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Record W3215731520 · doi:10.1182/blood-2021-152314

Snake Venom Botrocetin Induces GPIb-Independent Platelet Aggregation

2021· article· en· W3215731520 on OpenAlexaff
Chuanbin Shen, Daniel Thomas MacKeigan, Guangheng Zhu, Miguel A. D. Neves, Wenjing Ma, Eric G. Cerenzia, Heyu Ni

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsVon Willebrand factorGlycoprotein IbPlateletBernard–Soulier syndromePlatelet membrane glycoproteinPlatelet Glycoprotein GPIb-IX ComplexVon Willebrand diseaseMolecular biologyChemistryBiochemistryCell biologyBiologyImmunology

Abstract

fetched live from OpenAlex

Abstract Abstract Introduction:Snake venom-derived botrocetin facilitates von Willebrand factor (VWF) binding to GPIbα, and has been used clinically for the detection of von Willebrand disease (VWD) and GPIb-related disorders. Botrocetin has also been widely used experimentally for the development and characterization of potential antithrombotic drugs targeting the GPIb-VWF axis. Although compelling evidence suggests GPIb is responsible for botrocetin-induced VWF binding and platelet aggregation, some reports suggest that botrocetin could induce platelet aggregation in some Bernard-Soulier syndrome (BSS) patients who lack a functional GPIb complex. However, the alternative mechanism for botrocetin-induced BSS platelet aggregation and the receptor(s) mediating this action are unclear. Methods: Botrocetin was purified from the lyophilized venom of Bothrops jararaca using ion-exchange column chromatography. Light transmission aggregometry assay was performed using platelet-rich plasma (PRP) from human, wild type (WT) mice, GPIbα-deficient mice, αIIbβ3-deficient mice and VWF-deficient mice, or CHO cells stably transfected with αIIbβ3 integrin. O-sialoglycoprotein endopeptidase (OSGE) was used to cleave the N-terminal extracellular domain of GPIbα. The binding of botrocetin, VWF and fibrinogen to platelets from WT or the gene-deficient mice were measured by flow cytometry. Antibodies against GPIbα (SZ2, NIT A) and integrin αIIbβ3 (abciximab, JON/A, M1, PSI E1) were used to investigate the binding site of botrocetin. Perfusion chamber assay was used to measure thrombus formation under different shear stresses. Results: We discovered that botrocetin induced aggregation of human platelets lacking the N-terminal extracellular domain of GPIbα and platelets from GPIbα-deficient mice in the presence of VWF. This VWF-dependent, GPIbα-independent platelet aggregation induced by botrocetin was inhibited by αIIbβ3 antagonists. Botrocetin also induced aggregation of CHO cells stably transfected with αIIbβ3 in VWF-dependent manner. Further experiments with gel-filtered platelets showed that botrocetin competitively bound to the ligand-binding area exposed on αIIbβ3 and blocked fibrinogen and other ligands from binding to the active state of αIIbβ3 in the absence of VWF. Botrocetin inhibited platelet aggregation and thrombus formation in VWF-deficient mice. Conclusion: Integrin αIIbβ3 is the alternative receptor that mediates VWF-dependent, GPIb-independent platelet aggregation induced by botrocetin. However, via targeting αIIbβ3, botrocetin itself inhibits platelet aggregation in the absence of VWF. These results demonstrate versatility in the mechanism of botrocetin, which may provide snakes containing this toxin the adaptability necessary to aggregate platelets/thrombocytes of different prey or predators. Our data reveals a previously unknown role of botrocetin in the integrin-VWF interaction and also provides insight into developing new antithrombotic drugs that target the active conformation of integrin αIIbβ3. The target switching of botrocetin between GPIb-VWF and αIIbβ3-VWF may explain the possible misdiagnosis of the GPIb-related congenital disorders evaluated by botrocetin. Disclosures No relevant conflicts of interest to declare.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.248
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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