IVIg and LPS Co-stimulation Induces IL-10 Production by Human Monocytes, Which Is Compromised by an FcγRIIA Disease-Associated Gene Variant
Bibliographic record
Abstract
Intravenous Immunoglobulin (IVIg) is used to treat autoimmune or inflammatory diseases, but its mechanism of action is not completely understood. We asked whether IVIg can induce interleukin-10 (IL-10) and reduce pro-inflammatory cytokine production in human monocytes, and whether this response is reduced in monocytes from people with an Fcγ receptor IIA (FcγRIIA) gene variant, which is associated with increased risk of inflammatory diseases and poor response to antibody-based biological therapy. IVIg increased IL-10 production and reduced pro-inflammatory cytokine production in response to bacterial lipopolysaccharide (LPS), which required FcγRI and FcγRIIB and activation of MAPKs, extracellular signal-regulated kinase 1/2 (ERK1/2), and p38. IL-10 production was lower and pro-inflammatory cytokine production was higher in monocytes from people with the FcγRIIA risk variant and the risk variant prevented IL-10 production in response to (IVIg+LPS). Finally, we show that IVIg did not induce MAPK activation in monocytes from people with the risk variant. Our results demonstrate that IVIg can skew human monocytes to an anti-inflammatory, IL-10-producing activation state, which is compromised in monocytes from people with the FcγRIIA risk variant. This research has profound implications for the use of IVIg because 25% of the population is homozygous for the FcγRIIA risk variant and its efficacy may be reduced in those individuals. In addition, this research may be useful to develop new therapeutic strategies to replace IVIg by cross-linking FcγRIs and FcγRIIBs to promote anti-inflammatory macrophage activation, independent of the FcγRIIA genotype.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".