T-Cell Tolerance by Dendritic Cells and Macrophages as a Mechanism for the Major Histocompatibility Complex-Linked Resistance to Autoimmune Diabetes
Bibliographic record
Abstract
For poorly understood reasons, the development of autoimmune diabetes in humans and mice is dominantly inhibited by major histocompatibility complex (MHC) class II molecules with diverse antigen-binding sites. We have previously shown that thymocytes expressing a highly diabetogenic I-A(g7)-restricted T-cell receptor (TCR) (4.1-TCR) undergo negative selection in mice carrying one copy of the antidiabetogenic H-2(b) haplotype in an I-A(b)-dependent but superantigen-independent manner. Here, we show that 4.1-TCR-transgenic thymocytes undergo different forms of tolerance in NOD mice expressing antidiabetogenic I-A(d), I-A(g7PD), or I-Ealpha(k) transgenes. The ability of protective MHC class II molecules to induce thymocyte tolerance in 4.1-TCR-transgenic NOD mice correlates with their ability to prevent diabetes in non-TCR-transgenic mice and is associated with polymorphisms within positions 56-67 of their beta1 domains. The 4.1-thymocyte tolerogenic activity of these MHC class II molecules is mediated by dendritic cells and macrophages but not by B-cells or thymic epithelial cells and is a peptide-dependent process. Antidiabetogenic MHC class II molecules may thus afford diabetes resistance by presenting, on dendritic cells and macrophages, tolerogenic peptides to a subset of highly diabetogenic and MHC-promiscuous CD4(+) T-cells that play a critical role in the initiation of diabetes.
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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".