A Notch‐dependent transcriptional hierarchy promotes mesenchymal transdifferentiation in the cardiac cushion
Bibliographic record
Abstract
BACKGROUND: Valvuloseptal defects are the most common congenital heart defects. Notch signaling-induced endothelial-to-mesenchymal transition (EMT) in the atrioventricular canal (AVC) cushions at murine embryonic day (E)9.5 is a required step during early valve development. Insights to the transcriptional network that is activated in endocardial cells (EC) during EMT and how these pathways direct valve maturation are lacking. RESULTS: We show that at E11.5, AVC-EC retain the ability to undergo Notch-dependent EMT when explanted on collagen. EC-Notch inhibition at E10.5 blocks expression of known mesenchymal genes in E11.5 AVC-EC. To understand the genetic network and AVC development downstream of Notch signaling beyond E9.5, we constructed Tag-Seq libraries corresponding to different cell types of the E11.5 AVC and atrium in wild-type mice and in EC-Notch inhibited mice. We identified 1,400 potential Notch targets in the AVC-EC, of which 124 are transcription factors (TF). From the 124 TFs, we constructed a transcriptional hierarchy and identify 10 upstream TFs within the network. CONCLUSIONS: We validated 4 of the upstream TFs as Notch targets that are enriched in AVC-EC. Functionally, we show these 4 TFs regulate EMT in AVC explant assays. These novel signaling pathways downstream of Notch are potentially relevant to valve development.
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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) | 0.002 | 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".