Neuropilin‐1 and neuropilin‐2 enhance VEGF <sub>121</sub> stimulated signal transduction by the VEGFR‐2 receptor
Bibliographic record
Abstract
The neuropilin-1 (np1) receptor binds the 165 amino-acid form of vascular endothelial growth factor165 (VEGF165) and functions as an enhancer that potentiates VEGF165 signaling via the VEGFR-2 tyrosine-kinase receptor. To study the mechanism by which neuropilins potentiate VEGF activity we produced a VEGF165 mutant (VEGF165KF) that binds to neuropilins but displays a much lower affinity toward VEGFR-1 and VEGFR-2. VEGF165KF failed to induce VEGFR-2 phosphorylation in cells lacking neuropilins. However, in the presence of np1, VEGF165KF bound weakly to VEGFR-2, induced VEGFR-2 phosphorylation, and activated ERK1/2. Interestingly, VEGF165KF did not promote formation of VEGFR-2/np1 complexes nor did high concentrations of VEGF165KF inhibit VEGF165 induced formation of such complexes, suggesting that VEGF165 does not stabilize VEGFR-2/np1 complexes by forming bridges spanning VEGFR-2 and np1. VEGF121 is a VEGF form that does not bind to neuropilins. Surprisingly, both np1 and neuropilin-2 (np2) enhanced VEGF121-induced phosphorylation of VEGFR-2 and VEGF121-induced proliferation of endothelial cells. The enhancement of VEGF121 activity by np1 was accompanied by a 10-fold increase in binding affinity to VEGFR-2 and was not associated with the formation of new VEGFR-2/np1 complexes. These observations suggest that neuropilins enhance the activity of VEGF forms that do not bind to neuropilins, indicate that np2 is a functional VEGF receptor, and imply that spontaneously formed VEGFR-2/np1 complexes suffice for efficient neuropilin mediated enhancement of VEGF activity.
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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 | 0.000 | 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) | 0.000 | 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".