Local Autoantigen Expression as Essential Gatekeeper of Memory T-Cell Recruitment to Islet Grafts in Diabetic Hosts
Bibliographic record
Abstract
It is generally believed that inflammatory cues can attract noncognate, "bystander" T-cell specificities to sites of inflammation. We have shown that recruitment of naive and in vitro activated autoreactive CD8⁺ T cells into endogenous islets requires local autoantigen expression. Here, we demonstrate that absence of an autoantigen in syngeneic extrapancreatic islet grafts in diabetic hosts renders the grafts "invisible" to cognate memory (and naive) T cells. We monitored the recruitment of islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP)₂₀₆₋₂₁₄-reactive CD8⁺ T cells into IGRP₂₀₆₋₂₁₄-competent and IGRP₂₀₆₋₂₁₄-deficient islet grafts in diabetic wild-type or IGRP₂₀₆₋₂₁₄(-/-) nonobese diabetic hosts (harboring either naive and memory T cells or only naive IGRP₂₀₆₋₂₁₄-specific T-cells, respectively). All four host-donor combinations had development of recurrent diabetes within 2 weeks. Wild-type hosts recruited IGRP₂₀₆₋₂₁₄-specific T cells into IGRP₂₀₆₋₂₁₄(+/+) but not IGRP₂₀₆₋₂₁₄(-/-) grafts. In IGRP₂₀₆₋₂₁₄(-/-) hosts, there was no recruitment of IGRP₂₀₆₋₂₁₄-specific T cells, regardless of donor type. Graft-derived IGRP₂₀₆₋₂₁₄ activated naive IGRP₂₀₆₋₂₁₄-specific T cells, but graft destruction invariably predated their recruitment. These results indicate that recurrent diabetes is exclusively driven by autoreactive T cells primed during the primary autoimmune response, and demonstrate that local antigen expression is a sine qua non requirement for accumulation of memory T cells into islet grafts. These findings underscore the importance of tackling autoreactive T-cell memory after β-cell replacement therapy.
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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".