mRNA stability as a function of striated muscle oxidative capacity
Bibliographic record
Abstract
A change in mRNA stability alters the abundance of mRNA available for translation and is emerging as a critical pathway influencing gene expression. Variations in the stability of functional and regulatory mitochondrial proteins may contribute to the divergent mitochondrial densities observed in striated muscle. Thus we hypothesized that the stability of mRNAs encoding for regulatory nuclear and mitochondrial transcription factors would be inversely proportional to muscle oxidative capacity and would be facilitated by the activity of RNA binding proteins (RBPs). The stability of mitochondrial transcription factor A (Tfam), peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α), and nuclear respiratory factor 2α (NRF-2α) mRNA was assessed in striated muscles with distinct oxidative capacities using in vitro decay assays. All three mitochondrial regulators were rapidly degraded in cardiac and slow-twitch red (STR) muscle, resulting in a ∼60-65% lower (P < 0.05) mRNA half-life (t(1/2)) compared with fast-twitch white (FTW) fibers. This accelerated rate of Tfam mRNA decay was matched by a 2.5-fold increase in Tfam transcription in slow- compared with fast-twitch muscle (P = 0.05). Protein expression of four unique RBPs [AU-rich binding factor 1 (AUF1), human antigen R (HuR), KH-homology splicing regulatory protein (KSRP), and CUG binding protein 1 (CUGBP1)] believed to modulate mRNA stability was elevated in cardiac and STR muscles (P < 0.05) and was moderately associated with the decay of Tfam, PGC-1α, and NRF-2α mRNA. Variable rates of transcript degradation were apparent when comparing all transcripts within the same muscle type. Thus the distribution of RBPs appears to follow a fiber-type specific pattern and subsequently functions to alter the stability of specific mitochondrial regulators in a transcript- and tissue-specific fashion.
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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 | 0.000 | 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".