Serum IgA Immune Complexes Promote Proinflammatory Cytokine Production by Human Macrophages, Monocytes, and Kupffer Cells through FcαRI–TLR Cross-Talk
Bibliographic record
Abstract
IgA is predominantly recognized to play an important role in host defense at mucosal sites, where it prevents invasion of pathogens by neutralization. Although it has recently become clear that IgA also mediates other immunological processes, little remains known about the potential of IgA to actively contribute to induction of inflammation, particularly in nonmucosal organs and tissues. In this article, we provide evidence that immune complex formation of serum IgA plays an important role in orchestration of inflammation in response to pathogens at various nonmucosal sites by eliciting proinflammatory cytokines by human macrophages, monocytes, and Kupffer cells. We show that opsonization of bacteria with serum IgA induced cross-talk between FcαRI and different TLRs, leading to cell type-specific amplification of proinflammatory cytokines, such as TNF-α, IL-1β, IL-6, and IL-23. Furthermore, we demonstrate that the increased protein production of cytokines was regulated at the level of gene transcription, which was dependent on activation of kinases Syk and PI3K. Taken together, these data demonstrate that the immunological function of IgA is substantially more extensive than previously considered and suggest that serum IgA-induced inflammation plays an important role in orchestrating host defense by different cell types in nonmucosal tissues, including the liver, skin, and peripheral 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) | 0.002 | 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".