Phage proteomics: applications of mass spectrometry

DPI_CDF: druggable protein identifier using cascade deep forest

BACKGROUND

Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory.

METHODS

In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF.

RESULTS

The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process.

AVAILABILITY

The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Genomic and proteomic characterization of vB_SauM-UFV_DC4, a novel Staphylococcus jumbo phage

Staphylococcus aureus is one of the most relevant mastitis pathogens in dairy cattle, and the acquisition of antimicrobial resistance genes presents a significant health issue in both veterinary and human fields. Among the different strategies to tackle S. aureus infection in livestock, bacteriophages have been thoroughly investigated in the last decades; however, few specimens of the so-called jumbo phages capable of infecting S. aureus have been described. Herein, we report the biological, genomic, and structural proteomic features of the jumbo phage vB_SauM-UFV_DC4 (DC4). DC4 exhibited a remarkable killing activity against S. aureus isolated from the veterinary environment and stability at alkaline conditions (pH 4 to 12). The complete genome of DC4 is 263,185 bp (GC content: 25%), encodes 263 predicted CDSs (80% without an assigned function), 1 tRNA (Phe-tRNA), multisubunit RNA polymerase, and an RNA-dependent DNA polymerase. Moreover, comparative analysis revealed that DC4 can be considered a new viral species belonging to a new genus DC4 and showed a similar set of lytic proteins and depolymerase activity with closely related jumbo phages. The characterization of a new S. aureus jumbo phage increases our understanding of the diversity of this group and provides insights into the biotechnological potential of these viruses. KEY POINTS: • vB_SauM-UFV_DC4 is a new viral species belonging to a new genus within the class Caudoviricetes. • vB_SauM-UFV_DC4 carries a set of RNA polymerase subunits and an RNA-directed DNA polymerase. • vB_SauM-UFV_DC4 and closely related jumbo phages showed a similar set of lytic proteins.

Stability of bacteriophages in organic solvents for formulations

Bacteriophages or phages used as an alternative therapy for treating multi-drug resistant infections require formulation consideration. Current strategies to produce phage formulations involving organic solvents are based on empirical practices without a good understanding of phage stability during formulation development. In this study, we investigated the effect of common formulation organic solvents (ethanol, isopropyl alcohol, tetrahydrofuran (THF) and dimethyl sulfoxide (DMSO)) on the stability of Pseudomonas aeruginosa-specific myovirus (PEV1, PEV20) and podovirus (PEV31) phages using biological assay, transmission electron microscopy (TEM) and scattering near field optical microscopy (SNOM). The three phages were mixed with the solvents at different concentrations (25%, 50%, and 75% (v/v)) for 20 min. All phages were fully viable in the organic solvents at 25% (v/v) showing negligible titre changes. At the higher solvent concentration of 50% (v/v), the myoviruses PEV1 and PEV20 remained relatively stable (titre loss 0.4-1.3 log10), whereas the podovirus PEV31 became less stable (titre loss 0.25-3.8 log10), depending on the solvent used. Increasing the solvent level to 75% (v/v) caused increased morphological changes in TEM and decreased viability as indicated by the titre loss (0.32-7.4 log10), with DMSO being the most phage-destabilising solvent. SNOM spectra showed differences in the signal intensity and peak positions in the amide I and amide II regions, revealing altered phage proteins by the solvents. In conclusion, the choice of the solvents for phage formulation depends on both the phages and solvent types. Our results showed (1) the phages are more stable in the alcohols than DMSO and THF, and (2) the myoviruses tend to be more stable than the podovirus in the solvents. Overall, a low to moderate (25-50 % v/v) level of organic solvents (except 50% THF) can be used in formulation of the phages without a substantial titre loss.

