Tissue-specific splicing of a ubiquitously expressed transcription factor is essential for muscle differentiation
Bibliographic record
Abstract
Alternate splicing contributes extensively to cellular complexity by generating protein isoforms with divergent functions. However, the role of alternate isoforms in development remains poorly understood. Mef2 transcription factors are essential transducers of cell signaling that modulate differentiation of many cell types. Among Mef2 family members, Mef2D is unique, as it undergoes tissue-specific splicing to generate a muscle-specific isoform. Since the ubiquitously expressed (Mef2Dα1) and muscle-specific (Mef2Dα2) isoforms of Mef2D are both expressed in muscle, we examined the relative contribution of each Mef2D isoform to differentiation. Using both in vitro and in vivo models, we demonstrate that Mef2D isoforms act antagonistically to modulate differentiation. While chromatin immunoprecipitation (ChIP) sequencing analysis shows that the Mef2D isoforms bind an overlapping set of genes, only Mef2Dα2 activates late muscle transcription. Mechanistically, the differential ability of Mef2D isoforms to activate transcription depends on their susceptibility to phosphorylation by protein kinase A (PKA). Phosphorylation of Mef2Dα1 by PKA provokes its association with corepressors. Conversely, exon switching allows Mef2Dα2 to escape this inhibitory phosphorylation, permitting recruitment of Ash2L for transactivation of muscle genes. Thus, our results reveal a novel mechanism in which a tissue-specific alternate splicing event has evolved that permits a ubiquitously expressed transcription factor to escape inhibitory signaling for temporal regulation of gene expression.
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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) | 0.002 | 0.002 |
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".