Differential role of β‐catenin in VEGF and histamine‐induced MMP‐2 production in microvascular endothelial cells
Bibliographic record
Abstract
Increases in endothelial cell permeability and production of matrix-degrading enzymes are two early steps in the angiogenic process. Factors such as vascular endothelial growth factor (VEGF) and histamine induce the angiogenic process through alterations in both permeability and proteolysis. We hypothesized that beta-catenin acts as a positive regulator of MMP-2 and MT1-MMP transcription following VEGF or histamine stimulation. Rat microvascular endothelial cells were exposed to VEGF or histamine overnight and MMP-2 protein production was assessed by gelatin zymography. Latent MMP-2 protein levels were increased following VEGF and histamine treatment as were MMP-2 mRNA transcript levels. Endothelial cells exposed to VEGF and histamine had an increased level of nuclear beta-catenin, which was sensitive to inhibition of the PI3-kinase signaling pathway. Promoter assays indicated increased transcriptional activity of both MMP-2 and MT1-MMP in endothelial cells co-transfected with luciferase reporter constructs and beta-catenin. Inhibition of beta-catenin signaling with inhibitor of catenin and T cell factor (ICAT) revealed that the VEGF-induced increase in MMP-2 mRNA is beta-catenin dependent. Interestingly, while MMP-2 mRNA levels increased in response to histamine H1 or H2 receptor activation, significantly larger increases were observed in cells co-treated with ICAT and histamine or the histamine receptor agonists, HTMT and dimaprit. While both VEGF and histamine increase nuclear beta-catenin and MMP-2 production, the role of beta-catenin in MMP-2 regulation differs between the two stimuli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".