Aryl hydrocarbon receptor expressing Treg17 cells suppress type 1 diabetes in NOD mice (BA11P.140)
Bibliographic record
Abstract
Abstract The role of Th17 cells in type 1 diabetes (T1D) remains inconclusive. We found polarizing CD4+ T cells with TGF-beta+IL-6 or with IL-23+IL-6 cytokines induces different subsets of Th17 cells. Cells polarized with TGF-beta+IL-6 express high levels of transcription factor aryl hydrocarbon receptor (AhR) and IL-10 but minimal amounts of IL-22. Cells from T cell receptor transgenic BDC2.5 NOD mice polarized with IL-23+IL-6 produced large amounts of IL-22, expressed little AhR expression but induce T1D in young NOD mice. Cells derived with TGF-beta+IL-6 and termed as Treg17 cells were nonpathogenic and did not induce T1D. All adoptive transfer studies were done in young NOD mice and not NOD.SCID mice to prevent the conversion of Th17 cells into Th1 cells. We have also found IL-22-producing Th17 cells in the pancreas of diabetic NOD mice. The expression of IL-22 receptor in the pancreas of NOD mice increased with disease progression. Neutralization of IL-22 by antibody in vivo during adoptive transfer of pathogenic Th17 cells or splenocytes from diabetic mice did not significantly alter disease progression in the recipient mice. Therefore, Th17 derived IL-22 is not directly pathogenic to beta cells. We conclude regulatory Treg17 cells induced by TGF-beta+IL-6 that express high levels of AhR are protective while Th17 cells with a very low level of AhR induced by IL-23 + IL-6 are pathogenic. Both EN and SMB equally contributed to this work.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".