Integrating Low-Order and High-Order Correlation Information for Identifying Phage Virion Proteins

Phage virion proteins (PVPs) play an important role in the host cell. Fast and accurate identification of PVPs is beneficial for the discovery and development of related drugs. Although wet experimental approaches are the first choice to identify PVPs, they are costly and time-consuming. Thus, researchers have turned their attention to computational models, which can speed up related studies. Therefore, we proposed a novel machine-learning model to identify PVPs in the current study. First, 50 different types of physicochemical properties were used to denote protein sequences. Next, two different approaches, including Pearson's correlation coefficient (PCC) and maximal information coefficient (MIC), were employed to extract discriminative information. Further, to capture the high-order correlation information, we used PCC and MIC once again. After that, we adopted the least absolute shrinkage and selection operator algorithm to select the optimal feature subset. Finally, these chosen features were fed into a support vector machine to discriminate PVPs from phage non-virion proteins. We performed experiments on two different datasets to validate the effectiveness of our proposed method. Experimental results showed a significant improvement in performance compared with state-of-the-art approaches. It indicates that the proposed computational model may become a powerful predictor in identifying PVPs.

Prediction of Phage Virion Proteins Using Machine Learning Methods

Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design.

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.

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/.

Ensemble Learning-Based Feature Selection for Phage Protein Prediction

Phage has high specificity for its host recognition. As a natural enemy of bacteria, it has been used to treat super bacteria many times. Identifying phage proteins from the original sequence is very important for understanding the relationship between phage and host bacteria and developing new antimicrobial agents. However, traditional experimental methods are both expensive and time-consuming. In this study, an ensemble learning-based feature selection method is proposed to find important features for phage protein identification. The method uses four types of protein sequence-derived features, quantifies the importance of each feature by adding perturbations to the features to influence the results, and finally splices the important features among the four types of features. In addition, we analyzed the selected features and their biological significance.

The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics

Phages have certain features, such as their ability to form protein-protein interactions, that make them good candidates for use in a variety of beneficial applications, such as in human or animal health, industry, food science, food safety, and agriculture. It is essential to identify and characterize the proteins produced by particular phages in order to use these viruses in a variety of functional processes, such as bacterial detection, as vehicles for drug delivery, in vaccine development, and to combat multidrug resistant bacterial infections. Furthermore, phages can also play a major role in the design of a variety of cheap and stable sensors as well as in diagnostic assays that can either specifically identify specific compounds or detect bacteria. This article reviews recently developed phage-based techniques, such as the use of recombinant tempered phages, phage display and phage amplification-based detection. It also encompasses the application of phages as capture elements, biosensors and bioreceptors, with a special emphasis on novel bacteriophage-based mass spectrometry (MS) applications.

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins

Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).

Large-scale comparative review and assessment of computational methods for phage virion proteins identification

Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.

iPVP-MCV: A Multi-Classifier Voting Model for the Accurate Identification of Phage Virion Proteins

The classic structure of a bacteriophage is commonly characterized by complex symmetry. The head of the structure features icosahedral symmetry, whereas the tail features helical symmetry. The phage virion protein (PVP), a type of bacteriophage structural protein, is an essential material of the infectious viral particles and is responsible for multiple biological functions. Accurate identification of PVPs is of great significance for comprehending the interaction between phages and host bacteria and developing new antimicrobial drugs or antibiotics. However, traditional experimental approaches for identifying PVPs are often time-consuming and laborious. Therefore, the development of computational methods that can efficiently and accurately identify PVPs is desired. In this study, we proposed a multi-classifier voting model called iPVP-MCV to enhance the predictive performance of PVPs based on their amino acid sequences. First, three types of evolutionary features were extracted from the position-specific scoring matrix (PSSM) profiles to represent PVPs and non-PVPs. Then, a set of baseline models were trained based on the support vector machine (SVM) algorithm combined with each type of feature descriptors. Finally, the outputs of these baseline models were integrated to construct the proposed method iPVP-MCV by using the majority voting strategy. Our results demonstrated that the proposed iPVP-MCV model was superior to existing methods when performing the rigorous independent dataset test.

