Bmp9 regulates Notch signaling and the temporal dynamics of angiogenesis via Lunatic Fringe
Bibliographic record
Abstract
In brief: The mechanisms regulating the signaling pathways involved in angiogenesis are not fully known. Ristori et al. show that Lunatic Fringe (LFng) mediates the crosstalk between Bone Morphogenic Protein 9 (Bmp9) and Notch signaling, thereby regulating the endothelial cell behavior and temporal dynamics of their identity during sprouting angiogenesis. Highlights: Bmp9 upregulates the expression of LFng in endothelial cells.LFng regulates the temporal dynamics of tip/stalk selection and rearrangement.LFng indicated to play a role in hereditary hemorrhagic telangiectasia.Bmp9 and LFng mediate the endothelial cell-pericyte crosstalk.Bone Morphogenic Protein 9 (Bmp9), whose signaling through Activin receptor-like kinase 1 (Alk1) is involved in several diseases, has been shown to independently activate Notch target genes in an additive fashion with canonical Notch signaling. Here, by integrating predictive computational modeling validated with experiments, we uncover that Bmp9 upregulates Lunatic Fringe (LFng) in endothelial cells (ECs), and thereby also regulates Notch activity in an inter-dependent, multiplicative fashion. Specifically, the Bmp9-upregulated LFng enhances Notch receptor activity creating a much stronger effect when Dll4 ligands are also present. During sprouting, this LFng regulation alters vessel branching by modulating the timing of EC phenotype selection and rearrangement. Our results further indicate that LFng can play a role in Bmp9-related diseases and in pericyte-driven vessel stabilization, since we find LFng contributes to Jag1 upregulation in Bmp9-stimulated ECs; thus, Bmp9-upregulated LFng results in not only enhanced EC Dll4-Notch1 activation, but also Jag1-Notch3 activation in pericytes.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".