Activation of invariant NKT cells confers protection against Chlamydia trachomatis-induced arthritis
Bibliographic record
Abstract
The role of invariant NKT (iNKT) cells in reactive arthritis is unknown. We explored the functional role of NKT cells in reactive arthritis using an established murine model of Chlamydia trachomatis-induced arthritis (CtIA). CtIA in wild-type and CD1d knockout (KO) mice was induced by intra-articular injection of C. trachomatis. The effect of alpha-galactosylceramide (alpha-GalCer) activation of iNKT cells was investigated by intra-peritoneal administration of alpha-GalCer. Histopathological and phenotypic changes, chlamydial clearance and cytokine and chemokine production in synovial tissue of the knee joint were investigated after onset of the arthritis. The severity of CtIA was significantly increased in CD1d KO mice, which was associated with decrease in bactericidal cytokine IFN-gamma, regulatory cytokines IL-4 and IL-10 and increase in pro-inflammatory chemokines macrophage inflammatory protein-2 (MIP-2) and IFN-gamma-inducible protein-10 (IP-10). Local clearance of the pathogen from the joint was also decreased. Prior treatment of mice with alpha-GalCer, a potent activator of iNKT cells, significantly reduced the severity of CtIA in mice. The amelioration of CtIA was associated with decrease in chlamydial load and induction of cytokines IFN-gamma, IL-4 and IL-10 and significant suppression of MIP-2 and IP-10. Treatment of established CtIA with alpha-GalCer also demonstrated modulation of CtIA and decrease in chlamydial load. These results suggest that iNKT cells are protective against CtIA and alpha-GalCer-activated iNKT cells have an immunoregulatory role not only in preventing the induction of reactive arthritis but also in modulating established disease.
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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 | 0.000 |
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 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".