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.

Bathyarchaeota, formerly known as the Miscellaneous Crenarchaeotal Group, is a phylum of global generalists that are widespread in anoxic sediments, which host relatively high abundance archaeal communities. Until now, 25 subgroups have been identified in the Bathyarchaeota. The distinct bathyarchaeotal subgroups diverged to adapt to marine and freshwater environments. Based on the physiological and genomic evidence, acetyl-coenzyme A-centralized heterotrophic pathways of energy conservation have been proposed to function in Bathyarchaeota; these microbes are able to anaerobically utilize (i) detrital proteins, (ii) polymeric carbohydrates, (iii) fatty acids/aromatic compounds, (iv) methane (or short chain alkane) and methylated compounds, and/or (v) potentially other organic matter. Furthermore, bathyarchaeotal members have wide metabolic capabilities, including acetogenesis, methane metabolism, and dissimilatory nitrogen and sulfur reduction, and they also have potential interactions with anaerobic methane-oxidizing archaea, acetoclastic methanogens and heterotrophic bacteria. These results have not only demonstrated multiple and important ecological functions of this archaeal phylum, but also paved the way for a detailed understanding of the evolution and metabolism of archaea as such. This review summarizes the recent findings pertaining to the ecological, physiological and genomic aspects of Bathyarchaeota, highlighting the vital role of this phylum in global carbon cycling.

BackgroundAs a recently discovered member of the DPANN superphylum, Woesearchaeota account for a wide diversity of 16S rRNA gene sequences, but their ecology, evolution, and metabolism remain largely unknown.ResultsHere, we assembled 133 global clone libraries/studies and 19 publicly available genomes to profile these patterns for Woesearchaeota. Phylogenetic analysis shows a high diversity with 26 proposed subgroups for this recently discovered archaeal phylum, which are widely distributed in different biotopes but primarily in inland anoxic environments. Ecological patterns analysis and ancestor state reconstruction for specific subgroups reveal that oxic status of the environments is the key factor driving the distribution and evolutionary diversity of Woesearchaeota. A selective distribution to different biotopes and an adaptive colonization from anoxic to oxic environments can be proposed and supported by evidence of the presence of ferredoxin-dependent pathways in the genomes only from anoxic biotopes but not from oxic biotopes. Metabolic reconstructions support an anaerobic heterotrophic lifestyle with conspicuous metabolic deficiencies, suggesting the requirement for metabolic complementarity with other microbes. Both lineage abundance distribution and co-occurrence network analyses across diverse biotopes confirmed metabolic complementation and revealed a potential syntrophic relationship between Woesearchaeota and methanogens, which is supported by metabolic modeling. If correct, Woesearchaeota may impact methanogenesis in inland ecosystems.ConclusionsThe findings provide an ecological and evolutionary framework for Woesearchaeota at a global scale and indicate their potential ecological roles, especially in methanogenesis.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0488-2) contains supplementary material, which is available to authorized users.

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.