MVP: a microbe-phage interaction database

Prophage-DB: A comprehensive database to explore diversity, distribution, and ecology of prophages

Background

Viruses that infect prokaryotes (phages) constitute the most abundant group of biological agents, playing pivotal roles in microbial systems. They are known to impact microbial community dynamics, microbial ecology, and evolution. Efforts to document the diversity, host range, infection dynamics, and effects of bacteriophage infection on host cell metabolism are extremely underexplored. Phages are classified as virulent or temperate based on their life cycles. Temperate phages adopt the lysogenic mode of infection, where the genome integrates into the host cell genome forming a prophage. Prophages enable viral genome replication without host cell lysis, and often contribute novel and beneficial traits to the host genome. Current phage research predominantly focuses on lytic phages, leaving a significant gap in knowledge regarding prophages, including their biology, diversity, and ecological roles.

Results

Here we develop and describe Prophage-DB, a database of prophages, their proteins, and associated metadata that will serve as a resource for viral genomics and microbial ecology. To create the database, we identified and characterized prophages from genomes in three of the largest publicly available databases. We applied several state-of-the-art tools in our pipeline to annotate these viruses, cluster and taxonomically classify them, and detect their respective auxiliary metabolic genes. In total, we identify and characterize over 350,000 prophages and 35,000 auxiliary metabolic genes. Our prophage database is highly representative based on statistical results and contains prophages from a diverse set of archaeal and bacterial hosts which show a wide environmental distribution.

Conclusion

Prophages are particularly overlooked in viral ecology and merit increased attention due to their vital implications for microbiomes and their hosts. Here, we created Prophage-DB to advance our comprehension of prophages in microbiomes through a comprehensive characterization of prophages in publicly available genomes. We propose that Prophage-DB will serve as a valuable resource for advancing phage research, offering insights into viral taxonomy, host relationships, auxiliary metabolic genes, and environmental distribution.

Precise microbiome engineering using natural and synthetic bacteriophages targeting an artificial bacterial consortium

In natural microbiomes, microorganisms interact with each other and exhibit diverse functions. Microbiome engineering, which enables bacterial knockdown, is a promising method to elucidate the functions of targeted bacteria in microbiomes. However, few methods to selectively kill target microorganisms in the microbiome without affecting the growth of nontarget microorganisms are available. In this study, we focused on the host-specific lytic ability of virulent phages and validated their potency for precise microbiome engineering. In an artificial microbiome consisting of Escherichia coli, Pseudomonas putida, Bacillus subtilis, and Lactiplantibacillus plantarum, the addition of bacteriophages infecting their respective host strains specifically reduced the number of these bacteria more than 102 orders. Remarkably, the reduction in target bacteria did not affect the growth of nontarget bacteria, indicating that bacteriophages were effective tools for precise microbiome engineering. Moreover, a virulent derivative of the λ phage was synthesized from prophage DNA in the genome of λ lysogen by in vivo DNA assembly and phage-rebooting techniques, and E. coli-targeted microbiome engineering was achieved. These results propose a novel approach for precise microbiome engineering using bacteriophages, in which virulent phages are synthesized from prophage DNA in lysogenic strains without isolating phages from environmental samples.

A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments

BACKGROUND

Ruminants are important livestock animals that have a unique digestive system comprising multiple stomach compartments. Despite significant progress in the study of microbiome in the gastrointestinal tract (GIT) sites of ruminants, we still lack an understanding of the viral community of ruminants. Here, we surveyed its viral ecology using 2333 samples from 10 sites along the GIT of 8 ruminant species.

RESULTS

We present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the GITs of ruminants including 64,922 non-redundant phage genomes. We characterized the distributions of the phage genomes in different ruminants and GIT sites and found that most phages were organism-specific. We revealed that ~ 60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phages, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic archaea.

CONCLUSIONS

The URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Phages have great potential for controlling pathogenic bacterial/archaeal species and reducing methane emissions. Our findings provide insights into the virome ecology research of the ruminant GIT and offer a starting point for future research on phage therapy in ruminants. Video Abstract.

Efficient Recovery of Complete Gut Viral Genomes by Combined Short- and Long-Read Sequencing

Current metagenome assembled human gut phage catalogs contained mostly fragmented genomes. Here, comprehensive gut virome detection procedure is developed involving virus-like particle (VLP) enrichment from ≈500 g feces and combined sequencing of short- and long-read. Applied to 135 samples, a Chinese Gut Virome Catalog (CHGV) is assembled consisting of 21,499 non-redundant viral operational taxonomic units (vOTUs) that are significantly longer than those obtained by short-read sequencing and contained ≈35% (7675) complete genomes, which is ≈nine times more than those in the Gut Virome Database (GVD, ≈4%, 1,443). Interestingly, the majority (≈60%, 13,356) of the CHGV vOTUs are obtained by either long-read or hybrid assemblies, with little overlap with those assembled from only the short-read data. With this dataset, vast diversity of the gut virome is elucidated, including the identification of 32% (6,962) novel vOTUs compare to public gut virome databases, dozens of phages that are more prevalent than the crAssphages and/or Gubaphages, and several viral clades that are more diverse than the two. Finally, the functional capacities are also characterized of the CHGV encoded proteins and constructed a viral-host interaction network to facilitate future research and applications.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

ESKtides: a comprehensive database and mining method for ESKAPE phage-derived antimicrobial peptides

'Superbugs' have received increasing attention from researchers, such as ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.), which directly led to about 1 270 000 death cases in 2019. Recently, phage peptidoglycan hydrolases (PGHs)-derived antimicrobial peptides were proposed as new antibacterial agents against multidrug-resistant bacteria. However, there is still a lack of methods for mining antimicrobial peptides based on phages or phage PGHs. Here, by using a collection of 6809 genomes of ESKAPE isolates and corresponding phages in public databases, based on a unified annotation process of all the genomes, PGHs were systematically identified, from which peptides were mined. As a result, a total of 12 067 248 peptides with high antibacterial activities were respectively determined. A user-friendly tool was developed to predict the phage PGHs-derived antimicrobial peptides from customized genomes, which also allows the calculation of peptide phylogeny, physicochemical properties, and secondary structure. Finally, a user-friendly and intuitive database, ESKtides (http://www.phageonehealth.cn:9000/ESKtides), was designed for data browsing, searching and downloading, which provides a rich peptide library based on ESKAPE prophages and phages. Database URL:  10.1093/database/baae022.

