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Epstein-Barr virus (EBV) is a complex oncogenic symbiont. The molecular mechanisms governing EBV carcinogenesis remain elusive and the functional interactions between virus and host cells are incompletely defined. Some of the known mechanisms include viral integration into the host genome, expression and mutation(s) of viral genes and the host response to the virus. Despite decades of research there is a lack of effective treatment options for EBV-positive cancer patients underscoring an urgent need to further investigate the mechanisms underlying tumorigenesis as well as explore and develop personalized treatment strategies for patients with EBV-positive cancers. In Chapter 1, I introduce Epstein-Barr Virus (EBV), the two phases of EBV lifecycle and an overview of certain EBV-associated carcinomas. I will also discuss the underlying mechanisms and few current therapeutic strategies against EBV infection. Next, I will discuss some of the preclinical model systems and high-throughput computation techniques that are commonly used by researchers in the field of EBV. In chapter 2, we have systematically analyzed RNA-sequencing from >1000 patients with 15 different cancer types, comparing virus and host factors of EBV+ to EBV- tissues to reveal novel insights into EBV-positive tumors. First, we observed that EBV preferentially integrates at highly accessible regions of the cancer genome with significant enrichment in super-enhancer architecture. Second, we determined that the expression of twelve EBV transcripts, including LMP1 and LMP2, correlated inversely with EBV reactivation signature. Over-expression of these genes significantly suppressed viral reactivation, consistent with a ‘Virostatic’ function. Third, we identified hundreds of novel frequent missense and nonsense variations in Virostatic genes in cancer samples, and that the variant genes failed to regulate their viral and cellular targets in cancer. Lastly, we were able to dichotomously classify EBV-positive tumors based on patterns of host interferon signature genes and immune checkpoint markers, such as PD-L1 and IDO1. In chapter 3, we probed the lifecycle of EBV on a cell-by-cell basis using single cell RNA sequencing (scRNA-seq) data from six EBV-immortalized lymphoblastoid cell lines (LCL). While the majority of LCLs comprised cells containing EBV in the latent phase of its life cycle, we identified two additional clusters that had distinct expression of both host and viral genes. Both clusters were high expressors of EBV Latent Membrane Protein-1 (LMP1) but differed in their expression of other EBV lytic genes, including glycoprotein gene GP350. We further probed into the transcriptional landscape of these clusters to identify potential regulators which will be discussed in further detail in the chapter. Importantly, I was able to demonstrate enhancing HIF1-a signaling by using Pevonedistat, a compound that stabilized HIF1-a can preferentially induce the transcriptional program specific to one of the three identified clusters. In Chapter 4, I describe some of my recent work. In this project, we have used an intuitive in-silico drug prediction approach to rapidly screen and identify FDA-approved or clinically available compounds that can be repurposed to induce lytic cycle in different EBV+ tumors. Using this strategy, we identified Ciclopirox, an antifungal drug, as a potent inducer of lytic cycle in EBV+ epithelial cancers. We used EBV+ GC cells to determine the effect of Ciclopirox on EBV reactivation as well as identify the underlying mechanisms. In summary, we discovered that reactivation of EBV lytic cycle by Ciclopirox is mediated by multiple pathways, two of the major ones being the HIF1-a and NF-kB pathways. Although, Ciclopirox treatment enhanced the killing effect of antiviral, further investigation is needed to effectively deliver this drug in vivo. Throughout this chapter, I have discussed findings that needs further investigation and proposed necessary experiments. Finally, in Chapter 5 I have summarized my work and described how our work can provide novel insights that can help delineate some of the complexities of host-pathogen interactions in EBV-associated malignancies.

Pathogenic mechanisms underlying severe SARS-CoV2 infection remain largely unelucidated. High throughput sequencing technologies that capture genome and transcriptome information are key approaches to gain detailed mechanistic insights from infected cells. These techniques readily detect both pathogen and host-derived sequences, providing a means of studying host-pathogen interactions. Recent studies have reported the presence of host-virus chimeric (HVC) RNA in RNA-seq data from SARS-CoV2 infected cells and interpreted these findings as evidence of viral integration in the human genome as a potential pathogenic mechanism. Since SARS-CoV2 is a positive sense RNA virus that replicates in the cytoplasm it does not have a nuclear phase in its life cycle, it is biologically unlikely to be in a location where splicing events could result in genome integration. Here, we investigated the biological authenticity of HVC events. In contrast to true biological events such as mRNA splicing and genome rearrangement events, which generate reproducible chimeric sequencing fragments across different biological isolates, we found that HVC events across >100 RNA-seq libraries from patients with COVID-19 and infected cell lines, were highly irreproducible. RNA-seq library preparation is inherently error-prone due to random template switching during reverse transcription of RNA to cDNA. By counting chimeric events observed when constructing an RNA-seq library from human RNA and spike-in RNA from an unrelated species, such as fruit-fly, we estimated that ~1% of RNA-seq reads are artifactually chimeric. In SARS-CoV2 RNA-seq we found that the frequency of HVC events was, in fact, not greater than this background "noise". Finally, we developed a novel experimental approach to enrich SARS-CoV2 sequences from bulk RNA of infected cells. This method enriched viral sequences but did not enrich for HVC events, suggesting that the majority of HVC events are, in all likelihood, artifacts of library construction. In conclusion, our findings indicate that HVC events observed in RNA-sequencing libraries from SARS-CoV2 infected cells are extremely rare and are likely artifacts arising from either random template switching of reverse-transcriptase and/or sequence alignment errors. Therefore, the observed HVC events do not support SARS-CoV2 fusion to cellular genes and/or integration into human genomes.