PHIST: fast and accurate prediction of prokaryotic hosts from metagenomic viral sequences

A Novel Framework for Predicting Phage-Host Interactions via Host Specificity-Aware Graph Autoencoder

Due to the abuse of antibiotics, some pathogenic bacteria have developed resistance to most antibiotics, leading to the emergence of antibiotic-resistant superbugs. Therefore, researchers resort to phage therapy for bacterial infections. For phage therapy, the fundamental step is to accurately identify phage-host interactions. Although various methods have been proposed, the existing methods suffer from the following two shortcomings: 1) they fail to make full use of genetic information including both genome and protein sequence of phages; 2) host specificity of phages is not explicitly utilized when learning representations of phages and bacteria. In this paper, we present an efficient computational method called PHISGAE for predicting phage-host interactions, in which the host specificity is explicitly employed. Firstly, initial phage-phage connections are efficiently constructed via utilizing phage genome and protein sequence. Then, the refined heterogeneous network is derived by applying K-nearest neighbor strategy, keeping relatively more meaningful local semantics among phages and bacteria. Finally, a host specificity-aware graph autoencoder is proposed to learn high-quality representations of phages and bacteria for predicting phage-host interactions. Experimental results show that PHISGAE outperforms the state-of-the-art methods on predicting phage-host interactions at both species level and genus level (AUC values of 94.73% and 96.32%, respectively). Moreover, results of case study demonstrate that PHISGAE is able to identify candidate hosts with high probability for previously unseen phages identified from metagenomics, effectively predicting potential phage-host interactions in real-world applications.

A novel framework for phage-host prediction via logical probability theory and network sparsification

Bacterial resistance has emerged as one of the greatest threats to human health, and phages have shown tremendous potential in addressing the issue of drug-resistant bacteria by lysing host. The identification of phage-host interactions (PHI) is crucial for addressing bacterial infections. Some existing computational methods for predicting PHI are suboptimal in terms of prediction efficiency due to the limited types of available information. Despite the emergence of some supporting information, the generalizability of models using this information is limited by the small scale of the databases. Additionally, most existing models overlook the sparsity of association data, which severely impacts their predictive performance as well. In this study, we propose a dual-view sparse network model (DSPHI) to predict PHI, which leverages logical probability theory and network sparsification. Specifically, we first constructed similarity networks using the sequences of phages and hosts respectively, and then sparsified these networks, enabling the model to focus more on key information during the learning process, thereby improving prediction efficiency. Next, we utilize logical probability theory to compute high-order logical information between phages (hosts), which is known as mutual information. Subsequently, we connect this information in node form to the sparse phage (host) similarity network, resulting in a phage (host) heterogeneous network that better integrates the two information views, thereby reducing the complexity of model computation and enhancing information aggregation capabilities. The hidden features of phages and hosts are explored through graph learning algorithms. Experimental results demonstrate that mutual information is effective information in predicting PHI, and the sparsification procedure of similarity networks significantly improves the model's predictive performance.

PHPGAT: predicting phage hosts based on multimodal heterogeneous knowledge graph with graph attention network

Antibiotic resistance poses a significant threat to global health, making the development of alternative strategies to combat bacterial pathogens increasingly urgent. One such promising approach is the strategic use of bacteriophages (or phages) to specifically target and eradicate antibiotic-resistant bacteria. Phages, being among the most prevalent life forms on Earth, play a critical role in maintaining ecological balance by regulating bacterial communities and driving genetic diversity. Accurate prediction of phage hosts is essential for successfully applying phage therapy. However, existing prediction models may not fully encapsulate the complex dynamics of phage-host interactions in diverse microbial environments, indicating a need for improved accuracy through more sophisticated modeling techniques. In response to this challenge, this study introduces a novel phage-host prediction model, PHPGAT, which leverages a multimodal heterogeneous knowledge graph with the advanced GATv2 (Graph Attention Network v2) framework. The model first constructs a multimodal heterogeneous knowledge graph by integrating phage-phage, host-host, and phage-host interactions to capture the intricate connections between biological entities. GATv2 is then employed to extract deep node features and learn dynamic interdependencies, generating context-aware embeddings. Finally, an inner product decoder is designed to compute the likelihood of interaction between a phage and host pair based on the embedding vectors produced by GATv2. Evaluation results using two datasets demonstrate that PHPGAT achieves precise phage host predictions and outperforms other models. PHPGAT is available at https://github.com/ZhaoZMer/PHPGAT.

