Exposure to IL-15 and IL-21 Enables Autoreactive CD8 T Cells To Respond to Weak Antigens and Cause Disease in a Mouse Model of Autoimmune Diabetes
Bibliographic record
Abstract
Autoreactive CD8(+) T lymphocytes play a key role in the pathogenesis of several autoimmune diseases. It is not yet well understood how autoreactive CD8(+) T cells, which express TCRs with low reactivity toward self-Ags, gain the ability to respond to autoantigens to cause disease. Previously, we have shown that prior stimulation of CD8(+) T cells with synergistic combinations of cytokines produced by the innate immune response, such as IL-21 and IL-15, induces Ag-independent proliferation. Such "cytokine-primed" CD8 T cells displayed increased responsiveness to limiting quantities of the cognate Ag. In this paper, we report that prior stimulation with IL-15 and IL-21 also enables CD8(+) T cells to respond to weakly agonistic TCR ligands, resulting in proliferation, cytokine secretion, and cytolytic activity. Using a transgenic mouse model of autoimmune diabetes, we show that cytokine-primed autoreactive CD8(+) T cells induce disease following stimulation by weak TCR ligands, but their diabetogenic potential is dependent on continuous availability of IL-15 in vivo. These findings suggest that inflammatory cytokines could facilitate the triggering of autoreactive CD8(+) T cells by weak autoantigens, and this mechanism may have important implications for autoimmune diseases associated with microbial infections and chronic inflammation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".