PhageScope: a well-annotated bacteriophage database with automatic analyses and visualizations

Bacteriophages are viruses that infect bacteria or archaea. Understanding the diverse and intricate genomic architectures of phages is essential to study microbial ecosystems and develop phage therapy strategies. However, the existing phage databases are short of meticulous annotations. To this end, we propose PhageScope (https://phagescope.deepomics.org), an online phage database with comprehensive annotations. PhageScope harbors a collection of 873 718 phage sequences from various sources. Applying fifteen state-of-the-art tools to perform systematic annotations and analyses, PhageScope provides annotations on genome completeness, host range, lifestyle information, taxonomy classification, nine types of structural and functional genetic elements, and three types of comparative genomic studies for curated phages. Additionally, PhageScope incorporates automatic analyses and visualizations for curated and customized phages, serving as an efficient platform for phage study.

The multi-kingdom microbiome of the goat gastrointestinal tract

BACKGROUND

Goat is an important livestock worldwide, which plays an indispensable role in human life by providing meat, milk, fiber, and pelts. Despite recent significant advances in microbiome studies, a comprehensive survey on the goat microbiomes covering gastrointestinal tract (GIT) sites, developmental stages, feeding styles, and geographical factors is still unavailable. Here, we surveyed its multi-kingdom microbial communities using 497 samples from ten sites along the goat GIT.

RESULTS

We reconstructed a goat multi-kingdom microbiome catalog (GMMC) including 4004 bacterial, 71 archaeal, and 7204 viral genomes and annotated over 4,817,256 non-redundant protein-coding genes. We revealed patterns of feeding-driven microbial community dynamics along the goat GIT sites which were likely associated with gastrointestinal food digestion and absorption capabilities and disease risks, and identified an abundance of large intestine-enriched genera involved in plant fiber digestion. We quantified the effects of various factors affecting the distribution and abundance of methane-producing microbes including the GIT site, age, feeding style, and geography, and identified 68 virulent viruses targeting the methane producers via a comprehensive virus-bacterium/archaea interaction network.

CONCLUSIONS

Together, our GMMC catalog provides functional insights of the goat GIT microbiota through microbiome-host interactions and paves the way to microbial interventions for better goat and eco-environmental qualities. Video Abstract.

Efficient screening of adsorbed receptors for Salmonella phage LP31 and identification of receptor-binding protein

The adsorption process is the first step in the lifecycle of phages and plays a decisive role in the entire infection process. Identifying the adsorption mechanism of phages not only makes phage therapy more precise and efficient but also enables the exploration of other potential applications and modifications of phages. Phage LP31 can lyse multiple Salmonella serotypes, efficiently clearing biofilms formed by Salmonella enterica serovar Enteritidis (S. Enteritidis) and significantly reducing the concentration of S. Enteritidis in chicken feces. Therefore, LP31 has great potential for many practical applications. In this study, we established an efficient screening method for phage infection-related genes and identified a total of 10 genes related to the adsorption process of phage LP31. After the construction of strain C50041ΔrfaL 58-358, it was found that the knockout strain had a rough phenotype as an O-antigen-deficient strain. Adsorption rate and transmission electron microscopy experiments showed that the receptor for phage LP31 was the O9 antigen of S. Enteritidis. Homology comparison and adsorption experiments confirmed that the tail fiber protein Lp35 of phage LP31 participated in the adsorption process as a receptor-binding protein. IMPORTANCE A full understanding of the interaction between phages and their receptors can help with the development of phage-related products. Phages like LP31 with the tail fiber protein Lp35, or a closely related protein, have been reported to effectively recognize and infect multiple Salmonella serotypes. However, the role of these proteins in phage infection has not been previously described. In this study, we established an efficient screening method to detect phage adsorption to host receptors. We found that phage LP31 can utilize its tail fiber protein Lp35 to adsorb to the O9 antigen of S. Enteritidis, initiating the infection process. This study provides a great model system for further studies of how a phage-encoded receptor-binding protein (RBP) interacts with its host's RBP binding target, and this new model offers opportunities for further theoretical and experimental studies to understand the infection mechanism of phages.

Long-Read Sequencing Reveals Extensive DNA Methylations in Human Gut Phagenome Contributed by Prevalently Phage-Encoded Methyltransferases

DNA methylation plays a crucial role in the survival of bacteriophages (phages), yet the understanding of their genome methylation remains limited. In this study, DNA methylation patterns are analyzed in 8848 metagenome-assembled high-quality phages from 104 fecal samples using single-molecule real-time sequencing. The results demonstrate that 97.60% of gut phages exhibit methylation, with certain factors correlating with methylation densities. Phages with higher methylation densities appear to have potential viability advantages. Strikingly, more than one-third of the phages possess their own DNA methyltransferases (MTases). Increased MTase copies are associated with higher genome methylation densities, specific methylation motifs, and elevated prevalence of certain phage groups. Notably, the majority of these MTases share close homology with those encoded by gut bacteria, suggesting their exchange during phage-bacterium interactions. Furthermore, these MTases can be employed to accurately predict phage-host relationships. Overall, the findings indicate the widespread utilization of DNA methylation by gut DNA phages as an evasion mechanism against host defense systems, with a substantial contribution from phage-encoded MTases.

Protein embeddings improve phage-host interaction prediction

With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.

GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information

Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/.

Enrichment Culture but Not Metagenomic Sequencing Identified a Highly Prevalent Phage Infecting Lactiplantibacillus plantarum in Human Feces

Lactiplantibacillus plantarum (previously known as Lactobacillus plantarum) is increasingly used as a probiotic to treat human diseases, but its phages in the human gut remain unexplored. Here, we report its first gut phage, Gut-P1, which we systematically screened using metagenomic sequencing, virus-like particle (VLP) sequencing, and enrichment culture from 35 fecal samples. Gut-P1 is virulent, belongs to the Douglaswolinvirus genus, and is highly prevalent in the gut (~11% prevalence); it has a genome of 79,928 bp consisting of 125 protein coding genes and displaying low sequence similarities to public L. plantarum phages. Physiochemical characterization shows that it has a short latent period and adapts to broad ranges of temperatures and pHs. Furthermore, Gut-P1 strongly inhibits the growth of L. plantarum strains at a multiplicity of infection (MOI) of 1e-6. Together, these results indicate that Gut-P1 can greatly impede the application of L. plantarum in humans. Strikingly, Gut-P1 was identified only in the enrichment culture, not in our metagenomic or VLP sequencing data nor in any public human phage databases, indicating the inefficiency of bulk sequencing in recovering low-abundance but highly prevalent phages and pointing to the unexplored hidden diversity of the human gut virome despite recent large-scale sequencing and bioinformatics efforts. IMPORTANCE As Lactiplantibacillus plantarum (previously known as Lactobacillus plantarum) is increasingly used as a probiotic to treat human gut-related diseases, its bacteriophages may pose a certain threat to their further application and should be identified and characterized more often from the human intestine. Here, we isolated and identified the first gut L. plantarum phage that is prevalent in a Chinese population. This phage, Gut-P1, is virulent and can strongly inhibit the growth of multiple L. plantarum strains at low MOIs. Our results also show that bulk sequencing is inefficient at recovering low-abundance but highly prevalent phages such as Gut-P1, suggesting that the hidden diversity of human enteroviruses has not yet been explored. Our results call for innovative approaches to isolate and identify intestinal phages from the human gut and to rethink our current understanding of the enterovirus, particularly its underestimated diversity and overestimated individual specificity.

