Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

Biochemical characterisation and production kinetics of high molecular-weight (HMW) putative antibacterial proteins of insect pathogenic Brevibacillus laterosporus isolates

BACKGROUND

Bacterial genomes often encode structures similar to phage capsids (encapsulins) and phage tails which can be induced spontaneously or using genotoxic compounds such as mitomycin C. These high molecular-weight (HMW) putative antibacterial proteins (ABPs) are used against the competitive strains under natural environment. Previously, it was unknown whether these HMW putative ABPs originating from the insect pathogenic Gram-positive, spore-forming bacterium Brevibacillus laterosporus (Bl) isolates (1821L, 1951) are spontaneously induced during the growth and pose a detrimental effect on their own survival. Furthermore, no prior work has been undertaken to determine their biochemical characteristics.

RESULTS

Using a soft agar overlay method with polyethylene glycol precipitation, a narrow spectrum of bioactivity was found from the precipitated lysate of Bl 1951. Electron micrographs of mitomycin C- induced filtrates showed structures similar to phage capsids and contractile tails. Bioactivity assays of cell free supernatants (CFS) extracted during the growth of Bl 1821L and Bl 1951 suggested spontaneous induction of these HMW putative ABPs with an autocidal activity. Sodium dodecyl sulphate-polyacrylamide gel electrophoresis of spontaneously induced putative ABPs showed appearance of ~ 30 kDa and ~ 48 kDa bands of varying intensity across all the time intervals during the bacterial growth except in the initial hours. Statistically, spontaneously induced HMW putative ABPs of Bl 1951 exhibited a significant decrease in the number of viable cells of its producer strain after 18 h of growth in liquid. In addition, a significant change in pH and prominent bioactivity of the CFS of this particular time period was noted. Biochemically, the filtered supernatant derived from either Bl 1821L or Bl 1951 maintained bioactivity over a wide range of pH and temperature.

CONCLUSION

This study reports the spontaneous induction of HMW putative ABPs (bacteriocins) of Bl 1821L and Bl 1951 isolates during the course of growth with potential autocidal activity which is critically important during production as a potential biopesticide. A narrow spectrum of putative antibacterial activity of Bl 1951 precipitate was found. The stability of HMW putative ABPs of Bl 1821L and Bl 1951 over a wide range of pH and temperature can be useful in expanding the potential of this useful bacterium beyond the insecticidal value.

Phage therapy as a glimmer of hope in the fight against the recurrence or emergence of surgical site bacterial infections

PURPOSE

Over the last decade, surgery rates have risen alarmingly, and surgical-site infections are expanding these concerns. In spite of advances in infection control practices, surgical infections continue to be a significant cause of death, prolonged hospitalization, and morbidity. As well as the presence of bacterial infections and their antibiotic resistance, biofilm formation is one of the challenges in the treatment of surgical wounds.

METHODS

This review article was based on published studies on inpatients and laboratory animals receiving phage therapy for surgical wounds, phage therapy for tissue and bone infections treated with surgery to prevent recurrence, antibiotic-resistant wound infections treated with phage therapy, and biofilm-involved surgical wounds treated with phage therapy which were searched without date restrictions.

RESULTS

It has been shown in this review article that phage therapy can be used to treat surgical-site infections in patients and animals, eliminate biofilms at the surgical site, prevent infection recurrence in wounds that have been operated on, and eradicate antibiotic-resistant infections in surgical wounds, including multi-drug resistance (MDR), extensively drug resistance (XDR), and pan-drug resistance (PDR). A cocktail of phages and antibiotics can also reduce surgical-site infections more effectively than phages alone.

CONCLUSION

In light of these encouraging results, clinical trials and research with phages will continue in the near future to treat surgical-site infections, biofilm removal, and antibiotic-resistant wounds, all of which could be used to prescribe phages as an alternative to antibiotics.

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.

Nano-emulsion encapsulation for the efficient delivery of bacteriophage therapeutics

Antibiotic resistance has become the major concern for global public health. Phage therapy is being considered as an alternative for antibiotics to treat the multidrug resistant bacterial infections. Bacteriophage therapeutic developments has faced many challenges, including the drug formulations for sustainable phage delivery. The nano-emulsion platform has been described as the best approach to retain phage efficacy, shelf life and stability. Encapsulated phage drugs ensure stable delivery of phages to the target site and integrate in the system. In this review, our main focus is on the nano-emulsion encapsulation of bacteriophages and its effects towards the phage therapeutic development.

