Discovering the complete enhancer map of human herpesviruses using a natural language processing model.

Abstract

Enhancers are distal cis-regulatory elements that dictate complex transcriptional repertoire. Herpes viruses exhibit programmed latent and lytic gene expression depending on the infected tissue and physiological state. Previously, using a systematic functional assay, we identified six enhancers within the genome of Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we present a natural language processing model (NLP)-based tool, ENHAvir, that is trained with these six enhancers and non-enhancer control sequences from the KSHV genome. ENHAvir identifies known enhancers and predicted novel enhancer elements in human herpesviruses. The activity of the predicted enhancers in HSV-2, HCMV, HHV-6, HHV-7, and EBV is confirmed in an enhancer reporter assay. The terminal repeats of all the herpes viruses also serve as a strong enhancer. Comparing herpesvirus enhancers with human enhancers reveals conserved enhancer signatures and the involvement of Alu elements. Here, we present an AI tool that successfully predicts enhancers in both viral and human genomes.