RhoA/ROCK Signaling Regulates TGFβ-Induced Epithelial-Mesenchymal Transition of Lens Epithelial Cells through MRTF-A
Bibliographic record
Abstract
Transforming growth factor (TGF)-β-induced epithelial-mesenchymal transition (EMT) leads to the formation of ocular fibrotic pathologies, such as anterior subcapsular cataract and posterior capsule opacification. Remodeling of the actin cytoskeleton, mediated by the Rho family of GTPases, plays a key role in EMT, however, how actin dynamics affect downstream markers of EMT has not been fully determined. Our previous work suggests that myocardin related transcription factor A (MRTF-A), an actin-binding protein, might be an important mediator of TGFβ-induced EMT in lens epithelial cells. The aim of the current study was to determine the requirement of RhoA/ROCK signaling in mediating TGFβ-induced nuclear accumulation of MRTF-A, and ultimate expression of α-smooth muscle actin (αSMA), a marker of a contractile, myofibroblast phenotype. Using rat lens epithelial explants, we demonstrate that ROCK inhibition using Y-27632 prevents TGFβ-induced nuclear accumulation of MRTF-A, E-cadherin/β-catenin complex disassembly, and αSMA expression. Using a novel inhibitor specifically targeting MRTF-A signaling, CCG-203971, we further demonstrate the requirement of MRTF-A nuclear localization and activity in the induction of αSMA expression. Overall, our findings suggest that TGFβ-induced cytoskeletal reorganization through RhoA/ROCK/MRTF-A signaling is critical to EMT of lens epithelial cells.
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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 | 0.001 |
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".