IDO Activates Regulatory T Cells and Blocks Their Conversion into Th17-Like T Cells
Bibliographic record
Abstract
TLR ligands are effective vaccine adjuvants because they stimulate robust proinflammatory and immune effector responses and they abrogate suppression mediated by regulatory T cells (Tregs). Paradoxically, systemic administration of high doses of CpGs that bind to TLR9 ligands stimulated Tregs in mouse spleen to acquire potent suppressor activity dependent on interactions between programmed death-1 and its ligands. This response to CpG treatment manifested 8-12 h and was mediated by a rare population of plasmacytoid dendritic cells (CD19(+) pDC) induced to express the immunosuppressive enzyme IDO after TLR9 ligation. When IDO was blocked, CpG treatment did not activate Tregs, but instead stimulated pDCs to uniformly express the proinflammatory cytokine IL-6, which in turn reprogrammed Foxp3-lineage Tregs to express IL-17. Thus, CpG-induced IDO activity in pDCs acted as a pivotal molecular switch that induced Tregs to acquire a stable suppressor phenotype, while simultaneously blocking CpG-induced IL-6 expression required to reprogram Tregs to become Th17-like effector T cells. These findings support the hypothesis that IDO dominantly controls the functional status of Tregs in response to inflammatory stimuli in physiological settings.
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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".