Angiopoietin-1 and Vascular Endothelial Growth Factor Regulation of Leukocyte Adhesion to Endothelial Cells
Bibliographic record
Abstract
OBJECTIVE: Vascular endothelial growth factor (VEGF) promotes leukocyte adhesion to endothelial cells (ECs). Angiopoietin-1 (Ang-1) inhibits this response. Nuclear receptor-77 (Nur77) is a proangiogenic nuclear receptor. In the present study, we assessed the influence of Ang-1 and VEGF on Nur77 expression in ECs, and evaluated its role in Ang-1/VEGF-mediated leukocyte adhesion. METHODS AND RESULTS: Expression of Nur77 was evaluated with real-time polymerase chain reaction and immunoblotting. Adhesion of leukocytes to ECs was monitored with inverted microscopy. Nur77 expression or activity was inhibited using adenoviruses expressing dominant-negative form of Nur77, retroviruses expressing Nur77 in the antisense direction, and small interfering RNA oligos. Both Ang-1 and VEGF induce Nur77 expression, by >5- and 30-fold, respectively. When combined, Ang-1 potentiates VEGF-induced Nur77 expression. Ang-1 induces Nur77 through the phosphoinositide 3-kinase and extracellular signal-regulated protein kinase 1/2 pathways. VEGF induces Nur77 expression through the protein kinase D/histone deacetylase 7/myocyte enhancer factor 2 and extracellular signal-regulated protein kinase 1/2 pathways. VEGF induces nuclear factor-kappaB transcription factor, vascular cell adhesion molecule-1, and E-selectin expressions, and promotes leukocyte adhesion to ECs. Ang-1 inhibits these responses. This inhibitory effect of Ang-1 disappears when Nur77 expression is disrupted, restoring the inductive effects of VEGF on adhesion molecule expression, and increased leukocyte adhesion to ECs. CONCLUSIONS: Nur77 promotes anti-inflammatory effects of Ang-1, and functions as a negative feedback inhibitor of VEGF-induced EC activation.
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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".