Forty high-intensity interval training sessions blunt exercise-induced changes in the nuclear protein content of PGC-1α and p53 in human skeletal muscle
Bibliographic record
Abstract
Exercise-induced increases in peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) and p53 protein content in the nucleus mediate the initial phase of exercise-induced mitochondrial biogenesis. Here, we investigated whether exercise-induced increases in these and other markers of mitochondrial biogenesis were altered after 40 sessions of twice-daily high-volume, high-intensity interval training (HVT) in human skeletal muscle. Vastus lateralis muscle biopsies were collected from 10 healthy recreationally active participants before, immediately postexercise, and 3 h after a session of high-intensity interval exercise (HIIE) performed at the same absolute exercise intensity before and after HVT (pre-HVT and post-HVT, respectively). The protein content of common markers of exercise-induced mitochondrial biogenesis was assessed in nuclear- and cytosolic-enriched fractions by immunoblotting; mRNA contents of key transcription factors and mitochondrial genes were assessed by qPCR. Despite exercise-induced increases in PGC-1α, p53, and plant homeodomain finger-containing protein 20 (PHF20) protein content, the phosphorylation of p53 and acetyl-CoA carboxylase (p-p53 Ser 15 and p-ACC Ser 79 , respectively), and PGC-1α mRNA Pre-HVT, no significant changes were observed post-HVT. Forty sessions of twice-daily high-intensity interval training blunted all of the measured exercise-induced molecular events associated with mitochondrial biogenesis that were observed pre-HVT. Future studies should determine whether this loss relates to the decrease in relative exercise intensity, habituation to the same exercise stimulus, or a combination of both.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".