Comprehensive resolution and classification of the Epstein Barr virus transcriptome
Virus genomes harbor highly compacted repertoires of genes and regulatory elements. Here, we report the most comprehensive Epstein Barr virus (EBV) transcriptome analysis to date, significantly expanding the number of known transcript isoforms to 1453 and resolving the major isoform of all but one lytic open reading frame. We also categorize each transcript according to their dependence on viral DNA replication, classifying transcripts as “early”, “leaky late”, or “late”. We identify a role for the viral preinitiation complex, vPIC in not only facilitating late gene expression but also in contributing to the expression of early promoters/genes. These studies also increased our understanding of the complexity of viral regulatory programs by identifying significantly active alternate promoters with distinct dependencies on viral DNA replication as well as biphasic promoters with embedded properties of both early and late promoter features. Genetic analyses identified an enhancer function for the viral lytic origin of replication (OriLyt) throughout the virus genome. We found substantial viral read-through transcription that is predicted to cause transcriptional interference and fine tuning of the temporal regulation of viral promoters. Further, in some loci with same direction overlapping gene configurations, polyA read-through is necessary to facilitate transcription through the entire ORF, but also gives rise to highly abundant viral lncRNAs due to the partial nature of read-through. Altogether, this study identified extreme viral transcriptome diversity, it resolved the major isoforms for nearly all lytic ORFs, and it identified novel regulatory modes driving and fine-tuning the temporal regulation of EBV lytic gene expression. Overall design: In this study, we aimed to significantly improve the resolution of the EBV transcriptome and to gain insights into regulatory processes controlling progression through the lytic gene expression cascade. To achieve this, we performed high-depth Oxford Nanopore Technologies (ONT) sequencing, used public CAGE-seq, and RNA-seq datasets through utilizing two distinct EBV reactivation models and we employed purification methods to selectively isolate reactivated cells from contaminating latent cells.