Nuclear translocation of myocardin-related transcription factor-A during transforming growth factor beta-induced epithelial to mesenchymal transition of lens epithelial cells.
Bibliographic record
Abstract
PURPOSE: Transforming growth factor beta (TGFβ) is a known inducer of epithelial to mesenchymal transition (EMT), and studies in other systems have shown that nuclear localization of the myocardin-related transcription factor (MRTF) is downstream of TGFβ. In the following study, we investigated whether nuclear translocation of MRTF-A or MRTF-B is involved in TGFβ-induced EMT of lens epithelial cells (LECs). We further investigated the relationship between matrix metalloproteinase-2 and -9 (MMP-2/9) and MRTF in the EMT of LECs. METHODS: Rat lens explant cultures were used as the model system. Explants were treated with TGFβ, an MMP-2/9 inhibitor, or actin binding drugs and immunostained for alpha smooth muscle actin (αSMA), MRTF-A, and MRTF-B. Cytoplasmic and nuclear intensities of cells were measured using ImageJ. Production of αSMA was measured using western blot analysis and ImageJ. RESULTS: Untreated explant cells exhibited little αSMA expression, and MRTF-A and B were found to reside primarily in the cytosol. However, when stimulated with TGFβ, a significantly greater number of cells exhibited nuclear expression of MRTF-A, accompanied by an increase in αSMA expression. However, MRTF-B remained in the cytoplasm following TGFβ treatment. Cotreatment with an MMP-2/9 inhibitor and TGFβ resulted in reduced MRTF-A nuclear localization and αSMA expression compared to cells treated with TGFβ alone. CONCLUSIONS: Our results are the first to demonstrate the expression of MRTF-A in LECs and that its nuclear translocation can be stimulated by TGFβ. Our data further suggest that MMP-2 and -9 are involved in the translocation of MRTF-A in LECs during TGFβ-induced EMT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".