The response of VEGF-stimulated endothelial cells to angiostatic molecules is substrate-dependent
Bibliographic record
Abstract
BACKGROUND: The microenvironment surrounding cells can exert multiple effects on their biological responses. In particular the extracellular matrix surrounding cells can profoundly influence their behavior. It has been shown that the extracellular matrix composition in tumors is vastly different than that found in normal tissue with increased amounts of certain matrices such as collagen I. It has been previously demonstrated that VEGF stimulation of endothelial cells growing on type I collagen results in the induction of bcl-2 expression and enhanced endothelial cell survival. We sought to investigate whether this increased endothelial cell survival resulted in the failure of angiostatic molecules to inhibit angiogenesis. RESULTS: We now demonstrate that VEGF-induced survival on collagen I impairs the ability of three known angiostatic molecules, TSP-1, IP-10 and endostatin to inhibit endothelial cell proliferation. Apoptosis of endothelial cells, growing on collagen I, induced by TSP-1 and IP-10 was also inhibited following VEGF stimulation. In contrast, endostatin induced apoptosis in these same cells. Further analysis determined that endostatin did not decrease the expression of bcl-2 nor did it increase activation of caspase-3 in the presence of VEGF. Alternatively, it appeared that in the presence of VEGF, endostatin induced the activation of caspase-8 in endothelial cells grown on collagen I. Furthermore, only endostatin had the ability to inhibit VEGF-induced sprout formation in collagen I gels. CONCLUSION: These data suggest that TSP-1, IP-10 and endostatin inhibit endothelial cells via different mechanisms and that only endostatin is effective in inhibiting angiogenic activities in the presence of collagen I. Our results suggest that the efficacy of angiostatic treatments may be impaired depending on the context of the extracellular matrix within the tumor environment and thus could impede the efficacy of angiostatic therapies.
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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) | 0.003 | 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".