Comparative expression profiling identifies differential roles for Myogenin and p38α MAPK signaling in myogenesis
Bibliographic record
Abstract
Skeletal muscle differentiation is mediated by a complex gene expression program requiring both the muscle-specific transcription factor Myogenin (Myog) and p38α MAPK (p38α) signaling. However, the relative contribution of Myog and p38α to the formation of mature myotubes remains unknown. Here, we have uncoupled the activity of Myog from that of p38α to gain insight into the individual roles of these proteins in myogenesis. Comparative expression profiling confirmed that Myog activates the expression of genes involved in muscle function. Furthermore, we found that in the absence of p38α signaling, Myog expression leads to the down-regulation of genes involved in cell cycle progression. Consistent with this, the expression of Myog is sufficient to induce cell cycle exit. Interestingly, p38α-defective, Myog-expressing myoblasts fail to form multinucleated myotubes, suggesting an important role for p38α in cell fusion. Through the analysis of p38α up-regulated genes, the tetraspanin CD53 was identified as a candidate fusion protein, a role confirmed both ex vivo in primary myoblasts, and in vivo during myofiber regeneration in mice. Thus, our study has revealed an unexpected role for Myog in mediating cell cycle exit and has identified an essential role for p38α in cell fusion through the up-regulation of CD53.
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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 | 0.001 | 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".