Proteomic Characterization of Bacteriophage Peptides from the Mastitis Producer Staphylococcus aureus by LC-ESI-MS/MS and the Bacteriophage Phylogenomic Analysis

The present work describes LC-ESI-MS/MS MS (liquid chromatography-electrospray ionization-tandem mass spectrometry) analyses of tryptic digestion peptides from phages that infect mastitis-causing Staphylococcus aureus isolated from dairy products. A total of 1933 nonredundant peptides belonging to 1282 proteins were identified and analyzed. Among them, 79 staphylococcal peptides from phages were confirmed. These peptides belong to proteins such as phage repressors, structural phage proteins, uncharacterized phage proteins and complement inhibitors. Moreover, eighteen of the phage origin peptides found were specific to S. aureus strains. These diagnostic peptides could be useful for the identification and characterization of S. aureus strains that cause mastitis. Furthermore, a study of bacteriophage phylogeny and the relationship among the identified phage peptides and the bacteria they infect was also performed. The results show the specific peptides that are present in closely related phages and the existing links between bacteriophage phylogeny and the respective Staphylococcus spp. infected.

Genome Sequence of the Bacteriophage CL31 and Interaction with the Host Strain Corynebacterium glutamicum ATCC 13032

In this study, we provide a comprehensive analysis of the genomic features of the phage CL31 and the infection dynamics with the biotechnologically relevant host strain Corynebacterium glutamicum ATCC 13032. Genome sequencing and annotation of CL31 revealed a 45-kbp genome composed of 72 open reading frames, mimicking the GC content of its host strain (54.4%). An ANI-based distance matrix showed the highest similarity of CL31 to the temperate corynephage Φ16. While the C. glutamicum ATCC 13032 wild type strain showed only mild propagation of CL31, a strain lacking the cglIR-cglIIR-cglIM restriction-modification system was efficiently infected by this phage. Interestingly, the prophage-free strain C. glutamicum MB001 featured an even accelerated amplification of CL31 compared to the ∆resmod strain suggesting a role of cryptic prophage elements in phage defense. Proteome analysis of purified phage particles and transcriptome analysis provide important insights into structural components of the phage and the response of C. glutamicum to CL31 infection. Isolation and sequencing of CL31-resistant strains revealed SNPs in genes involved in mycolic acid biosynthesis suggesting a role of this cell envelope component in phage adsorption. Altogether, these results provide an important basis for further investigation of phage-host interactions in this important biotechnological model organism.

Recent Advances of Computational Methods for Identifying Bacteriophage Virion Proteins

Phage Virion Proteins (PVP) are essential materials of bacteriophage, which participate in a series of biological processes. Accurate identification of phage virion proteins is helpful to understand the mechanism of interaction between the phage and its host bacteria. Since experimental method is labor intensive and time-consuming, in the past few years, many computational approaches have been proposed to identify phage virion proteins. In order to facilitate researchers to select appropriate methods, it is necessary to give a comprehensive review and comparison on existing computational methods on identifying phage virion proteins. In this review, we summarized the existing computational methods for identifying phage virion proteins and also assessed their performances on an independent dataset. Finally, challenges and future perspectives for identifying phage virion proteins were presented. Taken together, we hope that this review could provide clues to researches on the study of phage virion proteins.

Characterization of Bacteriophage Peptides of Pathogenic Streptococcus by LC-ESI-MS/MS: Bacteriophage Phylogenomics and Their Relationship to Their Host