CRISPR spacers acquired from plasmids primarily target backbone genes, making them valuable for predicting potential hosts and host range

In recent years, there has been a surge in metagenomic studies focused on identifying plasmids in environmental samples. Although these studies have unearthed numerous novel plasmids, enriching our understanding of their environmental roles, a significant gap remains: the scarcity of information regarding the bacterial hosts of these newly discovered plasmids. Furthermore, even when plasmids are identified within bacterial isolates, the reported host is typically limited to the original isolate, with no insights into alternative hosts or the plasmid's potential host range. Given that plasmids depend on hosts for their existence, investigating plasmids without the knowledge of potential hosts offers only a partial perspective. This study introduces a method for identifying potential hosts and host ranges for plasmids through alignment with CRISPR spacers. To validate the method, we compared the PLSDB plasmids database with the CRISPR spacers database, yielding host predictions for 46% of the plasmids. When compared with reported hosts, our predictions achieved 84% concordance at the family level and 99% concordance at the phylum level. Moreover, the method frequently identified multiple potential hosts for a plasmid, thereby enabling predictions of alternative hosts and the host range. Notably, we found that CRISPR spacers predominantly target plasmid backbone genes while sparing functional genes, such as those linked to antibiotic resistance, aligning with our hypothesis that CRISPR spacers are acquired from plasmid-specific regions rather than insertion elements from diverse sources. Finally, we illustrate the network of connections among different bacterial taxa through plasmids, revealing potential pathways for horizontal gene transfer.IMPORTANCEPlasmids are notorious for their role in distributing antibiotic resistance genes, but they may also carry and distribute other environmentally important genes. Since plasmids are not free-living entities and rely on host bacteria for survival and propagation, predicting their hosts is essential. This study presents a method for predicting potential hosts for plasmids and offers insights into the potential paths for spreading functional genes between different bacteria. Understanding plasmid-host relationships is crucial for comprehending the ecological and clinical impact of plasmids and implications for various biological processes.

CHAMP delivers accurate taxonomic profiles of the prokaryotes, eukaryotes, and bacteriophages in the human microbiome

Introduction

Accurate taxonomic profiling of the human microbiome composition is crucial for linking microbial species to health outcomes. Therefore, we created the Clinical Microbiomics Human Microbiome Profiler (CHAMP), a comprehensive tool designed for the profiling of prokaryotes, eukaryotes, and viruses across all body sites.

Methods

CHAMP uses a reference database derived from 30,382 human microbiome samples, covering 6,567 prokaryotic and 244 eukaryotic species, as well as 64,003 viruses. We benchmarked CHAMP against established profiling tools (MetaPhlAn 4, Bracken 2, mOTUs 3, and Phanta) using a diverse set of in silico metagenomes and DNA mock communities.

Results

CHAMP demonstrated unparalleled species recall, F1 score, and significantly reduced false positives compared to all other tools benchmarked. The false positive relative abundance (FPRA) for CHAMP was, on average, 50-fold lower than the second-best performing profiler. CHAMP also proved to be more robust than other tools at low sequencing depths, highlighting its application for low biomass samples.

Discussion

Taken together, this establishes CHAMP as a best-in-class human microbiome profiler of prokaryotes, eukaryotes, and viruses in diverse and complex communities across low and high biomass samples. CHAMP profiling is offered as a service by Clinical Microbiomics A/S and is available for a fee at https://cosmosidhub.com.

