Inhibition of Th17 Cells Regulates Autoimmune Diabetes in NOD Mice
Bibliographic record
Abstract
OBJECTIVE: The T helper 17 (Th17) population, a subset of CD4-positive T-cells that secrete interleukin (IL)-17, has been implicated in autoimmune diseases, including multiple sclerosis and lupus. Therapeutic agents that target the Th17 effector molecule IL-17 or directly inhibit the Th17 population (IL-25) have shown promise in animal models of autoimmunity. The role of Th17 cells in type 1 diabetes has been less clear. The effect of neutralizing anti-IL-17 and recombinant IL-25 on the development of diabetes in NOD mice, a model of spontaneous autoimmune diabetes, was investigated in this study. RESEARCH DESIGN AND METHODS AND RESULTS: Although treatment with either anti-IL-17 or IL-25 had no effect on diabetes development in young (<5 weeks) NOD mice, either intervention prevented diabetes when treatment was started at 10 weeks of age (P < 0.001). Insulitis scoring and immunofluorescence staining revealed that both anti-IL-17 and IL-25 significantly reduced peri-islet T-cell infiltrates. Both treatments also decreased GAD65 autoantibody levels. Analysis of pancreatic lymph nodes revealed that both treatments increased the frequency of regulatory T-cells. Further investigation demonstrated that IL-25 therapy was superior to anti-IL-17 during mature diabetes because it promoted a period of remission from new-onset diabetes in 90% of treated animals. Similarly, IL-25 delayed recurrent autoimmunity after syngeneic islet transplantation, whereas anti-IL-17 was of no benefit. GAD65-specific ELISpot and CD4-positive adoptive transfer studies showed that IL-25 treatment resulted in a T-cell-mediated dominant protective effect against autoimmunity. CONCLUSIONS: These studies suggest that Th17 cells are involved in the pathogenesis of autoimmune diabetes. Further development of Th17-targeted therapeutic agents may be of benefit in this 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".