Advances in the field of phage-based therapy with special emphasis on computational resources

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

Mr.Vc v2: An updated version of database with increased data of transcriptome and experimental validated interactions

Mr.Vc is a database of curated Vibrio cholerae transcriptome data and annotated information. The main objective is to facilitate the accessibility and reusability of the rapidly growing Vibrio cholerae omics data and relevant annotation. To achieve these goals, we performed manual curation on the transcriptome data and organized the datasets in an experiment-centric manner. We collected unknown operons annotated through text-mining analysis that would provide more clues about how Vibrio cholerae modulates gene regulation. Meanwhile, to understand the relationship between genes or experiments, we performed gene co-expression analysis and experiment-experiment correlation analysis. In additional, functional module named “Interactions” which dedicates to collecting experimentally validated interactions about Vibrio cholerae from public databases, MEDLINE documents and literature in life science journals. To date, Mr.Vc v2, which is significantly increased from the previous version, contains 107 microarray experiments, 106 RNA-seq experiments, and 3 Tn-seq projects, covering 56,839 entries of DEGs (Differentially Expressed Genes) from transcriptomes and 7,463 related genes from Tn-seq, respectively. and a total of 270,129 gene co-expression entries and 11,990 entries of experiment-experiment correlation was obtained, in total 1,316 entries of interactions were collected, including 496 protein-chemical signaling molecule interactions, 472 protein–protein interactions, 306 TF (Transcription Factor)-gene interactions and 42 Vibrio cholerae-virus interactions, most of which obtained from 402 literature through text-mining analysis. To make the information easier to access, Mr.Vc v2 is equipped with a search widget, enabling users to query what they are interested in. Mr.Vc v2 is freely available at http://mrvcv2.biownmc.info.

microbioTA: an atlas of the microbiome in multiple disease tissues of Homo sapiens and Mus musculus

microbioTA (http://bio-annotation.cn/microbiota) was constructed to provide a comprehensive, user-friendly resource for the application of microbiome data from diseased tissues, helping users improve their general knowledge and deep understanding of tissue-derived microbes. Various microbes have been found to colonize cancer tissues and play important roles in cancer diagnoses and outcomes, with many studies focusing on developing better cancer-related microbiome data. However, there are currently no independent, comprehensive open resources cataloguing cancer-related microbiome data, which limits the exploration of the relationship between these microbes and cancer progression. Given this, we propose a new strategy to re-align the existing next-generation sequencing data to facilitate the mining of hidden sequence data describing the microbiome to maximize available resources. To this end, we collected 417 publicly available datasets from 25 human and 14 mouse tissues from the Gene Expression Omnibus database and use these to develop a novel pipeline to re-align microbiome sequences facilitating in-depth analyses designed to reveal the microbial profile of various cancer tissues and their healthy controls. microbioTA is a user-friendly online platform which allows users to browse, search, visualize, and download microbial abundance data from various tissues along with corresponding analysis results, aimimg at providing a reference for cancer-related microbiome research.

Revealing bacteria-phage interactions in human microbiome through the CRISPR-Cas immune systems

The human gut microbiome is composed of a diverse consortium of microorganisms. Relatively little is known about the diversity of the bacteriophage population and their interactions with microbial organisms in the human microbiome. Due to the persistent rivalry between microbial organisms (hosts) and phages (invaders), genetic traces of phages are found in the hosts' CRISPR-Cas adaptive immune system. Mobile genetic elements (MGEs) found in bacteria include genetic material from phage and plasmids, often resultant from invasion events. We developed a computational pipeline (BacMGEnet), which can be used for inference and exploratory analysis of putative interactions between microbial organisms and MGEs (phages and plasmids) and their interaction network. Given a collection of genomes as the input, BacMGEnet utilizes computational tools we have previously developed to characterize CRISPR-Cas systems in the genomes, which are then used to identify putative invaders from publicly available collections of phage/prophage sequences. In addition, BacMGEnet uses a greedy algorithm to summarize identified putative interactions to produce a bacteria-MGE network in a standard network format. Inferred networks can be utilized to assist further examination of the putative interactions and for discovery of interaction patterns. Here we apply the BacMGEnet pipeline to a few collections of genomic/metagenomic datasets to demonstrate its utilities. BacMGEnet revealed a complex interaction network of the Phocaeicola vulgatus pangenome with its phage invaders, and the modularity analysis of the resulted network suggested differential activities of the different P. vulgatus' CRISPR-Cas systems (Type I-C and Type II-C) against some phages. Analysis of the phage-bacteria interaction network of human gut microbiome revealed a mixture of phages with a broad host range (resulting in large modules with many bacteria and phages), and phages with narrow host range. We also showed that BacMGEnet can be used to infer phages that invade bacteria and their interactions in wound microbiome. We anticipate that BacMGEnet will become an important tool for studying the interactions between bacteria and their invaders for microbiome research.

An Engineered λ Phage Enables Enhanced and Strain-Specific Killing of Enterohemorrhagic Escherichia coli

Bacteriophages (phages) are ideal alternatives to traditional antimicrobial agents in a world where antimicrobial resistance (AMR) is emerging and spreading at an unprecedented speed. In addition, due to their narrow host ranges, phages are also ideal tools to modulate the gut microbiota in which alterations of specific bacterial strains underlie human diseases, while dysbiosis caused by broad-spectrum antibiotics can be harmful. Here, we engineered a lambda phage (Eλ) to target enterohemorrhagic Escherichia coli (EHEC) that causes a severe, sometimes lethal intestinal infection in humans. We enhanced the killing ability of the Eλ phage by incorporating a CRISPR-Cas3 system into the wild-type λ (wtλ) and the specificity by introducing multiple EHEC-targeting CRISPR spacers while knocking out the lytic gene cro. In vitro experiments showed that the Eλ suppressed the growth of EHEC up to 18 h compared with only 6 h with the wtλ; at the multiplicity of infection (MOI) of 10, the Eλ killed the EHEC cells with ~100% efficiency and did not affect the growth of other laboratory- and human-gut isolated E. coli strains. In addition, the EHEC cells did not develop resistance to the Eλ. Mouse experiments further confirmed the enhanced and strain-specific killing of the Eλ to EHEC, while the overall mouse gut microbiota was not disturbed. Our methods can be used to target other genes that are responsible for antibiotic resistance genes and/or human toxins, engineer other phages, and support in vivo application of the engineered phages. IMPORTANCE Pathogenic strains of Escherichia coli are responsible for 0.8 million deaths per year and together ranked the first among all pathogenic species. Here, we obtained, for the first time, an engineered phage, Eλ, that could specifically and efficiently eliminate EHEC, one of the most common and often lethal pathogens that can spread from person to person. We verified the superior performance of the Eλ over the wild-type phage with in vitro and in vivo experiments and showed that the Eλ could suppress EHEC growth to nondetectable levels, fully rescue the EHEC-infected mice, and rescore disturbed mouse gut microbiota. Our results also indicated that the EHEC did not develop resistance to the Eλ, which has been the biggest challenge in phage therapy. We believe our methods can be used to target other pathogenic strains of E. coli and support in vivo application of the engineered phages.

