The Influence of Exhaustive Exercise and Glycogen Repletion on PGC-1alpha mRNA and Protein Expression
Bibliographic record
Abstract
Altered substrate availability and muscle glycogen concentration may mediate exercise-induced gene transcription in skeletal muscle during and following exercise. To determine the influence of prolonged exercise and rapid glycogen repletion on regulation of peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1α), a nuclear co-activator involved in coordinating metabolic gene expression and mitochondrial biogenesis, 7 male subjects (23+1.3 y, VO2max 48.4+0.8 mL kg-1 min-1) completed 2 trials consisting of exhaustive cycling exercise (∼2h) at 65% VO2max followed by ingestion of either a high-(HC) or low-carbohydrate (LC) diet for the ensuing 52 h recovery period. Biopsies were taken at rest, exhaustion, and at 2-, 24-, and 52-h of recovery. Glycogen content remained depressed throughout recovery in LC (P<0.05), while returning to resting levels by 24 h of recovery in HC. Exercise induced a 6.2-fold increase in PGC-1α mRNA (P<0.05) that returned to resting levels within 24 h of recovery. There was also a 23% increase in PGC-1α protein (P<0.05) at the end of exercise, and it remained elevated for at least 24 h (P<0.05). While there was no direct treatment effect with HC vs. LC for PGC-1α mRNA, there were trends (P=0.11 and 0.06 in exercise and recovery, respectively) between the changes in glycogen and PGC-1α protein. PGC-1α protein content is increased by a single bout of exercise and its up-regulation may be associated with changes in muscle glycogen stores.
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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".