Analysis of the role of von Willebrand factor, platelet glycoprotein VI-, and α2β1-mediated collagen binding in thrombus formation
Bibliographic record
Abstract
Rare missense mutations in the von Willebrand factor (VWF) A3 domain that disrupt collagen binding have been found in patients with a mild bleeding phenotype. However, the analysis of these aberrant VWF-collagen interactions has been limited. Here, we have developed mouse models of collagen-binding mutants and analyzed the function of the A3 domain using comprehensive in vitro and in vivo approaches. Five loss-of-function (p.S1731T, p.W1745C, p.S1783A, p.H1786D, A3 deletion) and 1 gain-of-function (p.L1757A) variants were generated in the mouse VWF complementary DNA. The results of these various assays were consistent, although the magnitude of the effects were different: the gain-of-function (p.L1757A) variant showed consistent enhanced collagen binding whereas the loss-of-function mutants showed variable degrees of functional deficit. We further analyzed the impact of direct platelet-collagen binding by blocking glycoprotein VI (GPVI) and integrin α2β1 in our ferric chloride murine thrombosis model. The inhibition of GPVI demonstrated a comparable functional defect in thrombosis formation to the VWF(-/-) mice whereas α2β1 inhibition demonstrated a milder bleeding phenotype. Furthermore, a delayed and markedly reduced thrombogenic response was still evident in VWF(-/-), GPVI, and α2β1 blocked animals, suggesting that alternative primary hemostatic mechanisms can partially rescue the bleeding phenotype associated with these defects.
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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) | 0.001 | 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".