Hepatocyte growth factor mediates angiopoietin-induced smooth muscle cell recruitment
Bibliographic record
Abstract
Communication between endothelial cells (ECs) and mural cells is critical in vascular maturation. Genetic studies suggest that angiopoietin/Tie2 signaling may play a role in the recruitment of pericytes or smooth muscle cells (SMCs) during vascular maturation. However, the molecular mechanism is unclear. We used microarray technology to analyze genes regulated by angiopoietin-1 (Ang1), an agonist ligand for Tie2, in endothelial cells (ECs). We observed that hepatocyte growth factor (HGF), a mediator of mural cell motility, was up-regulated by Ang1 stimulation. We confirmed this finding by Northern blot and Western blot analyses in cultured vascular endothelial cells. Furthermore, stimulation of ECs with Ang1 increased SMC migration toward endothelial cells in a coculture assay. Addition of a neutralizing anti-HGF antibody inhibited Ang1-induced SMC recruitment, indicating that the induction of SMC migration by Ang1 was caused by the increase of HGF. Interestingly, Ang2, an antagonist ligand of Tie2, inhibited Ang1-induced HGF production and Ang1-induced SMC migration. Finally, we showed that deletion of Tie2 in transgenic mouse reduced HGF production. Collectively, our data reveal a novel mechanism of Ang/Tie2 signaling in regulating vascular maturation and suggest that a delicate balance between Ang1 and Ang2 is critical in this process.
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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) | 0.001 | 0.001 |
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".