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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. 

Summary Microcystisis a cyanobacterium that forms toxic blooms in freshwater ecosystems around the world. Biological variation among taxa within the genus is apparent through genetic and phenotypic differences between strains and via the spatial and temporal distribution of strains in the environment, and this fine‐scale diversity exerts strong influence over bloom toxicity. Yet we do not know how varying traits ofMicrocystisstrains govern their environmental distribution, the tradeoffs and links between these traits, or how they are encoded at the genomic level. Here we synthesize current knowledge on the importance of diversity withinMicrocystisand on the genes and traits that likely underpin ecological differentiation of taxa. We briefly review spatial and environmental patterns ofMicrocystisdiversity in the field and genetic evidence for cohesive groups withinMicrocystis. We then compile data on strain‐level diversity regarding growth responses to environmental conditions and explore evidence for variation of community interactions acrossMicrocystisstrains. Potential links and tradeoffs between traits are identified and discussed. The resulting picture, while incomplete, highlights key knowledge gaps that need to be filled to enable new models for predicting strain‐level dynamics, which influence the development, toxicity and cosmopolitan nature ofMicrocystisblooms. 

Abstract Among its many impacts, climate warming is leading to increasing winter air temperatures, decreasing ice cover extent, and changing winter precipitation patterns over the Laurentian Great Lakes and their watershed. Understanding and predicting the consequences of these changes is impeded by a shortage of winter‐period studies on most aspects of Great Lake limnology. In this review, we summarize what is known about the Great Lakes during their 3–6 months of winter and identify key open questions about the physics, chemistry, and biology of the Laurentian Great Lakes and other large, seasonally frozen lakes. Existing studies show that winter conditions have important effects on physical, biogeochemical, and biological processes, not only during winter but in subsequent seasons as well. Ice cover, the extent of which fluctuates dramatically among years and the five lakes, emerges as a key variable that controls many aspects of the functioning of the Great Lakes ecosystem. Studies on the properties and formation of Great Lakes ice, its effect on vertical and horizontal mixing, light conditions, and biota, along with winter measurements of fundamental state and rate parameters in the lakes and their watersheds are needed to close the winter knowledge gap. Overcoming the formidable logistical challenges of winter research on these large and dynamic ecosystems may require investment in new, specialized research infrastructure. Perhaps more importantly, it will demand broader recognition of the value of such work and collaboration between physicists, geochemists, and biologists working on the world's seasonally freezing lakes and seas.