Combination of Tumor Necrosis Factor-α Ablation and Matrix Metalloproteinase Inhibition Prevents Heart Failure After Pressure Overload in Tissue Inhibitor of Metalloproteinase-3 Knock-Out Mice
Bibliographic record
Abstract
Cytokine and extracellular matrix (ECM) homeostasis are distinct systems that are each dysregulated in heart failure. Here we show that tissue inhibitor of metalloproteinase (TIMP)-3 is a critical regulator of both systems in a mouse model of left ventricular (LV) dilation and dysfunction. Timp-3(-/-) mice develop precipitous LV dilation and dysfunction reminiscent of dilated cardiomyopathy (DCM), culminating in early onset of heart failure by 6 weeks, compared with wild-type aortic-banding (AB). Timp-3 deficiency resulted in increased TNFalpha converting enzyme (TACE) activity within 6 hours after AB leading to enhanced tumor necrosis factor-alpha (TNFalpha) processing. In addition, TNFalpha production increased in timp-3(-/-)-AB myocardium. A significant elevation in gelatinase and collagenase activities was observed 1 week after AB, with localized ECM degradation in timp-3(-/-)-AB myocardium. Timp-3(-/-)/tnfalpha(-/-) mice were generated and subjected to AB for comparative analyses with timp-3(-/-)-AB mice. This revealed the critical role of TNFalpha in the early phase of LV remodeling, de novo expression of Matrix metalloproteinases (MMP)-8 in the absence of TNFalpha, and highlighted the importance of interstitial collagenases (MMP-2, MMP-13, and MT1-MMP) for cardiac ECM degradation. Ablation of TNFalpha, or limiting MMP activity with a synthetic MMP inhibitor (PD166793), each partially attenuated LV dilation and cardiac dysfunction in timp-3(-/-)-AB mice. Notably, combining TNFalpha ablation with MMP inhibition completely rescued heart disease in timp-3(-/-)-AB mice. This study provides a basis for anti-TNFalpha and MMP inhibitor combination therapy in 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".