Tissue factor pathway inhibitor (TFPI) interferes with endothelial cell migration via ERK parthway and proteins involved in focal complexes
Bibliographic record
Abstract
A32 Introduction: Tissue factor pathway inhibitor (TFPI) is a plasma Kunitz-type serine protease inhibitor that is mainly known for its inhibition of tissue factor-mediated coagulation. In addition to its anticoagulant properties, emerging data showed that TFPI may also regulate endothelial cell functions via a non-haemostatic pathway. Methodology: In order to evaluate the antiangiogenic effects of TFPI on endothelial cells, recombinant human TFPI was obtained by cloning the full-length human TFPI cDNA and subsequent expression of the protein in Escherichia coli. After refolding, TFPI was used in vitro to characterize its effect on endothelial cell migration and capillary-like formation. The impact of TFPI on intracellular signalling pathway was studied using western blot and immunoprecipitation techniques. Finally, cell morphology and focal adhesion proteins were visualized by immunofluorescence and confocal microscopy. Results: In this work, we demonstrate physiologic concentration of TFPI inhibits endothelial cell migration and their differentiation into capillary-like structures in vitro. These effects were specific to endothelial cells since no inhibitory effect was observed on the migration of tumor (glioblastoma) cells. Inhibition of endothelial cell migration was correlated with a concomitant loss in cell adhesion, suggesting an alteration of focal adhesion complex integrity. Accordingly, we observed that TFPI inhibited the phosphorylation of focal adhesion kinase and paxillin, two key proteins involved in the formation of these complexes and that this effect was specific to endothelial cells. Conclusion: These results thus suggest that TFPI influences the angiogenic process via a non-haemostatic pathway, by downregulating the migratory mechanics of endothelial 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) | 0.002 | 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".