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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.

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. Conclusions The ability to accurately recover viruses and explore viral impacts on microbial community metabolism will greatly advance our understanding of microbiomes, host-microbe interactions, and ecosystem dynamics. 7uVs82Bc4tfSLymdHQuU_3 Video Abstract

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. Some bacteriophage encode auxiliary metabolic genes (AMGs) that impact host metabolism and biogeochemical cycling during infection. Here the authors identify hundreds of AMGs in environmental phage encoding sulfur oxidation genes and use their global distribution to infer phage-mediated biogeochemical impacts.

Lake Tanganyika (LT) is the largest tropical freshwater lake, and the largest body of anoxic freshwater on Earth’s surface. LT’s mixed oxygenated surface waters float atop a permanently anoxic layer and host rich animal biodiversity. However, little is known about microorganisms inhabiting LT’s 1470 meter deep water column and their contributions to nutrient cycling, which affect ecosystem-level function and productivity. Here, we applied genome-resolved metagenomics and environmental analyses to link specific taxa to key biogeochemical processes across a vertical depth gradient in LT. We reconstructed 523 unique metagenome-assembled genomes (MAGs) from 34 bacterial and archaeal phyla, including many rarely observed in freshwater lakes. We identified sharp contrasts in community composition and metabolic potential with an abundance of typical freshwater taxa in oxygenated mixed upper layers, and Archaea and uncultured Candidate Phyla in deep anoxic waters. Genomic capacity for nitrogen and sulfur cycling was abundant in MAGs recovered from anoxic waters, highlighting microbial contributions to the productive surface layers via recycling of upwelled nutrients, and greenhouse gases such as nitrous oxide. Overall, our study provides a blueprint for incorporation of aquatic microbial genomics in the representation of tropical freshwater lakes, especially in the context of ongoing climate change, which is predicted to bring increased stratification and anoxia to freshwater lakes.

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 . 8JzsRQQL6mihmS_qxcrFZs Video abstract

Exponential increases in microbial and viral genomic data demand transformational advances in scalable, generalizable frameworks for their interpretation. Standard homology-based functional analyses are hindered by the rapid divergence of microbial and especially viral genomes and proteins that significantly decreases the volume of usable data. Here, we present Protein Set Transformer (PST), a protein-based genome language model that models genomes as sets of proteins without considering sparsely available functional labels. Trained on >100k viruses, PST outperforms other homology- and language model-based approaches for relating viral genomes based on shared protein content. Further, PST demonstrates protein structural and functional awareness by clustering capsid-fold-containing proteins with known capsid proteins and uniquely clustering late gene proteins within related viruses. Our data establish PST as a valuable method for diverse viral genomics, ecology, and evolutionary applications. We posit that the PST framework can be a foundation model for microbial genomics when trained on suitable data. A genome language model, Protein Set Transformer, trained on viral datasets, uncovers evolutionary rules of protein content and organization driving precise virus identification, host prediction, and protein annotation for viral genomics and ecology.

Candidate phyla radiation (CPR) bacteria and DPANN (an acronym of the names of the first included phyla) archaea are massive radiations of organisms that are widely distributed across Earth’s environments, yet we know little about them. Initial indications are that they are consistently distinct from essentially all other bacteria and archaea owing to their small cell and genome sizes, limited metabolic capacities and often episymbiotic associations with other bacteria and archaea. In this Analysis, we investigate their biology and variations in metabolic capacities by analysis of approximately 1,000 genomes reconstructed from several metagenomics-based studies. We find that they are not monolithic in terms of metabolism but rather harbour a diversity of capacities consistent with a range of lifestyles and degrees of dependence on other organisms. Notably, however, certain CPR and DPANN groups seem to have exceedingly minimal biosynthetic capacities, whereas others could potentially be free living. Understanding of these microorganisms is important from the perspective of evolutionary studies and because their interactions with other organisms are likely to shape natural microbiome function.

