Human MHC Class I Transgenic Mice Deficient for H2 Class I Expression Facilitate Identification and Characterization of New HLA Class I-Restricted Viral T Cell Epitopes
Bibliographic record
Abstract
Although mice transgenic (Tg) for human MHC (HLA) class I alleles could provide an important model for characterizing HLA-restricted viral and tumor Ag CTL epitopes, the extent to which Tg mouse T cells become HLA restricted in the presence of endogenous H2 class I and recognize the same peptides as in HLA allele-matched humans is not clear. We previously described Tg mice carrying the HLA-B27, HLA-B7, or HLA-A2 alleles expressed as fully native (HLA(nat)) (with human beta(2)-microglobulin) and as hybrid human/mouse (HLA(hyb)) molecules on the H2(b) background. To eliminate the influence of H2(b) class I, each HLA Tg strain was bred with a H2-K(b)/H2-D(b)-double knockout (DKO) strain to generate mice in which the only classical class I expression was the human molecule. Expression of each HLA(hyb) molecule and HLA-B27(nat)/human beta(2)-microglobulin led to peripheral CD8(+) T cell levels comparable with that for mice expressing a single H2-K(b) or H2-D(b) gene. Influenza A infection of Tg HLA-B27(hyb)/DKO generated a strong CD8(+) T cell response directed at the same peptide (flu nucleoprotein NP383-391) recognized by CTLs from flu-infected B27(+) humans. As HLA-B7/flu epitopes were not known from human studies, we used flu-infected Tg HLA-B7(hyb)/DKO mice to examine the CTL response to candidate peptides identified based on the B7 binding motif. We have identified flu NP418-426 as a major HLA-B7-restricted flu CTL epitope. In summary, the HLA class I Tg/H2-K/H2-D DKO mouse model described in this study provides a sensitive and specific approach for identifying and characterizing HLA-restricted CTL epitopes for a variety of human disease-associated Ags.
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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) | 0.004 | 0.001 |
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".