Systematic investigation reveals extensive Epstein-Barr virus transcriptional regulation of the human genome
Bibliographic record
Abstract
We systematically investigate interactions between Epstein-Barr virus (EBV) transcriptional regulators (viral transcriptional regulators [vTRs]) and the human genome. Starting with 16 known and candidate vTRs, we identify nine whose introduction into human cells results in substantial alterations to host gene expression. Genome-scale profiling of vTR binding, chromatin accessibility alterations, and gene expression impact reveals extensive EBV functional interactions with the human genome, including >100,000 vTR binding events impacting almost a quarter of human genes. BMRF1 emerges as a potent regulator, impacting >7,000 genes and altering >37,000 chromatin regions. Our results reveal that EBV RTA interacts with and stabilizes the binding of human RBPJ. Network analysis reveals that many human genes are targeted by multiple EBV-vTRs, highlighting the vast coordinated impact of EBV on human gene expression. This study provides a valuable, extensive resource for examining EBV-induced alterations to human gene regulation, with data available on multiple platforms.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".