Intravenous immunoglobulin skews macrophages to an anti-inflammatory, IL-10-producing activation state
Bibliographic record
Abstract
Intravenous Ig is used to treat autoimmune or autoinflammatory disorders, but the mechanism by which it exerts its immunosuppressive activity is not understood completely. To examine the impact of intravenous Ig on macrophages, we compared cytokine production by LPS-activated macrophages in the presence and absence of intravenous Ig. Intravenous Ig treatment induced robust production of IL-10 in response to LPS, relative to LPS stimulation alone, and reduced production of proinflammatory cytokines. This anti-inflammatory, intravenous Ig-induced activation was sustained for 24 h but could only be induced if intravenous Ig were provided within 1 h of LPS stimulation. Intravenous Ig activation led to enhanced and prolonged activation of MAPKs, Erk1/2, p38, and Erk5, and inhibition of each reduced intravenous Ig-induced IL-10 production and suppression of IL-12/23p40. IL-10 production occurred rapidly in response to intravenous Ig + LPS and was sufficient to reduce proinflammatory IL-12/23p40 production in response to LPS. IL-10 induction and reduced IL-12/23p40 production were transcriptionally regulated. IL-10 played a direct role in reducing proinflammatory cytokine production by macrophages treated with intravenous Ig + LPS, as macrophages from mice deficient in the IL-10R β chain or in IL-10 were compromised in their ability to reduce proinflammatory cytokine production. Finally, intraperitoneal injection of intravenous Ig or intravenous Ig + LPS into mice activated macrophages to produce high levels of IL-10 during subsequent or concurrent LPS challenge, respectively. These findings identify IL-10 as a key anti-inflammatory mediator produced by intravenous Ig-treated macrophages and provide insight into a novel mechanism by which intravenous Ig may dampen down inflammatory responses in patients with autoimmune or autoinflammatory diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".