Nucleocytoplasmic large DNA viruses (NCLDVs) are highly diverse and abundant in marine environments. However, the knowledge of their hosts is limited because only a few NCLDVs have been isolated so far. Taking advantage of the recent large-scale marine metagenomics census, in silico host prediction approaches are expected to fill the gap and further expand our knowledge of virus-host relationships for unknown NCLDVs. In this study, we built co-occurrence networks of NCLDVs and eukaryotic taxa to predict virus-host interactions using Tara Oceans sequencing data. Using the positive likelihood ratio to assess the performance of host prediction for NCLDVs, we benchmarked several co-occurrence approaches and demonstrated an increase in the odds ratio of predicting true positive relationships 4-fold compared to random host predictions. To further refine host predictions from high-dimensional co-occurrence networks, we developed a phylogeny-informed filtering method, Taxon Interaction Mapper, and showed it further improved the prediction performance by 12-fold. Finally, we inferred virophage-NCLDV networks to corroborate that co-occurrence approaches are effective for predicting interacting partners of NCLDVs in marine environments.IMPORTANCE NCLDVs can infect a wide range of eukaryotes, although their life cycle is less dependent on hosts compared to other viruses. However, our understanding of NCLDV-host systems is highly limited because few of these viruses have been isolated so far. Co-occurrence information has been assumed to be useful to predict virus-host interactions. In this study, we quantitatively show the effectiveness of co-occurrence inference for NCLDV host prediction. We also improve the prediction performance with a phylogeny-guided method, which leads to a concise list of candidate host lineages for three NCLDV families. Our results underpin the usage of co-occurrence approaches for the metagenomic exploration of the ecology of this diverse group of viruses.
Keywords: NCLDV; Tara Oceans; assessment; co-occurrence; host prediction.
Copyright © 2021 Meng et al.