Antarctic lake viromes reveal potential virus associated influences on nutrient cycling in ice-covered lakes

The McMurdo Dry Valleys (MDVs) of Antarctica are a mosaic of extreme habitats which are dominated by microbial life. The MDVs include glacial melt holes, streams, lakes, and soils, which are interconnected through the transfer of energy and flux of inorganic and organic material via wind and hydrology. For the first time, we provide new data on the viral community structure and function in the MDVs through metagenomics of the planktonic and benthic mat communities of Lakes Bonney and Fryxell. Viral taxonomic diversity was compared across lakes and ecological function was investigated by characterizing auxiliary metabolic genes (AMGs) and predicting viral hosts. Our data suggest that viral communities differed between the lakes and among sites: these differences were connected to microbial host communities. AMGs were associated with the potential augmentation of multiple biogeochemical processes in host, most notably with phosphorus acquisition, organic nitrogen acquisition, sulfur oxidation, and photosynthesis. Viral genome abundances containing AMGs differed between the lakes and microbial mats, indicating site specialization. Using procrustes analysis, we also identified significant coupling between viral and bacterial communities (p = 0.001). Finally, host predictions indicate viral host preference among the assembled viromes. Collectively, our data show that: (i) viruses are uniquely distributed through the McMurdo Dry Valley lakes, (ii) their AMGs can contribute to overcoming host nutrient limitation and, (iii) viral and bacterial MDV communities are tightly coupled.

Extremely halophilic brine community manipulation shows higher robustness of microbiomes inhabiting human-driven solar saltern than naturally driven lake

Hypersaline ecosystems display taxonomically similar assemblages with low diversities and highly dense accompanying viromes. The ecological implications of viral infection on natural microbial populations remain poorly understood, especially at finer scales of diversity. Here, we sought to investigate the influence of changes in environmental physicochemical conditions and viral predation pressure by autochthonous and allochthonous viruses on host dynamics. For this purpose, we transplanted two microbiomes coming from distant hypersaline systems (solar salterns of Es Trenc in Spain and the thalassohaline lake of Aran-Bidgol lake in Iran), by exchanging the cellular fractions with the sterile-filtered accompanying brines with and without the free extracellular virus fraction. The midterm exposure (1 month) of the microbiomes to the new conditions showed that at the supraspecific taxonomic range, the assemblies from the solar saltern brine more strongly resisted the environmental changes and viral predation than that of the lake. The metagenome-assembled genomes (MAGs) analysis revealed an intraspecific transition at the ecotype level, mainly driven by changes in viral predation pressure, by both autochthonous and allochthonous viruses.

IMPORTANCE

Viruses greatly influence succession and diversification of their hosts, yet the effects of viral infection on the ecological dynamics of natural microbial populations remain poorly understood, especially at finer scales of diversity. By manipulating the viral predation pressure by autochthonous and allochthonous viruses, we uncovered potential phage-host interaction, and their important role in structuring the prokaryote community at an ecotype level.

Tools and methodology to in silico phage discovery in freshwater environments

Freshwater availability is essential, and its maintenance has become an enormous challenge. Due to population growth and climate changes, freshwater sources are becoming scarce, imposing the need for strategies for its reuse. Currently, the constant discharge of waste into water bodies from human activities leads to the dissemination of pathogenic bacteria, negatively impacting water quality from the source to the infrastructure required for treatment, such as the accumulation of biofilms. Current water treatment methods cannot keep pace with bacterial evolution, which increasingly exhibits a profile of multidrug resistance to antibiotics. Furthermore, using more powerful disinfectants may affect the balance of aquatic ecosystems. Therefore, there is a need to explore sustainable ways to control the spreading of pathogenic bacteria. Bacteriophages can infect bacteria and archaea, hijacking their host machinery to favor their replication. They are widely abundant globally and provide a biological alternative to bacterial treatment with antibiotics. In contrast to common disinfectants and antibiotics, bacteriophages are highly specific, minimizing adverse effects on aquatic microbial communities and offering a lower cost-benefit ratio in production compared to antibiotics. However, due to the difficulty involving cultivating and identifying environmental bacteriophages, alternative approaches using NGS metagenomics in combination with some bioinformatic tools can help identify new bacteriophages that can be useful as an alternative treatment against resistant bacteria. In this review, we discuss advances in exploring the virome of freshwater, as well as current applications of bacteriophages in freshwater treatment, along with current challenges and future perspectives.

