Genome-wide identification of enhancers in skeletal muscle: the role of MyoD1
Bibliographic record
Abstract
To identify the compendium of distal regulatory elements that govern myogenic differentiation, we generated chromatin state maps based on histone modifications and recruitment of factors that typify enhancers in myoblasts and myotubes. We found a striking concordance between the locations of these newly defined enhancers, MyoD1-binding events, and noncoding RNA transcripts. These enhancers recruit several sequence-specific transcription factors in a spatially constrained manner around MyoD1-binding sites. Remarkably, MyoD1-null myoblasts show a wholesale loss of recruitment of these factors as well as diminished monomethylation of H3K4 (H3K4me1) and acetylation of H3K27 (H3K27ac) and reduced recruitment of Set7, an H3K4 monomethylase. Surprisingly, we found that H3K4me1, but not H3K27ac, could be restored by re-expression of MyoD1 in MyoD1(-/-) myoblasts, although re-expression of this factor in MyoD1-null myotubes restored both histone modifications. Our studies identified a role for MyoD1 in condition-specific enhancer assembly through recruitment of transcription factors and histone-modifying enzymes that shape muscle differentiation.
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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".