Natural killer cell memory precedes HLH in monozygotic twins discordant for chronic active Epstein-Barr virus disease
Bibliographic record
Abstract
Severe mosquito bite allergy (SMBA) is a manifestation of chronic active Epstein-Barr virus (CAEBV) infection defined by necrotic ulceration of the stings. CAEBV with SMBA has a high mortality rate as most patients eventually develop fulminant and refractory hemophagocytic lymphohistiocytosis (HLH). However, how self-resolving SMBA escalates to systemic lethal HLH remains unclear. Through comprehensive immune profiling of a SMBA patient with CAEBV and her healthy monozygotic twin, we found that both twins were seropositive for EBV but showed high discordance in their circulating natural killer (NK) cells. The patient's EBV-infected NK cells displayed memory-like properties, including low CD16, high CD226 and induction of enhanced IFNγ production by IL-2 or IL-12. Her leukocytes also produced high levels of IL-2 and IL-12 when stimulated with salivary gland extract (SGE) specifically from A. albopictus mosquitoes, connected again with hyperproduction of IFNγ by her NK cells. Strikingly, pharmacological inhibition of STAT3 suppressed the NK memory-associated cytokine axis of IFNγ, IL-2 and IL-12 that is generated by A. albopictus SGE stimulation. Altogether, this study shows that NK memory is promoted during CAEBV with SMBA by repeated cytokine restimulation leading up to lethal HLH, and proposes STAT3 as a therapeutic target to halt its progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".