Epstein-Barr Virus Nuclear Antigen 1 Hijacks the Host Kinase CK2 To Disrupt PML Nuclear Bodies
Bibliographic record
Abstract
Latent Epstein-Barr virus (EBV) infection is an important causative factor in the development of several cancers, including nasopharyngeal carcinoma (NPC). The one EBV protein expressed in the nucleus of NPC cells, EBNA1, has been shown to disrupt promyelocitic leukemia (PML) nuclear bodies (NBs) by inducing the degradation of PML proteins, leading to impaired DNA repair and increased cell survival. Although EBNA1-mediated PML disruption is likely to be an important factor in the development of NPC, little is known about its mechanism. We now show that an interaction between EBNA1 and the host CK2 kinase is crucial for EBNA1 to disrupt PML bodies and degrade PML proteins. EBNA1 increases the association of CK2 with PML proteins, thereby increasing the phosphorylation of PML proteins by CK2, a modification that is known to trigger the polyubiquitylation and degradation of PML. The interaction between EBNA1 and CK2 is direct and occurs through the β regulatory subunit of CK2 and EBNA1 amino acids 387 to 394. The binding of EBNA1 to the host ubiquitin specific protease USP7 has also been shown to be important for EBNA1-mediated PML disruption. We show that EBNA1 also increases the occupancy of USP7 at PML NBs and that CK2 and USP7 bind independently and simultaneously to EBNA1 to form a ternary complex. The combined results indicate that EBNA1 usurps two independent cellular pathways to trigger the loss of PML NBs.
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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) | 0.002 | 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".