In silico analysis reveals the co-existence of CRISPR-Cas type I-F1 and type I-F2 systems and its association with restricted phage invasion in Acinetobacter baumannii

Introduction

Acinetobacter baumannii, an opportunistic pathogen, rapidly acquires antibiotic resistance, thus compelling researchers to develop alternative treatments at utmost priority. Phage-based therapies are of appreciable benefit; however, CRISPR-Cas systems are a major constraint in this approach. Hence for effective implementation and a promising future of phage-based therapies, a multifaceted understanding of the CRISPR-Cas systems is necessary.

Methods

This study investigated 4,977 RefSeq genomes of A. baumannii from the NCBI database to comprehend the distribution and association of CRISPR-Cas systems with genomic determinants.

Results

Approximately 13.84% (n = 689/4,977) isolates were found to carry the CRSIPR-Cas system, and a small fraction of isolates, 1.49% (n = 74/4,977), exhibited degenerated CRISPR-Cas systems. Of these CRISPR-Cas positive (+) isolates, 67.48% (465/689) isolates harbored type I-F1, 28.59% (197/689) had type I-F2, and 3.7% (26/689) had co-existence of both type I-F1 and type I-F2 systems. Co-existing type I-F1 and type I-F2 systems are located distantly (∼1.733 Mb). We found a strong association of CRISPR-Cas systems within STs for type I-F1 and type I-F2, whereas the type I-F1 + F2 was not confined to any particular ST. Isolates with type I-F1 + F2 exhibited a significantly high number of mean spacers (n = 164.58 ± 46.41) per isolate as compared to isolates with type I-F2 (n = 82.87 ± 36.14) and type I-F1 (n = 54.51 ± 26.27) with majority targeting the phages. Isolates with type I-F1 (p < 0.0001) and type I-F2 (p < 0.0115) displayed significantly larger genome sizes than type I-F1 + F2. A significantly reduced number of integrated phages in isolates with co-existence of type I-F1 + F2 compared with other counterparts was observed (p = 0.0041). In addition, the isolates carrying type I-F1 + F2 did not exhibit reduced resistance and virulence genes compared to CRISPR-Cas(–) and CRISPR-Cas (+) type I-F1 and type I-F2, except for bap, abaI, and abaR.

Conclusion

Our observation suggests that the co-existence of type I-F1 and F2 is more effective in constraining the horizontal gene transfer and phage invasion in A. baumannii than the isolates exhibiting only type I-F1 and only type I-F2 systems.

Diversity and dynamics of the CRISPR-Cas systems associated with Bacteroides fragilis in human population

Background

CRISPR-Cas (clustered regularly interspaced short palindromic repeats—CRISPR-associated proteins) systems are adaptive immune systems commonly found in prokaryotes that provide sequence-specific defense against invading mobile genetic elements (MGEs). The memory of these immunological encounters are stored in CRISPR arrays, where spacer sequences record the identity and history of past invaders. Analyzing such CRISPR arrays provide insights into the dynamics of CRISPR-Cas systems and the adaptation of their host bacteria to rapidly changing environments such as the human gut.

Results

In this study, we utilized 601 publicly available Bacteroides fragilis genome isolates from 12 healthy individuals, 6 of which include longitudinal observations, and 222 available B. fragilis reference genomes to update the understanding of B. fragilis CRISPR-Cas dynamics and their differential activities. Analysis of longitudinal genomic data showed that some CRISPR array structures remained relatively stable over time whereas others involved radical spacer acquisition during some periods, and diverse CRISPR arrays (associated with multiple isolates) co-existed in the same individuals with some persisted over time. Furthermore, features of CRISPR adaptation, evolution, and microdynamics were highlighted through an analysis of host-MGE network, such as modules of multiple MGEs and hosts, reflecting complex interactions between B. fragilis and its invaders mediated through the CRISPR-Cas systems.

Conclusions

We made available of all annotated CRISPR-Cas systems and their target MGEs, and their interaction network as a web resource at https://omics.informatics.indiana.edu/CRISPRone/Bfragilis. We anticipate it will become an important resource for studying of B. fragilis, its CRISPR-Cas systems, and its interaction with mobile genetic elements providing insights into evolutionary dynamics that may shape the species virulence and lead to its pathogenicity.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08770-8).

Daily Reports on Phage-Host Interactions

Understanding phage-host relationships is crucial for the study of virus biology and the application of phages in biotechnology and medicine. However, information concerning the range of hosts for bacterial and archaeal viruses is scattered across numerous databases and is difficult to obtain. Therefore, here we present PHD (Phage & Host Daily), a web application that offers a comprehensive, up-to-date catalog of known phage-host associations that allows users to select viruses targeting specific bacterial and archaeal taxa of interest. Our service combines the latest information on virus-host interactions from seven source databases with current taxonomic classification retrieved directly from the groups and institutions responsible for its maintenance. The web application also provides summary statistics on host and virus diversity, their pairwise interactions, and the host range of deposited phages. PHD is updated daily and available at http://phdaily.info or http://combio.pl/phdaily.

Short- and long-read metagenomics expand individualized structural variations in gut microbiomes

In-depth profiling of genetic variations in the gut microbiome is highly desired for understanding its functionality and impacts on host health and disease. Here, by harnessing the long read advantage provided by Oxford Nanopore Technology (ONT), we characterize fine-scale genetic variations of structural variations (SVs) in hundreds of gut microbiomes from healthy humans. ONT long reads dramatically improve the quality of metagenomic assemblies, enable reliable detection of a large, expanded set of structural variation types (notably including large insertions and inversions). We find SVs are highly distinct between individuals and stable within an individual, representing gut microbiome fingerprints that shape strain-level differentiations in function within species, complicating the associations to metabolites and host phenotypes such as blood glucose. In summary, our study strongly emphasizes that incorporating ONT reads into metagenomic analyses expands the detection scope of genetic variations, enables profiling strain-level variations in gut microbiome, and their intricate correlations with metabolome.

Here, Wang and colleagues combine short and long sequencing reads to characterize structural variations, prophage and CRISPR spacer elements in human gut microbiomes, and reveal functional differences at a finer level of bacterial strains.

