IL-33 Mediates Lung Inflammation by the IL-6-Type Cytokine Oncostatin M
Bibliographic record
Abstract
The interleukin-1 family member IL-33 participates in both innate and adaptive T helper-2 immune cell responses in models of lung disease. The IL-6-type cytokine Oncostatin M (OSM) elevates lung inflammation, Th2-skewed cytokines, alternatively activated (M2) macrophages, and eosinophils in C57Bl/6 mice in vivo. Since OSM induces IL-33 expression, we here test the IL-33 function in OSM-mediated lung inflammation using IL-33-/- mice. Adenoviral OSM (AdOSM) markedly induced IL-33 mRNA and protein levels in wild-type animals while IL-33 was undetectable in IL-33-/- animals. AdOSM treatment showed recruitment of neutrophils, eosinophils, and elevated inflammatory chemokines (KC, eotaxin-1, MIP1a, and MIP1b), Th2 cytokines (IL-4/IL-5), and arginase-1 (M2 macrophage marker) whereas these responses were markedly diminished in IL-33-/- mice. AdOSM-induced IL-33 was unaffected by IL-6-/- deficiency. AdOSM also induced IL-33R+ ILC2 cells in the lung, while IL-6 (AdIL-6) overexpression did not. Flow-sorted ILC2 responded in vitro to IL-33 (but not OSM or IL-6 stimulation). Matrix remodelling genes col3A1, MMP-13, and TIMP-1 were also decreased in IL-33-/- mice. In vitro, IL-33 upregulated expression of OSM in the RAW264.7 macrophage cell line and in bone marrow-derived macrophages. Taken together, IL-33 is a critical mediator of OSM-driven, Th2-skewed, and M2-like responses in mouse lung inflammation and contributes in part through activation of ILC2 cells.
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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) | 0.002 | 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".