IgM and IgA anti-erythrocyte autoantibodies induce anemia in a mouse model through multivalency-dependent hemagglutination but not through complement activation
Bibliographic record
Abstract
By generating IgM and IgA switch variants of the 34-3C IgG2a anti-red blood cell (RBC) autoantibody, we evaluated the pathogenic activity of these 2 isotypes in view of the Fc-associated effector functions (ie, complement activation and polyvalency-dependent agglutination). We found that polymeric forms of 34-3C IgM and IgA anti-RBC autoantibody were as pathogenic as IgG2a, which was the most pathogenic among 4 different IgG subclasses, whereas their monomeric variants completely lacked pathogenic effects. Histological examination showed that 34-3C IgM and IgA autoantibodies caused anemia as a result of multivalency-dependent hemaggultination and subsequent sequestration of RBC in the spleen, in contrast to Fc receptor- and complement receptor-mediated erythrophagocytosis by Kupffer cells with IgG isotypes. In addition, the development of anemia induced by IgM and IgA isotypes of 34-3C antibody and by 2 additional IgM anti-RBC monoclonal autoantibodies was not inhibited at all in C3-deficient mice, indicating the lack of involvement of complement activation in the pathogenesis of IgM- and IgA-induced anemia. Our data demonstrate a remarkably high pathogenic potential of polymeric forms of IgM and IgA anti-RBC autoantibodies due to their ability to induce hemagglutination but completely independent of complement activation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".