Marine picoplankton metagenomes and MAGs from eleven vertical profiles obtained by the Malaspina Expedition

The Ocean microbiome has a crucial role in Earth's biogeochemical cycles. During the last decade, global cruises such as Tara Oceans and the Malaspina Expedition have expanded our understanding of the diversity and genetic repertoire of marine microbes. Nevertheless, there are still knowledge gaps regarding their diversity patterns throughout depth gradients ranging from the surface to the deep ocean. Here we present a dataset of 76 microbial metagenomes (MProfile) of the picoplankton size fraction (0.2-3.0 µm) collected in 11 vertical profiles covering contrasting ocean regions sampled during the Malaspina Expedition circumnavigation (7 depths, from surface to 4,000 m deep). The MProfile dataset produced 1.66 Tbp of raw DNA sequences from which we derived: 17.4 million genes clustered at 95% sequence similarity (M-GeneDB-VP), 2,672 metagenome-assembled genomes (MAGs) of Archaea and Bacteria (Malaspina-VP-MAGs), and over 100,000 viral genomic sequences. This dataset will be a valuable resource for exploring the functional and taxonomic connectivity between the photic and bathypelagic tropical and sub-tropical ocean, while increasing our general knowledge of the Ocean microbiome.

Ultraconserved bacteriophage genome sequence identified in 1300-year-old human palaeofaeces

Bacteriophages are widely recognised as rapidly evolving biological entities. However, knowledge about ancient bacteriophages is limited. Here, we analyse DNA sequence datasets previously generated from ancient palaeofaeces and human gut-content samples, and identify an ancient phage genome nearly identical to present-day Mushuvirus mushu, a virus that infects gut commensal bacteria. The DNA damage patterns of the genome are consistent with its ancient origin and, despite 1300 years of evolution, the ancient Mushuvirus genome shares 97.7% nucleotide identity with its modern counterpart, indicating a long-term relationship between the prophage and its host. In addition, we reconstruct and authenticate 297 other phage genomes from the last 5300 years, including those belonging to unknown families. Our findings demonstrate the feasibility of reconstructing ancient phage genome sequences, thus expanding the known virosphere and offering insights into phage-bacteria interactions spanning several millennia.

Comparative Analyses of Bacteriophage Genomes

Bacterial viruses (bacteriophages or phages) are the most abundant and diverse biological entities on Earth. There is a renewed worldwide interest in phage-centered research motivated by their enormous potential as antimicrobials to cope with multidrug-resistant pathogens. An ever-growing number of complete phage genomes are becoming available, derived either from newly isolated phages (cultivated phages) or recovered from metagenomic sequencing data (uncultivated phages). Robust comparative analysis is crucial for a comprehensive understanding of genotypic variations of phages and their related evolutionary processes, and to investigate the interaction mechanisms between phages and their hosts. In this chapter, we present a protocol for phage comparative genomics employing tools selected out of the many currently available, focusing on complete genomes of phages classified in the class Caudoviricetes. This protocol provides accurate identification of similarities, differences, and patterns among new and previously known complete phage genomes as well as phage clustering and taxonomic classification.

Computational host range prediction-The good, the bad, and the ugly

The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges-that is the bacterial strains or species that a bacteriophage can successfully infect and kill-is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision-however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of in silico host prediction to combat the challenge of guiding experimental designs to identify the most promising bacteriophage candidates for any given application.

