Differential Regulation of Transforming Growth Factor β Signaling Pathways by Notch in Human Endothelial Cells
Bibliographic record
Abstract
Notch and transforming growth factor beta (TGFbeta) play critical roles in endothelial-to-mesenchymal transition (EndMT), a process that is essential for heart development. Previously, we have shown that Notch and TGFbeta signaling synergistically induce Snail expression in endothelial cells, which is required for EndMT in cardiac cushion morphogenesis. Here, we report that Notch activation modulates TGFbeta signaling pathways in a receptor-activated Smad (R-Smad)-specific manner. Notch activation inhibits TGFbeta/Smad1 and TGFbeta/Smad2 signaling pathways by decreasing the expression of Smad1 and Smad2 and their target genes. In contrast, Notch increases SMAD3 mRNA expression and protein half-life and regulates the expression of TGFbeta/Smad3 target genes in a gene-specific manner. Inhibition of Notch in the cardiac cushion of mouse embryonic hearts reduces Smad3 expression. Notch and TGFbeta synergistically up-regulate a subset of genes by recruiting Smad3 to both Smad and CSL binding sites and cooperatively inducing histone H4 acetylation. This is the first evidence that Notch activation affects R-Smad expression and that cooperative induction of histone acetylation at specific promoters underlies the selective synergy between Notch and TGFbeta signaling pathways.
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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".