Differential modulation of cell cycle progression distinguishes members of the myogenic regulatory factor family of transcription factors
Bibliographic record
Abstract
The muscle-specific basic helix-loop-helix proteins MyoD, Myf5, myogenin (Myog) and MRF4 constitute the myogenic regulatory factor (MRF) family of transcription factors that drive muscle gene expression during myogenesis. Having evolved from a single ancestral gene, the spatial and temporal specificity of expression for each family member has been used to define a hierarchical relationship between the four MRFs. Molecular characterization of two of the MRFs (MyoD and Myog) suggests an important distinction between these factors, whereby MyoD establishes an open chromatin structure at muscle-specific genes, whereas Myog drives high levels of transcription of genes within this open chromatin state. Furthermore, recent data have provided an additional distinction between MRF function with respect to cell cycle regulation. Indeed, MyoD has been shown to directly activate genes involved in cell cycle progression, leading to myoblast proliferation. In contrast, Myog has antiproliferative activity through the activation of genes that shut down the cell proliferation machinery, leading to cell cycle exit and myoblast differentiation. Although the transcriptional activities of MyoD and Myog synergize to drive muscle differentiation, it is the expression of Myog that sets in motion a gene expression program that constitutes a 'point of no return', leading to cell cycle exit. In this review, we compare and contrast the current literature with respect to MRF function, with a particular emphasis on the differential role of MRFs in modulating the cell cycle.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".