MMP-2 Mediates Angiotensin II–Induced Hypertension Under the Transcriptional Control of MMP-7 and TACE
Bibliographic record
Abstract
Development of cardiovascular disease induced by excessive Gq protein-coupled receptor agonist stimulation depends on signaling networks involving multiple matrix metalloproteinases (MMPs) and metalloproteinase disintegrins (ADAMs). Here, we hypothesized that MMP-2, being a major gelatinase in cardiac and vascular tissue, was likely to play a key role in cardiovascular homeostasis. We targeted MMP-2 using complementary and overlapping approaches involving pharmacological inhibition and RNA interference in mice treated with angiotensin II (1.4 mg/kg per day) for 12 days. We studied the development of hypertension (by tail cuff plethysmography), cardiac hypertrophy (by M-mode echocardiography, cardiomyocyte cross-sectional area, and quantitative real-time polymerase chain reaction (qRT-PCR) analysis of hypertrophy marker genes), and fibrosis (by picrosirius red collagen staining and qRT-PCR analysis of fibrosis marker genes) in mice receiving angiotensin II. We found that angiotensin II infusion upregulated MMP-2 concurrent with the development of hypertension, hypertrophy, and fibrosis. This upregulation of MMP-2 depended on MMP-7 and TACE (tumor necrosis factor-α convertase, ADAM-17). RNA interference targeting MMP-7 and TACE attenuated the angiotensin II-induced upregulation of MMP-2 and prevented the development of hypertension, as well as development of cardiac hypertrophy and fibrosis. In contrast, pharmacological inhibition and RNA interference of MMP-2 attenuated angiotensin II-induced hypertension, without influencing development of cardiac hypertrophy or fibrosis. Downstream of MMP-7 and TACE, MMP-2 mediated angiotensin II-induced hypertension, but did not mediate cardiac hypertrophy or fibrosis. This suggests a functional specialization of MMP-2 in agonist-induced cardiovascular disease development that has potential implications for the design of metalloproteinase-based therapeutic strategies.
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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 | 0.000 |
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".