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 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) | 0.002 | 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".