Overlapping Peptides Elicit Distinct CD8+ T Cell Responses following Influenza A Virus Infection
Bibliographic record
Abstract
Abstract The presentation of pathogen-derived peptides on MHC class I molecules is essential for the initiation of adaptive CD8+ T cell immunity, which in turn is critical for effective control of many significant human infections. The identification of immunogenic pathogen-derived epitopes and a detailed understanding of how they are recognized by TCRs is essential for the design of effective T cell–based vaccines. In this study, we have characterized the T cell recognition and immune responses in mice to two naturally presented influenza A virus–derived peptides previously identified from virally infected cells via mass spectrometry. These neuraminidase-derived peptides, NA181–190 (SGPDNGAVAV) and NA181–191 (SGPDNGAVAVL), are completely overlapping with the exception of a 1 aa extension at the C terminus of the longer peptide. This minor peptidic difference results in the induction of two completely independent and non–cross-reactive T cell populations that show distinct functional characteristics after influenza A virus infection of B6 mice. We show that the unique TCR reactivity to the overlapping peptides is present in the naive repertoire prior to immune expansion in B6 mice. Moreover, we provide a structural explanation underlying the distinct CD8+ T cell reactivities, which reinforces the concept that peptide length is a key determinant of Ag specificity in CD8+ T cell responses.
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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".