PHISDetector: A Tool to Detect Diverse In Silico Phage-host Interaction Signals for Virome Studies

Phage-microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage-microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage-host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein-protein interaction signals. In the present study, we developed a novel tool phage-host interaction signal detector (PHISDetector) to predict phage-host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage-host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage-host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage-host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies.

Computational design of phage cocktails based on phage-bacteria infection networks

The misuse and overuse of antibiotics have boosted the proliferation of multidrug-resistant (MDR) bacteria, which are considered a major public health issue in the twenty-first century. Phage therapy may be a promising way in the treatment of infections caused by MDR pathogens, without the side effects of the current available antimicrobials. Phage therapy is based on phage cocktails, that is, combinations of phages able to lyse the target bacteria. In this work, we present and explain in detail two innovative computational methods to design phage cocktails taking into account a given phage-bacteria infection network. One of the methods (Exhaustive Search) always generates the best possible phage cocktail, while the other method (Network Metrics) always keeps a very reduced runtime (a few milliseconds). Both methods have been included in a Cytoscape application that is available for any user. A complete experimental study has been performed, evaluating and comparing the biological quality, runtime, and the impact when additional phages are included in the cocktail.

Comparative genomics of Chinese and international isolates of Escherichia albertii: population structure and evolution of virulence and antimicrobial resistance

Escherichia albertii is a recently recognized species in the genus Escherichia that causes diarrhoea. The population structure, genetic diversity and genomic features have not been fully examined. Here, 169 E. albertii isolates from different sources and regions in China were sequenced and combined with 312 publicly available genomes (from additional 14 countries) for genomic analyses. The E. albertii population was divided into two clades and eight lineages, with lineage 3 (L3), L5 and L8 more common in China. Clinical isolates were observed in all clades/lineages. Virulence genes were found to be distributed differently among lineages: subtypes of the intimin encoding gene eae and the cytolethal distending toxin gene cdtB were lineage associated, and the second type three secretion system (ETT2) island was truncated in L3 and L6. Seven new eae subtypes and one new cdtB subtype (cdtB-VI) were identified. Alarmingly, 85.9 % of the Chinese E. albertii isolates were predicted to be multidrug-resistant (MDR) with 35.9 % harbouring genes capable of conferring resistance to 10 to 14 different drug classes. The majority of the MDR isolates were of poultry source from China and belonged to four sequence types (STs) [ST4638, ST4479, ST4633 and ST4488]. Thirty-four plasmids with some carrying MDR and virulence genes, and 130 prophages were identified from 17 complete E. albertii genomes. The 130 intact prophages were clustered into five groups, with group five prophages harbouring more virulence genes. We further identified three E. albertii specific genes as markers for the identification of this species. Our findings provided fundamental insights into the population structure, virulence variation and drug resistance of E. albertii.

Comparison of CRISPR-Cas Immune Systems in Healthcare-Related Pathogens

The ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and Clostridium difficile have been identified as the leading global cause of multidrug-resistant bacterial infections in hospitals. CRISPR-Cas systems are bacterial immune systems, empowering the bacteria with defense against invasive mobile genetic elements that may carry the antimicrobial resistance (AMR) genes, among others. On the other hand, the CRISPR-Cas systems are themselves mobile. In this study, we annotated and compared the CRISPR-Cas systems in these pathogens, utilizing their publicly available large numbers of sequenced genomes (e.g., there are more than 12 thousands of S. aureus genomes). The presence of CRISPR-Cas systems showed a very broad spectrum in these pathogens: S. aureus has the least tendency of obtaining the CRISPR-Cas systems with only 0.55% of its isolates containing CRISPR-Cas systems, whereas isolates of C. difficile we analyzed have CRISPR-Cas systems each having multiple CRISPRs. Statistical tests show that CRISPR-Cas containing isolates tend to have more AMRs for four of the pathogens (A. baumannii, E. faecium, P. aeruginosa, and S. aureus). We made available all the annotated CRISPR-Cas systems in these pathogens with visualization at a website (https://omics.informatics.indiana.edu/CRISPRone/pathogen), which we believe will be an important resource for studying the pathogens and their arms-race with invaders mediated through the CRISPR-Cas systems, and for developing potential clinical applications of the CRISPR-Cas systems for battles against the antibiotic resistant pathogens.

mBodyMap: a curated database for microbes across human body and their associations with health and diseases

mBodyMap is a curated database for microbes across the human body and their associations with health and diseases. Its primary aim is to promote the reusability of human-associated metagenomic data and assist with the identification of disease-associated microbes by consistently annotating the microbial contents of collected samples using state-of-the-art toolsets and manually curating the meta-data of corresponding human hosts. mBodyMap organizes collected samples based on their association with human diseases and body sites to enable cross-dataset integration and comparison. To help users find microbes of interest and visualize and compare their distributions and abundances/prevalence within different body sites and various diseases, the mBodyMap database is equipped with an intuitive interface and extensive graphical representations of the collected data. So far, it contains a total of 63 148 runs, including 14 401 metagenomes and 48 747 amplicons related to health and 56 human diseases, from within 22 human body sites across 136 projects. Also available in the database are pre-computed abundances and prevalence of 6247 species (belonging to 1645 genera) stratified by body sites and diseases. mBodyMap can be accessed at: https://mbodymap.microbiome.cloud.

Taxonomy-aware, sequence similarity ranking reliably predicts phage-host relationships

Background

Characterizing phage–host interactions is critical to understanding the ecological role of both partners and effective isolation of phage therapeuticals. Unfortunately, experimental methods for studying these interactions are markedly slow, low-throughput, and unsuitable for phages or hosts difficult to maintain in laboratory conditions. Therefore, a number of in silico methods emerged to predict prokaryotic hosts based on viral sequences. One of the leading approaches is the application of the BLAST tool that searches for local similarities between viral and microbial genomes. However, this prediction method has three major limitations: (i) top-scoring sequences do not always point to the actual host; (ii) mosaic virus genomes may match to many, typically related, bacteria; and (iii) viral and host sequences may diverge beyond the point where their relationship can be detected by a BLAST alignment.

Results

We created an extension to BLAST, named Phirbo, that improves host prediction quality beyond what is obtainable from standard BLAST searches. The tool harnesses information concerning sequence similarity and bacteria relatedness to predict phage–host interactions. Phirbo was evaluated on three benchmark sets of known virus–host pairs, and it improved precision and recall by 11–40 percentage points over currently available, state-of-the-art, alignment-based, alignment-free, and machine-learning host prediction tools. Moreover, the discriminatory power of Phirbo for the recognition of virus–host relationships surpassed the results of other tools by at least 10 percentage points (area under the curve = 0.95), yielding a mean host prediction accuracy of 57% and 68% at the genus and family levels, respectively, and drops by 12 percentage points when using only a fraction of viral genome sequences (3 kb). Finally, we provide insights into a repertoire of protein and ncRNA genes that are shared between phages and hosts and may be prone to horizontal transfer during infection.

