Cross-Priming of a Single Viral Protein from Lymphocytic Choriomeningitis Virus Alters Immunodominance Hierarchies of CD8 <sup>+</sup> T Cells during Subsequent Viral Infections
Bibliographic record
Abstract
Immunogenic epitopes that stimulate CD8+ T cells can be organized into an immunodominance hierarchy, based on their ability to induce T-cell priming and subsequent expansion. Cytotoxic CD8+ T cells can be primed through the cross-priming pathway, where exogenous viral proteins are acquired by professional antigen-presenting cells (pAPCs). We have previously reported that lymphocytic choriomeningitis nucleoprotein (LCMV-NP) expressed in HEK cells (HEK-NP) induces cross-priming of CD8+ T cells in vivo. In this study, we have used this HEK-NP model to study the effects of LCMV-NP cross-priming on the LCMV immunodominance hierarchy following viral challenge. Our results highlight the contribution of cross-priming to the immune response, since the T-cell hierarchy was significantly altered as a result of exogenous processing of a single virus protein, and this phenomenon was maintained throughout the memory response. Moreover, as a result of cross-priming, in vivo CD8+ T-cell killer activity was enhanced during subsequent virus assaults. These findings have significant implications for immunotherapy because they demonstrate that exogenous delivery of specific T-cell epitopes can be utilized to manipulate the host's CD8+ T-cell memory immunodominance 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".