Induction of RNA‐binding proteins in denervated skeletal muscle
Bibliographic record
Abstract
Several transcriptional mechanisms are known to be involved in the atrophic response of skeletal muscle. However, emerging data lead us to hypothesize that post‐transcriptional events, operating at the level of mRNA stability, are also contributing. We thus initiated a series of studies to determine the role of post‐transcriptional mechanisms in the response of muscle to disuse. Given the key role of AU‐rich elements (ARE) located in the 3′UTR of multiple mRNAs in controlling their stability, we focused on the contribution of RNA‐binding proteins (RBP) known to interact with this cis‐element. Specifically, we examined expression of HuR, AUF1, TTP, BRF1 and KSRP in slow vs fast muscles as well as in denervated muscles. In general, expression of these RBP was higher in slow muscles. Additionally, a time course of hindlimb denervation ranging from 12 hours to 14 days revealed that the major changes occurred early, i.e. within 2 days of denervation. The most dramatic changes consisted in a substantial increase in expression of the mRNA destabilizing factors TTP and its homolog BRF1 in fast muscles. Since these factors bind to ARE found in multiple important mRNAs, our results identify new molecular mechanisms that likely play a key role in the atrophic response of muscle. Also, they provide additional targets that may be useful for developing novel therapeutics aimed at countering muscle atrophy. Funded by CNES, AFM, MDA and CIHR.
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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) | 0.002 | 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".