IL-33–Stimulated Murine Mast Cells Polarize Alternatively Activated Macrophages, Which Suppress T Cells That Mediate Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Abstract IL-33 is known to promote type 2 immune responses through ST2, a component of the IL-33R complex, expressed primarily on mast cells, Th2 cells, group 2 innate lymphoid cells and regulatory T cells, and to a lesser extent, on NK cells and Th1 cells. Consistent with previous studies, we found that IL-33 polarized alternatively activated macrophages (AAMΦ) in vivo. However, in vitro stimulation of murine bone marrow–derived or peritoneal macrophages with IL-33 failed to promote arginase activity or expression of YM-1 or Retnla, markers of AAMΦ. Furthermore, macrophages have low/no basal expression of ST2. This suggested that alternative activation of macrophages may involve an IL-33–responsive third-party cell. Because mast cells have the highest expression of ST2 relative to other leukocytes, we focused on this cell type. Coculture experiments showed that IL-33–stimulated mast cells polarized AAMΦ through production of soluble factors. IL-33–stimulated mast cells produced a range of cytokines, including IL-6 and IL-13. Mast cell–derived IL-13 was required for induction of AAMΦ, whereas mast cell–derived IL-6 enhanced macrophage responsiveness to IL-13 via upregulation of the IL-4Rα receptor. Furthermore, we found that AAMΦ polarized by IL-33–stimulated mast cells could suppress proliferation and IL-17 and IFN-γ production by T cells. Finally, we show that AAMΦ polarized by IL-33–stimulated mast cells attenuated the encephalitogenic function of T cells in the experimental autoimmune encephalomyelitis model. Our findings reveal that IL-33 can promote immunosuppressive responses by polarizing AAMΦ via mast cell–derived IL-6 and IL-13.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".