A structural basis of T cell cross-reactivity to native and spliced self-antigens presented by HLA-DQ8
Bibliographic record
Abstract
Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease that has a strong HLA association, where a number of self-epitopes have been implicated in disease pathogenesis. Human pancreatic islet-infiltrating CD4 + T cell clones not only respond to proinsulin C-peptide (PI 40-54; GQVELGGGPGAGSLQ) but also cross-react with a hybrid insulin peptide (HIP; PI 40-47 -IAPP 74-80; GQVELGGG-NAVEVLK) presented by HLA-DQ8. How T cell receptors recognize self-peptide and cross-react to HIPs is unclear. We investigated the cross-reactivity of the CD4 + T cell clones reactive to native PI 40-54 epitope and multiple HIPs fused at the same N-terminus (PI 40-54 ) to the degradation products of two highly expressed pancreatic islet proteins, neuropeptide Y (NPY 68-74 ) and amyloid polypeptide (IAPP 23-29 and IAPP 74-80 ). We observed that five out of the seven selected SKW3 T cell lines expressing TCRs isolated from CD4 + T cells of people with T1D responded to multiple HIPs. Despite shared TRAV26-1-TRBV5-1 gene usage in some T cells, these clones cross-reacted to varying degrees with the PI 40-54 and HIP epitopes. Crystal structures of two TRAV26-1 + -TRBV5-1 + T cell receptors (TCRs) in complex with PI 40-54 and HIPs bound to HLA-DQ8 revealed that the two TCRs had distinct mechanisms responsible for their differential recognition of the PI 40-54 and HIP epitopes. Alanine scanning mutagenesis of the PI 40-54 and HIPs determined that the P2, P7, and P8 residues in these epitopes were key determinants of TCR specificity. Accordingly, we provide a molecular basis for cross-reactivity towards native insulin and HIP epitopes presented by HLA-DQ8.
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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) | 0.002 | 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".