Phage fibers and spikes: a nanoscale Swiss army knife for host infection

Bacteriophages are being rediscovered as potent agents for medical and industrial applications. However, finding a suitable phage relies on numerous factors, including host specificity, burst size, and infection cycle. The host range of a phage is, besides phage defense systems, initially determined by the recognition and attachment of receptor-binding proteins (RBPs) to the target receptors of susceptible bacteria. RBPs include tail (or occasionally head) fibers and tailspikes. Owing to the potential flexibility and heterogeneity of these structures, they are often overlooked during structural studies. Recent advances in cryo-electron microscopy studies and computational approaches have begun to unravel their structural and fundamental mechanisms during phage infection. In this review, we discuss the current state of research on different phage tail and head fibers, spike models, and molecular mechanisms. These details may facilitate the manipulation of phage-host specificity, which in turn will have important implications for science and society.

Complete genome sequences and characteristics of mycobacteriophages Diminimus, Dulcita, Glaske16, and Koreni

Complete genome sequences of four novel mycobacteriophages, Diminimus, Dulcita, Glaske16, and Koreni, isolated from soil are presented. All these bacteriophages belong to subcluster M1, except Koreni that belongs to subcluster A4. Moreover, all have siphovirus morphologies, with genome sizes ranging from 51,055 to 81,156 bp.

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.

Bacteriophage-based biosensors for detection of pathogenic microbes in wastewater

Wastewater is discarded from several sources, including industry, livestock, fertilizer application, and municipal waste. If the disposed of wastewater has not been treated and processed before discharge to the environment, pathogenic microorganisms and toxic chemicals are accumulated in the disposal area and transported into the surface waters. The presence of harmful microbes is responsible for thousands of human deaths related to water-born contamination every year. To be able to take the necessary step and quick action against the possible presence of harmful microorganisms and substances, there is a need to improve the effective speed of identification and treatment of these problems. Biosensors are such devices that can give quantitative information within a short period of time. There have been several biosensors developed to measure certain parameters and microorganisms. The discovered biosensors can be utilized for the detection of axenic and mixed microbial strains from the wastewaters. Biosensors can further be developed for specific conditions and environments with an in-depth understanding of microbial organization and interaction within that community. In this regard, bacteriophage-based biosensors have become a possibility to identify specific live bacteria in an infected environment. This paper has investigated the current scenario of microbial community analysis and biosensor development in identifying the presence of pathogenic microorganisms.

Knowing and Naming: Phage Annotation and Nomenclature for Phage Therapy

Bacteriophages, or phages, are viruses that infect bacteria shaping microbial communities and ecosystems. They have gained attention as potential agents against antibiotic resistance. In phage therapy, lytic phages are preferred for their bacteria killing ability, while temperate phages, which can transfer antibiotic resistance or toxin genes, are avoided. Selection relies on plaque morphology and genome sequencing. This review outlines annotating genomes, identifying critical genomic features, and assigning functional labels to protein-coding sequences. These annotations prevent the transfer of unwanted genes, such as antimicrobial resistance or toxin genes, during phage therapy. Additionally, it covers International Committee on Taxonomy of Viruses (ICTV)-an established phage nomenclature system for simplified classification and communication. Accurate phage genome annotation and nomenclature provide insights into phage-host interactions, replication strategies, and evolution, accelerating our understanding of the diversity and evolution of phages and facilitating the development of phage-based therapies.

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.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer

MOTIVATION

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification.

RESULTS

In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins.

AVAILABILITY AND IMPLEMENTATION

The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

Deciphering Phage-Host Specificity Based on the Association of Phage Depolymerases and Bacterial Surface Glycan with Deep Learning

Phage tailspike proteins are depolymerases that target diverse bacterial surface glycans with high specificity, determining the host-specificity of numerous phages. To address the challenge of identifying tailspike proteins due to their sequence diversity, we developed SpikeHunter, an approach based on the ESM-2 protein language model. Using SpikeHunter, we successfully identified 231,965 tailspike proteins from a dataset comprising 8,434,494 prophages found within 165,365 genomes of five common pathogens. Among these proteins, 143,035 tailspike proteins displayed strong associations with serotypes. Moreover, we observed highly similar tailspike proteins in species that share closely related serotypes. We found extensive domain swapping in all five species, with the C-terminal domain being significantly associated with host serotype highlighting its role in host range determination. Our study presents a comprehensive cross-species analysis of tailspike protein to serotype associations, providing insights applicable to phage therapy and biotechnology.

