Cortactin activation by FVIIa/tissue factor and PAR2 promotes endothelial cell migration
Bibliographic record
Abstract
Cellular migration is a complex process that requires the polymerization of actin filaments to drive cellular extension. Smooth muscle and cancer cell migration has been shown to be affected by coagulation factors, notably the factor VII (FVIIa) and tissue factor (TF) complex. The present studies delineated mediators involved with the process of FVIIa/TF-induced cell migration and utilized a simple, precise, and reproducible, migration assay. Both FVIIa and protease-activated receptor-2 (PAR2)-activating peptide, SLIGRL, increased the migration rate of porcine cerebral microvascular endothelial cells (pCMVECs) overexpressing human TF. Ras homolog gene family member A (RhoA) and cortactin were upregulated during the process; expression of HIF, actin polymerization nuclear diaphanous-related formin-1 and -2 (Dia1, and Dia2) were unaffected. Gene silencing by shRNA to PAR2, RhoA, and cortactin attenuated this gene upregulation and migration induced by FVIIa/TF. Utilizing immunocellular localization, we demonstrate that during FVIIa/TF and PAR2 activation, cortactin molecules translocate from the cytoplasm to the cell periphery and assist in lamellipodia formation of pCMVECs. Overall, we demonstrate a novel regulation and role for cortactin in FVIIa/TF-mediated endothelial cell migration that occurs through a PAR2 and RhoA dependent mechanism.
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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".