PGC-1α regulation by exercise training and its influences on muscle function and insulin sensitivity
Bibliographic record
Abstract
The peroxisome proliferator-activated receptor-gamma (PPARgamma) coactivator-1alpha (PGC-1alpha) is a major regulator of exercise-induced phenotypic adaptation and substrate utilization. We provide an overview of 1) the role of PGC-1alpha in exercise-mediated muscle adaptation and 2) the possible insulin-sensitizing role of PGC-1alpha. To these ends, the following questions are addressed. 1) How is PGC-1alpha regulated, 2) what adaptations are indeed dependent on PGC-1alpha action, 3) is PGC-1alpha altered in insulin resistance, and 4) are PGC-1alpha-knockout and -transgenic mice suitable models for examining therapeutic potential of this coactivator? In skeletal muscle, an orchestrated signaling network, including Ca(2+)-dependent pathways, reactive oxygen species (ROS), nitric oxide (NO), AMP-dependent protein kinase (AMPK), and p38 MAPK, is involved in the control of contractile protein expression, angiogenesis, mitochondrial biogenesis, and other adaptations. However, the p38gamma MAPK/PGC-1alpha regulatory axis has been confirmed to be required for exercise-induced angiogenesis and mitochondrial biogenesis but not for fiber type transformation. With respect to a potential insulin-sensitizing role of PGC-1alpha, human studies on type 2 diabetes suggest that PGC-1alpha and its target genes are only modestly downregulated (< or =34%). However, studies in PGC-1alpha-knockout or PGC-1alpha-transgenic mice have provided unexpected anomalies, which appear to suggest that PGC-1alpha does not have an insulin-sensitizing role. In contrast, a modest ( approximately 25%) upregulation of PGC-1alpha, within physiological limits, does improve mitochondrial biogenesis, fatty acid oxidation, and insulin sensitivity in healthy and insulin-resistant skeletal muscle. Taken altogether, there is substantial evidence that the p38gamma MAPK-PGC-1alpha regulatory axis is critical for exercise-induced metabolic adaptations in skeletal muscle, and strategies that upregulate PGC-1alpha, within physiological limits, have revealed its insulin-sensitizing effects.
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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".