Initiation of plasminogen activation on the surface of monocytes expressing the type II transmembrane serine protease matriptase
Bibliographic record
Abstract
uPA (urokinase-type plasminogen activator) activates plasminogen with high efficiency when bound to its cellular receptor uPAR, but only after a prolonged lag phase during which generated plasmin activates pro-uPA. How the activity of this proteolytic system might be rapidly initiated is unknown. We have now found that 2 monocytic cell lines display distinct patterns of plasminogen activation. U937 cells, but not THP-1 cells, displayed the expected lag phase, suggesting a constitutive initiation mechanism on the latter. This was shown to be due to the plasmin-independent activation of uPAR-bound pro-uPA by a cell surface-associated protease and to correlate with the expression of matriptase, a type II transmembrane serine protease that was highly expressed in THP-1 cells but undetectable in U937 cells. Kinetic analysis demonstrated that matriptase is a relatively poor activator of pro-uPA in solution, approximately 100-fold less efficient than plasmin (k(cat)/K(m) 1.16 x 10(5) M(-1)s(-1) cf 1.21 x 10(7) M(-1)s(-1)). However, down-regulation of matriptase expression in THP-1 cells by siRNA reduced the activation of cell-associated pro-uPA and the subsequent rapid initiation of plasminogen activation by 76% to 93%. Matriptase was also found to be expressed by peripheral blood monocytes and may therefore be a specific mechanism for the rapid initiation and regulation of plasminogen activation by these 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".