Engineering bacteriophages for enhanced host range and efficacy: insights from bacteriophage-bacteria interactions

Bacteriophages, the most abundant organisms on earth, have the potential to address the rise of multidrug-resistant bacteria resulting from the overuse of antibiotics. However, their high specificity and limited host range can hinder their effectiveness. Phage engineering, through the use of gene editing techniques, offers a means to enhance the host range of bacteria, improve phage efficacy, and facilitate efficient cell-free production of phage drugs. To engineer phages effectively, it is necessary to understand the interaction between phages and host bacteria. Understanding the interaction between the receptor recognition protein of bacteriophages and host receptors can serve as a valuable guide for modifying or replacing these proteins, thereby altering the receptor range of the bacteriophage. Research and development focused on the CRISPR-Cas bacterial immune system against bacteriophage nucleic acids can provide the necessary tools to promote recombination and counter-selection in engineered bacteriophage programs. Additionally, studying the transcription and assembly functions of bacteriophages in host bacteria can facilitate the engineered assembly of bacteriophage genomes in non-host environments. This review highlights a comprehensive summary of phage engineering methods, including in-host and out-of-host engineering, and the use of high-throughput methods to understand their role. The main aim of these techniques is to harness the intricate interactions between bacteriophages and hosts to inform and guide the engineering of bacteriophages, particularly in the context of studying and manipulating the host range of bacteriophages. By employing advanced high-throughput methods to identify specific bacteriophage receptor recognition genes, and subsequently introducing modifications or performing gene swapping through in-host recombination or out-of-host synthesis, it becomes possible to strategically alter the host range of bacteriophages. This capability holds immense significance for leveraging bacteriophages as a promising therapeutic approach against antibiotic-resistant bacteria.

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases

Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents.

Current Clinical Landscape and Global Potential of Bacteriophage Therapy

In response to the global spread of antimicrobial resistance, there is an increased demand for novel and innovative antimicrobials. Bacteriophages have been known for their potential clinical utility in lysing bacteria for almost a century. Social pressures and the concomitant introduction of antibiotics in the mid-1900s hindered the widespread adoption of these naturally occurring bactericides. Recently, however, phage therapy has re-emerged as a promising strategy for combatting antimicrobial resistance. A unique mechanism of action and cost-effective production promotes phages as an ideal solution for addressing antibiotic-resistant bacterial infections, particularly in lower- and middle-income countries. As the number of phage-related research labs worldwide continues to grow, it will be increasingly important to encourage the expansion of well-developed clinical trials, the standardization of the production and storage of phage cocktails, and the advancement of international collaboration. In this review, we discuss the history, benefits, and limitations of bacteriophage research and its current role in the setting of addressing antimicrobial resistance with a specific focus on active clinical trials and case reports of phage therapy administration.

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

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

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.

What Lies Beneath? Taking the Plunge into the Murky Waters of Phage Biology

The sequence revolution revealed that bacteria-infecting viruses, known as phages, are Earth's most abundant biological entities. Phages have far-reaching impacts on the form and function of microbial communities and play a fundamental role in ecological processes. However, even well into the sequencing revolution, we have only just begun to explore the murky waters around the phage biology iceberg. Many viral reads cannot be assigned to a culturable isolate, and reference databases are biased toward more easily collectible samples, which likely distorts our conclusions. This minireview points out alternatives to mapping reads to reference databases and highlights innovative bioinformatic and experimental approaches that can help us overcome some of the challenges in phage research and better decipher the impact of phages on microbial communities. Moving beyond the identification of novel phages, we highlight phage metabolomics as an important influencer of bacterial host cell physiology and hope to inspire the reader to consider the effects of phages on host metabolism and ecosystems at large. We encourage researchers to report unassigned/unknown sequencing reads and contigs and to continue developing alternative methods to investigate phages within sequence data.

A perfect fit: Bacteriophage receptor-binding proteins for diagnostic and therapeutic applications

Bacteriophages are the most abundant biological entity on earth, acting as the predators and evolutionary drivers of bacteria. Owing to their inherent ability to specifically infect and kill bacteria, phages and their encoded endolysins and receptor-binding proteins (RBPs) have enormous potential for development into precision antimicrobials for treatment of bacterial infections and microbial disbalances; or as biocontrol agents to tackle bacterial contaminations during various biotechnological processes. The extraordinary binding specificity of phages and RBPs can be exploited in various areas of bacterial diagnostics and monitoring, from food production to health care. We review and describe the distinctive features of phage RBPs, explain why they are attractive candidates for use as therapeutics and in diagnostics, discuss recent applications using RBPs, and finally provide our perspective on how synthetic technology and artificial intelligence-driven approaches will revolutionize how we use these tools in the future.

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.

