MMP9 Differentially Regulates Proteins Involved in Actin Polymerization and Cell Migration during TGF-β-Induced EMT in the Lens
Bibliographic record
Abstract
Fibrotic cataracts have been attributed to transforming growth factor-beta (TGF-β)-induced epithelial-to-mesenchymal transition (EMT). Using mouse knockout (KO) models, our laboratory has identified MMP9 as a crucial protein in the TGF-β-induced EMT process. In this study, we further revealed an absence of alpha-smooth muscle actin (αSMA) and filamentous-actin (F-actin) stress fibers in MMP9KO mouse lens epithelial cell explants (LECs). Expression analysis using NanoString revealed no marked differences in αSMA (ACTA2) and beta-actin (β-actin) (ACTB) mRNA between the lenses of TGF-β-overexpressing (TGF-βtg) mice and TGF-βtg mice on a MMP9KO background. We subsequently conducted a protein array that revealed differential regulation of proteins known to be involved in actin polymerization and cell migration in TGF-β-treated MMP9KO mouse LECs when compared to untreated controls. Immunofluorescence analyses using rat LECs and the novel MMP9-specific inhibitor, JNJ0966, revealed similar differential regulation of cortactin, FAK, LIMK1 and MLC2 as observed in the array. Finally, a reduction in the nuclear localization of MRTF-A, a master regulator of cytoskeletal remodeling during EMT, was observed in rat LECs co-treated with JNJ0966 and TGF-β. In conclusion, MMP9 deficiency results in differential regulation of proteins involved in actin polymerization and cell migration, and this in turn prevents 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".