Increased expression of the mitochondrial derived peptide, MOTS-c, in skeletal muscle of healthy aging men is associated with myofiber composition
Bibliographic record
Abstract
Mitochondria putatively regulate the aging process, in part, through the small regulatory peptide, mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) that is encoded by the mitochondrial genome. Here we investigated the regulation of MOTS-c in the plasma and skeletal muscle of healthy aging men. Circulating MOTS-c reduced with age, but older (70-81 y) and middle-aged (45-55 y) men had ~1.5-fold higher skeletal muscle MOTS-c expression than young (18-30 y). Plasma MOTS-c levels only correlated with plasma in young men, was associated with markers of slow-type muscle, and associated with improved muscle quality in the older group (maximal leg-press load relative to thigh cross-sectional area). Using small mRNA assays we provide evidence that MOTS-c transcription may be regulated independently of the full length 12S rRNA gene in which it is encoded, and expression is not associated with antioxidant response element (ARE)-related genes as previously seen in culture. Our results suggest that plasma and muscle MOTS-c are differentially regulated with aging, and the increase in muscle MOTS-c expression with age is consistent with fast-to-slow type muscle fiber transition. Further research is required to determine the molecular targets of endogenous MOTS-c in human muscle but they may relate to factors that maintain muscle quality.
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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".