Ocean viruses are abundant and infect 20-40% of surface microbes. Infected cells, termed virocells, are thus a predominant microbial state. Yet, virocells and their ecosystem impacts are understudied, thus precluding their incorporation into ecosystem models. Here we investigated how unrelated bacterial viruses (phages) reprogram one host into contrasting virocells with different potential ecosystem footprints. We independently infected the marine Pseudoalteromonas bacterium with siphovirus PSA-HS2 and podovirus PSA-HP1. Time-resolved multi-omics unveiled drastically different metabolic reprogramming and resource requirements by each virocell, which were related to phage-host genomic complementarity and viral fitness. Namely, HS2 was more complementary to the host in nucleotides and amino acids, and fitter during infection than HP1. Functionally, HS2 virocells hardly differed from uninfected cells, with minimal host metabolism impacts. HS2 virocells repressed energy-consuming metabolisms, including motility and translation. Contrastingly, HP1 virocells substantially differed from uninfected cells. They repressed host transcription, responded to infection continuously, and drastically reprogrammed resource acquisition, central carbon and energy metabolisms. Ecologically, this work suggests that one cell, infected versus uninfected, can have immensely different metabolisms that affect the ecosystem differently. Finally, we relate phage-host genome complementarity, virocell metabolic reprogramming, and viral fitness in a conceptual model to guide incorporating viruses into ecosystem models.

Recently discovered submerged sinkholes in Lake Huron are high-sulfur, lowoxygen extreme environments for microbial life. In order to understand the relationship between the physical environment, photophysiology and community composition, we measured the physical conditions, photophysiological indices, and genetic diversity at 3 microbial mat sites bathed in high conductivity groundwater under a natural light gradient during 2012 and 2013. A strong seasonal trend prevailed at all sites, characterized by decreased photosynthetic yield (F v '/F m '; 0.25 to 0.40) during the summer (April to August) and increased yield (0.70 to 0.75) during the winter (November to March). Chlorophyll a content varied seasonally in a similar manner to photosynthetic yield. All sites were dominated by > 80% abundance of one cyanobacterial group, most closely related to Phormidium sp. Phycobilins (phycocyanin and phycoerythrin) were consistently higher in concentration than chlorophyll. Photosynthetic yield was statistically indistinguishable between sites, suggesting that these mat communities are able to acclimate across a wide range of photosynthetically active radiation (PAR). Interestingly, these cyanobacteria carried out oxygenic photosynthesis in the presence of in vitro H 2 S, further suggestive of their versatile photophysiologies under variable redox conditions. Collectively, our study provides insight into the adaptive capabilities of cyanobacteria by revealing how they photophysiologically respond to changes in light climate and redox conditions, and are thereby able to inhabit a wide range of physico-chemical environments. Such versatile physiologies may have enabled their ancestors to thrive across a range of habitats on early Earth.

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BackgroundThe identification of viruses from environmental metagenomic samples using informatics tools has offered critical insights in microbiome studies. However, it remains difficult for researchers to know, for their specific study, which tool(s) and settings are best suited to minimize false positives and maximize capture of true viruses. Studies are increasingly combining multiple tool outputs attempting to recover more viruses, but no combined approach has been benchmarked for accuracy. Here, we benchmarked 63 viral identification ‘rulesets’ against mock metagenomes composed of publically available viral, bacterial, archaeal, fungal, and protist sequences. These rulesets are based on combinations of four single-tool rules and two multi-tool tuning rules. We applied these rulesets to various aquatic metagenomes (fresh and saltwater, drinking water, wastewater) and filtering strategies (targeting the virus-enriched and cell-enriched fractions of microbial communities) to evaluate the impact of habitat and viral enrichment on individual and combined tool performance.ResultsWe found that combining rules increased viral recall, but at the expense of increased false positives. Of the 63 rulesets, 6 had MCCs (Matthew’s Correlation Coefficient, our accuracy measure) that were statistically equivalent (padj ≥ 0.05) to the highest MCC ruleset (MCC=0.77). These rulesets all included VirSorter2, 5 included our “tuning removal” rule, and none used more than 4 of our 6 rules.DeepVirFinder, VIBRANT, and VirSorter were each found once in the “high MCC” rulesets, but never in combination with each other. Our validation suggests that the MCC plateau is caused by inaccurate classification of the data that viral identification tools rely on for training and validation. In the aquatic metagenomes, our “highest MCC” ruleset identified a higher proportion of viral sequences in the virus-enriched samples (44-46%) than the non-enriched, cellular metagenomes (7-19%).ConclusionWhile improved algorithms may lead to more accurate viral identification tools, this should be done in tandem with curating accurately labeled viral gene and sequence databases. For most applications, we recommend the use of the ruleset that uses VirSorter2 and our data-driven tuning removal rules. By providing a rigorous overview of the behavior ofin silicoviral identification strategies, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.