Reverse Engineering of a Pathogenic Antibody Reveals the Molecular Mechanism of Vaccine-Induced Immune Thrombotic Thrombocytopenia
Bibliographic record
Abstract
The massive COVID-19 vaccine roll-out campaign illuminated a range of rare side effects, the most dangerous of which─vaccine-induced immune thrombotic thrombocytopenia (VITT)─is caused by adenoviral (Ad)-vectored vaccines. VITT occurrence had been linked to the production of pathogenic antibodies that recognize an endogenous chemokine, platelet factor 4 (PF4). Mass spectrometry (MS)-based evaluation of the ensemble of anti-PF4 antibodies obtained from a VITT patient’s blood indicates that the major component is a monoclonal antibody. Structural characterization of this antibody reveals several unusual characteristics, such as the presence of an N -glycan in the Fab segment and high density of acidic amino acid residues in the complementarity-determining regions. A recombinant version of this antibody (RVT1) was generated by transient expression in mammalian cells based on the newly determined sequence. It captures the key properties of VITT antibodies such as their ability to activate platelets in a PF4 concentration-dependent fashion. Homology modeling of the Fab segment reveals a well-defined polyanionic paratope, and the docking studies indicate that the polycationic segment of PF4 readily accommodates two Fab segments, cross-linking the antibodies to yield polymerized immune complexes. Their existence was verified with native MS by detecting assemblies as large as (RVT1) 3 (PF4) 2, pointing out at FcγRIIa-mediated platelet activation as the molecular mechanism underlying VITT clinical manifestations. In addition to the high PF4 affinity, RVT1 readily binds other polycationic targets, indicating a polyreactive nature of this antibody. This surprising promiscuity not only sheds light on VITT etiology but also opens up a range of opportunities to manage this pathology.
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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 | 0.000 |
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".