Multi-Omics Approach to Dissect the Mechanisms of Rexinoid Signaling in Myoblast Differentiation
Bibliographic record
Abstract
Stem cells represent a key resource in regenerative medicine, however, there is a critical need for pharmacological modulators to promote efficient conversion of stem cells into a myogenic lineage. We have previously shown that bexarotene, an agonist of retinoid X receptor (RXR) approved for cancer therapy, promotes the specification and differentiation of skeletal muscle progenitors. To decipher the molecular regulation of rexinoid signaling in myogenic differentiation, we have integrated RNA-seq transcription profiles with ChIP-seq of H4K8, H3K9, H3K18, H3K27 acetylation, and H3K27 methylation in addition to that of histone acetyl-transferase p300 in rexinoid-promoted myoblast differentiation. Here, we provide details regarding data collection, validation and omics integration analyses to offer strategies for future data application and replication. Our analyses also reveal molecular pathways underlying different patterns of gene expression and p300-associated histone acetylation at distinct chromatin states in rexinoid-enhanced myoblast differentiation. These datasets can be repurposed for future studies to examine the relationship between signaling molecules, chromatin modifiers and histone acetylation in myogenic regulation, providing a framework for discovery and functional characterization of muscle-specific loci.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".