Relative effects of VEGF‐A and VEGF‐C on endothelial cell proliferation, migration and PAF synthesis: Role of neuropilin‐1
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF-A) is an inducer of endothelial cell (EC) proliferation, migration, and synthesis of inflammatory agents such as platelet-activating factor (PAF). Recently, neuropilin-1 (NRP-1) has been described as a coreceptor of KDR which potentiates VEGF-A activity. However, the role of NRP-1 in numerous VEGF-A activities remains unclear. To assess the contribution of NRP-1 to VEGF-A mediated EC proliferation, migration, and PAF synthesis, we used porcine aortic EC (PAEC) recombinantly expressing Flt-1, NRP-1, KDR or KDR and NRP-1. Cells were stimulated with VEGF-A, which binds to Flt-1, KDR and NRP-1, and VEGF-C, which binds to KDR only. VEGF-A was 12.4-fold more potent than VEGF-C in inducing KDR phosphorylation in PAEC-KDR. VEGF-A and VEGF-C showed similar potency to mediate PAEC-KDR proliferation, migration, and PAF synthesis. On PAEC-KDR/NRP-1, VEGF-A was 28.6-fold more potent than VEGF-C in inducing KDR phosphorylation and PAEC-KDR/NRP-1 proliferation (1.3-fold), migration (1.7-fold), and PAF synthesis (4.6-fold). These results suggest that cooperative binding of VEGF-A to KDR and NRP-1 enhances KDR phosphorylation and its biological activities. Similar results were obtained with bovine aortic EC that endogenously express both KDR and NRP-1 receptors. In contrast, stimulation of PAEC-Flt-1 and PAEC-NRP-1 with VEGF-A or VEGF-C did not induce proliferation, migration, or PAF synthesis. In conclusion, the presence of NRP-1 on EC preferentially increases KDR activation by VEGF-A as well as KDR-mediated biological activities, and may elicit novel intracellular events. On the other hand, VEGF-A and VEGF-C have equipotent biological activities on EC in absence of NRP-1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".