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

Analysis and culturing of the prototypic crAssphage reveals a phage-plasmid lifestyle

The prototypic crAssphage (Carjivirus communis) is one of the most abundant, prevalent, and persistent gut bacteriophages, yet it remains uncultured and its lifestyle uncharacterized. For the last decade, crAssphage has escaped plaque-dependent culturing efforts, leading us to investigate alternative lifestyles that might explain its widespread success. Through genomic analyses and culturing, we find that crAssphage uses a phage-plasmid lifestyle to persist extrachromosomally. Plasmid-related genes are more highly expressed than those implicated in phage maintenance. Leveraging this finding, we use a plaque-free culturing approach to measure crAssphage replication in culture with Phocaeicola vulgatus, Phocaeicola dorei, and Bacteroides stercoris, revealing a broad host range. We demonstrate that crAssphage persists with its hosts in culture without causing major cell lysis events or integrating into host chromosomes. The ability to switch between phage and plasmid lifestyles within a wide range of hosts contributes to the prolific nature of crAssphage in the human gut microbiome.

Comparative evaluation of bioinformatic tools for virus-host prediction and their application to a highly diverse community in the Cuatro Ciénegas Basin, Mexico

Due to the enormous diversity of non-culturable viruses, new viruses must be characterized using culture-independent techniques. The associated host is an important phenotypic feature that can be inferred from metagenomic viral contigs thanks to the development of several bioinformatic tools. Here, we compare the performance of recently developed virus-host prediction tools on a dataset of 1,046 virus-host pairs and then apply the best-performing tools to a metagenomic dataset derived from a highly diverse transiently hypersaline site known as the Archaean Domes (AD) within the Cuatro Ciénegas Basin, Coahuila, Mexico. Among host-dependent methods, alignment-based approaches had a precision of 66.07% and a sensitivity of 24.76%, while alignment-free methods had an average precision of 75.7% and a sensitivity of 57.5%. RaFAH, a virus-dependent alignment-based tool, had the best overall performance (F1_score = 95.7%). However, when predicting the host of AD viruses, methods based on public reference databases (such as RaFAH) showed lower inter-method agreement than host-dependent methods run against custom databases constructed from prokaryotes inhabiting AD. Methods based on custom databases also showed the greatest agreement between the source environment and the predicted host taxonomy, habitat, lifestyle, or metabolism. This highlights the value of including custom data when predicting hosts on a highly diverse metagenomic dataset, and suggests that using a combination of methods and qualitative validations related to the source environment and predicted host biology can increase the number of correct predictions. Finally, these predictions suggest that AD viruses infect halophilic archaea as well as a variety of bacteria that may be halophilic, halotolerant, alkaliphilic, thermophilic, oligotrophic, sulfate-reducing, or marine, which is consistent with the specific environment and the known geological and biological evolution of the Cuatro Ciénegas Basin and its microorganisms.

Exploring the Archaeal Virosphere by Metagenomics

During the past decade, environmental research has demonstrated that archaea are abundant and widespread in nature and play important ecological roles at a global scale. Currently, however, the majority of archaeal lineages cannot be cultivated under laboratory conditions and are known exclusively or nearly exclusively through metagenomics. A similar trend extends to the archaeal virosphere, where isolated representatives are available for a handful of model archaeal virus-host systems. Viral metagenomics provides an alternative way to circumvent the limitations of culture-based virus discovery and offers insight into the diversity, distribution, and environmental impact of uncultured archaeal viruses. Presently, metagenomics approaches have been successfully applied to explore the viromes associated with various lineages of extremophilic and mesophilic archaea, including Asgard archaea (Asgardarchaeota), ANME-1 archaea (Methanophagales), thaumarchaea (Nitrososphaeria), altiarchaea (Altiarchaeota), and marine group II archaea (Poseidoniales). Here, we provide an overview of methods widely used in archaeal virus metagenomics, covering metavirome preparation, genome annotation, phylogenetic and phylogenomic analyses, and archaeal host assignment. We hope that this summary will contribute to further exploration and characterization of the enigmatic archaeal virome lurking in diverse environments.

Unraveling the genome of Bacillus velezensis MEP218, a strain producing fengycin homologs with broad antibacterial activity: comprehensive comparative genome analysis

Bacillus sp. MEP218, a soil bacterium with high potential as a source of bioactive molecules, produces mostly C16-C17 fengycin and other cyclic lipopeptides (CLP) when growing under previously optimized culture conditions. This work addressed the elucidation of the genome sequence of MEP218 and its taxonomic classification. The genome comprises 3,944,892 bp, with a total of 3474 coding sequences and a G + C content of 46.59%. Our phylogenetic analysis to determine the taxonomic position demonstrated that the assignment of the MEP218 strain to Bacillus velezensis species provides insights into its evolutionary context and potential functional attributes. The in silico genome analysis revealed eleven gene clusters involved in the synthesis of secondary metabolites, including non-ribosomal CLP (fengycins and surfactin), polyketides, terpenes, and bacteriocins. Furthermore, genes encoding phytase, involved in the release of phytic phosphate for plant and animal nutrition, or other enzymes such as cellulase, xylanase, and alpha 1-4 glucanase were detected. In vitro antagonistic assays against Salmonella typhimurium, Acinetobacter baumanii, Escherichia coli, among others, demonstrated a broad spectrum of C16-C17 fengycin produced by MEP218. MEP218 genome sequence analysis expanded our understanding of the diversity and genetic relationships within the Bacillus genus and updated the Bacillus databases with its unique trait to produce antibacterial fengycins and its potential as a resource of biotechnologically useful enzymes.

