β-Catenin/CBP–Dependent Signaling Regulates TGF-β–Induced Epithelial to Mesenchymal Transition of Lens Epithelial Cells
Bibliographic record
Abstract
PURPOSE: Transforming growth factor-β-induced epithelial-mesenchymal transition (EMT) is one of the main causes of posterior capsular opacification (PCO) or secondary cataract; however, the signaling events involved in TGF-β-induced PCO have not been fully characterized. Here, we focus on examining the role of β-catenin/cyclic AMP response element-binding protein (CREB)-binding protein (CBP) and β-catenin/T-cell factor (TCF)-dependent signaling in regulating cytoskeletal dynamics during TGF-β-induced EMT in lens epithelial explants. METHODS: Rat lens epithelial explants were cultured in medium M199 in the absence of serum. Explants were treated with TGF-β2 in the presence or absence of the β-catenin/CBP interaction inhibitor, ICG-001, or the β-catenin/TCF interaction inhibitor, PNU-74654. Western blot and immunofluorescence experiments were carried out and analyzed. RESULTS: An increase in the expression of fascin, an actin-bundling protein, was observed in the lens explants upon stimulation with TGF-β, and colocalized with F-actin filaments. Inhibition of β-catenin/CBP interactions, but not β-catenin/TCF interactions, led to a decrease in TGF-β-induced fascin and stress fiber formation, as well as a decrease in the expression of known markers of EMT, α-smooth muscle actin (α-SMA) and matrix metalloproteinase 9 (MMP9). In addition, inhibition of β-catenin/CBP-dependent signaling also prevented TGF-β-induced downregulation of epithelial cadherin (E-cadherin) in lens explants. CONCLUSIONS: We show that β-catenin/CBP-dependent signaling regulates fascin, MMP9, and α-SMA expression during TGF-β-induced EMT. We demonstrate that β-catenin/CBP-dependent signaling is crucial for TGF-β-induced EMT in the lens.
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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".