Conclusions

Our results suggest that Phirbo is a simple and effective tool for predicting phage–host relationships.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01146-6.

PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion

Phage therapy has become one of the most promising alternatives to antibiotics in the treatment of bacterial diseases, and identifying phage-host interactions (PHIs) helps to understand the possible mechanism through which a phage infects bacteria to guide the development of phage therapy. Compared with wet experiments, computational methods of identifying PHIs can reduce costs and save time and are more effective and economic. In this paper, we propose a PHI prediction method with a generative adversarial network (GAN)-based data augmentation and sequence-based feature fusion (PHIAF). First, PHIAF applies a GAN-based data augmentation module, which generates pseudo PHIs to alleviate the data scarcity. Second, PHIAF fuses the features originated from DNA and protein sequences for better performance. Third, PHIAF utilizes an attention mechanism to consider different contributions of DNA/protein sequence-derived features, which also provides interpretability of the prediction model. In computational experiments, PHIAF outperforms other state-of-the-art PHI prediction methods when evaluated via 5-fold cross-validation (AUC and AUPR are 0.88 and 0.86, respectively). An ablation study shows that data augmentation, feature fusion and an attention mechanism are all beneficial to improve the prediction performance of PHIAF. Additionally, four new PHIs with the highest PHIAF score in the case study were verified by recent literature. In conclusion, PHIAF is a promising tool to accelerate the exploration of phage therapy.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Viral Metagenome-Based Precision Surveillance of Pig Population at Large Scale Reveals Viromic Signatures of Sample Types and Influence of Farming Management on Pig Virome

Pigs are a major meat source worldwide and a pillar of Chinese animal husbandry; hence, their health and safety are a prioritized concern of the national economy. Although pig viruses have been continuously investigated, the full extent of the pig virome has remained unknown and emerging viruses are still a major threat to the pig industry. Here, we report a comprehensive study to delineate the pig virome of 1,841 healthy weaned pigs from 45 commercial farms collected from 25 major pig-producing regions across China. A viromic sequence data set, named Pigs_VIRES, which matched 96,586 viral genes from at least 249 genera within 66 families and which almost tripled the number of previously published pig viromic genes, was established. The majority of the mammalian viruses were closely related to currently known ones. A comparison with previously published viromes of bovines, avians, and humans has revealed the distinct composition of Pigs_VIRES, which has provided characteristic viromic signatures of serum, pharyngeal, and anal samples that were significantly influenced by farming management and disease control measures. Taken together, Pigs_VIRES has revealed the most complete viromic data set of healthy pigs to date. The compiled data also provide useful guidance to pig viral disease control and prevention and the biosafety management of pig farms. Especially, the established viromic protocol has created a precision surveillance strategy to potentially innovate currently used surveillance methods of animal infectious diseases, particularly by making precision surveillance available to other animal species on a large scale or even during a nationwide surveillance campaign.

IMPORTANCE Pigs are deeply involved in human lives; hence, their viruses are associated with public health. Here, we established the most comprehensive virome of healthy piglets to date, which provides a viromic baseline of weaned pigs for disease prevention and control, highlighting that longitudinal viromic monitoring is needed to better understand the dynamics of the virome in pig development and disease occurrence. The present study also shows how high standards of animal farm management with strict biosafety measures can significantly minimize the risk of introduction of pathogenic viruses into pig farms. Particularly, the viromic strategy established, i.e., high-throughput detection and analyses of various known and unknown pathogenic viruses in a single test at large scale, has completely innovated current surveillance measures in provision of timely and precise detection of all potentially existing pathogenic viruses and can be widely applied in other animal species.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

mMGE: a database for human metagenomic extrachromosomal mobile genetic elements

Extrachromosomal mobile genetic elements (eMGEs), including phages and plasmids, that can move across different microbes, play important roles in genome evolution and shaping the structure of microbial communities. However, we still know very little about eMGEs, especially their abundances, distributions and putative functions in microbiomes. Thus, a comprehensive description of eMGEs is of great utility. Here we present mMGE, a comprehensive catalog of 517 251 non-redundant eMGEs, including 92 492 plasmids and 424 759 phages, derived from diverse body sites of 66 425 human metagenomic samples. About half the eMGEs could be further grouped into 70 074 clusters using relaxed criteria (referred as to eMGE clusters below). We provide extensive annotations of the identified eMGEs including sequence characteristics, taxonomy affiliation, gene contents and their prokaryotic hosts. We also calculate the prevalence, both within and across samples for each eMGE and eMGE cluster, enabling users to see putative associations of eMGEs with human phenotypes or their distribution preferences. All eMGE records can be browsed or queried in multiple ways, such as eMGE clusters, metagenomic samples and associated hosts. The mMGE is equipped with a user-friendly interface and a BLAST server, facilitating easy access/queries to all its contents easily. mMGE is freely available for academic use at: https://mgedb.comp-sysbio.org.

IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses

Viruses are integral components of all ecosystems and microbiomes on Earth. Through pervasive infections of their cellular hosts, viruses can reshape microbial community structure and drive global nutrient cycling. Over the past decade, viral sequences identified from genomes and metagenomes have provided an unprecedented view of viral genome diversity in nature. Since 2016, the IMG/VR database has provided access to the largest collection of viral sequences obtained from (meta)genomes. Here, we present the third version of IMG/VR, composed of 18 373 cultivated and 2 314 329 uncultivated viral genomes (UViGs), nearly tripling the total number of sequences compared to the previous version. These clustered into 935 362 viral Operational Taxonomic Units (vOTUs), including 188 930 with two or more members. UViGs in IMG/VR are now reported as single viral contigs, integrated proviruses or genome bins, and are annotated with a new standardized pipeline including genome quality estimation using CheckV, taxonomic classification reflecting the latest ICTV update, and expanded host taxonomy prediction. The new IMG/VR interface enables users to efficiently browse, search, and select UViGs based on genome features and/or sequence similarity. IMG/VR v3 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.

Ecological Structuring of Temperate Bacteriophages in the Inflammatory Bowel Disease-Affected Gut

The aim of this study was to elucidate the ecological structure of the human gut temperate bacteriophage community and its role in inflammatory bowel disease (IBD). Temperate bacteriophages make up a large proportion of the human gut microbiota and are likely to play a role in IBD pathogenesis. However, many of these bacteriophages await characterization in reference databases. Therefore, we conducted a large-scale reconstruction of temperate bacteriophage and bacterial genomes from the whole-metagenome sequence data generated by the IBD Multi’omics Database project. By associating phages with their hosts via genome comparisons, we found that temperate bacteriophages infect a phylogenetically wide range of bacteria. The majority of variance in bacteriophage community composition was explained by variation among individuals, but differences in the abundance of temperate bacteriophages were identified between IBD and non-IBD patients. Of note, in active ulcerative colitis patients, temperate bacteriophages infecting Bacteroides uniformis and Bacteroides thetaiotaomicron—two species experimentally proven to be beneficial to gut homeostasis—were over-represented, whereas their hosts were under-represented in comparison with non-IBD patients. Supporting the mounting evidence that gut viral community plays a vital role in IBD, our results show potential association between temperate bacteriophages and IBD pathogenesis.

