Myocyte Enhancer Factor 2C and Myogenin Up-regulate Each Other's Expression and Induce the Development of Skeletal Muscle in P19 Cells
Bibliographic record
Abstract
Two families of transcription factors, myogenic regulatory factors (MRFs) and myocyte enhancer factor 2 (MEF2), function synergistically to regulate myogenesis. In addition to activating structural muscle-specific genes, MRFs and MEF2 activate each other's expression. The MRF, myogenin, can activate MEF2 DNA binding activity when transfected into fibroblasts and, in turn, the myogenin promoter contains essential MEF2 DNA binding elements. To determine which MEF2 is involved in this regulation, P19 cells stably expressing MyoD and myogenin were compared for their ability to activate the expression of MEF2 family members. There was very little cross-activation of MyoD expression by myogenin and vice versa. Myogenin expression, and not MyoD, was found to up-regulate MEF2C expression. MEF2A, -B, and -D expression levels were not up-regulated by overexpression of either MyoD or myogenin. To examine whether MEF2C can differentially regulate MyoD or myogenin expression, P19 cell lines overexpressing MEF2C were analyzed. MEF2C induced myogenesis in P19 cells and up-regulated the expression of myogenin with 25-fold greater efficiency than that of MyoD. Therefore, myogenin and MEF2C participate in a regulatory loop in differentiating stem cells. This positive regulation does not extend to MyoD or the other MEF2 family members. Consequently, MEF2C appears to play a specific role in early events of myogenesis.
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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".