A Lymphotoxin/Type I IFN Axis Programs CD8+ T Cells To Infiltrate a Self-Tissue and Propagate Immunopathology
Bibliographic record
Abstract
Type I IFNs (IFN-I) are cytokines that can mediate both immune suppression and activation. Dendritic cells (DC) are significant producers of IFN-I, and depending on the context (nature of Ag, duration of exposure to Ag), DC-derived IFN-I can have varying effects on CD8(+) T cell responses. In this study, we report that in the context of a CD8(+) T cell response to a self-Ag, DC-intrinsic expression of IFN regulatory factor 3 is required to induce optimal proliferation and migration of autoreactive CD8(+) T cells, ultimately determining their ability to infiltrate a target tissue (pancreas), and the development of glucose intolerance in rat insulin promoter-glycoprotein (RIP-GP) mice. Moreover, we show that signals through the lymphotoxin-β receptor (LTβR) in DC are also required for the proliferation of autoreactive CD8(+) T cells, the upregulation of VLA4/LFA1 on activated CD8(+) T cells, and their subsequent infiltration into the pancreas both in vitro and in vivo. Importantly, the defects in autoreactive CD8(+) T cell proliferation, accumulation of CD8(+) T cells in the pancreas, and consequent glucose intolerance observed in the context of priming by LTβR(-/-) DC could be rescued by exogenous addition of IFN-I. Collectively, our data demonstrate that the LTβR/IFN-I axis is essential for programming of CD8(+) T cells to mediate immunopathology in a self-tissue. A further understanding of the IFN-I/LTβR axis will provide valuable therapeutic insights for treatment of CD8(+) T cell-mediated autoimmune diseases.
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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".