Bioinformatics approaches for unveiling virus-host interactions

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Bacteriophage-Mediated Cancer Gene Therapy

Bacteriophages have long been considered only as infectious agents that affect bacterial hosts. However, recent studies provide compelling evidence that these viruses are able to successfully interact with eukaryotic cells at the levels of the binding, entry and expression of their own genes. Currently, bacteriophages are widely used in various areas of biotechnology and medicine, but the most intriguing of them is cancer therapy. There are increasing studies confirming the efficacy and safety of using phage-based vectors as a systemic delivery vehicle of therapeutic genes and drugs in cancer therapy. Engineered bacteriophages, as well as eukaryotic viruses, demonstrate a much greater efficiency of transgene delivery and expression in cancer cells compared to non-viral gene transfer methods. At the same time, phage-based vectors, in contrast to eukaryotic viruses-based vectors, have no natural tropism to mammalian cells and, as a result, provide more selective delivery of therapeutic cargos to target cells. Moreover, numerous data indicate the presence of more complex molecular mechanisms of interaction between bacteriophages and eukaryotic cells, the further study of which is necessary both for the development of gene therapy methods and for understanding the cancer nature. In this review, we summarize the key results of research into aspects of phage-eukaryotic cell interaction and, in particular, the use of phage-based vectors for highly selective and effective systemic cancer gene therapy.

Understanding Bacteriophage Tail Fiber Interaction with Host Surface Receptor: The Key “Blueprint” for Reprogramming Phage Host Range

Bacteriophages (phages), as natural antibacterial agents, are being rediscovered because of the growing threat of multi- and pan-drug-resistant bacterial pathogens globally. However, with an estimated 10³¹ phages on the planet, finding the right phage to recognize a specific bacterial host is like looking for a needle in a trillion haystacks. The host range of a phage is primarily determined by phage tail fibers (or spikes), which initially mediate reversible and specific recognition and adsorption by susceptible bacteria. Recent significant advances at single-molecule and atomic levels have begun to unravel the structural organization of tail fibers and underlying mechanisms of phage–host interactions. Here, we discuss the molecular mechanisms and models of the tail fibers of the well-characterized T4 phage’s interaction with host surface receptors. Structure–function knowledge of tail fibers will pave the way for reprogramming phage host range and will bring future benefits through more-effective phage therapy in medicine. Furthermore, the design strategies of tail fiber engineering are briefly summarized, including machine-learning-assisted engineering inspired by the increasingly enormous amount of phage genetic information.

Microbiome-phage interactions in inflammatory bowel disease

BACKGROUND

Inflammatory bowel diseases (IBD) constitute a group of auto-inflammatory disorders impacting the gastrointestinal tract and other systemic organs. The gut microbiome contributes to IBD pathology through multiple mechanisms. Bacteriophages (hence termed phages) are viruses that are able to specifically infect bacteria. Considered as part of the gut microbiome, phages may impact bacterial community structure in various clinical contexts. Additionally, exogenous phage administration may represent a means of suppressing IBD-associated pathobionts, yet utilization of phage therapy remains at an early developmental phase.

OBJECTIVES

Herein, we summarize the latest advances in understanding endogenous phage impacts on the gut microbiome in health and in IBD. We highlight the prospect of phage utilization as a targeted mode of pathobiont eradication, in preventing and treating IBD manifestations and complications.

SOURCES

Selected peer-reviewed publications regarding the role of phages in health and in IBD, published between 2013 and 2022.

CONTENT

The human gut microbiome is increasingly suggested to play a significant role in the onset and progression of multiple non-communicable diseases such as IBD. Several studies suggest that this effect may be mediated by discrete disease-contributing commensals. However, eradication of such pathogenic bacteria remains a daunting unmet task. Altered community structure in IBD may be influenced by blooms of phages within the gut bacterial ecosystem. Moreover, combinations of phages specifically targeting disease-contributing pathobiont strain clades may be harnessed as potential eradication treatment preventing and treating IBD, while bearing minimal adverse impacts on the surrounding bacterial microbiome.

IMPLICATIONS

Understanding endogenous phage-gut commensal interactions in health and in IBD may enable phage utilization in precision gut microbiome editing, towards treating IBD and other non-communicable microbiome-associated diseases. Nevertheless, developing phage combination-mediated IBD pathobiont eradication treatment modalities will likely necessitate better strain-level bacterial target identification and resolution of treatment-related challenges, such as phage delivery, off-target effects, and bacterial resistance.

Enterobacteria Phage Ac3's Genome Annotation and Host Range Analysis Against the ECOR Reference Library

Host range analyses and genome sequencing/annotation of bacteriophage isolates allow more effective development of tools for applications in medicine, agriculture, and the environment and expand our understanding of phage biology. Here we present the complete sequence of phage Ac3's assembled and annotated genome (accession OK040907). Originally referred to simply as "3," Ac3 has previously been described as a T4-like bacteriophage belonging to the Myoviridae family in the Caudovirales order of tailed bacteriophages. Using a combination of spot tests and full plate plaque assays, Ac3's permissive and adsorptive host range were evaluated against the ECOR Reference Library; a panel of 72 Escherichia coli isolates meant to represent the diversity of E. coli. Spot assays revealed that Ac3 could adsorb to 43 of the 72 strains (59.7%), whereas plaque assays demonstrated Ac3's ability to complete replication within 27 of the 72 strains (37.5%). By overlaying spot test and plaque assay results, 16 of the 45 nonpermissive ECOR strains (35.5%) were highlighted as being able to support Ac3's adsorption and tail contraction, but not its replication. Further characterization of Ac3 is still needed, however, the study presented here provides a solid starting point for future research.

