Interleukin‐10 limits the expansion of immunoregulatory CD4<sup>−</sup>CD8<sup>−</sup> T cells in autoimmune‐prone non‐obese diabetic mice
Bibliographic record
Abstract
Regulatory T cells appear to show great potential for use in cellular therapy. In particular, CD4(-)CD8(-) (double negative (DN)) T cells, which compose 1-3% of the total number of T lymphocytes, exhibit prominent antigen-specific immune tolerance properties and confer immune tolerance in models of allografts and xenografts. We have recently shown that autoimmune-diabetes-prone mice carry fewer DN T cells and that this phenotype contributes to autoimmune-prone diabetes susceptibility, suggesting that increasing DN T-cell number in autoimmune-prone individuals may be of therapeutic interest. To achieve this goal, we must first determine whether the remaining DN T cells in autoimmune-prone mice are functional. In addition, we must identify the parameters that regulate the numbers of DN T cells. Herein, we evaluate the immunoregulatory properties of DN T cells in the autoimmune-prone non-obese diabetic (NOD) genetic background. Using 3A9 TCR transgenic mice, we show that DN T cells from both diabetes-resistant B10.Br and genetically autoimmune-prone NOD.H2(k) mice show an equivalent immunoregulatory potential on a per cell basis. However, upon stimulation, there is a 10-fold increase in the number of 3A9 TCR transgenic DN T cells that produce interleukin 10 (IL-10) from NOD.H2(k) mice in comparison with B10.Br mice. We further showed that IL-10 facilitates DN T-cell apoptosis and thus may regulate the number of DN T cells. Taken together, our results show that, although reduced in number, DN T cells from mice carrying an autoimmune-prone genetic background exhibit a potent cytotoxic potential and that DN T-cell expansion is regulated, at least in part, by IL-10.
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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".