Viral Host Range database, an online tool for recording, analyzing and disseminating virus-host interactions

Disruption of Biofilm by Bacteriophages in Clinically Relevant Settings

INTRODUCTION

Antibiotic-resistant bacteria are a growing threat to civilian and military health today. Although infections were once easily treatable by antibiotics and wound cleaning, the frequent mutation of bacteria has created strains impermeable to antibiotics and physical attack. Bacteria further their pathogenicity because of their ability to form biofilms on wounds, medical devices, and implant surfaces. Methods for treating biofilms in clinical settings are limited, and when formed by antibiotic-resistant bacteria, can generate chronic infections that are recalcitrant to available therapies. Bacteriophages are natural viral predators of bacteria, and their ability to rapidly destroy their host has led to increased attention in potential phage therapy applications.

MATERIALS AND METHODS

The present article sought to address a knowledge gap in the available literature pertaining to the usage of bacteriophage in clinically relevant settings and the resolution of infections particular to military concerns. PRISMA guidelines were followed for a systematic review of available literature that met the criteria for analysis and inclusion. The research completed for this review article originated from the U.S. Military Academy's library "Scout" search engine, which complies results from 254 available databases (including PubMed, Google Scholar, and SciFinder). The search criteria included original studies that employed bacteriophage use against biofilms, as well as successful phage therapy strategies for combating chronic bacterial infections. We specifically explored the use of bacteriophage against antibiotic- and treatment-resistant bacteria.

RESULTS

A total of 80 studies were identified that met the inclusion criteria following PRISMA guidelines. The application of bacteriophage has been demonstrated to robustly disrupt biofilm growth in wounds and on implant surfaces. When traditional therapies have failed to disrupt biofilms and chronic infections, a combination of these treatments with phage has proven to be effective, often leading to complete wound healing without reinfection.

CONCLUSIONS

This review article examines the available literature where bacteriophages have been utilized to treat biofilms in clinically relevant settings. Specific attention is paid to biofilms on implant medical devices, biofilms formed on wounds, and clinical outcomes, where phage treatment has been efficacious. In addition to the clinical benefit of phage therapies, the military relevance and treatment of combat-related infections is also examined. Phages offer the ability to expand available treatment options in austere environments with relatively low cost and effort, allowing the impacted warfighter to return to duty quicker and healthier.

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.

Characterization, taxonomic classification, and genomic analysis of two newly isolated bacteriophages with potential to infect Escherichia coli

Escherichia coli is a pathogenic bacterium that is widely distributed and can lead to serious illnesses in both humans and animals. As there is rising incidence of multidrug resistance among these bacteria, it has become imperative to discover alternative therapies beyond antibiotics to effectively treat such infections. Bacteriophage (phage) therapy has the potential to treat infections caused by E. coli, as phages contain enzymes that can cause lysis or destruction of bacterial cells. Simultaneously, the easy accessibility and cost-effectiveness of next-generation sequencing technologies have led to the accumulation of a vast amount of phage sequence data. Here, phages IME177 and IME267 were isolated from sewage water of a hospital in China. Modern phylogenetic approaches and key findings from the genomic analysis revealed that phages IME177 and IME267 are classified as members of the Kayfunavirus genus, Autographiviridae family, and a newly proposed Suseptimavirus genus under subfamily Gordonclarkvirinae, respectively. Further, the Kuravirus genus reshaped into three different genera: Kuravirus, Nieuwekanaalvirus, and Suspeptimavirus, which are classified together under a higher taxonomic rank (subfamily) named Gordonclarkvirinae. No genes related to virulence were detected in the genomes of the phages IME177 and IME267. Both phages exhibited a high degree of resilience to a wide range of conditions, including pH, temperature, exposure to chloroform, and UV radiation. Phages IME177 and IME267 are promising biological agents that can infect E. coli, making them suitable candidates for use in phage therapies.IMPORTANCEBiological and taxonomic characterization of phages is essential for facilitating the development of effective strategies for phage therapy and disease control. Escherichia coli phages are incredibly diverse, and their isolation and classification help us understand the scope and nature of this diversity. By identifying new phages and grouping them into families, we can better understand the genetic and structural variations between phages and how they affect their infectivity and interactions with bacteria. Overall, the isolation and classification of E. coli phages have broad implications for both basic and applied research, clinical practice, and public health.

Robust Approaches to the Quantitative Analysis of Genome Formula Variation in Multipartite and Segmented Viruses

When viruses have segmented genomes, the set of frequencies describing the abundance of segments is called the genome formula. The genome formula is often unbalanced and highly variable for both segmented and multipartite viruses. A growing number of studies are quantifying the genome formula to measure its effects on infection and to consider its ecological and evolutionary implications. Different approaches have been reported for analyzing genome formula data, including qualitative description, applying standard statistical tests such as ANOVA, and customized analyses. However, these approaches have different shortcomings, and test assumptions are often unmet, potentially leading to erroneous conclusions. Here, we address these challenges, leading to a threefold contribution. First, we propose a simple metric for analyzing genome formula variation: the genome formula distance. We describe the properties of this metric and provide a framework for understanding metric values. Second, we explain how this metric can be applied for different purposes, including testing for genome-formula differences and comparing observations to a reference genome formula value. Third, we re-analyze published data to illustrate the applications and weigh the evidence for previous conclusions. Our re-analysis of published datasets confirms many previous results but also provides evidence that the genome formula can be carried over from the inoculum to the virus population in a host. The simple procedures we propose contribute to the robust and accessible analysis of genome-formula data.

Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants

BACKGROUND

Phage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations presents a considerable challenge. Currently, there is a notable lack of tools designed for large-scale characterization of phage receptor-binding proteins, which are crucial in determining the phage host range.

RESULTS

In this study, we present SpikeHunter, a deep learning method based on the ESM-2 protein language model. With SpikeHunter, we identified 231,965 diverse phage-encoded tailspike proteins, a crucial determinant of phage specificity that targets bacterial polysaccharide receptors, across 787,566 bacterial genomes from 5 virulent, antibiotic-resistant pathogens. Notably, 86.60% (143,200) of these proteins exhibited strong associations with specific bacterial polysaccharides. We discovered that phages with identical tailspike proteins can infect different bacterial species with similar polysaccharide receptors, underscoring the pivotal role of tailspike proteins in determining host range. The specificity is mainly attributed to the protein's C-terminal domain, which strictly correlates with host specificity during domain swapping in tailspike proteins. Importantly, our dataset-driven predictions of phage-host specificity closely match the phage-host pairs observed in real-world phage therapy cases we studied.

CONCLUSIONS

Our research provides a rich resource, including both the method and a database derived from a large-scale genomics survey. This substantially enhances understanding of phage specificity determinants at the strain level and offers a valuable framework for guiding phage selection in therapeutic applications.

Navigating the Landscape: A Comprehensive Review of Current Virus Databases

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.

Using machine learning to detect coronaviruses potentially infectious to humans

Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses. Three viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 and Pipistrellus abramus bat coronavirus HKU5-related (both MERS related viruses), and Rhinolophus affinis coronavirus isolate LYRa3 (a SARS related virus). We further analyze the binding properties of BtCoV/133/2005 and LYRa3 using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-CoV-2 and all viral sequences released after the SARS-CoV-2 was published. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.

Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes

The biogeography of eukaryotes in drinking water systems is poorly understood relative to that of prokaryotes or viruses, limiting the understanding of their role and management. A challenge with studying complex eukaryotic communities is that metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore the factors affecting the relative abundance and diversity of eukaryotic communities in drinking water distribution systems (DWDSs). We developed an ensemble approach exploiting k-mer- and reference-based strategies to improve eukaryotic sequence identification and identified MetaBAT2 as the best-performing binning approach for their clustering. Applying this workflow to the DWDS metagenomes showed that eukaryotic sequences typically constituted small proportions (i.e., <1%) of the overall metagenomic data with higher relative abundances in surface water-fed or chlorinated systems with high residuals. The α and β diversities of eukaryotes were correlated with those of prokaryotic and viral communities, highlighting the common role of environmental/management factors. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose members' presence and abundance in DWDSs were affected by disinfection strategies, climate conditions, and source water types.

Genetic determinants of host tropism in Klebsiella phages

Bacteriophages play key roles in bacterial ecology and evolution and are potential antimicrobials. However, the determinants of phage-host specificity remain elusive. Here, we isolate 46 phages to challenge 138 representative clinical isolates of Klebsiella pneumoniae, a widespread opportunistic pathogen. Spot tests show a narrow host range for most phages, with <2% of 6,319 phage-host combinations tested yielding detectable interactions. Bacterial capsule diversity is the main factor restricting phage host range. Consequently, phage-encoded depolymerases are key determinants of host tropism, and depolymerase sequence types are associated with the ability to infect specific capsular types across phage families. However, all phages with a broader host range found do not encode canonical depolymerases, suggesting alternative modes of entry. These findings expand our knowledge of the complex interactions between bacteria and their viruses and point out the feasibility of predicting the first steps of phage infection using bacterial and phage genome sequences.

The role of the animal host in the management of bacteriophage resistance during phage therapy

Multi-drug-resistant bacteria are associated with significantly higher morbidity and mortality. The possibilities for discovering new antibiotics are limited, but phage therapy - the use of bacteriophages (viruses infecting bacteria) to cure infections - is now being investigated as an alternative or complementary treatment to antibiotics. However, one of the major limitations of this approach lies in the antagonistic coevolution between bacteria and bacteriophages, which determines the ultimate success or failure of phage therapy. Here, we review the possible influence of the animal host on phage resistance and its consequences for the efficacy of phage therapy. We also discuss the value of in vitro assays for anticipating the dynamics of phage resistance observed in vivo.

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.

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.

