The utrophin A 5'-UTR drives cap-independent translation exclusively in skeletal muscles of transgenic mice and interacts with eEF1A2
Bibliographic record
Abstract
The molecular mechanisms regulating expression of utrophin A are of therapeutic interest since upregulating its expression at the sarcolemma can compensate for the lack of dystrophin in animal models of Duchenne Muscular Dystrophy (DMD). The 5'-UTR of utrophin A has been previously shown to drive cap-independent internal ribosome entry site (IRES)-mediated translation in response to muscle regeneration and glucocorticoid treatment. To determine whether the utrophin A IRES displays tissue specific activity, we generated transgenic mice harboring control (CMV/betaGAL/CAT) or utrophin A 5'-UTR (CMV/betaGAL/UtrA/CAT) bicistronic reporter transgenes. Examination of multiple tissues from two CMV/betaGAL/UtrA/CAT lines revealed that the utrophin A 5'-UTR drives cap-independent translation of the reporter gene exclusively in skeletal muscles and no other examined tissues. This expression pattern suggested that skeletal muscle-specific factors are involved in IRES-mediated translation of utrophin A. We performed RNA-affinity chromatography experiments combined with mass spectrometry to identify trans-factors that bind the utrophin A 5'-UTR and identified eukaryotic elongation factor 1A2 (eEF1A2). UV-crosslinking experiments confirmed the specificity of this interaction. Regions of the utrophin A 5'-UTR that bound eEF1A2 also mediated cap-independent translation in C2C12 muscle cells. Cultured cells lacking eEF1A2 had reduced IRES activity compared with cells overexpressing eEF1A2. Together, these results suggest an important role for eEF1A2 in driving cap-independent translation of utrophin A in skeletal muscle. The trans-factors and signaling pathways driving skeletal-muscle specific IRES-mediated translation of utrophin A could provide unique targets for developing pharmacological-based DMD therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".