Peptide Dose, MHC Affinity, and Target Self-Antigen Expression Are Critical for Effective Immunotherapy of Nonobese Diabetic Mouse Prediabetes
Bibliographic record
Abstract
Cross-reactive T cells that recognize both Tep69 (dominant nonobese diabetic (NOD) T cell epitope in ICA69 (islet cell autoantigen of 69 kDa)) and ABBOS (dominant NOD T cell epitope in BSA) are routinely generated during human and NOD mouse prediabetes. Here we analyzed how systemic administration of these mimicry peptides affects progressive autoimmunity in adoptively transferred and cyclophosphamide-accelerated NOD mouse diabetes. These models were chosen to approximate mid to late stage prediabetes, the typical status of probands in human intervention trials. Unexpectedly, high dose (100 microg) i.v. ABBOS prevented, while Tep69 exacerbated, disease in both study models. Peptide effects required cognate recognition of endogenous self-Ag, because both treatments were ineffective in ICA69null NOD congenic mice adoptively transferred with wild-type, diabetic splenocytes. The affinity of ABBOS for NOD I-A(g7) was orders of magnitude higher than that of Tep69. This explained 1) the expansion of the mimicry T cell pool following i.v. Tep69, 2) the long-term unresponsiveness of these cells after i.v. ABBOS, and 3) precipitation of the disease after low dose i.v. ABBOS. Disease precipitation and prevention in mid to late stage prediabetes are thus governed by affinity profiles and doses of therapeutic peptides. ABBOS or ABBOS analogues with even higher MHC affinity may be candidates for experimental intervention strategies in human prediabetes, but the dose translation from NOD mice to humans requires caution.
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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".