The mRNA stability of regulators of mitochondrial biogenesis is inversely related to muscle oxidative capacity
Bibliographic record
Abstract
The stabilization of mRNA permits a greater amount of mRNA to be translated into protein. Accordingly, the mRNA stability of nuclear genes encoding mitochondrial proteins may be an important regulatory pathway in striated muscle mitochondrial biogenesis. We hypothesized that the transcript stability of these genes is highly regulated in oxidative tissue, prompting rapid rates of mRNA turnover. The stability of nuclear-encoded mitochondrial transcription factor A (Tfam) and nuclear respiratory factor 2 (NRF-2) was assessed in extracts of fast-twitch white (FTW), fast-twitch red (FTR), slow-twitch red (STR) and heart muscle using in vitro decay assays. mRNA decay rates were highest in oxidative tissue, as evident by the 2.4-fold and 3.6-fold lower half-life for Tfam and NRF-2, respectively, in heart compared to low oxidative FTW muscle. This corresponded well to the high ratio of destabilizing:stabilizing RNA binding protein expression (AUF1 p42:HuR) found in heart muscle. Additionally, there was a greater abundance of the destabilizing GRE-binding protein CUGBP1 in cardiac tissue. These values parallel the observed rates of transcript decay. Thus, the half-lives of mRNAs encoding mitochondrial biogenesis regulator proteins are inversely related to oxidative capacity, and provide an additional means for the regulation of mitochondrial transcripts in oxidative tissue.
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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 | 0.001 |
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".