IgA and IgG antineutrophil cytoplasmic antibody engagement of Fc receptor genetic variants influences granulomatosis with polyangiitis
Bibliographic record
Abstract
Granulomatosis with polyangiitis (Wegener's) is a rare autoimmune neutrophil-mediated vasculitis that can cause renal disease and mucosal manifestations. Antineutrophil cytoplasmic antibodies (ANCA) are present in many patients, vary in level over time, and induce neutrophil activation through engagement with Fc receptors (FcRs). Given roles for FcRs in ANCA-mediated neutrophil activation and IgA antibodies in mucosal immunity, we hypothesized that FcR genetics and previously unappreciated IgA ANCA affect clinical presentation. We assembled a total of 673 patients and 413 controls from two multicenter cohorts, performed ELISA and immunofluorescence assays to determine IgA and IgG ANCA positivity, and used Illumina, TaqMan, or Pyrosequencing to genotype eight haplotype-tagging SNPs in the IgA FcR (FCAR) and to determine NA1/NA2 genotype of FCGR3B, the most prevalent neutrophil IgG FcR. We evaluated neutrophil activation by measuring degranulation marker CD11b with flow cytometry or neutrophil extracellcular trap formation with confocal microscopy. Functional polymorphisms in FCGR3B and FCAR differed between patient groups stratified by renal involvement. IgA ANCA were found in ∼30% of patients and were less common in patients with severe renal disease. Neutrophil stimulation by IgA or IgG ANCA led to degranulation and neutrophil extracellcular trap formation in a FcR allele-specific manner (IgA:FCAR P = 0.008; IgG:FCGR3B P = 0.003). When stimulated with IgA and IgG ANCA together, IgG ANCA induced neutrophil activation was reduced (P = 0.0001). FcR genotypes, IgA ANCA, and IgG ANCA are potential prognostic and therapeutic targets for understanding the pathogenesis and presentation of granulomatosis with polyangiitis (Wegener's).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".