Challenges of Studying the Human Virome - Relevant Emerging Technologies

In this review we provide an overview of current challenges and advances in bacteriophage research within the growing field of viromics. In particular, we discuss, from a human virome study perspective, the current and emerging technologies available, their limitations in terms of de novo discoveries, and possible solutions to overcome present experimental and computational biases associated with low abundance of viral DNA or RNA. We summarize recent breakthroughs in metagenomics assembling tools and single-cell analysis, which have the potential to increase our understanding of phage biology, diversity, and interactions with both the microbial community and the human body. We expect that these recent and future advances in the field of viromics will have a strong impact on how we develop phage-based therapeutic approaches.

Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections.

Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting.

Predicting host taxonomic information from viral genomes: A comparison of feature representations

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus on nucleotide features, ignoring other representations of genomic information. Here we investigate the predictive potential of features generated from four different ‘levels’ of viral genome representation: nucleotide, amino acid, amino acid properties and protein domains. This more fully exploits the biological information present in the virus genomes. Over a hundred and eighty binary datasets for infecting versus non-infecting viruses at all taxonomic ranks of both eukaryote and prokaryote hosts were compiled. The viral genomes were converted into the four different levels of genome representation and twenty feature sets were generated by extracting k-mer compositions and predicted protein domains. We trained and tested Support Vector Machine, SVM, classifiers to compare the predictive capacity of each of these feature sets for each dataset. Our results show that all levels of genome representation are consistently predictive of host taxonomy and that prediction k-mer composition improves with increasing k-mer length for all k-mer based features. Using a phylogenetically aware holdout method, we demonstrate that the predictive feature sets contain signals reflecting both the evolutionary relationship between the viruses infecting related hosts, and host-mimicry. Our results demonstrate that incorporating a range of complementary features, generated purely from virus genome sequences, leads to improved accuracy for a range of virus host prediction tasks enabling computational assignment of host taxonomic information.

Elucidating the host of a newly identified virus species is an important challenge, with applications from knowing the source species of a newly emerged pathogen to understanding the bacteriophage-host relationships within the microbiome of any of earth’s ecosystems. Current high throughput methods used to identify viruses within biological or environmental samples have resulted in an unprecedented increase in virus discovery. However, for the majority of these virus genomes the host species/taxonomic classification remains unknown. To address this gap in our knowledge there is a need for fast, accurate computational methods for the assignment of putative host taxonomic information. Machine learning is an ideal approach but to maximise predictive accuracy the viral genomes need to be represented in a format (sets of features) that makes the discriminative information available to the machine learning algorithm. Here, we compare different types of features derived from the same viral genomes for their ability to predict host information. Our results demonstrate that all these feature sets are predictive of host taxonomy and when combined have the potential to improve accuracy over the use of individual feature sets across many virus host prediction applications.

GMrepo: a database of curated and consistently annotated human gut metagenomes

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data. This is achieved by consistently annotating the microbial contents of collected samples using state-of-art toolsets and by manual curation of the meta-data of the corresponding human hosts. GMrepo organizes the collected samples according to their associated phenotypes and includes all possible related meta-data such as age, sex, country, body-mass-index (BMI) and recent antibiotics usage. To make relevant information easier to access, GMrepo is equipped with a graphical query builder, enabling users to make customized, complex and biologically relevant queries. For example, to find (1) samples from healthy individuals of 18 to 25 years old with BMIs between 18.5 and 24.9, or (2) projects that are related to colorectal neoplasms, with each containing >100 samples and both patients and healthy controls. Precomputed species/genus relative abundances, prevalence within and across phenotypes, and pairwise co-occurrence information are all available at the website and accessible through programmable interfaces. So far, GMrepo contains 58 903 human gut samples/runs (including 17 618 metagenomes and 41 285 amplicons) from 253 projects concerning 92 phenotypes. GMrepo is freely available at: https://gmrepo.humangut.info.

Genomic analysis of 40 prophages located in the genomes of 16 carbapenemase-producing clinical strains of Klebsiella pneumoniae

Klebsiella pneumoniae is the clinically most important species within the genus Klebsiella and, as a result of the continuous emergence of multi-drug resistant (MDR) strains, the cause of severe nosocomial infections. The decline in the effectiveness of antibiotic treatments for infections caused by MDR bacteria has generated particular interest in the study of bacteriophages. In this study, we characterized a total of 40 temperate bacteriophages (prophages) with a genome range of 11.454-84.199 kb, predicted from 16 carbapenemase-producing clinical strains of K. pneumoniae belonging to different sequence types, previously identified by multilocus sequence typing. These prophages were grouped into the three families in the order Caudovirales (27 prophages belonging to the family Myoviridae, 10 prophages belonging to the family Siphoviridae and 3 prophages belonging to the family Podoviridae). Genomic comparison of the 40 prophage genomes led to the identification of four prophages isolated from different strains and of genome sizes of around 33.3, 36.1, 39.6 and 42.6 kb. These prophages showed sequence similarities (query cover >90 %, identity >99.9 %) with international Microbe Versus Phage (MVP) (http://mvp.medgenius.info/home) clusters 4762, 4901, 3499 and 4280, respectively. Phylogenetic analysis revealed the evolutionary proximity among the members of the four groups of the most frequently identified prophages in the bacterial genomes studied (33.3, 36.1, 39.6 and 42.6 kb), with bootstrap values of 100 %. This allowed the prophages to be classified into three clusters: A, B and C. Interestingly, these temperate bacteriophages did not infect the highest number of strains as indicated by a host-range assay, these results could be explained by the development of superinfection exclusion mechanisms. In addition, bioinformatic analysis of the 40 identified prophages revealed the presence of 2363 proteins. In total, 59.7 % of the proteins identified had a predicted function, mainly involving viral structure, transcription, replication and regulation (lysogenic/lysis). Interestingly, some proteins had putative functions associated with bacterial virulence (toxin expression and efflux pump regulators), phage defence profiles such as toxin-antitoxin modules, an anti-CRISPR/Cas9 protein, TerB protein (from terZABCDE operon) and methyltransferase proteins.

