Peptide‐activated double‐negative T cells can prevent autoimmune type‐1 diabetes development
Bibliographic record
Abstract
Autoimmune diseases may develop because of defective maturation, activation, differentiation and function of regulatory T cells. Previous studies have shown that exposure to donor antigen activates peripheral TCRalphabeta+CD3+CD4-CD8-NK1.1-, double-negative (DN) T cells, which specifically suppress anti-donor T cells and enhance survival of skin and heart grafts from allogeneic and xenogeneic donors. However, the role of DN T cells in preventing T cell-mediated autoimmune disease is unknown. Here, we analyzed the ability of DN T cells to recognize peptides expressed on self MHC and to suppress peptide-reactive CD8+ T cells, using the P14 mouse model that expresses a transgenic TCR specific for gp33 peptide presented on self MHC class I-Db. We found that injection of gp33 peptide resulted in increased DN and decreased CD8+ T cell numbers in the lymph nodes when compared to untreated mice. Injection of gp33, but not TCR-non-specific AV peptide, increased expression of T cell activation markers on DN T cells. Moreover, gp33-activated DN T cells suppressed proliferation of syngeneic CD8+ T cells via killing activated CD8+ T cells in an antigen-specific fashion in vitro. Furthermore, transferring gp33-activated DN T cells inhibited the development of autoimmune diabetes, suggesting that DN T cells may provide a novel therapy for T cell-mediated autoimmune diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".