Viruses that infect bacteria are key components of microbiomes and ecosystems. They can kill and manipulate microorganisms, drive planetary-scale processes and biogeochemical cycling, and influence the structures of entire food networks. Temperate phages (prophages) are ubiquitous in nature and persist as dormant components of host cells (lysogenic stage) before activating and lysing the host (lytic stage). Actively replicating prophages contribute to central community processes, such as enabling bacterial virulence, manipulating biogeochemical cycling, and driving microbial community diversification. Recent advances in sequencing technology have allowed for the identification and characterization of diverse phages, yet no approaches currently exist for identifying if a prophage has activated. Here, we present PropagAtE (Prophage Activity Estimator), an automated software tool for estimating if a prophage is in the lytic or lysogenic stage of infection. PropagAtE uses statistical analyses of prophage-to-host read coverage ratios to decipher actively replicating prophages, irrespective of whether prophages were induced or spontaneously activated. We demonstrate that PropagAtE is fast, accurate, and sensitive, regardless of sequencing depth. Application of PropagAtE to prophages from 348 complex metagenomes from human gut, murine gut, and soil environments identified distinct spatial and temporal prophage activation signatures, with the highest proportion of active prophages in murine gut samples. In infants treated with antibiotics or infants without treatment, we identified active prophage populations correlated with specific treatment groups. Within time series samples from the human gut, 11 prophage populations, some encoding the sulfur metabolism gene cysH or a rhuM -like virulence factor, were consistently present over time but not active. Overall, PropagAtE will facilitate accurate representations of viruses in microbiomes by associating prophages with their active roles in shaping microbial communities in nature. IMPORTANCE Viruses that infect bacteria are key components of microbiomes and ecosystems. They can kill and manipulate microorganisms, drive planetary-scale processes and biogeochemical cycling, and influence the structures of entire food networks. Prophages are viruses that can exist in a dormant state within the genome of their host (lysogenic stage) before activating in order to replicate and kill the host (lytic stage). Recent advances have allowed for the identification of diverse viruses in nature, but no approaches exist for characterizing prophages and their stages of infection (prophage activity). We develop and benchmark an automated approach, PropagAtE, to identify the stages of infection of prophages from genomic data. We provide evidence that active prophages vary in identity and abundance across multiple environments and scales. Our approach will enable accurate and unbiased analyses of viruses in microbiomes and ecosystems.

Blooms of planktonic cyanobacteria have long been of concern in lakes, but more recently, harmful impacts of riverine benthic cyanobacterial mats been recognized. As yet, we know little about how various benthic cyanobacteria are distributed in river networks, or how environmental conditions or other associated microbes in their consortia affect their biosynthetic capacities. We performed metagenomic sequencing for 22 Oscillatoriales-dominated (Cyanobacteria) microbial mats collected across the Eel River network in Northern California and investigated factors associated with anatoxin-a producing cyanobacteria. All microbial communities were dominated by one or two cyanobacterial species, so the key mat metabolisms involve oxygenic photosynthesis and carbon oxidation. Only a few metabolisms fueled the growth of the mat communities, with little evidence for anaerobic metabolic pathways. We genomically defined four cyanobacterial species, all which shared <96% average nucleotide identity with reference Oscillatoriales genomes and are potentially novel species in the genus Microcoleus . One of the Microcoleus species contained the anatoxin-a biosynthesis genes, and we describe the first anatoxin-a gene cluster from the Microcoleus clade within Oscillatoriales. Occurrence of these four Microcoleus species in the watershed was correlated with total dissolved nitrogen and phosphorus concentrations, and the species that contains the anatoxin-a gene cluster was found in sites with higher nitrogen concentrations. Microbial assemblages in mat samples with the anatoxin-a gene cluster consistently had a lower abundance of Burkholderiales (Betaproteobacteria) species than did mats without the anatoxin-producing genes. The associations of water nutrient concentrations and certain co-occurring microbes with anatoxin-a producing Microcoleus motivate further exploration for their roles as potential controls on the distributions of toxigenic benthic cyanobacteria in river networks.

Virus–host interactions are vital to microbiome ecology and evolution, yet their responses to environmental stressors under global change remain poorly understood. We perform a 10-month outdoor mesocosm experiment simulating multi-trophic freshwater shallow lake ecosystems. Using a fully factorial design comprising eight treatments with six replicates each, we assess the individual and combined effects of climate warming, nutrient loading, and pesticide loading on DNA viral communities and their interactions with microbial hosts. Metagenomic sequencing recovers 12,359 viral OTUs and 1628 unique prokaryotic metagenome-assembled genomes. Our analysis shows that combined nutrient and pesticide loading causes significant disruption by synergistically reducing viral alpha diversity while altering beta diversity and predator-prey linkages. Stressors lead to the simplification of virus-bacteria cross-kingdom networks, with nutrient-pesticide combinations exerting the strongest influence, although warming impacts diminish in the presence of pesticides. Stressor-driven changes also affect the abundance and composition of viral auxiliary metabolic genes, leading to complex shifts in virus-mediated metabolic pathways under multiple stress conditions. These findings underscore the importance of understanding the regulatory role of viruses on microbial communities to effectively address the challenges posed by global change. The impact of global change-related stressors on microbial communities is poorly studied. Here, the authors use mesocosm experiments to show that combined environmental stressors such as nutrient and pesticide loading disrupt freshwater viral communities and virus-host interactions even when single stressors do not.