Identification of CD4+ T Cell-Specific Epitopes of Islet-Specific Glucose-6-Phosphatase Catalytic Subunit-Related Protein: A Novel β Cell Autoantigen in Type 1 Diabetes
Bibliographic record
Abstract
Islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP) has been identified as a novel CD8(+) T cell-specific autoantigen in NOD mice. This study was undertaken to identify MHC class II-specific CD4(+) T cell epitopes of IGRP. Peptides named P1, P2, P3, P4, P5, P6, and P7 were synthesized by aligning the IGRP protein amino acid sequence with peptide-binding motifs of the NOD MHC class II (I-A(g7)) molecule. Peptides P1, P2, P3, and P7 were immunogenic and induced both spontaneous and primed responses. IGRP peptides P1-, P2-, P3-, and P7-induced responses were inhibited by the addition of anti-MHC class II (I-A(g7)) Ab, confirming that the response is indeed I-A(g7) restricted. Experiments using purified CD4(+) and CD8(+) T cells from IGRP peptide-primed mice also showed a predominant CD4(+) T cell response with no significant activation of CD8(+) T cells. T cells from P1-, P3-, and P7-primed mice secreted both IFN-gamma and IL-10 cytokines, whereas P2-primed cells secreted only IFN-gamma. Peptides P3 and P7 prevented the development of spontaneous diabetes and delayed adoptive transfer of diabetes. Peptides P1 and P2 delayed the onset of diabetes in both these models. In summary, we have identified two I-A(g7)-restricted CD4(+) T cell epitopes of IGRP that can modulate and prevent the development of diabetes in NOD mice. These results provide the first evidence on the role of IGRP-specific, MHC class II-restricted CD4(+) T cells in disease protection and may help in the development of novel therapies for type 1 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 | 0.001 | 0.000 |
| 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".