Nuclear SIRT1 activity, but not protein content, regulates mitochondrial biogenesis in rat and human skeletal muscle
Bibliographic record
Abstract
Silent mating type information regulator 2 homolog 1 (SIRT1)-mediated peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) deacetylation is potentially key for activating mitochondrial biogenesis. Yet, at the whole muscle level, SIRT1 is not associated with mitochondrial biogenesis (Gurd, BJ, Yoshida Y, Lally J, Holloway GP, Bonen A. J Physiol 587: 1817-1828, 2009). Therefore, we examined nuclear SIRT1 protein and activity in muscle with varied mitochondrial content and in response to acute exercise. We also measured these parameters after stimulating mitochondrial biogenesis with chronic muscle contraction and 5-aminoimidazole-4-carboxamide-1-β-d-ribofuranoside (AICAR) administration in rodents and exercise training in humans. In skeletal and heart muscles, nuclear SIRT1 protein was negatively correlated with indices of mitochondrial density (citrate synthase activity, CS; cytochrome oxidase IV, COX IV), but SIRT1 activity was positively correlated with these parameters (r > 0.98). Acute exercise did not alter nuclear SIRT1 protein but did induce a time-dependent increase in nuclear SIRT1 activity. This increase in SIRT1 activity was temporally related to increases in mRNA expression of genes activated by PGC-1α. Both chronic muscle stimulation and AICAR increased mitochondrial biogenesis and muscle PGC-1α, but not nuclear PGC-1α. Concomitantly, muscle and nuclear SIRT1 protein contents were reduced, but nuclear SIRT1 activity was increased. In human muscle, training-induced mitochondrial biogenesis did not alter muscle or nuclear SIRT1 protein content, but it did increase muscle and nuclear PGC-1α and SIRT1 activity. Thus, nuclear SIRT1 activity, but not muscle or nuclear SIRT1 protein content, is associated with contraction-stimulated mitochondrial biogenesis in rat and human muscle, possibly via AMPK activation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".