Acute upregulation of <i>PGC-1α</i> mRNA correlates with training-induced increases in SDH activity in human skeletal muscle
Bibliographic record
Abstract
The purpose of the present study was to determine if acute responses in PGC-1α, VEGFA, SDHA, and GPD1–2 mRNA expression predict their associated chronic skeletal muscle molecular (SDH–GPD activity and substrate storage) and morphological (fibre-type composition and capillary density) adaptations following training. Skeletal muscle biopsies were collected from 14 recreationally active men (age: 22.0 ± 2.4 years) before (PRE) and 3 h after (3HR) the completion of an acute bout of sprint interval training (SIT) (eight 20-s intervals at ∼170% peak oxygen uptake work rate separated by 10 s of recovery). Participants then completed 6 weeks of SIT 4 times per week with additional biopsies after 2 (MID) and 6 (POST) weeks of training. Acute increases in PGC-1α mRNA strongly predicted increases in SDH activity (a marker of oxidative capacity) from PRE and MID to POST (PRE–POST: r = 0.81, r2 = 0.65, p < 0.01; MID–POST: r = 0.79, r2 = 0.62, p < 0.01) and glycogen content from MID to POST (r = 0.60, r2 = 0.36, p < 0.05). No other significant relationships were found between acute responses in PGC-1α, VEGFA, SDHA, and GPD1–2 mRNA expression and chronic adaptations to training. These results suggest that acute upregulation of PGC-1α mRNA relates to the magnitude of subsequent training-induced increases in oxidative capacity, but not other molecular and morphological chronic skeletal muscle adaptations. Additionally, acute mRNA responses in PGC-1α correlated with VEGFA, but not SDHA, suggesting a coordinated upregulation between PGC-1α and only some of its proposed targets in human skeletal muscle.
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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".