Ski Modulates Myofibroblast Motility via Downregulation of MMP2 and Paxillin
Bibliographic record
Abstract
Cardiac fibrosis is component of a number of cardiovascular diseases, including myocardial infarction. Excessive formation of extracellular matrix (ECM) occurs in activated cardiac (myo)fibroblasts that reside in the infarct scar or those moving from adjacent viable tissue. We propose that Ski, an endogenous repressor of TGF‐β1, regulates ECM remodeling and cellular motility by influencing MMP function, and specifically MMP‐2. Moreover, Ski alters cardiac fibroblast motility by altering the expression of paxillin (a focal adhesion associated protein) and its kinases including FAK (Tyr 397) and PYK2 (Y402). Primary adult rat fibroblasts (P1) were subjected to either exogenous Ski overexpression by adenoviral Ski (Ad‐Ski) or Ad‐Lac‐Z control with the multiplicity of infection (MOI) of 50 and 150. Ski overexpressing cells exhibited significantly lower MMP‐2 secretion and significantly decreased MMP‐2 activity as detected using gelatin zymography. Using Transwell plates, Ski overexpression was associated with a significant decrease in migration of cells in the presence of a chemoattractant in the lower well. Moreover, reduced motility of P1 cells vs. control was observed by Ski overexpression group via scratch assay. Furthermore, downregulation of paxillin was detected in Ski overexpressing cells lysates vs. Lac‐Z and control samples. Both FAK (Tyr 397) and PYK2 (Y402) were decreased in Ski overexpressing cells vs control. We suggest that Ski may exert multiple effects eg, MMP2 and paxillin, which affects motility and adhesion, and thus represents a putative mechanism for modulation of myofibroblast function and cardiac fibrosis.
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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".