Prokaryotic virus host prediction with graph contrastive augmentaion

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

Genomic and transcriptomic insights into complex virus-prokaryote interactions in marine biofilms

Marine biofilms are complex communities of microorganisms that play a crucial ecological role in oceans. Although prokaryotes are the dominant members of these biofilms, little is known about their interactions with viruses. By analysing publicly available and newly sequenced metagenomic data, we identified 2446 virus-prokaryote connections in 84 marine biofilms. Most of these connections were between the bacteriophages in the Uroviricota phylum and the bacteria of Proteobacteria, Cyanobacteria and Bacteroidota. The network of virus-host pairs is complex; a single virus can infect multiple prokaryotic populations or a single prokaryote is susceptible to several viral populations. Analysis of genomes of paired prokaryotes and viruses revealed the presence of 425 putative auxiliary metabolic genes (AMGs), 239 viral genes related to restriction-modification (RM) systems and 38,538 prokaryotic anti-viral defence-related genes involved in 15 defence systems. Transcriptomic evidence from newly established biofilms revealed the expression of viral genes, including AMGs and RM, and prokaryotic defence systems, indicating the active interplay between viruses and prokaryotes. A comparison between biofilms and seawater showed that biofilm prokaryotes have more abundant defence genes than seawater prokaryotes, and the defence gene composition differs between biofilms and the surrounding seawater. Overall, our study unveiled active viruses in natural biofilms and their complex interplay with prokaryotes, which may result in the blooming of defence strategists in biofilms. The detachment of bloomed defence strategists may reduce the infectivity of viruses in seawater and result in the emergence of a novel role of marine biofilms.

Top-heavy trophic structure within benthic viral dark matter

A better understanding of system-specific viral ecology in diverse environments is needed to predict patterns of virus-host trophic structure in the Anthropocene. This study characterised viral-host trophic structure within coral reef benthic cyanobacterial mats-a globally proliferating cause and consequence of coral reef degradation. We employed deep longitudinal multi-omic sequencing to characterise the viral assemblage (ssDNA, dsDNA, and dsRNA viruses) and profile lineage-specific host-virus interactions within benthic cyanobacterial mats sampled from Bonaire, Caribbean Netherlands. We recovered 11,012 unique viral populations spanning at least 10 viral families across the orders Caudovirales, Petitvirales, and Mindivirales. Gene-sharing network analyses provided evidence for extensive genomic novelty of mat viruses from reference and environmental viral sequences. Analysis of coverage ratios of viral sequences and computationally predicted hosts spanning 15 phyla and 21 classes revealed virus-host abundance (from DNA) and activity (from RNA) ratios consistently exceeding 1:1, suggesting a top-heavy intra-mat trophic structure with respect to virus-host interactions. Overall, our article contributes a curated database of viral sequences found in Caribbean coral reef benthic cyanobacterial mats (vMAT database) and provides multiple lines of field-based evidence demonstrating that viruses are active members of mat communities, with broader implications for mat functional ecology and demography.

PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network

Identifying the potential bacteriophages (phage) candidate to treat bacterial infections plays an essential role in the research of human pathogens. Computational approaches are recognized as a valid way to predict bacteria and target phages. However, most of the current methods only utilize lower-order biological information without considering the higher-order connectivity patterns, which helps to improve the predictive accuracy. Therefore, we developed a novel microbial heterogeneous interaction network (MHIN)-based model called PTBGRP to predict new phages for bacterial hosts. Specifically, PTBGRP first constructs an MHIN by integrating phage-bacteria interaction (PBI) and six bacteria-bacteria interaction networks with their biological attributes. Then, different representation learning methods are deployed to extract higher-level biological features and lower-level topological features from MHIN. Finally, PTBGRP employs a deep neural network as the classifier to predict unknown PBI pairs based on the fused biological information. Experiment results demonstrated that PTBGRP achieves the best performance on the corresponding ESKAPE pathogens and PBI dataset when compared with state-of-art methods. In addition, case studies of Klebsiella pneumoniae and Staphylococcus aureus further indicate that the consideration of rich heterogeneous information enables PTBGRP to accurately predict PBI from a more comprehensive perspective. The webserver of the PTBGRP predictor is freely available at http://120.77.11.78/PTBGRP/.

