Subtype reclassification and viral mRNA expression profiling of Red Seabream Iridovirus (RSIV) via comparative genomic and transcriptomic analysis

Abstract

Red seabream iridovirus (RSIV), which causes significant economic losses in aquaculture, was first reported in the 1990s. Over the past few decades, it has infected a wide range of fish species, enhancing its genetic diversity. Classification of RSIV subtypes has generally been based on the major capsid protein (MCP) and adenosine triphosphatase (ATPase) genes. However, recent studies have reported inconsistencies, particularly in strains designated as mixed subtype I/II, where the same strain has been assigned to different subtypes depending on the gene used for phylogenetic analysis. To address this issue, this study reclassified RSIV subtypes from a full-genome perspective and investigated which genomic regions were capable of differentiating subtypes through a genome-wide association study (GWAS). We employed publicly available RSIV full genomes and transcriptomic data containing RSIV sequences. Through the development of bioinformatics pipelines, RSIV sequence reads were extracted from the transcriptomic data and the full genome of infected RSIV was constructed. Average nucleotide identity (ANI) analysis of the RSIV genomes revealed that RSIV could be classified into three distinct subtypes, rather than the two subtypes previously classified based on MCP and ATPase gene analyses. GWAS analysis between RSIV subtypes revealed significant single nucleotide polymorphisms (SNPs) in key open reading frames, indicating their potential as alternative markers for genetic diversity profiling. Transcriptomic analyses of RSIV-infected rock bream also identified dynamic ORF expression patterns, with early-stage infection marked by high expression of immune-modulatory proteins, such as E3 ubiquitin ligase, and later stages characterized by structural protein synthesis, like MCP. This research not only redefines the classification of RSIV subtypes through comparative genome analysis but also advances our understanding of RSIV genomic diversity and its host-pathogen interactions.