Immunoglobulin subclass determines ability of immunoglobulin (Ig)G to capture and activate neutrophils presented as normal human IgG or disease-associated anti-neutrophil cytoplasm antibody (ANCA)-IgG
Bibliographic record
Abstract
Immunoglobulin G (IgG) is a potent neutrophil stimulus, particularly when presented as anti-neutrophil cytoplasm antibody (ANCA) in ANCA-associated vasculitis. We assessed whether IgG subclasses had differential effects on neutrophil activation and whether differences were dependent on specific Fc-receptor engagement. Using a physiologically relevant flow model, we compared adhesion of neutrophils to different subclasses of normal IgG coated onto solid surfaces, with adhesion of neutrophils treated with different subclasses of soluble ANCA IgG to P-selectin surfaces or endothelial cells (EC). Normal IgG captured flowing neutrophils efficiently in the order IgG3 > IgG1 > IgG2 > IgG4. Fc-receptor blockade reduced capture, IgG3 being more dependent on CD16 and IgG1/2 on CD32. Blockade of the integrin CD18 reduced neutrophil spreading, while inhibition of calcium-dependent signalling reduced both capture and spreading, suggesting that both were active processes. Neutrophils treated with ANCA IgG subclasses 1, 3 and 4 showed stabilization of adhesion to P-selectin surfaces and EC. ANCA changed neutrophil behaviour from rolling to static adhesion and the potency of the subclasses followed the same pattern as above: IgG3 > IgG1 > IgG4. Blockade of Fc receptors resulted in neutrophils continuing to roll, i.e. they were not ANCA-activated; differential utilization of Fc receptor by particular IgG subclasses was not as apparent as during neutrophil capture by normal IgG. IgG3 is the most effective subclass for inducing neutrophil adhesion and altered behaviour, irrespective of whether the IgG is surface bound or docks onto neutrophil surface antigens prior to engaging Fc receptors. Engagement of Fc receptors underpins these responses; the dominant Fc receptor depends on IgG subclass.
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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) | 0.002 | 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".