Innate immune control of EBV-infected B cells by invariant natural killer T cells
Bibliographic record
Abstract
Individuals with X-linked lymphoproliferative disease lack invariant natural killer T (iNKT) cells and are exquisitely susceptible to Epstein-Barr virus (EBV) infection. To determine whether iNKT cells recognize or regulate EBV, resting B cells were infected with EBV in the presence or absence of iNKT cells. The depletion of iNKT cells increased both viral titers and the frequency of EBV-infected B cells. However, EBV-infected B cells rapidly lost expression of the iNKT cell receptor ligand CD1d, abrogating iNKT cell recognition. To determine whether induced CD1d expression could restore iNKT recognition in EBV-infected cells, lymphoblastoid cell lines (LCL) were treated with AM580, a synthetic retinoic acid receptor-α agonist that upregulates CD1d expression via the nuclear protein, lymphoid enhancer-binding factor 1 (LEF-1). AM580 significantly reduced LEF-1 association at the CD1d promoter region, induced CD1d expression on LCL, and restored iNKT recognition of LCL. CD1d-expressing LCL elicited interferon γ secretion and cytotoxicity by iNKT cells even in the absence of exogenous antigen, suggesting an endogenous iNKT antigen is expressed during EBV infection. These data indicate that iNKT cells may be important for early, innate control of B cell infection by EBV and that downregulation of CD1d may allow EBV to circumvent iNKT cell-mediated immune recognition.
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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) | 0.001 | 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".