R9AP is a common receptor for EBV infection in epithelial cells and B cells
Bibliographic record
Abstract
Epstein–Barr virus (EBV) persistently infects more than 90% of the human population, causing infectious mononucleosis1, susceptibility to autoimmune diseases2 and multiple malignancies of epithelial or B cell-origin3. EBV infects epithelial cells and B cells through interaction between viral glycoproteins and different host receptors4, but it has remained unknown whether a common receptor mediates infection of its two major host cell targets. Here, we establish R9AP as a crucial EBV receptor for entry into epithelial and B cells. R9AP silencing or knockout, R9AP-derived peptide and R9AP monoclonal antibody each significantly inhibit, whereas R9AP overexpression promotes, EBV uptake into both cell types. R9AP binds directly to the EBV glycoprotein gH/gL complex to initiate gH/gL–gB-mediated membrane fusion. Notably, the interaction of R9AP with gH/gL is inhibited by the highly competitive gH/gL-neutralizing antibody AMMO1, which blocks EBV epithelial and B cell entry. Moreover, R9AP mediates viral and cellular membrane fusion in cooperation with EBV gp42–human leukocyte antigen class II or gH/gL–EPHA2 complexes in B cells or epithelial cells, respectively. We propose R9AP as the crucial common receptor of B cells and epithelial cells and a potential prophylactic and vaccine target for EBV. R9AP is a key receptor for entry of Epstein–Barr virus into human epithelial and B cells, and interacts directly with the viral glycoprotein gH/gL complex to mediate virus–host membrane fusion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".