Exercise training increases the expression and nuclear localization of mRNA destabilizing proteins in skeletal muscle
Bibliographic record
Abstract
While a paucity of information exists regarding posttranscriptional mechanisms influencing mitochondrial biogenesis, in resting muscle the stability of peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) mRNA has been linked to mitochondrial content. Therefore, in the current study we have examined whether exercise promotes mRNA accumulation through the induction of proteins affiliated with mRNA stabilization (human antigen R, HuR) or conversely by decreasing the expression of mRNA destabilizing proteins [AU-rich binding factor (AUF1) and CUG binding protein (CUG-BP1)]. A single bout of exercise increased (P < 0.05) the mRNA content of the transcriptional coactivator PGC-1α ∼3.5-fold without affecting mRNA content for HuR, CUG-BP1, or AUF1. One week of treadmill exercise training did not alter markers of mitochondrial content, the mRNA stabilizing protein HuR, or the mRNA destabilizing protein AUF1. In contrast, the mRNA destabilizing protein CUG-BP1 increased ∼40%. Four weeks of treadmill training increased the content of subunits of the electron transport chain ∼50%, suggesting induction of mitochondrial biogenesis. Expression levels for HuR and CUG-BP1 were not altered with chronic training; however, AUF1 expression was increased posttraining. Specifically, training increased (P < 0.05) total muscle expression of two of four AUF1 isoforms ∼50% (AUF1(p37), AUF1(p40)). Interestingly, these two isoforms were not detected in isolated nuclei; however, a large band representing the other two isoforms (AUF1(p42), AUF1(p45)) was present in nuclei and increased ∼35% following chronic training. Altogether the current data provides evidence that mitochondrial biogenesis occurs in the presence of increased CUG-BP1 and AUF1, suggesting that reductions in known mRNA destabilizing proteins likely does not contribute to exercise-induced mitochondrial biogenesis.
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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 | 0.000 |
| 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".