CRISPRimmunity: an interactive web server for CRISPR-associated Important Molecular events and Modulators Used in geNome edIting Tool identifYing

The CRISPR-Cas system is a highly adaptive and RNA-guided immune system found in bacteria and archaea, which has applications as a genome editing tool and is a valuable system for studying the co-evolutionary dynamics of bacteriophage interactions. Here introduces CRISPRimmunity, a new web server designed for Acr prediction, identification of novel class 2 CRISPR-Cas loci, and dissection of key CRISPR-associated molecular events. CRISPRimmunity is built on a suite of CRISPR-oriented databases providing a comprehensive co-evolutionary perspective of the CRISPR-Cas and anti-CRISPR systems. The platform achieved a high prediction accuracy of 0.997 for Acr prediction when tested on a dataset of 99 experimentally validated Acrs and 676 non-Acrs, outperforming other existing prediction tools. Some of the newly identified class 2 CRISPR-Cas loci using CRISPRimmunity have been experimentally validated for cleavage activity in vitro. CRISPRimmunity offers the catalogues of pre-identified CRISPR systems to browse and query, the collected resources or databases to download, a well-designed graphical interface, a detailed tutorial, multi-faceted information, and exportable results in machine-readable formats, making it easy to use and facilitating future experimental design and further data mining. The platform is available at http://www.microbiome-bigdata.com/CRISPRimmunity. Moreover, the source code for batch analysis are published on Github (https://github.com/HIT-ImmunologyLab/CRISPRimmunity).

PhageTailFinder: A tool for phage tail module detection and annotation

Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (>80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder).

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.

A Novel Inovirus Reprograms Metabolism and Motility of Marine Alteromonas

Members from the Inoviridae family with striking features are widespread, highly diverse, and ecologically pervasive across multiple hosts and environments. However, a small number of inoviruses have been isolated and studied. Here, a filamentous phage infecting Alteromonas abrolhosensis, designated ϕAFP1, was isolated from the South China Sea and represented a novel genus of Inoviridae. ϕAFP1 consisted of a single-stranded DNA genome (5986 bp), encoding eight putative ORFs. Comparative analyses revealed ϕAFP1 could be regarded as genetic mosaics having homologous sequences with Ralstonia and Stenotrophomonas phages. The temporal transcriptome analysis of A. abrolhosensis to ϕAFP1 infection revealed that 7.78% of the host genes were differentially expressed. The genes involved in translation processes, ribosome pathways, and degradation of multiple amino acid pathways at the plateau period were upregulated, while host material catabolic and bacterial motility-related genes were downregulated, indicating that ϕAFP1 might hijack the energy of the host for the synthesis of phage proteins. ϕAFP1 exerted step-by-step control on host genes through the appropriate level of utilizing host resources. Our study provided novel information for a better understanding of filamentous phage characteristics and phage-host interactions. IMPORTANCE Alteromonas is widely distributed and plays a vital role in biogeochemical in marine environments. However, little information about Alteromonas phages is available. Here, we isolated and characterized the biological characteristics and genome sequence of a novel inovirus infecting Alteromonas abrolhosensis, designated ϕAFP1, representing a novel viral genus of Inoviridae. We then presented a comprehensive view of the ϕAFP1 phage-Alteromonas abrolhosensis interactions, elucidating reprogramed host metabolism and motility. Our study provided novel information for better comprehension of filamentous phage characteristics and phage-host interactions.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Bacteriophage Genetic Edition Using LSTM

Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic-resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning-driven components: a phage-bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of Pseudomonas aeruginosa. The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering.

DBSCAN-SWA: An Integrated Tool for Rapid Prophage Detection and Annotation

As an intracellular form of a bacteriophage in the bacterial host genome, a prophage usually integrates into bacterial DNA with high specificity and contributes to horizontal gene transfer (HGT). With the exponentially increasing number of microbial sequences uncovered in genomic or metagenomics studies, there is a massive demand for a tool that is capable of fast and accurate identification of prophages. Here, we introduce DBSCAN-SWA, a command line software tool developed to predict prophage regions in bacterial genomes. DBSCAN-SWA runs faster than any previous tools. Importantly, it has great detection power based on analysis using 184 manually curated prophages, with a recall of 85% compared with Phage_Finder (63%), VirSorter (74%), and PHASTER (82%) for (Multi-) FASTA sequences. Moreover, DBSCAN-SWA outperforms the existing standalone prophage prediction tools for high-throughput sequencing data based on the analysis of 19,989 contigs of 400 bacterial genomes collected from Human Microbiome Project (HMP) project. DBSCAN-SWA also provides user-friendly result visualizations including a circular prophage viewer and interactive DataTables. DBSCAN-SWA is implemented in Python3 and is available under an open source GPLv2 license from https://github.com/HIT-ImmunologyLab/DBSCAN-SWA/.