Going Viral: Virus-Based Biological Control Agents for Plant Protection

The most economically important biotic stresses in crop production are caused by fungi, oomycetes, insects, viruses, and bacteria. Often chemical control is still the most commonly used method to manage them. However, the development of resistance in the different pathogens/pests, the putative damage on the natural ecosystem, the toxic residues in the field, and, thus, the contamination of the environment have stimulated the search for saferalternatives such as the use of biological control agents (BCAs). Among BCAs, viruses, a major driver for controlling host populations and evolution, are somewhat underused, mostly because of regulatory hurdles that make the cost of registration of such host-specific BCAs not affordable in comparison with the limited potential market. Here, we provide a comprehensive overview of the state of the art of virus-based BCAs against fungi, bacteria, viruses, and insects, with a specific focus on new approaches that rely on not only the direct biocidal virus component but also the complex ecological interactions between viruses and their hosts that do not necessarily result in direct damage to the host.

Isolation, Purification, and Characterisation of a Phage Tail-Like Bacteriocin from the Insect Pathogenic Bacterium Brevibacillus laterosporus

The Gram-positive and spore-forming bacterium Brevibacillus laterosporus (Bl) belongs to the Brevibacillus brevis phylogenetic cluster. Isolates of the species have demonstrated pesticidal potency against a wide range of invertebrate pests and plant diseases. Two New Zealand isolates, Bl 1821L and Bl 1951, are under development as biopesticides for control of diamondback moth and other pests. However, due to the often-restricted growth of these endemic isolates, production can be an issue. Based on the previous work, it was hypothesised that the putative phages might be involved. During investigations of the cause of the disrupted growth, electron micrographs of crude lysate of Bl 1821L showed the presence of phages’ tail-like structures. A soft agar overlay method with PEG 8000 precipitation was used to differentiate between the antagonistic activity of the putative phage and phage tail-like structures (bacteriocins). Assay tests authenticated the absence of putative phage activity. Using the same method, broad-spectrum antibacterial activity of Bl 1821L lysate against several Gram-positive bacteria was found. SDS-PAGE of sucrose density gradient purified and 10 kD MWCO concentrated lysate showed a prominent protein band of ~48 kD, and transmission electron microscopy revealed the presence of polysheath-like structures. N-terminal sequencing of the ~48 kD protein mapped to a gene with weak predicted amino acid homology to a Bacillus PBSX phage-like element xkdK, the translated product of which shared >90% amino acid similarity to the phage tail-sheath protein of another Bl published genome, LMG15441. Bioinformatic analysis also identified an xkdK homolog in the Bl 1951 genome. However, genome comparison of the region around the xkdK gene between Bl 1821L and Bl 1951 found differences including two glycine rich protein encoding genes which contain imperfect repeats (1700 bp) in Bl 1951, while a putative phage region resides in the analogous Bl 1821L region. Although comparative analysis of the genomic organisation of Bl 1821L and Bl 1951 PBSX-like region with the defective phages PBSX, PBSZ, and PBP 180 of Bacillus subtilis isolates 168 and W23, and Bacillus phage PBP180 revealed low amino acids similarity, the genes encode similar functional proteins in similar arrangements, including phage tail-sheath (XkdK), tail (XkdO), holin (XhlB), and N-acetylmuramoyl-l-alanine (XlyA). AMPA analysis identified a bactericidal stretch of 13 amino acids in the ~48 kD sequenced protein of Bl 1821L. Antagonistic activity of the purified ~48 kD phage tail-like protein in the assays differed remarkably from the crude lysate by causing a decrease of 34.2% in the number of viable cells of Bl 1951, 18 h after treatment as compared to the control. Overall, the identified inducible phage tail-like particle is likely to have implications for the in vitro growth of the insect pathogenic isolate Bl 1821L.

DeePVP: Identification and classification of phage virion proteins using deep learning

BACKGROUND

Many biological properties of phages are determined by phage virion proteins (PVPs), and the poor annotation of PVPs is a bottleneck for many areas of viral research, such as viral phylogenetic analysis, viral host identification, and antibacterial drug design. Because of the high diversity of PVP sequences, the PVP annotation of a phage genome remains a particularly challenging bioinformatic task.