Prokaryotic Genome Expansion Is Facilitated by Phages and Plasmids but Impaired by CRISPR

Viruses and plasmids can introduce novel DNA into bacterial cells, thereby creating an opportunity for genome expansion; conversely, CRISPR, the prokaryotic adaptive immune system, which targets and eliminates foreign DNAs, may impair genome expansions. Recent studies presented conflicting results over the impact of CRISPR on genome expansion. In this study, we constructed a comprehensive dataset of prokaryotic genomes and identified their associations with viruses and plasmids. We found that genomes associated with viruses and/or plasmids were significantly larger than those without, indicating that both viruses and plasmids contribute to genome expansion. Genomes were increasingly larger with increasing numbers of associated viruses or plasmids. Conversely, genomes with CRISPR systems were significantly smaller than those without, indicating that CRISPR has a negative impact on genome size. These results confirmed that on evolutionary timescales, viruses and plasmids facilitate genome expansion, while CRISPR impairs such a process in prokaryotes. Furthermore, our results also revealed that CRISPR systems show a preference for targeting viruses over plasmids.

Metagenomic Insights Into the Taxonomic and Functional Features of Kinema, a Traditional Fermented Soybean Product of Sikkim Himalaya

Kinema is an ethnic, naturally fermented soybean product consumed in the Sikkim Himalayan region of India. In the present study, the whole metagenome sequencing approach was adopted to examine the microbial diversity and related functional potential of Kinema, consumed in different seasons. Firmicutes was the abundant phylum in Kinema, ranging from 82.31 to 93.99% in different seasons, followed by Actinobacteria and Proteobacteria. At the species level, the prevalent microorganisms were Bacillus subtilis, Bacillus amyloliquefaciens, Bacillus licheniformis, Corynebacterium glutamicum, Bacillus pumilus, and Lactococcus lactis. The abundance of microbial species varied significantly in different seasons. Further, the genomic presence of some undesirable microbes like Bacillus cereus, Proteus mirabilis, Staphylococcus aureus, Proteus penneri, Enterococcus faecalis, and Staphylococcus saprophyticus, were also detected in the specific season. The metagenomic analysis also revealed the existence of bacteriophages belonging to the family Siphoviridae, Myoviridae, and Podoviridae. Examination of the metabolic potential of the Kinema metagenome depicted information about the biocatalysts, presumably involved in the transformation of protein and carbohydrate polymers into bioactive molecules of health-beneficial effects. The genomic resource of several desirable enzymes was identified, such as β-galactosidase, β-glucosidase, β-xylosidase, and glutamate decarboxylase, etc. The catalytic function of a novel glutamate decarboxylase gene was validated for the biosynthesis of γ-aminobutyric acid (GABA). The results of the present study highlight the microbial and genomic resources associated with Kinema, and its importance in functional food industry.

Comparative genomics groups phages of Negativicutes and classical Firmicutes despite different Gram-staining properties

Negativicutes are gram-negative bacteria characterized by two cell membranes, but they are phylogenetically a side-branch of gram-positive Firmicutes that contain only a single membrane. We asked whether viruses (phages) infecting Negativicutes were horizontally acquired from gram-negative Proteobacteria, given the shared outer cell structure of their bacterial hosts, or if Negativicute phages co-evolved vertically with their hosts and thus resemble gram-positive Firmicute prophages. We predicted and characterized 485 prophages (mostly Caudovirales) from gram-negative Firmicute genomes plus 2977 prophages from other bacterial clades, and we used virome sequence data from 183 human stool samples to support our predictions. The majority of identified Negativicute prophages were lambdoids closer related to prophages from other Firmicutes than Proteobacteria by sequence relationship and genome organization (position of the lysis module). Only a single Mu-like candidate prophage and no clear P2-like prophages were identified in Negativicutes, both common in Proteobacteria. Given this collective evidence, it is unlikely that Negativicute phages were acquired from Proteobacteria. Sequence-related prophages, which occasionally harboured antibiotic resistance genes, were identified in two distinct Negativicute orders (Veillonellales and Acidaminococcales), possibly suggesting horizontal cross-order phage infection between human gut commensals. Our results reveal ancient genomic signatures of phage and bacteria co-evolution despite horizontal phage mobilization.

Virome Diversity Correlates with Intestinal Microbiome Diversity in Adult Monozygotic Twins

The virome is one of the most variable components of the human gut microbiome. Within twin pairs, viromes have been shown to be similar for infants, but not for adults, indicating that as twins age and their environments and microbiomes diverge, so do their viromes. The degree to which the microbiome drives the vast virome diversity is unclear. Here, we examine the relationship between microbiome and virome diversity in 21 adult monozygotic twin pairs selected for high or low microbiome concordance. Viromes derived from virus-like particles are unique to each individual, are dominated by Caudovirales and Microviridae, and exhibit a small core that includes crAssphage. Microbiome-discordant twins display more dissimilar viromes compared to microbiome-concordant twins, and the richer the microbiomes, the richer the viromes. These patterns are driven by bacteriophages, not eukaryotic viruses. Collectively, these observations support a strong role of the microbiome in patterning for the virome.

Minimum Information about an Uncultivated Virus Genome (MIUViG)

We present an extension of the Minimum Information about any (x) Sequence (MIxS) standard for reporting sequences of uncultivated virus genomes. Minimum Information about an Uncultivated Virus Genome (MIUViG) standards were developed within the Genomic Standards Consortium framework and include virus origin, genome quality, genome annotation, taxonomic classification, biogeographic distribution and in silico host prediction. Community-wide adoption of MIUViG standards, which complement the Minimum Information about a Single Amplified Genome (MISAG) and Metagenome-Assembled Genome (MIMAG) standards for uncultivated bacteria and archaea, will improve the reporting of uncultivated virus genomes in public databases. In turn, this should enable more robust comparative studies and a systematic exploration of the global virosphere.

Investigation of recombination-intense viral groups and their genes in the Earth's virome

Bacteriophages (phages), or bacterial viruses, are the most abundant and diverse biological entities that impact the global ecosystem. Recent advances in metagenomics have revealed their rampant abundance in the biosphere. A fundamental aspect of bacteriophages that remains unexplored in metagenomic data is the process of recombination as a driving force in evolution that occurs among different viruses within the same bacterial host. Here, we systematically examined signatures of recombination in every gene from 211 species-level viral groups in a recently obtained dataset of the Earth’s virome that contain corresponding information on the host bacterial species. Our study revealed that signatures of recombination are widespread (84%) among the diverse viral groups. We identified 25 recombination-intense viral groups, widely distributed across the viral taxonomy, and present in bacterial species living in the human oral cavity. We also revealed a significant inverse association between the recombination-intense viral groups and Type II restriction endonucleases, that could be effective in reducing recombination among phages in a cell. Furthermore, we identified recombination-intense genes that are significantly enriched for encoding phage morphogenesis proteins. Changes in the viral genomic sequence by recombination may be important to escape cleavage by the host bacterial immune systems.

The 2018 Nucleic Acids Research database issue and the online molecular biology database collection

The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.