Systemic overexpression of interleukin-22 induces the negative immune-regulator SOCS3 and potently reduces experimental arthritis in mice
Bibliographic record
Abstract
OBJECTIVE: High levels of IL-22 are present in serum and synovial fluid of patients with RA. As both pro- and anti-inflammatory roles for IL-22 have been described in studies using animal models of RA, its exact function in arthritis remains poorly defined. With this study we aimed to further unravel the mechanism by which IL-22 exerts its effects and to decipher its therapeutic potential by overexpression of IL-22 either locally or systemically during experimental arthritis. METHODS: CIA was induced in DBA-1 mice by immunization and booster injection with type II collagen (col II). Before arthritis onset, IL-22 was overexpressed either locally by intra-articular injection or systemically by i.v. injection using an adenoviral vector and clinical arthritis was scored for a period of 10 days. Subsequently, joints were isolated for histological analysis of arthritis severity and mRNA and protein expression of various inflammatory mediators was determined in the synovium, spleen and serum. RESULTS: Local IL-22 overexpression did not alter arthritis pathology, whereas systemic overexpression of IL-22 potently reduced disease incidence, severity and pathology during CIA. Mice systemically overexpressing IL-22 showed strongly reduced serum cytokine levels of TNF-α and macrophage inflammatory protein 1α that correlated significantly with the enhanced expression of the negative immune regulator SOCS3 in the spleen. CONCLUSION: With this study, we revealed clear anti-inflammatory effects of systemic IL-22 overexpression during CIA. Additionally, we are the first to show that the protective effect of systemic IL-22 during experimental arthritis is likely orchestrated via upregulation of the negative regulator SOCS3.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".