β-Catenin/Smad3 Interaction Regulates Transforming Growth Factor-β-Induced Epithelial to Mesenchymal Transition in the Lens
Bibliographic record
Abstract
Cataracts are the leading cause of blindness worldwide. Although surgery is a successful method to restore vision loss due to cataracts, post-surgical complications can occur, such as secondary cataracts, also known as posterior capsular opacification (PCO). PCO arises when lens epithelial cells (LEC) are left behind in the capsular bag following surgery and are induced to undergo epithelial to mesenchymal transition (EMT). Following EMT, LEC morphology and phenotype are altered leading to a loss of transparency and vision. Transforming growth factor (TGF)-β-induced signaling through both canonical, TGF-β/Smad, and non-canonical, β-catenin/Wnt and Rho/ROCK/MRTF-A, pathways have been shown to be involved in lens EMT, and thus PCO. However, the interactions between these signaling pathways in the lens have not been thoroughly explored. In the current study we use rat LEC explants as an ex vivo model, to examine the interplay between three TGF-β-mediated pathways using α-smooth muscle actin (α-SMA) as a molecular marker for EMT. We show that Smad3 inhibition via SIS3 prevents nuclear translocation of β-catenin and MRTF-A, and α-SMA expression, suggesting a key role of Smad3 in regulation of MRTF-A and β-catenin nuclear transport in LECs. Further, we demonstrate that inhibition of β-catenin/CBP interaction by ICG-001 decreased the amount of phosphorylated Smad3 upon TGF-β stimulation in addition to significantly decreasing the expression levels of TGF-β receptors, TBRII and TBRI. Overall, our findings demonstrate interdependence between the canonical and non-canonical TGF-β-mediated signaling pathways controlling 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 | 0.001 |
| 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".