PhageCocktail: An R package to design phage cocktails from experimental phage-bacteria infection networks

BACKGROUND AND OBJECTIVE

Phage therapy is a resurgent strategy used in medicine and the food industry to lyse bacteria that cause damage to health or spoil a food product. Frequently, phage-bacteria infection networks have a large size, making it impossible to manually study all possible phage cocktails. Thus, this article presents an R package called PhageCocktail to automatically design efficient phage cocktails from phage-bacteria infection networks.

METHODS

This R package includes four different methods for designing phage cocktails: ExhaustiveSearch, ExhaustivePhi, ClusteringSearch, and ClusteringPhi. These four methods are explained in detail and are evaluated using 13 empirical phage-bacteria infection networks. More specifically, runtime and expected success (fraction of lysed bacteria) are analyzed.

RESULTS

The four methods have variations in terms of runtime and quality of the results. ExhaustiveSearch always provides the best possible phage cocktail, but its runtime could be long. ExhaustivePhi only focuses on one cocktail size, the one estimated as the best; thus, its runtime is less than ExhaustiveSearch, but it can produce cocktails with more phages than necessary. ClusteringSearch and ClusteringPhi are very fast (generally, less than one millisecond), providing always immediate results due to clustering techniques, but their accuracies can be lower, yielding cocktails with lower expected successes.

CONCLUSIONS

The larger the phage-bacteria infection network is, the more complex its analysis is. Thus, this tool eases this task for scientists and other users while designing phage cocktails of good quality. This R package includes four different methods; therefore, users may choose among them, considering their preferences in speed and accuracy of results.

Systematic analysis of putative phage-phage interactions on minimum-sized phage cocktails

The application of bacteriophages as antibacterial agents has many benefits in the “post-antibiotic age”. To increase the number of successfully targeted bacterial strains, phage cocktails, instead of a single phage, are commonly formulated. Nevertheless, there is currently no consensus pipeline for phage cocktail development. Thus, although large cocktails increase the spectrum of activity, they could produce side effects such as the mobilization of virulence or antibiotic resistance genes. On the other hand, coinfection (simultaneous infection of one host cell by several phages) might reduce the potential for bacteria to evolve phage resistance, but some antagonistic interactions amongst phages might be detrimental for the outcome of phage cocktail application. With this in mind, we introduce here a new method, which considers the host range and each individual phage-host interaction, to design the phage mixtures that best suppress the target bacteria while minimizing the number of phages to restrict manufacturing costs. Additionally, putative phage-phage interactions in cocktails and phage-bacteria networks are compared as the understanding of the complex interactions amongst bacteriophages could be critical in the development of realistic phage therapy models in the future.

Computational Prediction of Bacteriophage Host Ranges

Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.

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.

Shopping for phages? Unpacking design rules for therapeutic phage cocktails

In bacteriophage therapy, the combination of different phages into a single cocktail is of critical importance to overcome the narrow host range of single phage isolates. Today, the design of therapeutic cocktails is often akin to a black box and relies largely on intuition and (pre-)availability of isolates in local collections. Here we show that straightforward host range analysis can disclose design rules and we propose to apply/translate a data mining approach, historically applied in the field of marketing ('shopping cart analysis') to explore patterns in phage combinations. The technique is broadly applicable to host range datasets and can serve in combination with other molecular-based approaches to propose rationales for phage cocktail design.

Digital phagograms: predicting phage infectivity through a multilayer machine learning approach

Machine learning has been broadly implemented to investigate biological systems. In this regard, the field of phage biology has embraced machine learning to elucidate and predict phage-host interactions, based on receptor-binding proteins, (anti-)defense systems, prophage detection, and life cycle recognition. Here, we highlight the enormous potential of integrating information from omics data with insights from systems biology to better understand phage-host interactions. We conceptualize and discuss the potential of a multilayer model that mirrors the phage infection process, integrating adsorption, bacterial pan-immune components and hijacking of the bacterial metabolism to predict phage infectivity. In the future, this model can offer insights into the underlying mechanisms of the infection process, and digital phagograms can support phage cocktail design and phage engineering.

Phage Cocktail Development for Bacteriophage Therapy: Toward Improving Spectrum of Activity Breadth and Depth

Phage therapy is the use of bacterial viruses as antibacterial agents. A primary consideration for commercial development of phages for phage therapy is the number of different bacterial strains that are successfully targeted, as this defines the breadth of a phage cocktail's spectrum of activity. Alternatively, phage cocktails may be used to reduce the potential for bacteria to evolve phage resistance. This, as we consider here, is in part a function of a cocktail's 'depth' of activity. Improved cocktail depth is achieved through inclusion of at least two phages able to infect a single bacterial strain, especially two phages against which bacterial mutation to cross resistance is relatively rare. Here, we consider the breadth of activity of phage cocktails while taking both depth of activity and bacterial mutation to cross resistance into account. This is done by building on familiar algorithms normally used for determination solely of phage cocktail breadth of activity. We show in particular how phage cocktails for phage therapy may be rationally designed toward enhancing the number of bacteria impacted while also reducing the potential for a subset of those bacteria to evolve phage resistance, all as based on previously determined phage properties.