolG mice. At 3‐mo, 36 PolG mice (♀ = ♂) were randomly assigned to a sedentary (SED) or forced‐endurance training (END; 15m/min for 45 min, 3x/week for 5 months) group. In skeletal muscle, END increased nuclear abundance of PGC‐1α (76%) while concomitantly decreasing RIP140 (a negative regulator of PGC‐1α; 33%) nuclear content (P<0.05). END also increased mtDNA copy number (2–3 fold), protein content of respiratory chain subunits (CI‐NUFA9, CII‐subunit 70 kDa, C‐III core 2, and COX‐IV; 2–3 fold), and COX activity (22–120%) in skeletal muscle, lungs, heart, brain, and gonads (P<0.05). These molecular adaptations conferred complete phenotypic protection, reduced multi‐organ pathology, and increased lifespan of PolG mice. We conclude that END training promotes PGC‐1α‐mediated systemic mitochondrial oxidative capacity, contributing to the complete rejuvenation of PolG mice. We propose that END training offers a valuable therapeutic intervention for attenuating and/or reversing mitochondrial abnormalities associated with aging. (Funded by CIHR, and Mr. Warren Lammert and family)
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".