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1. Single-virus genomics and beyond

   https://doi.org/10.1038/s41579-020-00444-0  

   Martínez Martínez, Joaquín; Martinez-Hernandez, Francisco; Martinez-Garcia, Manuel (December 2020, Nature Reviews Microbiology)

   null (Ed.)

   Viruses are extremely diverse and modulate important biological and ecological processes globally. However, much of viral diversity remains uncultured and yet to be discovered. Several powerful culture- independent tools, in particular metagenomics, have substantially advanced virus discovery. Among those tools is single- virus genomics, which yields sequenced reference genomes from individual sorted virus particles without the need for cultivation. This new method complements virus culturing and metagenomic approaches and its advantages include targeted investigation of specific virus groups and investigation of genomic microdiversity within viral populations. In this Review, we provide a brief history of single- virus genomics, outline how this emergent method has facilitated advances in virus ecology and discuss its current limitations and future potential. Finally, we address how this method may synergistically intersect with other single- virus and single- cell approaches. 

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2. Killer Archaea: Virus-Mediated Antagonism to CRISPR-Immune Populations Results in Emergent Virus-Host Mutualism

   https://doi.org/10.1128/mBio.00404-20  

   DeWerff, Samantha J.; Bautista, Maria A.; Pauly, Matthew; Zhang, Changyi; Whitaker, Rachel J.; Martiny, Jennifer B. (April 2020, mBio)

   ABSTRACT Theory, simulation, and experimental evolution demonstrate that diversified CRISPR-Cas immunity to lytic viruses can lead to stochastic virus extinction due to a limited number of susceptible hosts available to each potential new protospacer escape mutation. Under such conditions, theory predicts that to evade extinction, viruses evolve toward decreased virulence and promote vertical transmission and persistence in infected hosts. To better understand the evolution of host-virus interactions in microbial populations with active CRISPR-Cas immunity, we studied the interaction between CRISPR-immune Sulfolobus islandicus cells and immune-deficient strains that are infected by the chronic virus SSV9. We demonstrate that Sulfolobus islandicus cells infected with SSV9, and with other related SSVs, kill uninfected, immune strains through an antagonistic mechanism that is a protein and is independent of infectious virus. Cells that are infected with SSV9 are protected from killing and persist in the population. We hypothesize that this infection acts as a form of mutualism between the host and the virus by removing competitors in the population and ensuring continued vertical transmission of the virus within populations with diversified CRISPR-Cas immunity. IMPORTANCE Multiple studies, especially those focusing on the role of lytic viruses in key model systems, have shown the importance of viruses in shaping microbial populations. However, it has become increasingly clear that viruses with a long host-virus interaction, such as those with a chronic lifestyle, can be important drivers of evolution and have large impacts on host ecology. In this work, we describe one such interaction with the acidic crenarchaeon Sulfolobus islandicus and its chronic virus Sulfolobus spindle-shaped virus 9. Our work expands the view in which this symbiosis between host and virus evolved, describing a killing phenotype which we hypothesize has evolved in part due to the high prevalence and diversity of CRISPR-Cas immunity seen in natural populations. We explore the implications of this phenotype in population dynamics and host ecology, as well as the implications of mutualism between this virus-host pair. 

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3. The diel disconnect between cell growth and division in Aureococcus is interrupted by giant virus infection

   https://doi.org/10.3389/fmicb.2024.1426193  

   Truchon, Alexander R; Chase, Emily E; Stark, Ashton R; Wilhelm, Steven W (August 2024, Frontiers in Microbiology)

