Impact of exercise and fasting on mitochondrial regulators in human muscle
Bibliographic record
Abstract
Abstract Objectives To investigate the impact of acute energetic stress (acute HIIE and fasting) on ERRγ , PPARβ , NR1D1 , NR4A1 , and TFEB in human skeletal muscle. Methods The current study performed secondary analyses using muscle biopsy samples from two previously published studies: study 1) leg muscle biopsies from nine men and eight women were obtained pre and 3 h following acute high-intensity interval cycling exercise (HIIE); study 2) leg muscle biopsies were obtained from nine men pre-, during, and post-an 8 h fast with or without 2 h of arm ergometer exercise. RT-PCR was performed on samples from each study to determine the mRNA expression of ERRγ , PPARβ , NR1D1 , NR4A1 , and TFEB . Additionally, we retrieved data from meta-analyzed human muscle gene expression using the publicly available database MetaMex. Results PGC-1α (p<0.01, d=1.98) and NR4A1 (p<0.01, d=1.36) mRNA expression significantly increased while TFEB (p≤0.05, d=0.70) decreased following HIIE. Significant decreases in NR4A1 and NR1D1 mRNA expression were observed following an 8 h fast. Our MetaMex analyses revealed significant increases (p<0.05) in PGC-1α and NR4A1 expression following aerobic and resistance exercise, and in PPARβ expression following resistance exercise. Conclusions Our data indicate that acute HIIE stimulates increases in NR4A1 and PGC-1α and decreases in TFEB mRNA expression in human skeletal muscle. Additionally, a short term (8 h) fast reduced the mRNA expression of the transcriptional regulators NR4A1 and NR1D1 – potentially as a mechanism of decreasing mitochondrial biogenesis to reduce energy expenditure during a period of restricted energy availability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".