Human β-Cell Killing by Autoreactive Preproinsulin-Specific CD8 T Cells Is Predominantly Granule-Mediated With the Potency Dependent Upon T-Cell Receptor Avidity
Bibliographic record
Abstract
The end-stage immunopathology of type 1 diabetes resulting in β-cell destruction appears to be strongly dominated by cytotoxic CD8 T lymphocytes (CD8 T cells). However, the mechanism of cytotoxicity used by autoreactive CD8 T cells in the human setting remains unknown. Using type 1 diabetes patient-derived preproinsulin-specific CD8 T-cell clones recognizing either an HLA-A2 (A*0201) or HLA-A24 (A*2402)-restricted epitope (peptide of preproinsulin [PPI](15-24), ALWGPDPAAA; or PPI(3-11), LWMRLLPLL), we assessed the use of conventional mediators of cytotoxicity in the destruction of human β-cells in vitro compared with virus-specific cytotoxic CD8 T-cell clones. We show that PPI-specific CD8 T-cell clones are mainly reliant upon cytotoxic degranulation for inducing β-cell death. Furthermore, we find that in comparison with virus-specific CD8 T cells, there are differences in the killing potency of PPI-specific CD8 T cells that are not due to cell-intrinsic differences, but rather are mediated by differences in strength of signaling by peptide-HLA ligands. The study highlights the regulation of β-cell killing as a potential point for therapeutic control, including the possibility of blocking autoreactive CD8 T-cell function without impacting upon general immune competence.
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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 | 0.000 |
| 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".