Platelet glycoprotein Ibα forms catch bonds with human WT vWF but not with type 2B von Willebrand disease vWF
Bibliographic record
Abstract
Arterial blood flow enhances glycoprotein Ibalpha (GPIbalpha) binding to vWF, which initiates platelet adhesion to injured vessels. Mutations in the vWF A1 domain that cause type 2B von Willebrand disease (vWD) reduce the flow requirement for adhesion. Here we show that increasing force on GPIbalpha/vWF bonds first prolonged ("catch") and then shortened ("slip") bond lifetimes. Two type 2B vWD A1 domain mutants, R1306Q and R1450E, converted catch bonds to slip bonds by prolonging bond lifetimes at low forces. Steered molecular dynamics simulations of GPIbalpha dissociating from the A1 domain suggested mechanisms for catch bonds and their conversion by the A1 domain mutations. Catch bonds caused platelets and GPIbalpha-coated microspheres to roll more slowly on WT vWF and WT A1 domains as flow increased from suboptimal levels, explaining flow-enhanced rolling. Longer bond lifetimes at low forces eliminated the flow requirement for rolling on R1306Q and R1450E mutant A1 domains. Flowing platelets agglutinated with microspheres bearing R1306Q or R1450E mutant A1 domains, but not WT A1 domains. Therefore, catch bonds may prevent vWF multimers from agglutinating platelets. A disintegrin and metalloproteinase with a thrombospondin type 1 motif-13 (ADAMTS-13) reduced platelet agglutination with microspheres bearing a tridomain A1A2A3 vWF fragment with the R1450E mutation in a shear-dependent manner. We conclude that in type 2B vWD, prolonged lifetimes of vWF bonds with GPIbalpha on circulating platelets may allow ADAMTS-13 to deplete large vWF multimers, causing bleeding.
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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".