Induction of tolerance to a CD4 T cell hybrid insulin peptide epitope prolongs islet graft survival and suppresses autoreactive CD8 T cells
Bibliographic record
Abstract
Abstract Autoreactive T cells are thought to drive autoimmune diabetes by recognizing beta cell-derived peptide antigens. We previously discovered that hybrid insulin peptides (HIPs) are potent neoantigen peptide ligands for a subset of autoreactive CD4 T cells in both the NOD mouse model and human type 1 diabetes patients. Inducing tolerance to prominent T cell autoantigens through antigen-specific immunotherapy could prevent disease onset or recurrence after islet transplantation without the need for broad immunosuppression. Here we show that tolerogenic nanoparticle (NP) delivery of the 2.5HIP, a dominant CD4 T cell HIP epitope in the NOD mouse, can prolong islet graft survival in transplanted diabetic NOD mice. Tolerance induction to the 2.5HIP not only suppressed 2.5HIP tetramer+ CD4 T cells but also autoreactive CD4 and CD8 T cells specific for different islet antigens. 2.5HIP NP treatment induced a dysfunctional state in graft-infiltrating T cells characterized by increased expression of anergic markers on CD4 T cells and reduced inflammatory cytokine production in 2.5HIP tetramer+ CD4 T cells as well as IGRP (islet specific glucose-6-phosphatase catalytic subunit-related protein) tetramer+ CD8 T cells. In conclusion, we demonstrate that antigen-specific immunotherapy aimed at inducing tolerance to a single CD4 T cell HIP epitope can prolong islet graft survival and suppress autoreactive CD8 T cells in the NOD mouse model of autoimmune diabetes. Supported by grants from NIH (R01 DK122566, R01 DK081166, T32 DK120520) and JDRF (2-SRA-2018-566-S-B)
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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".