Cytokine priming enables triggering of autoreactive CD8 T lymphocytes by low affinity TCR ligands (83.7)
Bibliographic record
Abstract
Abstract The cellular and molecular events preceding the initial activation of autoreactive CD8 T cells in several autoimmune diseases remain unclear. Recent reports have implicated IL-6, IL-21, IL-15 and IL-7 in the triggering of autoreactive CD8 T cells in mouse models of autoimmune diseases. We have shown that IL-21 and IL-6 synergize with IL-7 or IL-15 to induce antigen-independent proliferation of naive CD8 T cells, with a concomitant increase in antigen responsiveness. We postulated that the cytokine-induced augmentation of TCR sensitivity (referred herein as ‘cytokine priming’) might enable stimulation of potentially autoreactive CD8 T cells by low affinity self antigens. Here, we show that cytokine priming enables naive TCR transgenic CD8 T cells to respond to low affinity TCR ligands, resulting in proliferation and acquisition of effector functions. We have used a transgenic mouse model of autoimmune type 1 diabetes to show that cytokine-primed autoreactive CD8 T cells acquire the capacity to cause disease following stimulation with weak TCR agonists, and that the diabetogenic potential of these cells is dependent on the continuous availability of IL-15. These findings demonstrate a novel mechanism by which cytokines contribute to the triggering of autoreactive CD8 T cells and have important implications for autoimmune diseases associated with 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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".