Inability To Detect Cross-Reactive Memory T Cells Challenges the Frequency of Heterologous Immunity among Common Viruses
Bibliographic record
Abstract
Human memory T cells that cross-react with epitopes from unrelated viruses can potentially modulate immune responses to subsequent infections by a phenomenon termed heterologous immunity. However, it is unclear whether similarities in structure rather than sequence underpin heterologous T cell cross-reactivity. In this study, we aimed to explore the mechanism of heterologous immunity involving immunodominant epitopes derived from common viruses restricted to high-frequency HLA allotypes (HLA-A*02:01, -B*07:02, and -B*08:01). We examined EBV-specific memory T cells for their ability to cross-react with CMV or influenza A virus-derived epitopes. Following T cell immunoassays to determine phenotype and function, complemented with biophysical and structural investigations of peptide/HLA complexes, we did not detect cross-reactivity of EBV-specific memory T cells toward either CMV or influenza A virus epitopes presented by any of the selected HLA allomorphs. Thus, despite the ubiquitous nature of these human viruses and the dominant immune response directed toward the selected epitopes, heterologous virus-specific T cell cross-reactivity was not detected. This suggests that either heterologous immunity is not as common as previously reported, or that it requires a very specific biological context to develop and be clinically relevant.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".