The present work focuses on LC-ESI-MS/MS (liquid chromatography-electrospray ionization-tandem mass spectrometry) analysis of phage-origin tryptic digestion peptides from mastitis-causing Streptococcus spp. isolated from milk. A total of 2,546 non-redundant peptides belonging to 1,890 proteins were identified and analyzed. Among them, 65 phage-origin peptides were determined as specific Streptococcus spp. peptides. These peptides belong to proteins such as phage repressors, phage endopeptidases, structural phage proteins, and uncharacterized phage proteins. Studies involving bacteriophage phylogeny and the relationship between phages encoding the peptides determined and the bacteria they infect were also performed. The results show how specific peptides are present in closely related phages, and a link exists between bacteriophage phylogeny and the Streptococcus spp. they infect. Moreover, the phage peptide M∗ATNLGQAYVQIM∗PSAK is unique and specific for Streptococcus agalactiae. These results revealed that diagnostic peptides, among others, could be useful for the identification and characterization of mastitis-causing Streptococcus spp., particularly peptides that belong to specific functional proteins, such as phage-origin proteins, because of their specificity to bacterial hosts.

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

vB_PaeM_MIJ3, a Novel Jumbo Phage Infecting Pseudomonas aeruginosa, Possesses Unusual Genomic Features

Phages are the most abundant biological entity on Earth. There are many variants in phage virion sizes, morphology, and genome sizes. Large virion sized phages, with genome sizes greater than 200 kbp have been identified and termed as Jumbo phages. These phages exhibit certain characteristics that have not been reported in phages with smaller genomes. In this work, a jumbo phage named MIJ3 (vB_PaeM_MIJ3) that infects Pseudomonas aeruginosa PAO1 was isolated from an equine livery yard in Leicestershire, United Kingdom. The genome and biological characteristics of this phage have been investigated. MIJ3 is a Myovirus with multiple long tail fibers. Assessment of the host range of MIJ3 revealed that it has the ability to infect many clinical isolates of P. aeruginosa. Bioinformatics analysis of the phage genome indicated that MIJ3 is closely related to the Pseudomonas phage, PA5oct. MIJ3 possesses several unusual features that are either rarely present in other phages or have not yet been reported. In particular, MIJ3 encodes a FtsH-like protein, and a putative lysidine synthase, TilS. These two proteins have not been reported in phages. MIJ3 also possesses a split DNA polymerase B with a novel intein. Of particular interest, unlike other jumbo phages infecting Pseudomonas spp., MIJ3 lacks the genetic elements required for the formation of the phage nucleus, which was believed to be conserved across jumbo Pseudomonas phages.

Anti-CRISPR-Associated Proteins Are Crucial Repressors of Anti-CRISPR Transcription

Phages express anti-CRISPR (Acr) proteins to inhibit CRISPR-Cas systems that would otherwise destroy their genomes. Most acr genes are located adjacent to anti-CRISPR-associated (aca) genes, which encode proteins with a helix-turn-helix DNA-binding motif. The conservation of aca genes has served as a signpost for the identification of acr genes, but the function of the proteins encoded by these genes has not been investigated. Here we reveal that an acr-associated promoter drives high levels of acr transcription immediately after phage DNA injection and that Aca proteins subsequently repress this transcription. Without Aca activity, this strong transcription is lethal to a phage. Our results demonstrate how sufficient levels of Acr proteins accumulate early in the infection process to inhibit existing CRISPR-Cas complexes in the host cell. They also imply that the conserved role of Aca proteins is to mitigate the deleterious effects of strong constitutive transcription from acr promoters.

Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods

Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins.

Urinary Virome Perturbations in Kidney Transplantation

The human microbiome is important for health and plays a role in essential metabolic functions and protection from certain pathogens. Conversely, dysbiosis of the microbiome is seen in the context of various diseases. Recent studies have highlighted that a complex microbial community containing hundreds of bacteria colonizes the healthy urinary tract, but little is known about the human urinary viruses in health and disease. To evaluate the human urinary virome in the context of kidney transplantation (tx), variations in the composition of the urinary virome were evaluated in urine samples from normal healthy volunteers as well as patients with kidney disease after they had undergone kidney tx. Liquid chromatography-mass spectrometry/mass spectrometry analysis was undertaken on a selected cohort of 142 kidney tx patients and normal healthy controls, from a larger biobank of 770 kidney biopsy matched urine samples. In addition to analysis of normal healthy control urine, the cohort of kidney tx patients had biopsy confirmed phenotype classification, coincident with the urine sample analyzed, of stable grafts (STA), acute rejection, BK virus nephritis, and chronic allograft nephropathy. We identified 37 unique viruses, 29 of which are being identified for the first time in human urine samples. The composition of the human urinary virome differs in health and kidney injury, and the distribution of viral proteins in the urinary tract may be further impacted by IS exposure, diet and environmental, dietary, or cutaneous exposure to various insecticides and pesticides.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

