Suppression of IGRP-specific CD8 T cells following induction of tolerance to a Hybrid Insulin Peptide CD4 neoepitope
Bibliographic record
Abstract
Abstract An autoimmune-mediated death of insulin-producing beta cells, orchestrated by effector CD4 and CD8 T cells that recognize islet antigens, results in Type 1 Diabetes. A dominant CD4 islet autoantigen in the NOD mouse model of autoimmune diabetes is a neoepitope termed the 2.5 Hybrid Insulin Peptide (2.5HIP). By delivering the 2.5HIP as an antigen-specific immunotherapy on tolerogenic PLG-nanoparticles, islet grafts in diabetic NOD mice survived longer and cytokine production in autoreactive CD4 and CD8 T cells was suppressed, including the well-studied IGRP tetramer+ (tet+) CD8 T cells. Using both diabetic transplant recipients and prediabetic NOD mice treated with 2.5HIP nanoparticles, we examined mechanisms of peripheral tolerance induction to a dominant CD4 neoepitope and the impact on the function of IGRP CD8 T cells. Following induction of tolerance to the 2.5HIP, there was an increase in dysfunctional surface marker expression (PD1+ TIM3+) on effector 2.5HIP tet+ CD4 T cells in the spleen of treated mice. Along with a decrease in effector function, we observed an increase in the fraction of both Treg and Tr1 2.5HIP tet+ T cells expressing IL10 in the islets, spleen, and draining lymph nodes of tolerized mice. Concurrently, IGRP tet+ CD8 T cells accumulated in the draining lymph nodes and were less able to traffic and infiltrate islets. Of the IGRP tet+ cells that entered islets, there were fewer cytolytic CX3CR1+ effector cells in tolerized mice. The robust functional increase in both Treg and Tr1 2.5HIP tet+ T cells may provide a potential mechanism for the accumulation of IGRP tet+ CD8 T cells in the draining lymph node and explain their inefficient trafficking into, and function within, islets following tolerance induction. NIH - T32 5T32DK120520-03, R01 2R01DK081166-11) JDRF 2-SRA-2020-907-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".