The Correlation Between Tfeb and PGC‐1α in Skeletal Muscle
Bibliographic record
Abstract
Muscle health is strongly dependent on the maintenance of functional mitochondria for the provision of ATP through oxidative phosphorylation. The mitochondrial pool is governed by two competing processes: mitochondrial biogenesis and autophagy (mitophagy). Peroxisome proliferator‐activated receptor gamma coactivator 1‐alpha (PGC‐1α) is the master regulator of mitochondrial biogenesis, while transcription factor EB (Tfeb) regulates lysosomal biogenesis and is required for autophagy. Since the two processes are strongly linked, we sought to study their ability to regulate one another. The purpose of this study was to further elucidate the relationship between Tfeb and PGC‐1α by investigating Tfeb family members, as well as its transcription factors and downstream targets. Protein levels of Tfeb were reduced by 70% in whole body PGC‐1α KO mice compared to their WT counterparts. In contrast, muscle‐specific overexpression of PGC‐1α resulted in a 25% increase in Tfeb protein expression. Tfe3, a family member of Tfeb, was reduced by 50% in PGC‐1α KO mice. Despite this similar reduction in Tfeb and Tfe3 in KO animals, there were no significant changes observed in YY1, a transcription factor upstream of Tfeb that may regulate Tfeb expression. Since Tfeb is involved in lysosomal biogenesis, markers of chaperone‐mediated autophagy, were measured in the PGC‐1α WT and KO mice in order to see if they were affected by PGC‐1α‐mediated Tfeb downregulation. However, no changes were observed in Hsc70 or Lamp‐2A between the two genotypes. Our findings confirm the positive correlation between the two master regulators of biogenesis and autophagy, PGC‐1α and Tfeb. PGC‐1α does not seem to have an effect on markers of chaperone‐mediated autophagy, despite its regulation of Tfeb expression. Thus, it is probable that this correlation is working to promote other forms of autophagy, as well as mitochondrial biogenesis, in order regulate the turnover of the mitochondrial network. Support or Funding Information Supported by NSERC
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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".