Robust T-cell stimulation by Epstein-Barr virus–transformed B cells after antigen targeting to DEC-205
Bibliographic record
Abstract
DEC-205 is a type I transmembrane multilectin receptor that is predominantly expressed on dendritic cells (DCs). Therefore, previous studies primarily focused on processing of DEC-205–targeted antigens by this potent antigen presenting cell type. Here we show that Epstein-Barr virus (EBV) transformed lymphoblastoid B-cell lines (LCLs) not only express DEC-205 at similar levels to DCs, but also efficiently present targeted EBV nuclear antigen 1 (EBNA1) and EBV-latent membrane protein 1 (LMP1) to EBNA1- and LMP1-specific CD4+ and CD8+ T-cell clones in vitro. Targeting of antigens to DEC-205 on B cells led to more efficient MHC class II than I loading, and stimulated T cells more efficiently than targeting to DEC-205 on DCs. Although LCLs internalized DEC-205–targeted antigens less efficiently than DCs, they retained them for longer time periods and delivered them to endosomal compartments that receive also B-cell receptor targeted proteins. This could facilitate prolonged T-cell stimulation and efficient MHC class II loading, and, indeed, CD4+ T-cell expansion by DEC-205–targeted vaccination was significantly compromised in B-cell deficient mice. These studies suggest that B cells, activated by virus transformation or other means, can contribute to T-cell stimulation after DEC-205 targeting of antigens during vaccination.
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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) | 0.002 | 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".