Endothelial gene regulatory elements associated with cardiopharyngeal lineage differentiation
Bibliographic record
Abstract
Endothelial cells (EC) differentiate from multiple sources, including the cardiopharyngeal mesoderm, which gives rise also to cardiac and branchiomeric muscles. The enhancers activated during endothelial differentiation within the cardiopharyngeal mesoderm are not completely known. Here, we use a cardiogenic mesoderm differentiation model that activates an endothelial transcription program to identify endothelial regulatory elements activated in early cardiogenic mesoderm. Integrating chromatin remodeling and gene expression data with available single-cell RNA-seq data from mouse embryos, we identify 101 putative regulatory elements of EC genes. We then apply a machine-learning strategy, trained on validated enhancers, to predict enhancers. Using this computational assay, we determine that 50% of these sequences are likely enhancers, some of which are already reported. We also identify a smaller set of regulatory elements of well-known EC genes and validate them using genetic and epigenetic perturbation. Finally, we integrate multiple data sources and computational tools to search for transcriptional factor binding motifs. In conclusion, we show EC regulatory sequences with a high likelihood to be enhancers, and we validate a subset of them using computational and cell culture models. Motif analyses show that the core EC transcription factors GATA/ETS/FOS is a likely driver of EC regulation in cardiopharyngeal mesoderm.
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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 | 0.001 |
| 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".