Novel Cell-Virus-Virophage Tripartite Infection Systems Discovered in the Freshwater Lake Dishui Lake in Shanghai, China

ABSTRACT Virophages are small parasitic double-stranded DNA (dsDNA) viruses of giant dsDNA viruses infecting unicellular eukaryotes. Except for a few isolated virophages characterized by parasitization mechanisms, features of virophages discovered in metagenomic data sets remain largely unknown. Here, the complete genomes of seven virophages (26.6 to 31.5 kbp) and four large DNA viruses (190.4 to 392.5 kbp) that coexist in the freshwater lake Dishui Lake, Shanghai, China, have been identified based on environmental metagenomic investigation. Both genomic and phylogenetic analyses indicate that Dishui Lake virophages (DSLVs) are closely related to each other and to other lake virophages, and Dishui Lake large DNA viruses are affiliated with the micro-green alga-infecting Prasinovirus of the Phycodnaviridae (named Dishui Lake phycodnaviruses [DSLPVs]) and protist (protozoan and alga)-infecting Mimiviridae (named Dishui Lake large alga virus [DSLLAV]). The DSLVs possess more genes with closer homology to that of large alga viruses than to that of giant protozoan viruses. Furthermore, the DSLVs are strongly associated with large green alga viruses, including DSLPV4 and DSLLAV1, based on codon usage as well as oligonucleotide frequency and correlation analyses. Surprisingly, a nonhomologous CRISPR-Cas like system is found in DSLLAV1, which appears to protect DSLLAV1 from the parasitization of DSLV5 and DSLV8. These results suggest that novel cell-virus-virophage (CVv) tripartite infection systems of green algae, large green alga virus (Phycodnaviridae- and Mimiviridae-related), and virophage exist in Dishui Lake, which will contribute to further deep investigations of the evolutionary interaction of virophages and large alga viruses as well as of the essential roles that the CVv plays in the ecology of algae. IMPORTANCE Virophages are small parasitizing viruses of large/giant viruses. To our knowledge, the few isolated virophages all parasitize giant protozoan viruses (Mimiviridae) for propagation and form a tripartite infection system with hosts, here named the cell-virus-virophage (CVv) system. However, the CVv system remains largely unknown in environmental metagenomic data sets. In this study, we systematically investigated the metagenomic data set from the freshwater lake Dishui Lake, Shanghai, China. Consequently, four novel large alga viruses and seven virophages were discovered to coexist in Dishui Lake. Surprisingly, a novel CVv tripartite infection system comprising green algae, large green alga viruses (Phycodnaviridae- and Mimiviridae-related), and virophages was identified based on genetic link, genomic signature, and CRISPR system analyses. Meanwhile, a nonhomologous CRISPR-like system was found in Dishui Lake large alga viruses, which appears to protect the virus host from the infection of Dishui Lake virophages (DSLVs). These findings are critical to give insight into the potential significance of CVv in global evolution and ecology.

Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion

Background Viruses are closely related to bacteria and human diseases. It is of great significance to predict associations between viruses and hosts for understanding the dynamics and complex functional networks in microbial community. With the rapid development of the metagenomics sequencing, some methods based on sequence similarity and genomic homology have been used to predict associations between viruses and hosts. However, the known virus-host association network was ignored in these methods. Results We proposed a kernelized logistic matrix factorization with integrating different information to predict potential virus-host associations on the heterogeneous network (ILMF-VH) which is constructed by connecting a virus network with a host network based on known virus-host associations. The virus network is constructed based on oligonucleotide frequency measurement, and the host network is constructed by integrating oligonucleotide frequency similarity and Gaussian interaction profile kernel similarity through similarity network fusion. The host prediction accuracy of our method is better than other methods. In addition, case studies show that the host of crAssphage predicted by ILMF-VH is consistent with presumed host in previous studies, and another potential host Escherichia coli is also predicted. Conclusions The proposed model is an effective computational tool for predicting interactions between viruses and hosts effectively, and it has great potential for discovering novel hosts of viruses.

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences

Viruses and their host genomes often share similar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similarity. We comprehensively compared 11 ONF metrics using several k-mer lengths for predicting host taxonomy from among ∼32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure $d_2^*$ at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for making predictions (thresholding) and taking the consensus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacteriophage and 2699 bacterial genomes, $d_2^*$ host prediction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Euclidian distance ONF (32%) or homology-based (22-62%) methods. When applied to metagenomically-assembled marine SUP05 viruses and the human gut virus crAssphage, $d_2^*$-based predictions overlapped (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metagenomic contigs from the same habitat or samples as the query viruses. The $d_2^*$ ONF method will greatly improve the characterization of novel, metagenomic viruses.

Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing

Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining7%–15%speed improvement.