FINDINGS

Based on deep learning, we developed DeePVP. The main module of DeePVP aims to discriminate PVPs from non-PVPs within a phage genome, while the extended module of DeePVP can further classify predicted PVPs into the 10 major classes of PVPs. Compared with the present state-of-the-art tools, the main module of DeePVP performs better, with a 9.05% higher F1-score in the PVP identification task. Moreover, the overall accuracy of the extended module of DeePVP in the PVP classification task is approximately 3.72% higher than that of PhANNs. Two application cases show that the predictions of DeePVP are more reliable and can better reveal the compact PVP-enriched region than the current state-of-the-art tools. Particularly, in the Escherichia phage phiEC1 genome, a novel PVP-enriched region that is conserved in many other Escherichia phage genomes was identified, indicating that DeePVP will be a useful tool for the analysis of phage genomic structures.

CONCLUSIONS

DeePVP outperforms state-of-the-art tools. The program is optimized in both a virtual machine with graphical user interface and a docker so that the tool can be easily run by noncomputer professionals. DeePVP is freely available at https://github.com/fangzcbio/DeePVP/.

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.

Battling Enteropathogenic Clostridia: Phage Therapy for Clostridioides difficile and Clostridium perfringens

The pathogenic Clostridioides difficile and Clostridium perfringens are responsible for many health care-associated infections as well as systemic and enteric diseases. Therefore, they represent a major health threat to both humans and animals. Concerns regarding increasing antibiotic resistance (related to C. difficile and C. perfringens) have caused a surge in the pursual of novel strategies that effectively combat pathogenic infections, including those caused by both pathogenic species. The ban on antibiotic growth promoters in the poultry industry has added to the urgency of finding novel antimicrobial therapeutics for C. perfringens. These efforts have resulted in various therapeutics, of which bacteriophages (in short, phages) show much promise, as evidenced by the Eliava Phage Therapy Center in Tbilisi, Georgia (https://eptc.ge/). Bacteriophages are a type of virus that infect bacteria. In this review, the (clinical) impact of clostridium infections in intestinal diseases is recapitulated, followed by an analysis of the current knowledge and applicability of bacteriophages and phage-derived endolysins in this disease indication. Limitations of phage and phage endolysin therapy were identified and require considerations. These include phage stability in the gastrointestinal tract, influence on gut microbiota structure/function, phage resistance development, limited host range for specific pathogenic strains, phage involvement in horizontal gene transfer, and-for phage endolysins-endolysin resistance, -safety, and -immunogenicity. Methods to optimize features of these therapeutic modalities, such as mutagenesis and fusion proteins, are also addressed. The future success of phage and endolysin therapies require reliable clinical trial data for phage(-derived) products. Meanwhile, additional research efforts are essential to expand the potential of exploiting phages and their endolysins for mitigating the severe diseases caused by C. difficile and C. perfringens.

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

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

Essential Topics for the Regulatory Consideration of Phages as Clinically Valuable Therapeutic Agents: A Perspective from Spain

Antibiotic resistance is one of the major challenges that humankind shall face in the short term. (Bacterio)phage therapy is a valuable therapeutic alternative to antibiotics and, although the concept is almost as old as the discovery of phages, its wide application was hindered in the West by the discovery and development of antibiotics in the mid-twentieth century. However, research on phage therapy is currently experiencing a renaissance due to the antimicrobial resistance problem. Some countries are already adopting new ad hoc regulations to favor the short-term implantation of phage therapy in clinical practice. In this regard, the Phage Therapy Work Group from FAGOMA (Spanish Network of Bacteriophages and Transducing Elements) recently contacted the Spanish Drugs and Medical Devices Agency (AEMPS) to promote the regulation of phage therapy in Spain. As a result, FAGOMA was asked to provide a general view on key issues regarding phage therapy legislation. This review comes as the culmination of the FAGOMA initiative and aims at appropriately informing the regulatory debate on phage therapy.

Genomic Characterisation of UFJF_PfDIW6: A Novel Lytic Pseudomonas fluorescens-Phage with Potential for Biocontrol in the Dairy Industry

In this study, we have presented the genomic characterisation of UFJF_PfDIW6, a novel lytic Pseudomonas fluorescens-phage with potential for biocontrol in the dairy industry. This phage showed a short linear double-stranded DNA genome (~42 kb) with a GC content of 58.3% and more than 50% of the genes encoding proteins with unknown functions. Nevertheless, UFJF_PfDIW6’s genome was organised into five functional modules: DNA packaging, structural proteins, DNA metabolism, lysogenic, and host lysis. Comparative genome analysis revealed that the UFJF_PfDIW6’s genome is distinct from other viral genomes available at NCBI databases, displaying maximum coverages of 5% among all alignments. Curiously, this phage showed higher sequence coverages (38–49%) when aligned with uncharacterised prophages integrated into Pseudomonas genomes. Phages compared in this study share conserved locally collinear blocks comprising genes of the modules’ DNA packing and structural proteins but were primarily differentiated by the composition of the DNA metabolism and lysogeny modules. Strategies for taxonomy assignment showed that UFJF_PfDIW6 was clustered into an unclassified genus in the Podoviridae clade. Therefore, our findings indicate that this phage could represent a novel genus belonging to the Podoviridae family.

