A comprehensive single cell data analysis of lymphoblastoid cells reveals the role of super-enhancers in maintaining EBV latency.

We probed the lifecycle of Epstein-Barr virus (EBV) on a cell-by-cell basis using single cell RNA sequencing (scRNA-seq) data from nine publicly available lymphoblastoid cell lines (LCLs). While the majority of LCLs comprised cells containing EBV in the latent phase, two other clusters of cells were clearly evident and were distinguished by distinct expression of host and viral genes. Notably, both were high expressors of EBV LMP1/BNLF2 and BZLF1 compared to another cluster that expressed neither gene. The two novel clusters differed from each other in their expression of EBV lytic genes, including glycoprotein gene GP350. The first cluster, comprising GP350- LMP1hi cells, expressed high levels of HIF1A and was transcriptionally regulated by HIF1-α. Treatment of LCLs with Pevonedistat, a drug that enhances HIF1-α signaling, markedly induced this cluster. The second cluster, containing GP350+ LMP1hi cells, expressed EBV lytic genes. Host genes that are controlled by super-enhancers (SEs), such as transcription factors MYC and IRF4, had the lowest expression in this cluster. Functionally, the expression of genes regulated by MYC and IRF4 in GP350+ LMP1hi cells were lower compared to other cells. Indeed, induction of EBV lytic reactivation in EBV+ AKATA reduced the expression of these SE-regulated genes. Furthermore, CRISPR-mediated perturbation of the MYC or IRF4 SEs in LCLs induced the lytic EBV gene expression, suggesting that host SEs and/or SE target genes are required for maintenance of EBV latency. Collectively, our study revealed EBV-associated heterogeneity among LCLs that may have functional consequence on host and viral biology.

Unbiased discovery of cancer pathways and therapeutics using Pathway Ensemble Tool and Benchmark.

Correctly identifying perturbed biological pathways is a critical step in uncovering basic disease mechanisms and developing much-needed therapeutic strategies. However, whether current tools are optimal for unbiased discovery of relevant pathways remains unclear. Here, we create "Benchmark" to critically evaluate existing tools and find that most function sub-optimally. We thus develop the "Pathway Ensemble Tool" (PET), which outperforms existing methods. Deploying PET, we identify prognostic pathways across 12 cancer types. PET-identified prognostic pathways offer additional insights, with genes within these pathways serving as reliable biomarkers for clinical outcomes. Additionally, normalizing these pathways using drug repurposing strategies represents therapeutic opportunities. For example, the top predicted repurposed drug for bladder cancer, a CDK2/9 inhibitor, represses cell growth in vitro and in vivo. We anticipate that using Benchmark and PET for unbiased pathway discovery will offer additional insights into disease mechanisms across a spectrum of diseases, enabling biomarker discovery and therapeutic strategies.