Viral disruption of promyelocytic leukemia (PML) nuclear bodies by hijacking host PML regulators
Bibliographic record
Abstract
Epstein-Barr virus (EBV) latent infection promotes cell survival and proliferation, in some cases contributing to tumourigenesis. EBV-immortalized cells and EBV-induced tumours express the viral EBNA1 protein which, in addition to its roles in replicating and maintaining EBV genomes, can alter cellular processes, including the disruption of promyelocytic leukemia (PML) nuclear bodies (NBs) through the degradation of PML proteins. PML NBs are based on PML proteins and mediate several cellular processes including apoptosis, DNA repair and antiviral responses. Accordingly, EBNA1 expression decreases apoptosis and DNA repair which may contribute to malignant transformation. The ability of EBNA1 to disrupt PML NBs has recently been shown to require EBNA1 binding to two host proteins, the protein kinase CK2 and deubiquitylating protein USP7/HAUSP, both of which are known to be partially associated with PML NBs. EBNA1 increases the association of both CK2 and USP7 with PML NBs and, as a result, increases phosphorylation of PML proteins by CK2, a modification that is known to trigger PML polyubiquitylation and degradation. Recent data also implicates USP7 as a negative regulator of PML proteins and nuclear bodies by a mechanism independent of its intrinsic ubiquitin cleavage activity. The results suggest that EBNA1 usurps two host PML regulators in order to promote degradation of PML proteins and 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".