SIGIRR Negatively Regulates IL-36–Driven Psoriasiform Inflammation and Neutrophil Infiltration in the Skin
Bibliographic record
Abstract
Abstract SIGIRR has been described as a negative regulator of several IL-1R/TLR family members and has been implicated in several inflammatory disease conditions. However, it is unknown whether it can suppress IL-36 family cytokines, which are members of the broader IL-1 superfamily that have emerged as critical orchestrators of psoriatic inflammation in both humans and mice. In this study, we demonstrate that SIGIRR is downregulated in psoriatic lesions in humans and mice, and this correlates with increased expression of IL-36 family cytokines. Using Sigirr−/− mice, we identify, for the first time (to our knowledge), SIGIRR as a negative regulator of IL-36 responses in the skin. Mechanistically, we identify dendritic cells and keratinocytes as the primary cell subsets in which IL-36 proinflammatory responses are regulated by SIGIRR. Both cell types displayed elevated IL-36 responsiveness in absence of SIGIRR activity, characterized by enhanced expression of neutrophil chemoattractants, leading to increased neutrophil infiltration to the inflamed skin. Blockade of IL-36R signaling ameliorated exacerbated psoriasiform inflammation in Sigirr−/− mice and inhibited neutrophil infiltration. These data identify SIGIRR activity as an important regulatory node in suppressing IL-36–dependent psoriatic inflammation in humans and mice.
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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".