Deploying Viruses against Phytobacteria: Potential Use of Phage Cocktails as a Multifaceted Approach to Combat Resistant Bacterial Plant Pathogens

Plants in nature are under the persistent intimidation of severe microbial diseases, threatening a sustainable food production system. Plant-bacterial pathogens are a major concern in the contemporary era, resulting in reduced plant growth and productivity. Plant antibiotics and chemical-based bactericides have been extensively used to evade plant bacterial diseases. To counteract this pressure, bacteria have evolved an array of resistance mechanisms, including innate and adaptive immune systems. The emergence of resistant bacteria and detrimental consequences of antimicrobial compounds on the environment and human health, accentuates the development of an alternative disease evacuation strategy. The phage cocktail therapy is a multidimensional approach effectively employed for the biocontrol of diverse resistant bacterial infections without affecting the fauna and flora. Phages engage a diverse set of counter defense strategies to undermine wide-ranging anti-phage defense mechanisms of bacterial pathogens. Microbial ecology, evolution, and dynamics of the interactions between phage and plant-bacterial pathogens lead to the engineering of robust phage cocktail therapeutics for the mitigation of devastating phytobacterial diseases. In this review, we highlight the concrete and fundamental determinants in the development and application of phage cocktails and their underlying mechanism, combating resistant plant-bacterial pathogens. Additionally, we provide recent advances in the use of phage cocktail therapy against phytobacteria for the biocontrol of devastating plant diseases.

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.

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.

Subtypes of tail spike proteins predicts the host range of Ackermannviridae phages

Phages belonging to the Ackermannviridae family encode up to four tail spike proteins (TSPs), each recognizing a specific receptor of their bacterial hosts. Here, we determined the TSPs diversity of 99 Ackermannviridae phages by performing a comprehensive in silico analysis. Based on sequence diversity, we assigned all TSPs into distinctive subtypes of TSP1, TSP2, TSP3 and TSP4, and found each TSP subtype to be specifically associated with the genera (Kuttervirus, Agtrevirus, Limestonevirus, Taipeivirus) of the Ackermannviridae family. Further analysis showed that the N-terminal XD1 and XD2 domains in TSP2 and TSP4, hinging the four TSPs together, are preserved. In contrast, the C-terminal receptor binding modules were only conserved within TSP subtypes, except for some Kuttervirus TSP1s and TSP3s that were similar to specific TSP4s. A conserved motif in TSP1, TSP3 and TSP4 of Kuttervirus phages may allow recombination between receptor binding modules, thus altering host recognition. The receptors for numerous uncharacterized phages expressing TSPs in the same subtypes were predicted using previous host range data. To validate our predictions, we experimentally determined the host recognition of three of the four TSPs expressed by kuttervirus S117. We confirmed that S117 TSP1 and TSP2 bind to their predicted host receptors, and identified the receptor for TSP3, which is shared by 51 other Kuttervirus phages. Kuttervirus phages were thus shown encode a vast genetic diversity of potentially exchangeable TSPs influencing host recognition. Overall, our study demonstrates that comprehensive in silico and host range analysis of TSPs can predict host recognition of Ackermannviridae phages.

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.

Isolation and characterization of a novel bacteriophage WO from Allonemobius socius crickets in Missouri

