Activation of pro-(matrix metalloproteinase-2) (pro-MMP-2) by thrombin is membrane-type-MMP-dependent in human umbilical vein endothelial cells and generates a distinct 63 kDa active species
Bibliographic record
Abstract
Thrombin, a critical enzyme in the coagulation cascade, has also been associated with angiogenesis and activation of the zymogen form of matrix metalloproteinase-2 (MMP-2 or gelatinase-A). We show that thrombin activated pro-MMP-2 in a dose- and time-dependent manner in cultured human umbilical-vein endothelial cells (HUVECs) to generate a catalytically active 63 kDa protein that accumulated as the predominant form in the conditioned medium. This 63 kDa thrombin-activated MMP-2 is distinct from the 62 kDa species found following concanavalin A or PMA stimulated pro-MMP-2 activation. Hirudin and leupeptin blocked thrombin-induced pro-MMP-2 activation, demonstrating that the proteolytic activity of thrombin is essential. However, activation was also dependent upon membrane-type-MMP (MT-MMP) action, since it was blocked by EDTA, o-phenanthroline, hydroxamate metalloproteinase inhibitors, tissue inhibitor of metalloproteinase-2 (TIMP-2) and TIMP-4, but not TIMP-1. Thrombin inefficiently cleaved recombinant 72 kDa pro-MMP-2, but efficiently cleaved the 64 kDa MT-MMP-processed intermediate form in the presence of cells. Thrombin also rapidly (within 1 h) increased cellular MT-MMP activity, and at longer time points (>6 h) it increased expression of MT1-MMP mRNA and protein. Thus signalling via proteinase-activated receptors (PARs) may play a role in thrombin-induced MMP-2 activation, though this does not appear to involve PAR1, PAR2, or PAR4 in HUVECs. These results indicate that in HUVECs the activation of pro-MMP-2 by thrombin involves increased MT-MMP activity and preferential cleavage of the MT-MMP-processed 64 kDa MMP-2 form in the presence of cells. The integration of these proteinase systems in the vascular endothelium may be important during thrombogenesis and tissue remodelling associated with neovascularization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".