New Phage-Derived Antibacterial Enzyme PolaR Targeting Rothia spp

Rothia is an opportunistic pathogen, particularly life-threatening for the immunocompromised. It is associated with pneumonia, endocarditis, peritonitis and many other serious infections, including septicemia. Of note, Rothia mucilaginousa produces metabolites that support and increase overgrowth of Pseudomonas aeruginosa, one of the ESKAPE bacteria. Endolysins are considered as antibacterial enzymes derived from bacteriophages that selectively and efficiently kill susceptible bacteria without harming human cells or the normal microbiome. Here, we applied a computational analysis of metagenomic sequencing data of the gastric mucosa phageome extracted from human patients' stomach biopsies. A selected candidate anti-Rothia sequence was produced in an expression system, purified and confirmed as a Rothia mucilaginosa- and Rothia dentocariosa-specific endolysin PolaR, able to destroy bacterial cells even when aggregated, as in a biofilm. PolaR had no cytotoxic or antiproliferative effects on mammalian cells. PolaR is the first described endolysin selectively targeting Rothia species, with a high potential to combat infections caused by Rothia mucilaginosa and Rothia dentocariosa, and possibly other bacterial groups. PolaR is the first antibacterial enzyme selected from the gastric mucosa phageome, which underlines the biological complexity and probably underestimated biological role of the phageome in the human gastric mucosa.

Water mass age structures the auxiliary metabolic gene content of free-living and particle-attached deep ocean viral communities

BACKGROUND

Viruses play important roles in the ocean's biogeochemical cycles. Yet, deep ocean viruses are one of the most under-explored fractions of the global biosphere. Little is known about the environmental factors that control the composition and functioning of their communities or how they interact with their free-living or particle-attached microbial hosts.

RESULTS

We analysed 58 viral communities associated with size-fractionated free-living (0.2-0.8 μm) and particle-attached (0.8-20 μm) cellular metagenomes from bathypelagic (2150-4018 m deep) microbiomes obtained during the Malaspina expedition. These metagenomes yielded 6631 viral sequences, 91% of which were novel, and 67 represented high-quality genomes. Taxonomic classification assigned 53% of the viral sequences to families of tailed viruses from the order Caudovirales. Computational host prediction associated 886 viral sequences to dominant members of the deep ocean microbiome, such as Alphaproteobacteria (284), Gammaproteobacteria (241), SAR324 (23), Marinisomatota (39), and Chloroflexota (61). Free-living and particle-attached viral communities had markedly distinct taxonomic composition, host prevalence, and auxiliary metabolic gene content, which led to the discovery of novel viral-encoded metabolic genes involved in the folate and nucleotide metabolisms. Water mass age emerged as an important factor driving viral community composition. We postulated this was due to changes in quality and concentration of dissolved organic matter acting on the host communities, leading to an increase of viral auxiliary metabolic genes associated with energy metabolism among older water masses.

CONCLUSIONS

These results shed light on the mechanisms by which environmental gradients of deep ocean ecosystems structure the composition and functioning of free-living and particle-attached viral communities. Video Abstract.

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.

