Rapid exercise-induced changes in PGC-1α mRNA and protein in human skeletal muscle
Bibliographic record
Abstract
The mRNA of the nuclear coactivator peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PGC-1alpha) increases during prolonged exercise and is influenced by carbohydrate availability. It is unknown if the increases in mRNA reflect the PGC-1alpha protein or if glycogen stores are an important regulator. Seven male subjects [23 +/- 1.3 yr old, maximum oxygen uptake (Vo(2 max)) 48.4 +/- 0.8 ml.kg(-1).min(-1)] exercised to exhaustion ( approximately 2 h) at 65% Vo(2 max) followed by ingestion of either a high-carbohydrate (HC) or low-carbohydrate (LC) diet (7 or 2.9 g.kg(-1).day(-1), respectively) for 52 h of recovery. Glycogen remained depressed in LC (P < 0.05) while returning to resting levels by 24 h in HC. PGC-1alpha mRNA increased both at exhaustion (3-fold) and 2 h later (6.2-fold) (P < 0.05) but returned to rest levels by 24 h. PGC-1alpha protein increased (P < 0.05) 23% at exhaustion and remained elevated for at least 24 h (P < 0.05). While there was no direct treatment effect (HC vs. LC) for PGC-1alpha mRNA or protein, there was a linear relationship between the changes in glycogen and those in PGC-1alpha protein during exercise and recovery (r = -0.68, P < 0.05). In contrast, PGC-1beta did not increase with exercise but rather decreased (P < 0.05) below rest level at 24 and 52 h, and the decrease was greater (P < 0.05) in LC. PGC-1alpha protein content increased in prolonged exercise and remained upregulated for 24 h, but this could not have been predicted by the changes in mRNA. The beta-isoform declined rather than increasing, and this was greater when glycogen was not resynthesized to rest levels.
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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) | 0.002 | 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".