Structure of pathogenic anti-PF4 autoantibodies reveals the mechanism of immune response formation in vaccine-induced thrombotic thrombocytopenia (VITT)
Bibliographic record
Abstract
Abstract Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a severe, but rare, side effect of adenoviral-vectored COVID-19 vaccines. It is attributed to formation of autoantibodies recognizing Platelet Factor 4 (PF4) and capable of triggering platelet activation, which leads to thrombosis. We used mass spectrometry (MS) to determine the structure of a VITT patient-derived anti-PF4 antibodies (VITT IgG) and identified their unique properties underlying pathogenicity. The VITT IgGs studied in this work were extracted from the plasma of a ChAdOx1 vaccine recipient, who developed VITT. Intact-mass MS analysis of these antibodies revealed one major clone, and its complete structural characterization was carried out using MS-based de novo sequencing. This allowed us to establish its subclass (IgG2), determine the amino acid sequence, and identify an N-glycan in the variable region. This information was used to build a 3D model of the VITT IgG, which revealed the presence of a large polyanionic patch within the paratope involving four CDR regions. Molecular dynamics simulations highlight the role of electrostatics as the major driver of the VITT IgG binding to PF4. The large equatorial belt of the positive charge circumscribing the PF4 tetramer serves as a distributed epitope, allowing it to cross-link up to three VITT IgG molecules, giving rise to large immune complexes capable of FcyRIIa-mediated platelet activation. The molecular mechanism of VITT pathogenesis emerging from this work explains how VITT IgGs and PF4 form large immune complexes in the absence of heparin, which is essential to for platelet activation in a similar disease, heparin-induced thrombocytopenia, and sheds light on the etiology of this devastating condition. Supported by grant from NIH R01 GM112666
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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".