The Herpesvirus Associated Ubiquitin Specific Protease, USP7, Is a Negative Regulator of PML Proteins and PML Nuclear Bodies
Bibliographic record
Abstract
The PML tumor suppressor is the founding component of the multiprotein nuclear structures known as PML nuclear bodies (PML-NBs), which control several cellular functions including apoptosis and antiviral effects. The ubiquitin specific protease USP7 (also called HAUSP) is known to associate with PML-NBs and to be a tight binding partner of two herpesvirus proteins that disrupt PML NBs. Here we investigated whether USP7 itself regulates PML-NBs. Silencing of USP7 was found to increase the number of PML-NBs, to increase the levels of PML protein and to inhibit PML polyubiquitylation in nasopharyngeal carcinoma cells. This effect of USP7 was independent of p53 as PML loss was observed in p53-null cells. PML-NBs disruption was induced by USP7 overexpression independently of its catalytic activity and was induced by either of the protein interaction domains of USP7, each of which localized to PML-NBs. USP7 also disrupted NBs formed from some single PML isoforms, most notably isoforms I and IV. CK2α and RNF4, which are known regulators of PML, were dispensable for USP7-associated PML-NB disruption. The results are consistent with a novel model of PML regulation where a deubiquitylase disrupts PML-NBs through recruitment of another cellular protein(s) to PML NBs, independently of its catalytic activity.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".