Modeling competition among autoreactive CD8+ T cells in autoimmune diabetes: implications for antigen-specific therapy
Bibliographic record
Abstract
Antigen therapy remains a promising strategy for prevention and treatment of autoimmune diseases, but translating this strategy to clinical therapy has been largely unsuccessful. We have shown that development of autoimmune diabetes in non-obese diabetic (NOD) mice involves prevalent recruitment of CD8+ T cells recognizing epitopes of islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP). Administration of peptide analogs of IGRP206-214, the dominant epitope, reduced disease incidence but only under conditions that led to selective deletion of high-avidity T-cell clones. Peptide types or doses that resulted in elimination of all IGRP206-214-reactive T cells, regardless of avidity, promoted the recruitment of sub-dominant epitope-specific T cells and failed to prevent disease development. Here, we mathematically model competition of IGRP-reactive T-cell clones during spontaneous disease, and in response to peptide treatment. Based on realistic T-cell activation, proliferation and differentiation parameter values, our model shows that progression of spontaneous disease is characterized by (i) initial expansion of all IGRP206-214-reactive T-cell clones (irrespective of avidity) and (ii) slow replacement of T-cell clones recognizing peptide/MHC with low avidity by their high-avidity counterparts. This model helps understand the paradoxical outcomes of IGRP-based peptide treatment experiments. Furthermore, it predicts that slight deviations in dose or peptide affinity can lead to treatment failure or disease progression. This will occur if the treatment (i) increases the imbalance between competing IGRP206-214-reactive T-cell clones such that it favors rapid takeover of high-avidity clones or (ii) deletes all IGRP206-214-reactive clones, thereby creating a vacuum that promotes the recruitment of pathogenic sub-dominant specificities. Our data and model urge caution in the application of peptide therapy in autoimmunity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".