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1. 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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2. Differential Synergistic Interactions among Four Different Wheat-infecting Viruses

   https://doi.org/10.3389/fmicb.2021.800318  

   Tatineni, S (December 2021, Frontiers in microbiology)

   Field-grown wheat (Triticum aestivum L.) plants can be co-infected by multiple viruses, including wheat streak mosaic virus (WSMV), Triticum mosaic virus (TriMV), brome mosaic virus (BMV), and barley stripe mosaic virus (BSMV). These viruses belong to four different genera in three different families and are, hence, genetically divergent. However, the impact of potential co-infections with two, three, or all four of them on the viruses themselves, as well as the wheat host, has yet to be examined. This study examined bi-, tri-, and quadripartite interactions among these viruses in wheat for disease development and accumulation of viral genomic RNAs, in comparison with single virus infections. Co-infection of wheat by BMV and BSMV resulted in BMV-like symptoms with a drastic reduction in BSMV genomic RNA copies and coat protein accumulation, suggesting an antagonism-like effect exerted by BMV toward BSMV. However, co-infection of either BMV or BSMV with WSMV or TriMV led to more severe disease than singly infected wheat, but with a decrease or no significant change in the titers of interacting viruses in the presence of BMV or BSMV, respectively. These results were in stark contrast with exacerbated disease phenotype accompanied with enhanced virus titers caused by WSMV and TriMV co-infection. Co-infection of wheat by WSMV, TriMV, and BMV or BSMV resulted in enhanced synergistic disease accompanied with increased accumulation of TriMV and BMV but not WSMV or BSMV. Quadripartite interactions in co-infected wheat by all four viruses resulted in very severe disease synergism, leading to the death of most infected plants, but paradoxically, a drastic reduction in BSMV titer. Our results indicate that interactions among different viruses infecting the same plant host are more complex than previously thought, do not always entail increases in virus titers, and likely involve multiple mechanisms. These findings lay the foundation for additional mechanistic dissections of synergistic interactions among unrelated plant viruses. 

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3. The Case for Studying New Viruses of New Hosts

   https://doi.org/10.1146/annurev-virology-100220-112915  

   Stenglein, Mark D. (September 2022, Annual Review of Virology)

   Virology has largely focused on viruses that are pathogenic to humans or to the other species that we care most about. There is no doubt that this has been a worthwhile investment. But many transformative advances have been made through the in-depth study of relatively obscure viruses that do not appear on lists of prioritized pathogens. In this review, I highlight the benefits that can accrue from the study of viruses and hosts off the beaten track. I take stock of viral sequence diversity across host taxa as an estimate of the bias that exists in our understanding of host-virus interactions. I describe the gains that have been made through the metagenomic discovery of thousands of new viruses in previously unsampled hosts as well as the limitations of metagenomic surveys. I conclude by suggesting that the study of viruses that naturally infect existing and emerging model organisms represents an opportunity to push virology forward in useful and hard to predict ways. 

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4. Taxonomic patterns in the zoonotic potential of mammalian viruses

   https://doi.org/10.7717/peerj.5979  

   Washburne, Alex D.; Crowley, Daniel E.; Becker, Daniel J.; Olival, Kevin J.; Taylor, Matthew; Munster, Vincent J.; Plowright, Raina K. (January 2018, PeerJ)

   Predicting and simplifying which pathogens may spill over from animals to humans is a major priority in infectious disease biology. Many efforts to determine which viruses are at risk of spillover use a subset of viral traits to find trait-based associations with spillover. We adapt a new method—phylofactorization—to identify not traits but lineages of viruses at risk of spilling over. Phylofactorization is used to partition the International Committee on Taxonomy of Viruses viral taxonomy based on non-human host range of viruses and whether there exists evidence the viruses have infected humans. We identify clades on a range of taxonomic levels with high or low propensities to spillover, thereby simplifying the classification of zoonotic potential of mammalian viruses. Phylofactorization by whether a virus is zoonotic yields many disjoint clades of viruses containing few to no representatives that have spilled over to humans. Phylofactorization by non-human host breadth yields several clades with significantly higher host breadth. We connect the phylogenetic factors above with life-histories of clades, revisit trait-based analyses, and illustrate how cladistic coarse-graining of zoonotic potential can refine trait-based analyses by illuminating clade-specific determinants of spillover risk. 

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5. Large, double‐stranded DNA viruses tend to suppress phytoplankton populations more effectively than small viruses of diverse genome type

   https://doi.org/10.1002/lol2.70035  

   Edwards, Kyle F; McBeain, Kelsey A; Schvarcz, Christopher R; Steward, Grieg F (June 2025, Limnology and Oceanography Letters)

   Viruses infecting aquatic microbes vary immensely in size, but the ecological consequences of virus size are poorly understood. Here we used a unique suite of diverse phytoplankton strains and their viruses, all isolated from waters around Hawai'i, to assess whether virus size affects the suppression of host populations. We found that small viruses of diverse genome type (3–24 kb genome size, 23–70 nm capsid diameter) have very similar effects on host populations, suppressing hosts less strongly and for a shorter period of time compared to large double‐stranded DNA viruses (214–1380 kb, 112–386 nm). Suppressive effects of larger viruses were more heterogeneous, but most isolates reduced host populations by many orders of magnitude, without recovery over the ~ 25‐d experiments. Our results suggest that disparate lineages of viruses may have ecological consequences that are predictable in part based on size, and that ecosystem impacts of viral infection may vary with the size structure of the viral community. 

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