Computational Tools for the Analysis of Uncultivated Phage Genomes

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.

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods

Understanding the ecological impacts of viruses on natural and engineered ecosystems relies on the accurate identification of viral sequences from community sequencing data. To maximize viral recovery from metagenomes, researchers frequently combine viral identification tools. However, the effectiveness of this strategy is unknown. Here, we benchmarked combinations of six widely used informatics tools for viral identification and analysis (VirSorter, VirSorter2, VIBRANT, DeepVirFinder, CheckV, and Kaiju), called "rulesets." Rulesets were tested against mock metagenomes composed of taxonomically diverse sequence types and diverse aquatic metagenomes to assess the effects of the degree of viral enrichment and habitat on tool performance. We found that six rulesets achieved equivalent accuracy [Matthews Correlation Coefficient (MCC) = 0.77, Padj ≥ 0.05]. Each contained VirSorter2, and five used our "tuning removal" rule designed to remove non-viral contamination. While DeepVirFinder, VIBRANT, and VirSorter were each found once in these high-accuracy rulesets, they were not found in combination with each other: combining tools does not lead to optimal performance. Our validation suggests that the MCC plateau at 0.77 is partly caused by inaccurate labeling within reference sequence databases. In aquatic metagenomes, our highest MCC ruleset identified more viral sequences in virus-enriched (44%-46%) than in cellular metagenomes (7%-19%). While improved algorithms may lead to more accurate viral identification tools, this should be done in tandem with careful curation of sequence databases. We recommend using the VirSorter2 ruleset and our empirically derived tuning removal rule. Our analysis provides insight into methods for in silico viral identification and will enable more robust viral identification from metagenomic data sets.

IMPORTANCE

The identification of viruses from environmental metagenomes using informatics tools has offered critical insights in microbial ecology. However, it remains difficult for researchers to know which tools optimize viral recovery for their specific study. In an attempt to recover more viruses, studies are increasingly combining the outputs from multiple tools without validating this approach. After benchmarking combinations of six viral identification tools against mock metagenomes and environmental samples, we found that these tools should only be combined cautiously. Two to four tool combinations maximized viral recovery and minimized non-viral contamination compared with either the single-tool or the five- to six-tool ones. By providing a rigorous overview of the behavior of in silico viral identification strategies and a pipeline to replicate our process, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.

Exploring the transcriptional landscape of phage-host interactions using novel high-throughput approaches

In the last decade, powerful high-throughput sequencing approaches have emerged to analyse microbial transcriptomes at a global scale. However, to date, applications of these approaches to microbial viruses such as phages remain scarce. Tailoring these techniques to virus-infected bacteria promises to obtain a detailed picture of the underexplored RNA biology and molecular processes during infection. In addition, transcriptome study of stress and perturbations induced by phages in their infected bacterial hosts is likely to reveal new fundamental mechanisms of bacterial metabolism and gene regulation. Here, we provide references and blueprints to implement emerging transcriptomic approaches towards addressing transcriptome architecture, RNA-RNA and RNA-protein interactions, RNA modifications, structures and heterogeneity of transcription profiles in infected cells that will provide guides for future directions in phage-centric therapeutic applications and microbial synthetic biology.

The insights into the phage communities of fermented foods in the age of viral metagenomics

Phages play a critical role in the assembly and regulation of fermented food microbiome through lysis and lysogenic lifestyle, which in turn affects the yield and quality of fermented foods. Therefore, it is important to investigate and characterize the diversity and function of phages under complex microbial communities and nutrient substrate conditions to provide novel insights into the regulation of traditional spontaneous fermentation. Viral metagenomics has gradually garnered increasing attention in fermented food research to elucidate phage functions and characterize the interactions between phages and the microbial community. Advances in this technology have uncovered a wide range of phages associated with the production of traditional fermented foods and beverages. This paper reviews the common methods of viral metagenomics applied in fermented food research, and summarizes the ecological functions of phages in traditional fermented foods. In the future, combining viral metagenomics with culturable methods and metagenomics will broaden the scope of research on fermented food systems, revealing the complex role of phages and intricate phage-bacterium interactions.

Campylobacter prophage diversity reveals pervasive recombination between prophages from different Campylobacter species

IMPORTANCE

Prophages play an important role in shaping the genetic diversity and evolution of their hosts. Acquisition or loss of prophages can lead to genomic variations, including changes in the bacterial phenotype promoted by recombination events, genetic repertoire exchanges and dissemination of virulence factors, and antibiotic resistance. By studying prophages in Campylobacter species, scientists can gain insights into the evolutionary patterns, pathogenicity mechanisms, epidemiology, and population dynamics of these species. This has implications for public health, antibiotic resistance surveillance, and the development of targeted therapeutic approaches.

