Lack of Heterologous Cross-reactivity toward HLA-A*02:01 Restricted Viral Epitopes Is Underpinned by Distinct αβT Cell Receptor Signatures
Bibliographic record
Abstract
αβT cell receptor (TCR) genetic diversity is outnumbered by the quantity of pathogenic epitopes to be recognized. To provide efficient protective anti-viral immunity, a single TCR ideally needs to cross-react with a multitude of pathogenic epitopes. However, the frequency, extent, and mechanisms of TCR cross-reactivity remain unclear, with conflicting results on anti-viral T cell cross-reactivity observed in humans. Namely, both the presence and lack of T cell cross-reactivity have been reported with HLA-A*02:01-restricted epitopes from the Epstein-Barr and influenza viruses (BMLF-1 and M158, respectively) or with the hepatitis C and influenza viruses (NS31073 and NA231, respectively). Given the high sequence similarity of these paired viral epitopes (56 and 88%, respectively), the ubiquitous nature of the three viruses, and the high frequency of the HLA-A*02:01 allele, we selected these epitopes to establish the extent of T cell cross-reactivity. We combined ex vivo and in vitro functional assays, single-cell αβTCR repertoire sequencing, and structural analysis of these four epitopes in complex with HLA-A*02:01 to determine whether they could lead to heterologous T cell cross-reactivity. Our data show that sequence similarity does not translate to structural mimicry of the paired epitopes in complexes with HLA-A*02:01, resulting in induction of distinct αβTCR repertoires. The differences in epitope architecture might be an obstacle for TCR recognition, explaining the lack of T cell cross-reactivity observed. In conclusion, sequence similarity does not necessarily result in structural mimicry, and despite the need for cross-reactivity, antigen-specific TCR repertoires can remain highly specific.
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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".