Global MEF2 target gene analysis in cardiac and skeletal muscle reveals novel regulation of DUSP6 by p38MAPK-MEF2 signaling
Bibliographic record
Abstract
MEF2 plays a profound role in the regulation of transcription in cardiac and skeletal muscle lineages. To define the overlapping and unique MEF2A genomic targets, we utilized ChIP-exo analysis of cardiomyocytes and skeletal myoblasts. Of the 2783 and 1648 MEF2A binding peaks in skeletal myoblasts and cardiomyocytes, respectively, 294 common binding sites were identified. Genomic targets were compared to differentially expressed genes in RNA-seq analysis of MEF2A depleted myogenic cells, revealing two prominent genetic networks. Genes largely associated with muscle development were down-regulated by loss of MEF2A while up-regulated genes reveal a previously unrecognized function of MEF2A in suppressing growth/proliferative genes. Several up-regulated (Tprg, Mctp2, Kitl, Prrx1, Dusp6) and down-regulated (Atp1a2, Hspb7, Tmem182, Sorbs2, Lmod3) MEF2A target genes were chosen for further investigation. Interestingly, siRNA targeting of the MEF2A/D heterodimer revealed a somewhat divergent role in the regulation of Dusp6, a MAPK phosphatase, in cardiac and skeletal myogenic lineages. Furthermore, MEF2D functions as a p38MAPK-dependent repressor of Dusp6 in myoblasts. These data illustrate that MEF2 orchestrates both common and non-overlapping programs of signal-dependent gene expression in skeletal and cardiac muscle lineages.
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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".