Functions of the Epstein-Barr Virus EBNA1 Protein in Viral Reactivation and Lytic Infection
Bibliographic record
Abstract
EBNA1 is the only nuclear Epstein-Barr virus (EBV) protein expressed in both latent and lytic modes of infection. While EBNA1 is known to play several important roles in latent infection, the reason for its continued expression in lytic infection is unknown. Here we identified two roles for EBNA1 in the reactivation of latent EBV to the lytic cycle in epithelial cells. First, EBNA1 depletion in latently infected cells was shown to positively contribute to spontaneous EBV reactivation, showing that EBNA1 has a role in suppressing reactivation. Second, when the lytic cycle was induced, EBNA1 depletion decreased lytic gene expression and DNA amplification, showing that it positively contributed to lytic infection. Since we have previously shown that EBNA1 disrupts promyelocytic leukemia (PML) nuclear bodies, we investigated whether this function could account for the effects of EBNA1 on lytic infection by repeating the experiments with cells lacking PML proteins. In the absence of PML, EBNA1 did not promote lytic infection, indicating that the EBNA1-mediated PML disruption is responsible for promoting lytic infection. In keeping with this conclusion, PML silencing was found to be sufficient to induce the EBV lytic cycle. Finally, by generating cells with single PML isoforms, we showed that individual PML isoforms were sufficient to suppress EBV lytic reactivation, although PML isoform IV (PML IV) was ineffective because it was most efficiently degraded by EBNA1. Our results provide the first function for EBNA1 in lytic infection and show that EBNA1 interactions with PML IV lead to a loss of PML nuclear bodies (NBs) that promotes lytic infection.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".