Tumor Necrosis Factor-α–Converting Enzyme (ADAM17) Mediates GPIbα Shedding From Platelets In Vitro and In Vivo
Bibliographic record
Abstract
Interaction of the platelet receptor glycoprotein (GP) Ib-V-IX with von Willebrand factor exposed at a site of vascular injury is an essential step in the initiation of a hemostatic plug. Proteolytic cleavage (shedding) of the GPIbalpha subunit was first described >25 years ago, the protease mediating this event as well as its physiological function, however, have not been elucidated. We reported recently that shedding of GPIbalpha induced by platelet storage or mitochondrial injury involves a platelet-derived metalloproteinase(s). Here we show that GPIbalpha shedding in response to mitochondrial injury or physiological activation is inhibited in platelets obtained from chimeric mice, which express inactive tumor necrosis factor-alpha converting enzyme (TACE(DeltaZn/DeltaZn)) in blood cells only. Shedding was also inhibited in mouse and human platelets in the presence of 2 potent TACE inhibitors: TAP1 and TMI-1. Our data further suggest that TACE is important in the regulation of GPIbalpha expression in vivo because we observed an approximately 90% reduction in soluble GPIbalpha (glycocalicin) in plasma of TACE(DeltaZn/DeltaZn) chimeras as well as significantly increased levels of GPIbalpha on circulating platelets. In contrast, shedding of P-selectin from activated platelets was not affected by the mutation in TACE. Damaged TACE(DeltaZn/DeltaZn) platelets were further characterized by a markedly improved post-transfusion recovery and hemostatic function in mice. In conclusion, our data demonstrate that TACE is expressed in platelets and that it is the key enzyme mediating shedding of GPIbalpha.
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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 | 0.001 |
| 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".