Separation of the DNA Replication, Segregation, and Transcriptional Activation Functions of Epstein-Barr Nuclear Antigen 1
Bibliographic record
Abstract
In latent Epstein-Barr virus infection, the viral EBNA1 protein binds to specific sites in the viral origin of DNA replication, oriP, to activate the initiation of DNA replication, enhance the expression of other viral latency proteins, and partition the viral episomes during cell division. The DNA binding domain of EBNA1 is required for all three function, and a Gly-Arg-rich sequence between amino acids 325 and 376 is required for both the transcriptional activation and partitioning functions. We have used mutational analysis to identify additional EBNA1 sequences that contribute to EBNA1 functions. We show that EBNA1 amino acids 8 to 67 contribute to, but are not absolutely required for, EBNA1 replication, partitioning, and transcriptional activation functions. A Gly-Arg-rich sequence (amino acids 33 to 53) that is similar to that of amino acids 325 to 376 and lies within the 8-to-67 region was not responsible for the functional contributions of residues 8 to 67, since deletion of amino acids 34 to 52 alone did not affect EBNA1 functions. We also found that deletion of amino acids 61 to 83 eliminated the transcriptional activity of EBNA1 without affecting partitioning. This mutant also exhibited an increased replication efficiency that resulted in the maintenance of oriP plasmids at a copy number approximately fourfold higher than for wild-type EBNA1. The results indicate that the three EBNA1 functions have overlapping but different sequence requirements. Transcriptional activation requires residues 61 to 83 and 325 to 376 and is stimulated by residues 8 to 67; partitioning requires residues 325 to 376 and is stimulated by residues 8 to 67; and replication involves redundant contributions of both the 325-to-376 and 8-to-67 regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".