Loci-specific histone acetylation profiles associated with transcriptional coactivator p300 during early myoblast differentiation
Bibliographic record
Abstract
Molecular regulation of stem cell differentiation is exerted through both genetic and epigenetic determinants over distal regulatory or enhancer regions. Understanding the mechanistic action of active or poised enhancers is therefore imperative for control of stem cell differentiation. Based on the genome-wide co-occurrence of different epigenetic marks in committed proliferating myoblasts, we have previously generated a 14-state chromatin state model to profile rexinoid-responsive histone acetylation in early myoblast differentiation. Here, we delineate the functional mode of transcription regulators during early myogenic differentiation using genome-wide chromatin state association. We define a role of transcriptional coactivator p300, when recruited by muscle master regulator MyoD, in the establishment and regulation of myogenic loci at the onset of myoblast differentiation. In addition, we reveal an enrichment of loci-specific histone acetylation at p300 associated active or poised enhancers, particularly when enlisted by MyoD. We provide novel molecular insights into the regulation of myogenic enhancers by p300 in concert with MyoD. Our studies present a valuable aptitude for driving condition-specific chromatin state or enhancers pharmacologically to treat muscle-related diseases and for the identification of additional myogenic targets and molecular interactions for therapeutic development. ABBREVIATIONS: MRF: Muscle regulatory factor; HAT: Histone acetyltransferase; CBP: CREB-binding protein; ES: Embryonic stem; ATCC: American type culture collection; DM: Differentiation medium; DMEM: Dulbecco's Modified Eagle Medium; GM: Growth medium; GO: Gene ontology; GREAT: Genomic regions enrichment of annotations tool; FPKM: Fragments per kilobase of transcript per million; GEO: Gene expression omnibus; MACS: Model-based analysis for ChIP-seq.
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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".