Spatial and temporal metagenomics of river compartments reveals viral community dynamics in an urban impacted stream

Although river ecosystems comprise less than 1% of Earth's total non-glaciated area, they are critical modulators of microbially and virally orchestrated global biogeochemical cycles. However, most studies either use data that is not spatially resolved or is collected at timepoints that do not reflect the short life cycles of microorganisms. As a result, the relevance of microbiome interactions and the impacts they have over time on biogeochemical cycles are poorly understood. To assess how viral and microbial communities change over time, we sampled surface water and pore water compartments of the wastewater-impacted River Erpe in Germany every 3 hours over a 48-hour period resulting in 32 metagenomes paired to geochemical and metabolite measurements. We reconstructed 6,500 viral and 1,033 microbial genomes and found distinct communities associated with each river compartment. We show that 17% of our vMAGs clustered to viruses from other ecosystems like wastewater treatment plants and rivers. Our results also indicated that 70% of the viral community was persistent in surface waters, whereas only 13% were persistent in the pore waters taken from the hyporheic zone. Finally, we predicted linkages between 73 viral genomes and 38 microbial genomes. These putatively linked hosts included members of the Competibacteraceae, which we suggest are potential contributors to carbon and nitrogen cycling. Together, these findings demonstrate that microbial and viral communities in surface waters of this urban river can exist as stable communities along a flowing river; and raise important considerations for ecosystem models attempting to constrain dynamics of river biogeochemical cycles.

IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata

Viruses are widely recognized as critical members of all microbiomes. Metagenomics enables large-scale exploration of the global virosphere, progressively revealing the extensive genomic diversity of viruses on Earth and highlighting the myriad of ways by which viruses impact biological processes. IMG/VR provides access to the largest collection of viral sequences obtained from (meta)genomes, along with functional annotation and rich metadata. A web interface enables users to efficiently browse and search viruses based on genome features and/or sequence similarity. Here, we present the fourth version of IMG/VR, composed of >15 million virus genomes and genome fragments, a ≈6-fold increase in size compared to the previous version. These clustered into 8.7 million viral operational taxonomic units, including 231 408 with at least one high-quality representative. Viral sequences in IMG/VR are now systematically identified from genomes, metagenomes, and metatranscriptomes using a new detection approach (geNomad), and IMG standard annotation are complemented with genome quality estimation using CheckV, taxonomic classification reflecting the latest taxonomic standards, and microbial host taxonomy prediction. IMG/VR v4 is available at https://img.jgi.doe.gov/vr, and the underlying data are available to download at https://genome.jgi.doe.gov/portal/IMG_VR.

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

BACKGROUND

Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.

RESULTS

We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.

CONCLUSIONS

The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.

AVAILABILITY AND IMPLEMENTATION

The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Three families of Asgard archaeal viruses identified in metagenome-assembled genomes

Asgardarchaeota harbour many eukaryotic signature proteins and are widely considered to represent the closest archaeal relatives of eukaryotes. Whether similarities between Asgard archaea and eukaryotes extend to their viromes remains unknown. Here we present 20 metagenome-assembled genomes of Asgardarchaeota from deep-sea sediments of the basin off the Shimokita Peninsula, Japan. By combining a CRISPR spacer search of metagenomic sequences with phylogenomic analysis, we identify three family-level groups of viruses associated with Asgard archaea. The first group, verdandiviruses, includes tailed viruses of the class Caudoviricetes (realm Duplodnaviria); the second, skuldviruses, consists of viruses with predicted icosahedral capsids of the realm Varidnaviria; and the third group, wyrdviruses, is related to spindle-shaped viruses previously identified in other archaea. More than 90% of the proteins encoded by these viruses of Asgard archaea show no sequence similarity to proteins encoded by other known viruses. Nevertheless, all three proposed families consist of viruses typical of prokaryotes, providing no indication of specific evolutionary relationships between viruses infecting Asgard archaea and eukaryotes. Verdandiviruses and skuldviruses are likely to be lytic, whereas wyrdviruses potentially establish chronic infection and are released without host cell lysis. All three groups of viruses are predicted to play important roles in controlling Asgard archaea populations in deep-sea ecosystems.

CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model

Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus–prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37%. In addition, CHERRY’s performance on short contigs is more stable than other tools.