bioRxivMicrobiology
Novel tri-segmented rhabdoviruses: A data mining expedition unveils the cryptic diversity of cytorhabdoviruses
Abstract
Cytorhabdoviruses (genus Cytorhabdovirus, family Rhabdoviridae) are plant-infecting viruses with enveloped, bacilliform virions. Established members of the genus Cytorhabdovirus have unsegmented single-stranded negative-sense RNA genomes (ca. 10-16 kb) which encode four to ten proteins. Here, by exploring large publicly available metatranscriptomics datasets, we report the identification and genomic characterization of 93 novel viruses with genetic and evolutionary cues of cytorhabdoviruses. Strikingly, five unprecedented cytorhabdoviruses with tri-segmented genomes were also identified. This finding represents the first tri-segmented viruses in the family Rhabdoviridae. Interestingly, the nucleocapsid and polymerase were the only typical rhabdoviral proteins encoded by those tri-segmented viruses, whereas in three of them, a protein similar to the emaravirus (family Fimoviridae) silencing suppressor was found, while the other predicted proteins had no matches in any sequence databases. Genetic distance and evolutionary insights suggest that all these novel viruses may represent members of novel species. Phylogenetic analyses, of both novel and previously classified plant rhabdoviruses, provide compelling support for the division of the genus Cytorhabdovirus into three distinct genera. This proposed reclassification not only enhances our understanding of the evolutionary dynamics within this group of plant rhabdoviruses but also illuminates the remarkable genomic diversity they encompass. This study not only represents a significant expansion of genomics of cytorhabdoviruses that will enable future research on the evolutionary peculiarity of this genus, but also shows the plasticity in the rhabdovirus genome organization with the discovery of tri-segmented members with a unique evolutionary trajectory.
- organism: plant (other)
- assay: metagenomics
- research focus: gene regulatory network inference
- approach: statistical model
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