Single-cell genomics unveils a canonical origin of the diverse mitochondrial genomes of euglenozoans.

BACKGROUND: The supergroup Euglenozoa unites heterotrophic flagellates from three major clades, kinetoplastids, diplonemids, and euglenids, each of which exhibits extremely divergent mitochondrial characteristics. Mitochondrial genomes (mtDNAs) of euglenids comprise multiple linear chromosomes carrying single genes, whereas mitochondrial chromosomes are circular non-catenated in diplonemids, but circular and catenated in kinetoplastids. In diplonemids and kinetoplastids, mitochondrial mRNAs require extensive and diverse editing and/or trans-splicing to produce mature transcripts. All known euglenozoan mtDNAs exhibit extremely short mitochondrial small (rns) and large (rnl) subunit rRNA genes, and absence of tRNA genes. How these features evolved from an ancestral bacteria-like circular mitochondrial genome remains unanswered. RESULTS: We sequenced and assembled 20 euglenozoan single-cell amplified genomes (SAGs). In our phylogenetic and phylogenomic analyses, three SAGs were placed within kinetoplastids, 14 within diplonemids, one (EU2) within euglenids, and two SAGs with nearly identical small subunit rRNA gene (18S) sequences (EU17/18) branched as either a basal lineage of euglenids, or as a sister to all euglenozoans. Near-complete mitochondrial genomes were identified in EU2 and EU17/18. Surprisingly, both EU2 and EU17/18 mitochondrial contigs contained multiple genes and one tRNA gene. Furthermore, EU17/18 mtDNA possessed several features unique among euglenozoans including full-length rns and rnl genes, six mitoribosomal genes, and nad11, all likely on a single chromosome. CONCLUSIONS: Our data strongly suggest that EU17/18 is an early-branching euglenozoan with numerous ancestral mitochondrial features. Collectively these data contribute to untangling the early evolution of euglenozoan mitochondria.

A Revised Taxonomy of Diplonemids Including the Eupelagonemidae n. fam. and a Type Species, Eupelagonema oceanica n. gen. & sp.

Recent surveys of marine microbial diversity have identified a previously unrecognized lineage of diplonemid protists as being among the most diverse heterotrophic eukaryotes in global oceans. Despite their monophyly (and assumed importance), they lack a formal taxonomic description, and are informally known as deep-sea pelagic diplonemids (DSPDs) or marine diplonemids. Recently, we documented morphology and molecular sequences from several DSPDs, one of which is particularly widespread and abundant in environmental sequence data. To simplify the communication of future work on this important group, here we formally propose to erect the family Eupelagonemidae to encompass this clade, as well as a formal genus and species description for the apparently most abundant phylotype, Eupelagonema oceanica, for which morphological information and single-cell amplified genome data are currently available.

VirSorter: mining viral signal from microbial genomic data.

Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.

Emerging methods to study bacteriophage infection at the single-cell level.

Bacteria and their viruses (phages) are abundant across diverse ecosystems and their interactions influence global biogeochemical cycles and incidence of disease. Problematically, both classical and metagenomic methods insufficiently assess the host specificity of phages and phage-host infection dynamics in nature. Here we review emerging methods to study phage-host interaction and infection dynamics with a focus on those that offer resolution at the single-cell level. These methods leverage ever-increasing sequence data to identify virus signals from single-cell amplified genome datasets or to produce primers/probes to target particular phage-bacteria pairs (digital PCR and phageFISH), even in complex communities. All three methods enable study of phage infection of uncultured bacteria from environmental samples, while the latter also discriminates between phage-host interaction outcomes (e.g., lytic, chronic, lysogenic) in model systems. Together these techniques enable quantitative, spatiotemporal studies of phage-bacteria interactions from environmental samples of any ecosystem, which will help elucidate and predict the ecological and evolutionary impacts of specific phage-host pairings in nature.