EBNA1 Partitions Epstein-Barr Virus Plasmids in Yeast Cells by Attaching to Human EBNA1-Binding Protein 2 on Mitotic Chromosomes
Bibliographic record
Abstract
Epstein-Barr virus (EBV) episomal genomes are stably maintained in human cells and are partitioned during cell division by mitotic chromosome attachment. Partitioning is mediated by the viral EBNA1 protein, which binds both the EBV segregation element (FR) and a mitotic chromosomal component. We previously showed that the segregation of EBV-based plasmids can be reconstituted in Saccharomyces cerevisiae and is absolutely dependent on EBNA1, the EBV FR sequence, and the human EBNA1-binding protein 2 (EBP2). We have now used this yeast system to elucidate the functional contribution of human EBP2 to EBNA1-mediated plasmid partitioning. Human EBP2 was found to attach to yeast mitotic chromosomes in a cell cycle-dependent manner and cause EBNA1 to associate with the mitotic chromosomes. The domain of human EBP2 that binds both yeast and human chromosomes was mapped and shown to be functionally distinct from the EBNA1-binding domain. The functionality and localization of human EBP2 mutants and fusion proteins indicated that the attachment of EBNA1 to mitotic chromosomes is crucial for EBV plasmid segregation in S. cerevisiae, as it is in humans, and that this is the contribution of human EBP2. The results also indicate that plasmid segregation in S. cerevisiae can occur through chromosome attachment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".