Exacerbated inflammatory arthritis in response to hyperactive gp130 signalling is independent of IL-17A
Bibliographic record
Abstract
OBJECTIVE: Interleukin (IL)-17A producing CD4 T-cells (TH-17 cells) are implicated in rheumatoid arthritis (RA). IL-6/STAT3 signalling drives TH-17 cell differentiation, and hyperactive gp130/STAT3 signalling in the gp130F/F mouse promotes exacerbated pathology. Conversely, STAT1-activating cytokines (eg, IL-27, IFN-γ) inhibit TH-17 commitment. Here, we evaluate the impact of STAT1 ablation on TH-17 cells during experimental arthritis and relate this to IL-17A-associated pathology. METHODS: Antigen-induced arthritis (AIA) was established in wild type (WT), gp130F/F mice displaying hyperactive gp130-mediated STAT signalling and the compound mutants gp130F/F:Stat1-/- and gp130F/F:Il17a-/- mice. Joint pathology and associated peripheral TH-17 responses were compared. RESULTS: Augmented gp130/STAT3 signalling enhanced TH-17 commitment in vitro and exacerbated joint pathology. Ablation of STAT1 in gp130F/F mice (gp130F/F:Stat1-/-) promoted the hyperexpansion of TH-17 cells in vitro and in vivo during AIA. Despite this heightened peripheral TH-17 cell response, disease severity and the number of joint-infiltrating T-cells were comparable with that of WT mice. Thus, gp130-mediated STAT1 activity within the inflamed synovium controls T-cell trafficking and retention. To determine the contribution of IL-17A, we generated gp130F/F:IL-17a-/- mice. Here, loss of IL-17A had no impact on arthritis severity. CONCLUSIONS: Exacerbated gp130/STAT-driven disease in AIA is associated with an increase in joint infiltrating T-cells but synovial pathology is IL-17A independent.
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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 | 0.001 | 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".