Mitogen-Activated Protein Kinases Mediate Matrix Metalloproteinase-9 Expression in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Expression of matrix metalloproteinase (MMP)-9 has been linked to the progression of plaque rupture and intimal formation in arterial lesions. In this study, we determined which factors and signaling pathways are involved in regulating the MMP-9 gene. Rat carotid arterial smooth muscle cells treated with tumor necrosis factor (TNF)-alpha showed a marked increase in MMP-9 activity and mRNA level, whereas platelet-derived growth factor (PDGF) showed a slight induction of the MMP-9 mRNA level. TNF-alpha treatment caused an increase in c-Jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (p38 MAPK), and extracellular signal-regulated kinase (ERK) activities, whereas PDGF treatment caused an increase in ERKs and p38 MAPK activities without any effect on JNK activity. Treatment with either SB203580 (inhibitor of p38 MAPK) or U0126 (inhibitor of the ERK pathway) downregulated the TNF-alpha-induced MMP-9 expression in a dose-dependent manner. Treatment of cells with TNF-alpha and PDGF together stimulated the MMP-9 expression at a level higher than that observed with either factor alone, suggesting that TNF-alpha and PDGF have a synergistic effect on MMP-9 expression in arterial smooth muscle cells. Furthermore, suboptimal inhibitory concentrations of SB203580 and U0126 together almost completely inhibited the MMP-9 expression. These results suggest that p38 MAPK and ERK pathways contribute to the transcriptional regulation of MMP-9 in arterial smooth muscle cells.
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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".