β-Catenin Mediates Mechanically Regulated, Transforming Growth Factor-β1–Induced Myofibroblast Differentiation of Aortic Valve Interstitial Cells
Bibliographic record
Abstract
OBJECTIVE: In calcific aortic valve disease, myofibroblasts and activation of the transforming growth factor-β1 (TGF-β1) and Wnt/β-catenin pathways are observed in the fibrosa, the stiffer layer of the leaflet, but their association is unknown. We elucidated the roles of β-catenin and extracellular matrix stiffness in TGF-β1-induced myofibroblast differentiation of valve interstitial cells (VICs). METHODS AND RESULTS: TGF-β1 induced rapid β-catenin nuclear translocation in primary porcine aortic VICs in vitro through TGF-β receptor I kinase. Degrading β-catenin pharmacologically or silencing it with small interfering RNA inhibited TGF-β1-induced myofibroblast differentiation without altering Smad2/3 activity. Conversely, increasing β-catenin availability with Wnt3A alone did not induce differentiation. However, combining TGF-β1 and Wnt3A caused greater myofibroblast differentiation than TGF-β1 treatment alone. Notably, in VICs grown on collagen-coated PA gels with physiological stiffnesses, TGF-β1-induced β-catenin nuclear translocation and myofibroblast differentiation occurred only on matrices with fibrosa-like stiffness, but not ventricularis-like stiffness. In diseased aortic valves from pigs fed an atherogenic diet, myofibroblasts colocalized with increased protein expression of Wnt3A, β-catenin, TGF-β1, and phosphorylated Smad2/3 in the fibrosa. CONCLUSIONS: Myofibroblast differentiation of VICs involves matrix stiffness-dependent crosstalk between TGF-β1 and Wnt signaling pathways and may explain in part why the stiffer fibrosa is more susceptible to disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".