Regulation of Tfam mRNA stability in skeletal muscle fiber types
Bibliographic record
Abstract
Changes in mRNA stability may serve as a regulatory mechanism for mitochondrial biogenesis in skeletal muscle. We hypothesized that oxidative capacity would be closely associated with a greater mRNA stability of proteins vital to organelle biogenesis, prompting an enhanced mitochondrial content. Using an in vitro decay assay, the mRNA stability of mitochondrial transcription factor A (Tfam) was assessed, since it regulates the expression of mitochondrial DNA (mtDNA). The decay of Tfam mRNA was slowest in low oxidative fast‐twitch white (FTW) muscle. Decay rates were 1.5‐ and 2.3‐fold greater in fast‐twitch red (FTR) and slow‐twitch red (STR) fibers. Despite this, steady Tfam mRNA levels were not different between fiber types, suggesting increases in transcription in oxidative muscle which parallel mRNA decay. Degradation of mRNA in fiber types matched similar differences in the mRNA destabilizing protein AUF1, particularly the p42 isoform, with a 4.5‐fold difference in protein expression between STR and FTW muscle. A more dramatic difference in the expression of the mRNA stabilizing protein HuR existed between fiber types, which may serve to restrain uncontrolled mRNA decay in highly oxidative muscle. Thus, oxidative muscle types exhibit faster rates of Tfam mRNA turnover than low oxidative muscle, suggesting more precise control of Tfam expression, and thus mtDNA levels, in response to metabolic stress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".