Discriminative T-cell receptor recognition of highly homologous HLA-DQ2–bound gluten epitopes
Bibliographic record
Abstract
Celiac disease (CeD) provides an opportunity to study the specificity underlying human T-cell responses to an array of similar epitopes presented by the same human leukocyte antigen II (HLA-II) molecule. Here, we investigated T-cell responses to the two immunodominant and highly homologous HLA-DQ2.5-restricted gluten epitopes, DQ2.5-glia-α1a (PFPQPELPY) and DQ2.5-glia-ω1 (PFPQPEQPF). Using HLA-DQ2.5-DQ2.5-glia-α1a and HLA-DQ2.5-DQ2.5-glia-ω1 tetramers and single-cell αβ T-cell receptor (TCR) sequencing, we observed that despite similarity in biased variable-gene usage in the TCR repertoire responding to these nearly identical peptide-HLA-II complexes, most of the T cells are specific for either of the two epitopes. To understand the molecular basis of this exquisite fine specificity, we undertook Ala substitution assays revealing that the p7 residue (Leu/Gln) is critical for specific epitope recognition by both DQ2.5-glia-α1a- and DQ2.5-glia-ω1-reactive T-cell clones. We determined high-resolution binary crystal structures of HLA-DQ2.5 bound to DQ2.5-glia-α1a (2.0 Å) and DQ2.5-glia-ω1 (2.6 Å). These structures disclosed that differences around the p7 residue subtly alter the neighboring substructure and electrostatic properties of the HLA-DQ2.5-peptide complex, providing the fine specificity underlying the responses against these two highly homologous gluten epitopes. This study underscores the ability of TCRs to recognize subtle differences in the peptide-HLA-II landscape in a human disease setting.
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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".