Simultaneous Transforming Growth Factor β-Tumor Necrosis Factor Activation and Cross-talk Cause Aberrant Remodeling Response and Myocardial Fibrosis in Timp3-deficient Heart
Bibliographic record
Abstract
The pleiotropic cytokines, transforming growth factor beta1 (TGFbeta1), and tumor necrosis factor (TNF) play critical roles in tissue homeostasis in response to injury and are implicated in multiple human diseases and cancer. We reported that the loss of Timp3 (tissue inhibitor of metalloproteinase 3) leads to abnormal TNF signaling and cardiovascular function. Here we show that parallel deregulation of TGFbeta1 and TNF signaling in Timp3(-/-) mice amplifies their cross-talk at the onset of cardiac response to mechanical stress (pressure overload), resulting in fibrosis and early heart failure. Microarray analysis showed a distinct gene expression profile in Timp3(-/-) hearts, highlighting activation of TGFbeta1 signaling as a potential mechanism underlying fibrosis. Neonatal cardiomyocyte-cardiofibroblast co-cultures were established to measure fibrogenic response to agonists known to be induced following mechanical stress in vivo. A stronger response occurred in neonatal Timp3(-/-) co-cultures, as determined by increased Smad signaling and collagen expression, due to increased TNF processing and precocious proteolytic maturation of TGFbeta1 to its active form. The relationship between TGFbeta1 and TNF was dissected using genetic and pharmacological manipulations. Timp3(-/-)/Tnf(-/-) mice had lower TGFbeta1 than Timp3(-/-), and anti-TGFbeta1 antibody (1D11) negated the abnormal TNF response, indicating their reciprocal stimulatory effects, with each manipulation abolishing fibrosis and improving heart function. Thus, TIMP3 is a common innate regulator of TGFbeta1 and TNF in tissue response to injury. The matrix-bound TIMP3 balances the anti-inflammatory and proinflammatory processes toward constructive tissue remodeling.
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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 | 0.001 |
| 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 | 0.001 |
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".