Mitochondrial DNA rejuvenescence: Why Resist?
Bibliographic record
Abstract
We previously reported that satellite cells from older adults do not accumulate aging‐associated mitochondrial DNA (mtDNA) deletions ( Safdar et al, 2007 ). Resistance (RES) training induces satellite cell activation and skeletal muscle hypertrophy, and is recommended as an effective countermeasure to sarcopenia in the elderly. We aimed to delineate the protective mechanism underlying this effect. Muscle biopsies were taken from the vastus lateralis of 16 older subjects (70 ± 5 y; 8 women) before and after 6 months of RES training and analyzed for protein content of electron transport chain (complex I ND6 , complex III core protein 2 , complex IV COX subunits –II and –IV , and complex V alpha subunit ), antioxidant enzymes (Mn‐SOD, Cu/Zn‐SOD, and catalase), markers of mitochondrial abundance (citrate synthase CS ) and oxidative damage (protein carbonyl PC , 4‐hydroxynonenal 4HNE , and total DNA 8‐hydroxy‐2‐deoxyguanosine 8‐OHdG ), mtDNA deletions, as well as CS, COX, and catalase enzyme activity. RES training increased protein content of ND6, core protein 2, COX‐II, COX‐IV, complex V alpha subunit and catalase by 25%, 41%, 62%, 69%, 31%, and 95% respectively (P < 0.042), and COX and catalase enzyme activity by 79% and 89%, respectively (P < 0.028). RES training decreased mtDNA deletions (63%), PC (45%), 4‐HNE (29%), and total DNA 8‐OHdG (45%) content (P < 0.01). We conclude that the improvements in mitochondrial function occurred through RES training‐induced recruitment of satellite cells resulting in mitochondrial gene shifting and rejuvenescence in the skeletal muscle of older adults. We propose that RES training is a viable therapy to attenuate and/or “reverse” mitochondrial abnormalities associated with sarcopenia. (Funded by CIHR Institute of Aging, and Warren Lammert and Kathy Corkins).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".