Matrix metalloproteinase-2 mediates cytokine-induced myocardial contractile dysfunction
Bibliographic record
Abstract
OBJECTIVE: Pro-inflammatory cytokines depress myocardial contractile function by enhancing peroxynitrite production, yet the mechanism by which peroxynitrite does this is unknown. As matrix metalloproteinases (MMPs) can be activated by peroxynitrite and can proteolytically cleave troponin I in hearts, we determined whether this occurs in cytokine-induced myocardial dysfunction. METHODS: Isolated working rat hearts were perfused with buffer containing interleukin-1 beta, interferon-gamma, and tumor necrosis factor-alpha. RESULTS: Cytokines induced a marked decline in mechanical function during 60-120 min of perfusion. This decline was accompanied by increased myocardial inducible NO synthase activity and perfusate dityrosine (a marker of peroxynitrite), compared to control hearts. Before the decline in mechanical function there was enhanced MMP-2 activity in the perfusate. This was accompanied by decreased tissue levels of MMP-2, tissue inhibitor of matrix metalloproteinases-4 and troponin I in cytokine-treated hearts. The collagen content of the heart was not affected by cytokine treatment. A neutralizing anti-MMP-2 antibody or the MMP inhibitors Ro31-9790 or PD166793 attenuated the decline in myocardial function. Moreover, the MMP-2 antibody prevented the decline in myocardial MMP-2 and troponin I levels. CONCLUSIONS: Myocardial contractile dysfunction caused by pro-inflammatory cytokines results in MMP-2 activation and a decline in tissue inhibitor of matrix metalloproteinases-4 in the heart. Troponin I is also a target for the proteolytic action of MMP-2 during acute heart failure triggered by pro-inflammatory cytokines. Inhibition of MMPs may be a novel pharmacological strategy for the treatment of acute inflammatory heart disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".