Monocyte Adhesion and Transmigration Induce Tissue Factor Expression: Role of the Mitogen-Activated Protein Kinases
Bibliographic record
Abstract
The expression of tissue factor (TF) by monocytes that have transmigrated across the endothelium to sites of extravascular inflammation acts both to focus and amplify the inflammatory response. Because clustering of the integrins responsible for endothelial adhesion and transmigration induces tyrosine phosphorylation and activation of the mitogen-activated protein (MAP) kinases, we postulated that transmigration might lead to monocyte activation and TF production. Monocytes were migrated across TNFalpha-primed ECV304 cells grown on fibronectin-coated Transwell chambers in response to FMLP (10(-8) M). After transmigration, monocytes showed a time-dependent increase in surface TF expression and biological procoagulant activity. TF expression was dependent on monocyte adhesion to ECV304 cells. Specifically, TF was not induced by FMLP treatment of suspended monocytes, by migration across fibronectin alone, or by soluble factors induced during migration, whereas monocyte-ECV304 adhesion was sufficient to stimulate TF. Antibodies against CD29 (beta1 integrin), but not against CD18 (beta2 integrin) or CD31 (PECAM-1), inhibited TF expression. Monocyte adhesion to ECV304 cells induced tyrosine phosphorylation of cellular proteins and specifically of the ERK and p38 MAP kinases. Tyrosine kinase inhibition with genistein (10 microg/mL) blocked transmigration, whereas selective ERK inhibition with PD98059 (50 microM) or p38 inhibition with SB203580 (20 microM) did not. However, both ERK and p38 inhibition dose dependently abolished TF expression. These studies suggest that an extravascular focus of infection or inflammation can promote both intravascular thrombosis and extravascular fibrin deposition during the process of adhesion and transmigration across the endothelial barrier. The selective inhibition of the mitogen-activated protein kinases may offer a novel therapeutic means of modulating this inflammatory sequence.
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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) | 0.001 | 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".