Interleukin-36γ is expressed by neutrophils and can activate microglia, but has no role in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
BACKGROUND: Experimental autoimmune encephalomyelitis (EAE) is a model of inflammatory demyelinating diseases mediated by different types of leukocytes. How these cells communicate with each other to orchestrate autoimmune attacks is not fully understood, especially in the case of neutrophils, whose importance in EAE is newly established. The present study aimed to determine the expression pattern and role of different components of the IL-36 signaling pathway (IL-36α, IL-36β, IL-36γ, IL-36R) in EAE. METHODS: EAE was induced by either active immunization with myelin peptide, passive transfer of myelin-reactive T cells or injection of pertussis toxin to transgenic 2D2 mice. The molecules of interest were analyzed using a combination of techniques, including quantitative real-time PCR (qRT-PCR), flow cytometry, Western blotting, in situ hybridization, and immunohistochemistry. Microglial cultures were treated with recombinant IL-36γ and analyzed using DNA microarrays. Different mouse strains were subjected to clinical evaluation and flow cytometric analysis in order to compare their susceptibility to EAE. RESULTS: Our observations indicate that both IL-36γ and IL-36R are strongly upregulated in nervous and hematopoietic tissues in different forms of EAE. IL-36γ is specifically expressed by neutrophils, while IL-36R is expressed by different immune cells, including microglia and other myeloid cells. In culture, microglia respond to recombinant IL-36γ by expressing molecules involved in neutrophil recruitment, such as Csf3, IL-1β, and Cxcl2. However, mice deficient in either IL-36γ or IL-36R develop similar clinical and histopathological signs of EAE compared to wild-type controls. CONCLUSION: This study identifies IL-36γ as a neutrophil-related cytokine that can potentially activate microglia, but that is only correlative and not contributory in EAE.
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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 | 0.000 | 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) | 0.000 | 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 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".