The Mouse <i>Idd2</i> Locus Is Linked to the Proportion of Immunoregulatory Double-Negative T Cells, a Trait Associated with Autoimmune Diabetes Resistance
Bibliographic record
Abstract
Autoimmune diseases result from a break in immune tolerance. Various mechanisms of peripheral tolerance can protect against autoimmunity, including immunoregulatory CD4(-)CD8(-) double-negative (DN) T cells. Indeed, we have previously shown that diabetes-prone mouse strains exhibit a low proportion of DN T cells relative to that of diabetes-resistant mice, and that a single autologous transfer of DN T cells can impede autoimmune diabetes development, at least in the 3A9 TCR transgenic setting. In this study, we aim to understand the genetic basis for the difference in DN T cell proportion between diabetes-resistant and diabetes-prone mice. We thus perform an unbiased linkage analysis in 3A9 TCR F2 (NOD.H2(k) × B10.BR) mice and reveal that a locus on chromosome 9, which coincides with Idd2, is linked to the proportion of DN T cells in the lymph nodes. We generate two NOD.H2(k).B10-Chr9 congenic mouse strains and validate the role of this genetic interval in defining the proportion of DN T cells. Moreover, we find that the increased proportion of DN T cells in lymphoid organs is associated with a decrease in both diabetes incidence and serum IgG Ab levels. Together, the data suggest that Idd2 is linked to DN T cell proportion and that a physiological increase in DN T cell number may be sufficient to confer resistance to autoimmune diabetes. Altogether, these findings could help identify new candidate genes for the development of therapeutic avenues aimed at modulating DN T cell number for the prevention of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".