Structural Characterization of a Pathogenic Antibody Underlying Vaccine-Induced Immune Thrombotic Thrombocytopenia (VITT)
Bibliographic record
Abstract
Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare but dangerous side effect of adenoviral-vectored COVID-19 vaccines. VITT had been linked to production of autoantibodies recognizing platelet factor 4 (PF4). Here, we characterize anti-PF4 antibodies obtained from a VITT patient’s blood. Intact mass measurements indicate that a significant fraction of these antibodies represent a limited number of clones. MS analysis of large antibody fragments (the light chain and the Fc/2 and Fd fragments of the heavy chain) confirms the monoclonal nature of this component of the anti-PF4 antibodies repertoire and reveals the presence of a mature complex biantennary N -glycan within the Fd segment. Peptide mapping using two complementary proteases and LC-MS/MS was used to determine the amino acid sequence of the entire light chain and over 98% of the heavy chain (excluding a short N-terminal segment). The sequence analysis allows the monoclonal antibody to be assigned to the IgG2 subclass and verifies that the light chain belongs to the λ-type. Incorporation of enzymatic de- N -glycosylation into the peptide mapping routine allows the N -glycan in the Fab region of the antibody to be localized to the framework 3 region of the V H domain. This novel N -glycosylation site is the result of a single mutation within the germline sequence. Peptide mapping also provides information on lower-abundance (polyclonal) components of the anti-PF4 antibody ensemble, revealing the presence of all four subclasses (IgG1–IgG4) and both types of the light chain (λ and κ). This case study demonstrates the power of combining the intact, middle-down, and bottom-up MS approaches for meaningful characterization of ultralow quantities of pathogenic antibodies extracted directly from patients’ blood.
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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".