Notch signaling inhibits cardiac fibroblast to myofibroblast transformation by antagonizing TGF‐β1/Smad3 signaling
Bibliographic record
Abstract
PURPOSE: During myocardial infarction (MI), cardiac fibroblasts (CFs) transform into myofibroblast (CMT). This study aimed to investigate the crosstalk of Notch1 and transforming growth factor-β1 (TGF-β1)/Smad3 signaling in the regulation of CMT and myocardial fibrosis. METHODS: Primary CFs were isolated from young rats and treated with TGF-β1 or adenovirus to overexpress or knockdown Notch1 intracellular domain (N1ICD) or Smad3. RESULTS: TGF-β1 decreased the expression of fibroblast markers but increased the expression of myofibroblast markers in rat CFs. TGF-β1 increased the proliferation, invasion, and adhesion, and the secretion of collagen I of CFs, and these effects were inhibited by N1ICD overexpression. Moreover, endogenous Smad3 phosphorylation in CFs was enhanced by N1ICD knockdown, whereas TGF-β1 induced Smad3 phosphorylation was antagonized by the N1ICD overexpression. Conversely, endogenous N1ICD activation in CFs was antagonized by Smad3, whereas TGF-β1 induced N1ICD inactivation was antagonized by Smad3 knockdown. Coimmunoprecipitation showed that N1ICD interacted with Smad3 and immunostaining revealed the colocalization of N1ICD and Smad3 in the nuclei of CFs. Moreover, we demonstrated the functional antagonism of N1ICD and Smad3 on the phenotypes of CFs. Finally, TGF-β1/Smad3 signaling promoted whereas Notch signaling inhibited myocardial fibrosis in rat MI model. CONCLUSION: Notch signaling inhibits CMT by antagonizing TGF-β1/Smad3 signaling. Notch signaling activators and TGF-β1/Smad3 signaling inhibitors could be exploited for therapeutic intervention to inhibit myocardial fibrosis after MI.
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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 | 0.000 |
| 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".