A Self MHC Class II β-Chain Peptide Prevents Diabetes in Nonobese Diabetic Mice
Bibliographic record
Abstract
We explored T cell responses to the self class II MHC (I-Ag7) beta-chain-derived peptides in diabetic and prediabetic nonobese diabetic (NOD) mice. We found that one of these immunodominant epitopes of the beta-chain of I-Ag7 molecule, peptide 54-76, could regulate autoimmunity leading to diabetes in NOD mice. T cells from prediabetic young NOD mice do not respond to the peptide 54-76, but T cells from diabetic NOD mice proliferated in response to this peptide. T cells from older nondiabetic mice or mice protected from diabetes do not respond to this peptide, suggesting a role for peptide 54-76-specific T cells in pathogenesis of diabetes. We show that this peptide is naturally processed and presented by the NOD APCs to self T cells. However, the peptide-specific T cells generated after immunization of young mice regulate autoimmunity in NOD mice by blocking the diabetogenic cells in adoptive transfer experiments. The NOD mice immunized with this peptide are protected from both spontaneous and cyclophosphamide-induced insulin-dependent diabetes mellitus. Immunization of young NOD mice with this peptide elicited T cell proliferation and production of Th2-type cytokines. In addition, immunization with this peptide induced peptide-specific Abs of IgG1 isotype that recognized native I-Ag7 molecule on the cell surface and inhibited the T cell proliferative responses. These results suggest that I-Abetag7(54-76) peptide-reactive T cells are involved in the pathogenesis of diabetes. However, immunization with this peptide at young age induces regulatory cells and the peptide-specific Abs that can modulate autoimmunity in NOD mice and prevent spontaneous and induced 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) | 0.001 | 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".