Epstein-Barr Virus EBNA1 Protein Regulates Viral Latency through Effects on let-7 MicroRNA and Dicer
Bibliographic record
Abstract
UNLABELLED: The EBNA1 protein of Epstein-Barr virus (EBV) plays multiple roles in EBV latent infection, including altering cellular pathways relevant for cancer. Here we used microRNA (miRNA) cloning coupled with high-throughput sequencing to identify the effects of EBNA1 on cellular miRNAs in two nasopharyngeal carcinoma cell lines. EBNA1 affected a small percentage of cellular miRNAs in both cell lines, in particular, upregulating multiple let-7 family miRNAs, including let-7a. The effects of EBNA1 on let-7a were verified by demonstrating that EBNA1 silencing in multiple EBV-positive carcinomas downregulated let-7a. Accordingly, the let-7a target, Dicer, was found to be partially downregulated by EBNA1 expression (at the mRNA and protein levels) and upregulated by EBNA1 silencing in EBV-positive cells. Reporter assays based on the Dicer 3' untranslated region with and without let-7a target sites indicated that the effects of EBNA1 on Dicer were mediated by let-7a. EBNA1 was also found to induce the expression of let-7a primary RNAs in a manner dependent on the EBNA1 transcriptional activation region, suggesting that EBNA1 induces let-7a by transactivating the expression of its primary transcripts. Consistent with previous reports that Dicer promotes EBV reactivation, we found that a let-7a mimic inhibited EBV reactivation to the lytic cycle, while a let-7 sponge increased reactivation. The results provide a mechanism by which EBNA1 could promote EBV latency by inducing let-7 miRNAs. IMPORTANCE: The EBNA1 protein of Epstein-Barr virus (EBV) contributes in multiple ways to the latent mode of EBV infection that leads to lifelong infection. In this study, we identify a mechanism by which EBNA1 helps to maintain EBV infection in a latent state. This involves induction of a family of microRNAs (let-7 miRNAs) that in turn decreases the level of the cellular protein Dicer. We demonstrate that let-7 miRNAs inhibit the reactivation of latent EBV, providing an explanation for our previous observation that EBNA1 promotes latency. In addition, since decreased levels of Dicer have been associated with metastatic potential, EBNA1 may increase metastases by downregulating Dicer.
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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) | 0.001 | 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".