Genome ResearchGenomicsPeer-reviewed journal article
LTRs activated by Epstein-Barr virus-induced transformation of B cells alter the transcriptome
Amy LeungiD, Candi Trac, Hiroyuki KatoiD, Kevin R CostelloiD, Zhaoxia CheniD, Rama D NatarajaniD, Dustin E. SchonesiD
Abstract
Endogenous retroviruses (ERVs) are ancient viral elements that have accumulated in the genome through retrotransposition events. Although they have lost their ability to transpose, many of the long terminal repeats (LTRs) that originally flanked full-length ERVs maintain the ability to regulate transcription. While these elements are typically repressed in somatic cells, when this repression is lost, they can function as transcriptional enhancers and promoters. The mechanisms driving LTR activation, however, are not well understood. Epstein-Barr virus (EBV), which transforms primary B cells into continuously proliferating cells, is a tumor virus associated with lymphomas. We report here that transformation of primary B cells by EBV leads to genome-wide activation of LTR enhancers and promoters. The activation of LTRs coincides with local DNA hypomethylation and binding by transcription factors such as RUNX3, EBF1, and EBNA2. The set of EBV-activated LTRs is unique to transformed B cells when compared to other cell lines known to have activated LTRs. Furthermore, we found that EBV-induced LTR activation impacts the B cell transcriptome by upregulating transcripts driven by cryptic LTR promoters. These transcripts include genes important to oncogenesis of Hodgkin lymphoma, as well as those found in other cancers such as HUWE1/HECTH9. These data suggest that the activation of LTRs by EBV may be important to the pathology of EBV-associated cancers. Altogether, our results indicate that EBV-induced transformation of B cells alters endogenous retroviral element activity, thereby impacting host gene regulatory networks and oncogenic potential.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 39
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