The ND10 Complex Represses Lytic Human Herpesvirus 6A Replication and Promotes Silencing of the Viral Genome
Bibliographic record
Abstract
Human herpesvirus 6A (HHV-6A) replicates in peripheral blood mononuclear cells (PBMCs) and various T-cell lines in vitro. Intriguingly, the virus can also establish latency in these cells, but it remains unknown what influences the decision between lytic replication and the latency of the virus. Incoming virus genomes are confronted with the nuclear domain 10 (ND10) complex as part of an intrinsic antiviral response. Most herpesviruses can efficiently subvert ND10, but its role in HHV-6A infection remains poorly understood. In this study, we investigated if the ND10 complex affects HHV-6A replication and contributes to the silencing of the virus genome during latency. We could demonstrate that ND10 complex was not dissociated upon infection, while the number of ND10 bodies was reduced in lytically infected cells. Virus replication was significantly enhanced upon knock down of the ND10 complex using shRNAs against its major constituents promyelocytic leukemia protein (PML), hDaxx, and Sp100. In addition, we could demonstrate that viral genes are more efficiently silenced in the presence of a functional ND10 complex. Our data thereby provides the first evidence that the cellular ND10 complex plays an important role in suppressing HHV-6A lytic replication and the silencing of the virus genome in latently infected cells.
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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 | 0.000 |
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".