Versican G3 Domain Promotes Blood Coagulation through Suppressing the Activity of Tissue Factor Pathway Inhibitor-1
Bibliographic record
Abstract
We have detected versican, a member of the large chondroitin sulfate proteoglycans, and its degraded C-terminal G3 fragments in human plasma and observed that the versican G3 domain promoted blood coagulation. Silencing G3 expression with small interfering RNA reduced the effect of G3 on coagulation. Plasma coagulation assays suggest that G3 enhances coagulation irrespective of its actions on platelets and white blood cells. To examine how versican affected blood coagulation, we used normal human plasma and different types of coagulation factor-deficient plasmas. The experiments indicated that versican enhanced coagulation through the extrinsic pathway, and that Factor VII was the target molecule. FVII activity assays showed that G3 activated FVII in the presence of plasma but not with purified FVII directly. Yeast two-hybrid, immunoprecipitation, and gel co-migration assays showed that G3 interacted with the tissue factor pathway inhibitor-1 (TFPI-1). TFPI-1 activity assays suggested that G3 inhibited TFPI-1 activity, allowing FVIIa and FXa to facilitate the coagulation process. G3-induced blood coagulation was further confirmed with a mouse model in a real-time manner. Taken together, these results indicate that versican may represent a new target for the development of therapies against atherosclerosis.
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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".