Background: Viruses are central to microbial community structure in all environments. The ability to generate large metagenomic assemblies of mixed microbial and viral sequences provides the opportunity to tease apart complex microbiome dynamics, but these analyses are currently limited by the tools available for analyses of viral genomes and assessing their metabolic impacts on microbiomes. Design: Here we present VIBRANT, the first method to utilize a hybrid machine learning and protein similarity approach that is not reliant on sequence features for automated recovery and annotation of viruses, determination of genome quality and completeness, and characterization of viral community function from metagenomic assemblies. VIBRANT uses neural networks of protein signatures and a newly developed v-score metric that circumvents traditional boundaries to maximize identification of lytic viral genomes and integrated proviruses, including highly diverse viruses. VIBRANT highlights viral auxiliary metabolic genes and metabolic pathways, thereby serving as a user-friendly platform for evaluating viral community function. VIBRANT was trained and validated on reference virus datasets as well as microbiome and virome data. Results: VIBRANT showed superior performance in recovering higher quality viruses and concurrently reduced the false identification of non-viral genome fragments in comparison to other virus identification programs, specifically VirSorter, VirFinder, and MARVEL. When applied to 120,834 metagenome-derived viral sequences representing several human and natural environments, VIBRANT recovered an average of 94% of the viruses, whereas VirFinder, VirSorter, and MARVEL achieved less powerful performance, averaging 48%, 87%, and 71%, respectively. Similarly, VIBRANT identified more total viral sequence and proteins when applied to real metagenomes. When compared to PHASTER, Prophage Hunter, and VirSorter for the ability to extract integrated provirus regions from host scaffolds, VIBRANT performed comparably and even identified proviruses that the other programs did not. To demonstrate applications of VIBRANT, we studied viromes associated with Crohn's disease to show that specific viral groups, namely Enterobacteriales-like viruses, as well as putative dysbiosis associated viral proteins are more abundant compared to healthy individuals, providing a possible viral link to maintenance of diseased states.

Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.

Proteobacteria constitute one of the most diverse and abundant groups of microbes on Earth. In productive marine environments like deep-sea hydrothermal systems, Proteobacteria are implicated in autotrophy coupled to sulfur, methane, and hydrogen oxidation, sulfate reduction, and denitrification. Beyond chemoautotrophy, little is known about the ecological significance of poorly studied Proteobacteria lineages that are globally distributed and active in hydrothermal systems. Here we apply multi-omics to characterize 51 metagenome-assembled genomes from three hydrothermal vent plumes in the Pacific and Atlantic Oceans that are affiliated with nine Proteobacteria lineages. Metabolic analyses revealed these organisms to contain a diverse functional repertoire including chemolithotrophic ability to utilize sulfur and C 1 compounds, and chemoorganotrophic ability to utilize environment-derived fatty acids, aromatics, carbohydrates, and peptides. Comparative genomics with marine and terrestrial microbiomes suggests that lineage-associated functional traits could explain niche specificity. Our results shed light on the ecological functions and metabolic strategies of novel Proteobacteria in hydrothermal systems and beyond, and highlight the relationship between genome diversification and environmental adaptation.

Highlights d Unique intestinal phage compositions correlate to at-risk RA anti-CCP serology d Lachnospiraceae phage-host interactions dominate in CCP+ individuals at risk for RA d Phages from CCP+ individuals may originate from disparate ecological niches d Phage AMGs contribute to cohort-associated differences

Microbial sulfur metabolism contributes to biogeochemical cycling on global scales. Sulfur metabolizing microbes are infected by phages that can encode auxiliary metabolic genes (AMGs) to alter sulfur metabolism within host cells but remain poorly characterized. Here we identified 191 phages derived from twelve environments that encoded 227 AMGs for oxidation of sulfur and thiosulfate (dsrA, dsrC/tusE, soxC, soxD and soxYZ). Evidence for retention of AMGs during niche-differentiation of diverse phage populations provided evidence that auxiliary metabolism imparts measurable fitness benefits to phages with ramifications for ecosystem biogeochemistry. Gene abundance and expression profiles of AMGs suggested significant contributions by phages to sulfur and thiosulfate oxidation in freshwater lakes and oceans, and a sensitive response to changing sulfur concentrations in hydrothermal environments. Overall, our study provides fundamental insights on the distribution, diversity, and ecology of phage auxiliary metabolism associated with sulfur and reinforces the necessity of incorporating viral contributions into biogeochemical configurations.

Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric ‘MW-score’ (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.Conclusion: METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

48Background 49Viruses are central to microbial community structure in all environments. The ability to generate 50 large metagenomic assemblies of mixed microbial and viral sequences provides the opportunity to 51 tease apart complex microbiome dynamics, but these analyses are currently limited by the tools 52 available for analyses of viral genomes and assessing their metabolic impacts on microbiomes. 53 54Design 55Here we present VIBRANT, the first method to utilize a hybrid machine learning and protein 56 similarity approach that is not reliant on sequence features for automated recovery and annotation 57 of viruses, determination of genome quality and completeness, and characterization of virome 58 function from metagenomic assemblies. VIBRANT uses neural networks of protein signatures and 59 a novel v-score metric that circumvents traditional boundaries to maximize identification of lytic 60 viral genomes and integrated proviruses, including highly diverse viruses. VIBRANT highlights 61 viral auxiliary metabolic genes and metabolic pathways, thereby serving as a user-friendly 62 platform for evaluating virome function. VIBRANT was trained and validated on reference virus 63 datasets as well as microbiome and virome data. 64 65Results 66VIBRANT showed superior performance in recovering higher quality viruses and concurrently 67 reduced the false identification of non-viral genome fragments in comparison to other virus 68 identification programs, specifically VirSorter and VirFinder. When applied to 120,834 69 metagenomically derived viral sequences representing several human and natural environments, 70VIBRANT recovered an average of 94.5% of the viruses, whereas VirFinder and VirSorter 71 achieved less powerful performance, averaging 48.1% and 56.0%, respectively. Similarly, 72VIBRANT identified more total viral sequence and proteins when applied to real metagenomes. 73When compared to PHASTER and Prophage Hunter for the ability to extract integrated provirus 74 regions from host scaffolds, VIBRANT performed comparably and even identified proviruses that 75 the other programs did not. To demonstrate applications of VIBRANT, we studied viromes 76 associated with Crohn's Disease to show that specific viral groups, namely Enterobacteriales-like 77 viruses, as well as putative dysbiosis associated viral proteins are more abundant compared to 78 healthy individuals, providing a possible viral link to maintenance of diseased states. 79 80Conclusions 81The ability to accurately recover viruses and explore viral impacts on microbial community 82 metabolism will greatly advance our understanding of microbiomes, host-microbe interactions and 83 ecosystem dynamics. 84 85Keywords 86 Virome, virus, bacteriophage, metagenome, machine learning, auxiliary metabolism 87 88 89 90 3

Highlights d Thousands of diverse viruses encode genes that manipulate organic sulfur metabolism d Infection in the presence of sulfide increases bacteriophage production d Engineered phage T7 retains cysteine synthase (cysK) over multiple generations d These viruses can influence human gut dysbiosis, microbiomes, and biogeochemistry