Freshwater Viral Metagenome Analyses Targeting dsDNA Viruses

Viral metagenomics is one of the most widely used approaches to study viral population genomics. With the recent development of bioinformatic tools, the number of molecular biological methods, programs, and software to analyze viral metagenome data have greatly increased. Here, we describe the basic analysis workflow along with bioinformatic tools that can be used to analyze viral metagenome data. Although this chapter assumes that the viral metagenome data are prepared from the freshwater samples and are subjected to dsDNA sequencing, the protocol can be applied and modified for other types of metagenome data collected from a variety of sources.

Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques

As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in the generation of antibiotic pathogens. Therefore, we have developed a bioinformatic pipeline that utilizes deep learning techniques to identify ARG-carrying phages and predict their hosts, with a special focus on pathogens. Using this method, we discovered that the predominant types of ARGs carried by temperate phages in a typical landscape lake, which is fully replenished by reclaimed water, were related to multidrug resistance and β-lactam antibiotics. MAGs containing virulent factors (VFs) were predicted to serve as hosts for these ARG-carrying phages, which suggests that the phages may have the potential to transfer ARGs. In silico analysis showed a significant positive correlation between temperate phages and host pathogens (R = 0.503, p < 0.001), which was later confirmed by qPCR. Interestingly, these MAGs were found to be more abundant than those containing both ARGs and VFs, especially in December and March. Seasonal variations were observed in the abundance of phages harboring ARGs (from 5.62 % to 21.02 %) and chromosomes harboring ARGs (from 18.01 % to 30.94 %). In contrast, the abundance of plasmids harboring ARGs remained unchanged. In summary, this study leverages deep learning to analyze phage-transferred ARGs and demonstrates an alternative method to track the production of potential antibiotic-resistant pathogens by metagenomics that can be extended to microbiological risk assessment.

KEGG tools for classification and analysis of viral proteins

The KEGG database and analysis tools (https://www.kegg.jp) have been developed mostly for understanding genes and genomes of cellular organisms. The KO (KEGG Orthology) dataset, which is a collection of functional orthologs, plays the role of linking genes in the genome to pathways and other molecular networks, enabling KEGG mapping to uncover hidden features in the genome. Although viruses were part of KEGG for some time, they were not fully integrated in the KEGG analysis tools, because the KO assignment rate is very low for virus genes. To supplement KOs a new dataset named virus ortholog clusters (VOCs) is computationally generated, covering 90% of viral proteins in KEGG. VOCs can be used, in place of KOs, for taxonomy mapping to uncover relationships of sequence similarity groups and taxonomic groups and for identifying conserved gene orders in virus genomes. Furthermore, selected VOCs are used to define tentative KOs for characterizing protein functions. Here an overview of KEGG tools is presented focusing on these extensions for viral protein analysis.

Genomic Analysis and Taxonomic Characterization of Seven Bacteriophage Genomes Metagenomic-Assembled from the Dishui Lake

Viruses in aquatic ecosystems exhibit remarkable abundance and diversity. However, scattered studies have been conducted to mine uncultured viruses and identify them taxonomically in lake water. Here, whole genomes (29-173 kbp) of seven uncultured dsDNA bacteriophages were discovered in Dishui Lake, the largest artificial lake in Shanghai. We analyzed their genomic signatures and found a series of viral auxiliary metabolic genes closely associated with protein synthesis and host metabolism. Dishui Lake phages shared more genes with uncultivated environmental viruses than with reference viruses based on the gene-sharing network classification. Phylogeny of proteomes and comparative genomics delineated three new genera within two known viral families of Kyanoviridae and Autographiviridae, and four new families in Caudoviricetes for these seven novel phages. Their potential hosts appeared to be from the dominant bacterial phyla in Dishui Lake. Altogether, our study provides initial insights into the composition and diversity of bacteriophage communities in Dishui Lake, contributing valuable knowledge to the ongoing research on the roles played by viruses in freshwater ecosystems.

Special Issue “Bacteriophage Genomics”: Editorial

Virus genomics as a separate branch of biology has emerged relatively recently [...]

The power of unbiased phenotypic screens - cellulose as a first receptor for the Schitoviridae phage S6 of Erwinia amylovora

Bacteriophages, host‐dependent replicative non‐cellular entities which significantly shape the microbial genomes and consequently physiological and ecological properties of the microbial populations are exploited to restrict plant, animal and human pathogens. Unravelling of phage characteristics aids the understanding of the basic molecular mechanisms of phage infections which can subsequently lead to the development of rationalized strategies to combat microbial pathogens. In an unbiased screen to investigate the molecular basis of infectivity of the fire blight pathogen Erwinia amylovora by the lytic Schitoviridae phage S6, the biofilm extracellular matrix component cellulose has been identified as a cyclic di‐GMP dependent first receptor required for infection with the phage to possess beta‐1,4‐glucosidases to degrade the exopolysaccharide. This absolute receptor dependency allows maintenance of a phage‐microbe equilibrium with a low bacterial density.