Activation of MMP-2 in response to vascular injury is mediated by phosphatidylinositol 3-kinase-dependent expression of MT1-MMP
Bibliographic record
Abstract
Phosphatidylinositol 3-kinase (PI3K) is required for smooth muscle cell (SMC) proliferation. This study reports that inhibitors of PI3K also prevent SMC migration and block neointimal hyperplasia in an organ culture model of restenosis. Inhibition of neointimal formation by LY-294002 was concentration and time dependent, with 10 muM yielding the maximal effect. Continuous exposure for at least the first 4-7 days of culture was essential for significant inhibition. To assess the role of matrix metalloproteinases (MMPs) in this process, we monitored MMP secretion by injured vessels in culture. Treatment with LY-294002 selectively reduced active MMP-2 in media samples according to zymography and Western blot analysis without concomitant changes in latent MMP-2. Parallel results with wortmannin indicate that MMP-2 activation is PI3K dependent. Previous research has shown a role for both furin and membrane-type 1 (MT1)-MMP (MMP-14) in the activation of MMP-2. The furin inhibitor decanoyl-Arg-Val-Lys-Arg-chloromethylketone did not prevent MMP-2 activation after balloon angioplasty. In contrast, balloon angioplasty induced a significant increase in the levels of MT1-MMP, which was suppressed by LY-294002. No change in MT1-MMP mRNA was observed with LY-294002, because equivalent amounts of this mRNA were present in both injured and noninjured vessels. These results implicate PI3K-dependent regulation of MT1-MMP protein synthesis and subsequent activation of latent MMP-2 as critical events in neointimal hyperplasia after vascular injury.
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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 | 0.001 |
| 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".