Epitope discovery in West Nile virus infection: Identification and immune recognition of viral epitopes
Bibliographic record
Abstract
Cytotoxic T lymphocytes (CTL) play an important role in the control and elimination of infection by West Nile virus (WNV), yet the class I human leukocyte antigen (HLA)-presented peptide epitopes that enable CTL recognition of WNV-infected cells remain uncharacterized. The goals of this work were first to discover the peptide epitopes that distinguish the class I HLA of WNV-infected cells and then to test the T cell reactivity of newly discovered WNV epitopes. To discover WNV-immune epitopes, class I HLA was harvested from WNV (NY99 strain)-infected and uninfected HeLa cells. Then peptide epitopes were eluted from affinity-purified HLA, and peptide epitopes from infected and uninfected cells were comparatively mapped by mass spectroscopy. Six virus-derived peptides from five different viral proteins (E, NS2b, NS3, NS4b, and NS5) were discovered as unique to HLA-A*0201 of infected cells, demonstrating that the peptides sampled by class I HLA are distributed widely throughout the WNV proteome. When tested with CTL from infected individuals, one dominant WNV target was apparent, two epitopes were subdominant, and three demonstrated little CTL reactivity. Finally, a sequence comparison of these epitopes with the hundreds of viral isolates shows that HLA-A*0201 presents epitopes derived from conserved regions of the virus. Detection and recovery from WNV infection are therefore functions of the ability of class I HLA molecules to reveal conserved WNV epitopes to an intact cellular immune system that subsequently recognizes infected cells.
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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) | 0.000 | 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".