Interleukin-4 but not Interleukin-10 Protects Against Spontaneous and Recurrent Type 1 Diabetes by Activated CD1d-Restricted Invariant Natural Killer T-Cells
Bibliographic record
Abstract
In nonobese diabetic (NOD) mice, a deficiency in the number and function of invariant natural killer T-cells (iNKT cells) contributes to the onset of type 1 diabetes. The activation of CD1d-restricted iNKT cells by alpha-galactosylceramide (alpha-GalCer) corrects these deficiencies and protects against spontaneous and recurrent type 1 diabetes. Although interleukin (IL)-4 and IL-10 have been implicated in alpha-GalCer-induced protection from type 1 diabetes, a precise role for these cytokines in iNKT cell regulation of susceptibility to type 1 diabetes has not been identified. Here we use NOD.IL-4(-/-) and NOD.IL-10(-/-) knockout mice to further evaluate the roles of IL-4 and IL-10 in alpha-GalCer-induced protection from type 1 diabetes. We found that IL-4 but not IL-10 expression mediates protection against spontaneous type 1 diabetes, recurrent type 1 diabetes, and prolonged syngeneic islet graft function. Increased transforming growth factor-beta gene expression in pancreatic lymph nodes may be involved in alpha-GalCer-mediated protection in NOD.IL-10(-/-) knockout mice. Unlike the requirement of IL-7 and IL-15 to maintain iNKT cell homeostasis, IL-4 and IL-10 are not required for alpha-GalCer-induced iNKT cell expansion and/or survival. Our data identify an important role for IL-4 in the protection against type 1 diabetes by activated iNKT cells, and these findings have important implications for cytokine-based therapy of type 1 diabetes and islet transplantation.
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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 | 0.000 | 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".