Tissue inhibitor of metalloproteinase-3 inhibits neonatal mouse cardiomyocyte proliferation via EGFR/JNK/SP-1 signaling
Bibliographic record
Abstract
We have recently demonstrated that tissue inhibitor of metalloproteinase-3 (TIMP-3) decreases neonatal cardiomyocyte proliferation (Hammoud L, Xiang F, Lu X, Brunner F, Leco K, Feng Q. Cardiovasc Res 75: 359-368, 2007). The aim of the present study was to delineate a pathway through which TIMP-3 exerts its antiproliferative effect. Experiments were conducted on neonatal cardiomyocyte cultures and heart tissues isolated from wild-type (WT) and TIMP-3(-/-) mice. Deficiency in TIMP-3 decreased p27 expression and increased cardiomyocyte proliferation in cardiomyocytes and neonatal hearts. A TIMP-3/epidermal growth factor (EGF) receptor (EGFR)/c-Jun NH(2)-terminal kinase (JNK)/SP-1/p27 pathway was investigated. JNK phosphorylation and EGFR protein levels were increased in TIMP-3(-/-) cardiomyocytes and heart tissues. Treatment with recombinant TIMP-3 decreased JNK phosphorylation and EGFR expression/phosphorylation. Inhibition of JNK activity using SP-600125 decreased SP-1 phosphorylation, increased p27 expression, and decreased cardiomyocyte proliferation. Furthermore, treatment with the EGFR specific inhibitor PD-168393 or the EGF-neutralizing antibody decreased cardiomyocyte proliferation as well as phosphorylation of JNK and SP-1 in both WT and TIMP-3(-/-) cardiomyocytes. We conclude that TIMP-3 inhibits neonatal mouse cardiomyocyte proliferation by upregulating p27 expression. The effects of TIMP-3 are mediated via inhibition of EGFR expression/phosphorylation, and decreases in JNK and SP-1 signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".