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Abstract Dinoflagellates from the family Symbiodiniaceae are phototrophic marine protists that engage in symbiosis with diverse hosts. Their large and distinct genomes are characterized by pervasive gene duplication and large-scale retroposition events. However, little is known about the role and scale of horizontal gene transfer (HGT) in the evolution of this algal family. In other dinoflagellates, high levels of HGTs have been observed, linked to major genomic transitions, such as the appearance of a viral-acquired nucleoprotein that originated via HGT from a large DNA algal virus. Previous work showed that Symbiodiniaceae from different hosts are actively infected by viral groups, such as giant DNA viruses and ssRNA viruses, that may play an important role in coral health. Latent viral infections may also occur, whereby viruses could persist in the cytoplasm or integrate into the host genome as a provirus. This hypothesis received experimental support; however, the cellular localization of putative latent viruses and their taxonomic affiliation are still unknown. In addition, despite the finding of viral sequences in some genomes of Symbiodiniaceae, viral origin, taxonomic breadth, and metabolic potential have not been explored. To address these questions, we searched for putative viral-derived proteins in thirteen Symbiodiniaceae genomes. We found fifty-nine candidate viral-derived HGTs that gave rise to twelve phylogenies across ten genomes. We also describe the taxonomic affiliation of these virus-related sequences, their structure, and their genomic context. These results lead us to propose a model to explain the origin and fate of Symbiodiniaceae viral acquisitions.
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Automated classification of giant virus genomes using a random forest model built on trademark protein families
Abstract Viruses of the phylumNucleocytoviricota, often referred to as “giant viruses,” are prevalent in various environments around the globe and play significant roles in shaping eukaryotic diversity and activities in global ecosystems. Given the extensive phylogenetic diversity within this viral group and the highly complex composition of their genomes, taxonomic classification of giant viruses, particularly incomplete metagenome-assembled genomes (MAGs) can present a considerable challenge. Here we developed TIGTOG (TaxonomicInformation ofGiant viruses usingTrademarkOrthologousGroups), a machine learning-based approach to predict the taxonomic classification of novel giant virus MAGs based on profiles of protein family content. We applied a random forest algorithm to a training set of 1531 quality-checked, phylogenetically diverseNucleocytoviricotagenomes using pre-selected sets of giant virus orthologous groups (GVOGs). The classification models were predictive of viral taxonomic assignments with a cross-validation accuracy of 99.6% at the order level and 97.3% at the family level. We found that no individual GVOGs or genome features significantly influenced the algorithm’s performance or the models’ predictions, indicating that classification predictions were based on a comprehensive genomic signature, which reduced the necessity of a fixed set of marker genes for taxonomic assigning purposes. Our classification models were validated with an independent test set of 823 giant virus genomes with varied genomic completeness and taxonomy and demonstrated an accuracy of 98.6% and 95.9% at the order and family level, respectively. Our results indicate that protein family profiles can be used to accurately classify large DNA viruses at different taxonomic levels and provide a fast and accurate method for the classification of giant viruses. This approach could easily be adapted to other viral groups.
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- Award ID(s):
- 2141862
- PAR ID:
- 10494642
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Viruses
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2948-1767
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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