Ligand-Dependent Induction of Noninflammatory Dendritic Cells by Anergic Invariant NKT Cells Minimizes Autoimmune Inflammation
Bibliographic record
Abstract
Stimulated by an agonistic ligand, alpha-galactosylceramide (alphaGalCer), invariant NKT (iNKT) cells are capable of both eliciting antitumor responses and suppressing autoimmunity, while they become anergic after an initial phase of activation. It is unknown how iNKT cells act as either activators or regulators in different settings of cellular immunity. We examined effects of alphaGalCer administration on autoimmune inflammation and characterized phenotypes and functional status of iNKT cells and dendritic cells in alphaGalCer-treated NOD mice. Although iNKT cells became and remained anergic after the initial exposure to their ligand, anergic iNKT cells induce noninflammatory DCs in response to alphaGalCer restimulation, whereas activated iNKT cells induce immunogenic maturation of DCs in a small time window after the priming. Induction of noninflammatory DCs results in the activation and expansion of islet-specific T cells with diminished proinflammatory cytokine production. The noninflammatory DCs function at inflammation sites in an Ag-specific fashion, and the persistence of noninflammatory DCs critically inhibits autoimmune pathogenesis in NOD mice. Anergic differentiation is a regulatory event that enables iNKT cells to transform from promoters to suppressors, down-regulating the ongoing inflammatory responses, similar to other regulatory T cells, through a ligand-dependent mechanism.
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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".