   Viruses of eukaryotic algae have become an important research focus due to their role(s) in nutrient cycling and top-down control of algal blooms. Omics-based studies have identified a boon of genomic and transcriptional potential among theNucleocytoviricota, a phylum of large dsDNA viruses which have been shown to infect algal and non-algal eukaryotes. However, little is still understood regarding the infection cycle of these viruses, particularly in how they take over a metabolically active host and convert it into a virocell state. Of particular interest are the roles light and the diel cycle in virocell development. Yet despite such a large proportion ofNucleocytoviricotainfecting phototrophs, little work has been done to tie infection dynamics to the presence, and absence, of light. Here, we examine the role of the diel cycle on the physiological and transcriptional state of the pelagophyteAureococcus anophagefferenswhile undergoing infection byKratosvirus quantuckensestrain AaV. Our observations demonstrate how infection by the virus interrupts the diel growth and division of this cell strain, and that infection further complicates the system by enhancing export of cell biomass. 

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4. High Transcriptional Activity and Diverse Functional Repertoires of Hundreds of Giant Viruses in a Coastal Marine System

   https://doi.org/10.1128/mSystems.00293-21  

   Ha, Anh D.; Moniruzzaman, Mohammad; Aylward, Frank O.; Etten, James Van (January 2021, mSystems)

   Bordenstein, Seth (Ed.)

   ABSTRACT Viruses belonging to the Nucleocytoviricota phylum are globally distributed and include members with notably large genomes and complex functional repertoires. Recent studies have shown that these viruses are particularly diverse and abundant in marine systems, but the magnitude of actively replicating Nucleocytoviricota present in ocean habitats remains unclear. In this study, we compiled a curated database of 2,431 Nucleocytoviricota genomes and used it to examine the gene expression of these viruses in a 2.5-day metatranscriptomic time-series from surface waters of the California Current. We identified 145 viral genomes with high levels of gene expression, including 90 Imitervirales and 49 Algavirales viruses. In addition to recovering high expression of core genes involved in information processing that are commonly expressed during viral infection, we also identified transcripts of diverse viral metabolic genes from pathways such as glycolysis, the TCA cycle, and the pentose phosphate pathway, suggesting that virus-mediated reprogramming of central carbon metabolism is common in oceanic surface waters. Surprisingly, we also identified viral transcripts with homology to actin, myosin, and kinesin domains, suggesting that viruses may use these gene products to manipulate host cytoskeletal dynamics during infection. We performed phylogenetic analysis on the virus-encoded myosin and kinesin proteins, which demonstrated that most belong to deep-branching viral clades, but that others appear to have been acquired from eukaryotes more recently. Our results highlight a remarkable diversity of active Nucleocytoviricota in a coastal marine system and underscore the complex functional repertoires expressed by these viruses during infection. IMPORTANCE The discovery of giant viruses has transformed our understanding of viral complexity. Although viruses have traditionally been viewed as filterable infectious agents that lack metabolism, giant viruses can reach sizes rivalling cellular lineages and possess genomes encoding central metabolic processes. Recent studies have shown that giant viruses are widespread in aquatic systems, but the activity of these viruses and the extent to which they reprogram host physiology in situ remains unclear. Here, we show that numerous giant viruses consistently express central metabolic enzymes in a coastal marine system, including components of glycolysis, the TCA cycle, and other pathways involved in nutrient homeostasis. Moreover, we found expression of several viral-encoded actin, myosin, and kinesin genes, indicating viral manipulation of the host cytoskeleton during infection. Our study reveals a high activity of giant viruses in a coastal marine system and indicates they are a diverse and underappreciated component of microbial diversity in the ocean. 

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5. Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks

   https://doi.org/10.1371/journal.pcbi.1011649  

   Bastien, G Eric; Cable, Rachel N; Batterbee, Cecelia; Wing, A J; Zaman, Luis; Duhaime, Melissa B (September 2024, PLOS Computational Biology)

   Scarpino, Samuel V (Ed.)

   Viruses of microbes are ubiquitous biological entities that reprogram their hosts’ metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only“who is there?”we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding“who do they infect?”Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, ‘Virus Host Range network’ (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which meaningful signals of viral adaptation to host were computed from genomic sequences. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is able to infer the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets. 

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