Characterization of Novel Bacteriophages for Biocontrol of Bacterial Blight in Leek Caused by Pseudomonas syringae pv. porri

Pseudomonas syringae pv. porri, the causative agent of bacterial blight in leek (Allium porrum), is increasingly frequent causing problems in leek cultivation. Because of the current lack of control measures, novel bacteriophages were isolated to control this pathogen using phage therapy. Five novel phages were isolated from infected fields in Flanders (vB_PsyM_KIL1, vB_PsyM_KIL2, vB_PsyM_KIL3, vB_PsyM_KIL4, and vB_PsyM_KIL5), and were complemented with one selected host range mutant phage (vB_PsyM_KIL3b). Genome analysis of the phages revealed genome sizes between 90 and 94 kb and an average GC-content of 44.8%. Phylogenomic networking classified them into a novel clade, named the "KIL-like viruses," related to the Felixounalikevirus genus, together with phage phiPsa374 from P. syringae pv. actinidiae. In vitro characterization demonstrated the stability and lytic potential of these phages. Host range analysis confirmed heterogeneity within P. syringae pv. porri, leading to the development of a phage cocktail with a range that covers the entire set of 41 strains tested. Specific bio-assays demonstrated the in planta efficacy of phages vB_PsyM_KIL1, vB_PsyM_KIL2, vB_PsyM_KIL3, and vB_PsyM_KIL3b. In addition, two parallel field trial experiments on three locations using a phage cocktail of the six phages showed variable results. In one trial, symptom development was attenuated. These data suggest some potential for phage therapy in controlling bacterial blight of leek, pending optimization of formulation and application methods.

Physiological Properties and Genome Structure of the Hyperthermophilic Filamentous Phage φOH3 Which Infects Thermus thermophilus HB8

A filamentous bacteriophage, φOH3, was isolated from hot spring sediment in Obama hot spring in Japan with the hyperthermophilic bacterium Thermus thermophilus HB8 as its host. Phage φOH3, which was classified into the Inoviridae family, consists of a flexible filamentous particle 830 nm long and 8 nm wide. φOH3 was stable at temperatures ranging from 70 to 90°C and at pHs ranging from 6 to 9. A one-step growth curve of the phage showed a 60-min latent period beginning immediately postinfection, followed by intracellular virus particle production during the subsequent 40 min. The released virion number of φOH3 was 109. During the latent period, both single stranded DNA (ssDNA) and the replicative form (RF) of phage DNA were multiplied from min 40 onward. During the release period, the copy numbers of both ssDNA and RF DNA increased sharply. The size of the φOH3 genome is 5688 bp, and eight putative open reading frames (ORFs) were annotated. These ORFs were encoded on the plus strand of RF DNA and showed no significant homology with any known phage genes, except ORF 5, which showed 60% identity with the gene VIII product of the Thermus filamentous phage PH75. All the ORFs were similar to predicted genes annotated in the Thermus aquaticus Y51MC23 and Meiothermus timidus DSM 17022 genomes at the amino acid sequence level. This is the first report of the whole genome structure and DNA multiplication of a filamentous T. thermophilus phage within its host cell.

