Regulation of Dystroglycan Gene Expression in Early Myoblast Differentiation
Bibliographic record
Abstract
Dystroglycan, a component of the dystrophin-associated glycoprotein complex, connects the extracellular matrix and cytoskeleton to maintain muscle membrane integrity. As such, abnormalities of dystroglycan are linked to different types of muscular dystrophies. In an effort to develop therapeutic approaches to re-establish signal integration for muscle repair and homeostasis, we have previously determined that a clinically approved agonist of retinoid X receptor enhances myoblast differentiation through direct regulation of gene expression of the muscle master regulator MyoD. Using comprehensive omics and molecular analyses, we found that dystroglycan gene expression is responsive to retinoid X receptor-selective signaling in early myoblast differentiation. In addition, the dystroglycan gene is a MyoD target, and residue-specific histone acetylation coincides with the occupancy of histone acetyltransferase p300 at the MyoD binding sites. Consequently, the p300 function is important for rexinoid-augmented dystroglycan gene expression. Finally, dystroglycan plays a role in myoblast differentiation. Our study sheds new light on dystroglycan regulation and function in myoblast differentiation and presents a potential avenue for re-establishing signal integration of a specific chromatin state pharmacologically to overcome muscle pathology and identify additional myogenic interactions for therapeutic applications.
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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 | 0.001 |
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".