Recognition of Multiple Hybrid Insulin Peptides by a Single Highly Diabetogenic T-Cell Receptor
Bibliographic record
Abstract
The mechanisms underlying the major histocompatibility complex class II (MHCII) type 1 diabetes (T1D) association remain incompletely understood. We have previously shown that thymocytes expressing the highly diabetogenic, I-A g7 -restricted 4.1-T-cell receptor (TCR) are MHCII-promiscuous, and that, in MHCII-heterozygous mice, they sequentially undergo positive and negative selection/Treg deviation by recognizing pro- and anti-diabetogenic MHCII molecules on cortical thymic epithelial cells and medullary hematopoietic antigen-presenting cells (APCs), respectively. Here, we use a novel autoantigen discovery approach to define the antigenic specificity of this TCR in the context of I-A g7 . This was done by screening the ability of random epitope–GS linker–I- A β g 7 chain fusion pools to form agonistic peptide–MHCII complexes on the surface of I- A α d chain-transgenic artificial APCs. Pool deconvolution, I-A g7 -binding register-fixing, TCR contact residue mapping, and alanine scanning mutagenesis resulted in the identification of a 4.1-TCR recognition motif XL(G/A)XEXE(D/E)X that was shared by seven agonistic hybrid insulin peptides (HIPs) resulting from the fusion of several different chromogranin A and/or insulin C fragments, including post-translationally modified variants. These data validate a novel, highly sensitive MHCII-restricted epitope discovery approach for orphan TCRs and suggest thymic selection of autoantigen-promiscuous TCRs as a mechanism for the murine T1D–I-A g7 -association.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".