Acute endurance exercise increases the nuclear abundance of PGC-1α in trained human skeletal muscle
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor gamma coactivator (PGC-1alpha) is a transcriptional coactivator that plays a key role in coordinating mitochondrial biogenesis. Recent evidence has linked p38 MAPK and AMPK with activation of PGC-1alpha. It was recently shown in rodent skeletal muscle that acute endurance exercise causes a shift in the subcellular localization of PGC-1alpha from the cytosol to the nucleus, allowing PGC-1alpha to coactivate transcription factors and increase mitochondrial gene expression, but human data are limited and equivocal in this regard. Our purpose was to examine p38 MAPK and AMPK activation, and PGC-1alpha protein content in whole muscle, cytosolic, and nuclear fractions of human skeletal muscle following an acute bout of endurance exercise. Eight trained men (29 +/- 3 yr; Vo(2peak) = 55 +/- 2 ml.kg(-1).min(-1)) cycled for 90 min at approximately 65% of Vo(2peak) and needle biopsy samples (vastus lateralis) were obtained before and immediately after exercise. At rest, the majority of PGC-1alpha was detected in cytosolic compared with the nuclear fractions. In response to exercise, nuclear PGC-1alpha protein increased by 54% (P < 0.05), yet whole muscle PGC-1alpha protein was unchanged compared with rest. Whole muscle and cytosolic p38 MAPK phosphorylation increased several-fold immediately after exercise compared with rest (P < 0.05). Acetyl CoA carboxylase (ACC) phosphorylation, a marker of AMPK activation, was increased by approximately 5-fold in cytosolic fractions following exercise (P < 0.05). These data provide evidence that, in human skeletal muscle, activation of cytosolic p38 MAPK and AMPK may be potential signals that lead to increased nuclear abundance and activation of PGC-1alpha in response to an acute bout of endurance exercise.
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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".