Modulation of utrophin A mRNA stability in fast versus slow muscles via an AU-rich element and calcineurin signaling
Bibliographic record
Abstract
We examined the role of post-transcriptional mechanisms in controlling utrophin A mRNA expression in slow versus fast skeletal muscles. First, we determined that the half-life of utrophin A mRNA is significantly shorter in the presence of proteins isolated from fast muscles. Direct plasmid injection experiments using reporter constructs containing the full-length or truncated variants of the utrophin 3'UTR into slow soleus and fast extensor digitorum longus muscles revealed that a region of 265 nucleotides is sufficient to confer lower levels of reporter mRNA in fast muscles. Further analysis of this region uncovered a conserved AU-rich element (ARE) that suppresses expression of reporter mRNAs in cultured muscle cells. Moreover, stability of reporter mRNAs fused to the utrophin full-length 3'UTR was lower in the presence of fast muscle protein extracts. This destabilization effect seen in vivo was lost upon deletion of the conserved ARE. Finally, we observed that calcineurin signaling affects utrophin A mRNA stability through the conserved ARE. These results indicate that ARE-mediated mRNA decay is a key mechanism that regulates expression of utrophin A mRNA in slow muscle fibers. This is the first demonstration of ARE-mediated mRNA decay regulating the expression of a gene associated with the slow myogenic program.
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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) | 0.001 | 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".