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The Identification of the Amino Acids on Platelet Factor 4 That Bind Pathogenic Antibodies in Heparin-Induced Thrombocytopenia

2017· article· en· W3158959319 on OpenAlexaffabout
Angela Huynh, Donald M. Arnold, Peter Horsewood, John G. Kelton, Ishac Nazy

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicHeparin-Induced Thrombocytopenia and Thrombosis
Canadian institutionsCanadian Blood ServicesMcMaster University
Fundersnot available
KeywordsPlatelet factor 4HeparinAlanineAlanine scanningAmino acidChemistryAntibodyHeparin-induced thrombocytopeniaMolecular biologyBiochemistryMutantBiologyMutagenesisImmunologyGene

Abstract

fetched live from OpenAlex

Abstract Introduction: Heparin induced thrombocytopenia (HIT) is an adverse drug reaction that occurs when heparin binds to platelet factor 4 (PF4) and forms immunogenic multimolecular complexes. As a result, anti-PF4/heparin IgG antibodies bind to the PF4/heparin complexes, leading to cross-linking of FcγIIa receptors on platelets and FcγRI on monocytes resulting in their activation and an increased risk of thrombosis. Approximately 50% of cardiac surgery patients produce anti-PF4/heparin antibodies, but only a subset ( Methods: We used alanine scanning mutagenesis (ASM) to produce 70 mutations of PF4, each with a unique amino acid substitution. Single point mutations were made by mutating non-alanine residues to alanine, and alanine residues to valine. The PF4 mutants were isolated from Escherichia coli and used in an EIA. The binding capacity of the monoclonal pathogenic HIT antibody (KKO) and a non-pathogenic anti-PF4/heparin antibody (RTO) to each of the PF4 mutants, relative to wild-type PF4, was determined. Point mutations of PF4 that resulted in >25% loss of binding were identified. We compared our candidate amino acids to binding sites previously identified in the PF4 and KKO crystal structure. In addition, we screened nine EIA-positive/SRA-positive (pathogenic) and six EIA-positive/SRA-negative (non-pathogenic) patient samples using the PF4 mutants. Student's t-test was used to determine whether the binding capacities of each amino acid was significantly different between the two patient sample groups. Results: We identified 12 amino acid mutations of PF4 that caused ≥25% reduction in KKO binding. Most implicated amino acids cluster in two regions within the PF4 molecule. We identified amino acids on PF4 that were required for KKO binding which were not previously identified based on crystallographic studies. These results are consistent with the previous mutagenesis and crystallographic studies (Cai et al, Nat Commun. 2015;6:8277) of the KKO and PF4 complex but also contradict some previously stated amino acids that were thought to be part of this binding site. ASM of RTO and PF4 revealed only one amino acid that was affected. We used pathogenic and non-pathogenic EIA-positive human sera to further refine the location of the binding sites. We identified 10 amino acids that were required for the binding of human pathogenic HIT sera. Conclusions: Using sequential point mutations, we identified the amino acids that are critical for the binding of pathogenic HIT antibodies to PF4. These amino acids are either directly involved in antibody binding or influence the antigenic conformation of PF4. This library of PF4 mutant proteins can help differentiate pathogenic and non-pathogenic HIT antibodies and this information can improve diagnostic assays. This study was funded by the Canadian Institutes for Health Research. Disclosures Arnold: Amgen: Consultancy, Research Funding; Bristol Myers Squibb: Research Funding; Dova: Consultancy; Novartis: Consultancy, Research Funding; UCB: Consultancy; Rigel: Consultancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.0010.000
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 head0.057
GPT teacher head0.312
Teacher spread0.254 · 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".

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Citations0
Published2017
Admission routes2
Has abstractyes

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