Wolbachia are endosymbionts of numerous arthropod and some nematode species, are important for their development and if present can cause distinct phenotypes of their hosts. Prophage DNA has been frequently detected in Wolbachia, but particles of Wolbachia bacteriophages (phage WO) have been only occasionally isolated. Here, we report the characterization and isolation of a phage WO of the southern ground cricket, Allonemobius socius, and provided the first whole-genome sequence of phage WO from this arthropod family outside of Asia. We screened A. socius abdomen DNA extracts from a cricket population in eastern Missouri by quantitative PCR for Wolbachia surface protein and phage WO capsid protein and found a prevalence of 55% and 50%, respectively, with many crickets positive for both. Immunohistochemistry using antibodies against Wolbachia surface protein showed many Wolbachia clusters in the reproductive system of female crickets. Whole-genome sequencing using Oxford Nanopore MinION and Illumina technology allowed for the assembly of a high-quality, 55 kb phage genome containing 63 open reading frames (ORF) encoding for phage WO structural proteins and host lysis and transcriptional manipulation. Taxonomically important regions of the assembled phage genome were validated by Sanger sequencing of PCR amplicons. Analysis of the nucleotides sequences of the ORFs encoding the large terminase subunit (ORF2) and minor capsid (ORF7) frequently used for phage WO phylogenetics showed highest homology to phage WOAu of Drosophila simulans (94.46% identity) and WOCin2USA1 of the cherry fruit fly, Rhagoletis cingulata (99.33% identity), respectively. Transmission electron microscopy examination of cricket ovaries showed a high density of phage particles within Wolbachia cells. Isolation of phage WO revealed particles characterized by 40–62 nm diameter heads and up to 190 nm long tails. This study provides the first detailed description and genomic characterization of phage WO from North America that is easily accessible in a widely distributed cricket species.

Influence of Caudovirales Phages on Humoral Immunity in Mice

Bacteriophages are promising antibacterial agents. Although they have been recognized as bacterial viruses and are considered to be non-interacting with eukaryotic cells, there is growing evidence that phages may have a significant impact on the immune system via interactions with macrophages, neutrophils, and T-cell polarization. In this study, the influence of phages of podovirus, siphovirus, and myovirus morphotypes on humoral immunity of CD-1 mice was investigated. In addition, tissue distribution of the phages was tested in these mice. No common patterns were found either in the distribution of phages in mice or in changes in the levels of cytokines in the sera of mice once injected with phages. Importantly, pre-existing IgM-class antibodies directed against capsid proteins of phages with myovirus and siphovirus morphotypes were identified in mice before immunization. After triple immunization of CD1-mice with phages without any adjuvant, levels of anti-phage serum polyclonal IgG antibodies increased. Immunogenic phage proteins recognized by IgM and/or IgG antibodies were identified using Western blot analysis and mass spectrometry. In addition, mice serum collected after immunization demonstrated neutralizing properties, leading to a substantial decrease in infectivity of investigated phages with myovirus and siphovirus morphotypes. Moreover, serum samples collected before administration of these phages exhibited some ability to reduce the phage infectivity. Furthermore, Proteus phage PM16 with podovirus morphotype did not elicit IgM or IgG antibodies in immunized mice, and no neutralizing activities against PM16 were revealed in mouse serum samples before and after immunization.

Global overview and major challenges of host prediction methods for uncultivated phages

Bacterial communities play critical roles across all of Earth's biomes, affecting human health and global ecosystem functioning. They do so under strong constraints exerted by viruses, that is, bacteriophages or 'phages'. Phages can reshape bacterial communities' structure, influence long-term evolution of bacterial populations, and alter host cell metabolism during infection. Metagenomics approaches, that is, shotgun sequencing of environmental DNA or RNA, recently enabled large-scale exploration of phage genomic diversity, yielding several millions of phage genomes now to be further analyzed and characterized. One major challenge however is the lack of direct host information for these phages. Several methods and tools have been proposed to bioinformatically predict the potential host(s) of uncultivated phages based only on genome sequence information. Here we review these different approaches and highlight their distinct strengths and limitations. We also outline complementary experimental assays which are being proposed to validate and refine these bioinformatic predictions.

Bacteriophage-Host Association in the Phytoplasma Insect Vector Euscelidius variegatus

Insect vectors transmit viruses and bacteria that can cause severe diseases in plants and economic losses due to a decrease in crop production. Insect vectors, like all other organisms, are colonized by a community of various microorganisms, which can influence their physiology, ecology, evolution, and also their competence as vectors. The important ecological meaning of bacteriophages in various ecosystems and their role in microbial communities has emerged in the past decade. However, only a few phages have been described so far in insect microbiomes. The leafhopper Euscelidius variegatus is a laboratory vector of the phytoplasma causing Flavescence dorée, a severe grapevine disease that threatens viticulture in Europe. Here, the presence of a temperate bacteriophage in E. variegatus (named Euscelidius variegatus phage 1, EVP-1) was revealed through both insect transcriptome analyses and electron microscopic observations. The bacterial host was isolated in axenic culture and identified as the bacterial endosymbiont of E. variegatus (BEV), recently assigned to the genus Candidatus Symbiopectobacterium. BEV harbors multiple prophages that become active in culture, suggesting that different environments can trigger different mechanisms, finely regulating the interactions among phages. Understanding the complex relationships within insect vector microbiomes may help in revealing possible microbe influences on pathogen transmission, and it is a crucial step toward innovative sustainable strategies for disease management in agriculture.