Exercise induces TFEB expression and activity in skeletal muscle in a PGC-1α-dependent manner
Bibliographic record
Abstract
The mitochondrial network in muscle is controlled by the opposing processes of mitochondrial biogenesis and mitophagy. The coactivator peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) regulates biogenesis, while the transcription of mitophagy-related genes is controlled by transcription factor EB (TFEB). PGC-1α activation is induced by exercise; however, the effect of exercise on TFEB is not fully known. We investigated the interplay between PGC-1α and TFEB on mitochondria in response to acute contractile activity in C 2 C 12 myotubes and following exercise in wild-type and PGC-1α knockout mice. TFEB nuclear localization was increased by 1.6-fold following 2 h of acute myotube contractile activity in culture, while TFEB transcription was also simultaneously increased by twofold to threefold. Viral overexpression of TFEB in myotubes increased PGC-1α and cytochrome- c oxidase-IV gene expression. In wild-type mice, TFEB translocation to the nucleus increased 2.4-fold in response to acute exercise, while TFEB transcription, assessed through the electroporation of a TFEB promoter construct, was elevated by fourfold. These exercise effects were dependent on the presence of PGC-1α. Our data indicate that acute exercise provokes TFEB expression and activation in a PGC-1α-dependent manner and suggest that TFEB, along with PGC-1α, is an important regulator of mitochondrial biogenesis in muscle as a result of 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 | 0.001 |
| 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".