Illuminating structural proteins in viral "dark matter" with metaproteomics

Viruses are ecologically important, yet environmental virology is limited by dominance of unannotated genomic sequences representing taxonomic and functional "viral dark matter." Although recent analytical advances are rapidly improving taxonomic annotations, identifying functional dark matter remains problematic. Here, we apply paired metaproteomics and dsDNA-targeted metagenomics to identify 1,875 virion-associated proteins from the ocean. Over one-half of these proteins were newly functionally annotated and represent abundant and widespread viral metagenome-derived protein clusters (PCs). One primarily unannotated PC dominated the dataset, but structural modeling and genomic context identified this PC as a previously unidentified capsid protein from multiple uncultivated tailed virus families. Furthermore, four of the five most abundant PCs in the metaproteome represent capsid proteins containing the HK97-like protein fold previously found in many viruses that infect all three domains of life. The dominance of these proteins within our dataset, as well as their global distribution throughout the world's oceans and seas, supports prior hypotheses that this HK97-like protein fold is the most abundant biological structure on Earth. Together, these culture-independent analyses improve virion-associated protein annotations, facilitate the investigation of proteins within natural viral communities, and offer a high-throughput means of illuminating functional viral dark matter.

Incomplete LPS Core-Specific Felix01-Like Virus vB_EcoM_VpaE1

Bacteriophages represent a valuable source for studying the mechanisms underlying virus-host interactions. A better understanding of the host-specificity of viruses at the molecular level can promote various phage applications, including bacterial diagnostics, antimicrobial therapeutics, and improve methods in molecular biology. In this study, we describe the isolation and characterization of a novel coliphage, vB_EcoM_VpaE1, which has different host specificity than its relatives. Morphology studies, coupled with the results of genomic and proteomic analyses, indicate that vB_EcoM_VpaE1 belongs to the newly proposed genus Felix01likevirus in the family Myoviridae. The genus Felix01likevirus comprises a group of highly similar phages that infect O-antigen-expressing Salmonella and Escherichia coli (E. coli) strains. Phage vB_EcoM_VpaE1 differs from the rest of Felix01-like viruses, since it infects O-antigen-deficient E. coli strains with an incomplete core lipopolysaccharide (LPS). We show that vB_EcoM_VpaE1 can infect mutants of E. coli that contain various truncations in their LPS, and can even recognize LPS that is truncated down to the inner-core oligosaccharide, showing potential for the control of rough E. coli strains, which usually emerge as resistant mutants upon infection by O-Ag-specific phages. Furthermore, VpaE1 can replicate in a wide temperature range from 9 to 49 °C, suggesting that this virus is well adapted to harsh environmental conditions. Since the structural proteins of such phages tend to be rather robust, the receptor-recognizing proteins of VpaE1 are an attractive tool for application in glycan analysis, bacterial diagnostics and antimicrobial therapeutics.

Genome, Proteome and Structure of a T7-Like Bacteriophage of the Kiwifruit Canker Phytopathogen Pseudomonas syringae pv. actinidiae

Pseudomonas syringae pv. actinidiae is an economically significant pathogen responsible for severe bacterial canker of kiwifruit (Actinidia sp.). Bacteriophages infecting this phytopathogen have potential as biocontrol agents as part of an integrated approach to the management of bacterial canker, and for use as molecular tools to study this bacterium. A variety of bacteriophages were previously isolated that infect P. syringae pv. actinidiae, and their basic properties were characterized to provide a framework for formulation of these phages as biocontrol agents. Here, we have examined in more detail φPsa17, a phage with the capacity to infect a broad range of P. syringae pv. actinidiae strains and the only member of the Podoviridae in this collection. Particle morphology was visualized using cryo-electron microscopy, the genome was sequenced, and its structural proteins were analysed using shotgun proteomics. These studies demonstrated that φPsa17 has a 40,525 bp genome, is a member of the T7likevirus genus and is closely related to the pseudomonad phages φPSA2 and gh-1. Eleven structural proteins (one scaffolding) were detected by proteomics and φPsa17 has a capsid of approximately 60 nm in diameter. No genes indicative of a lysogenic lifecycle were identified, suggesting the phage is obligately lytic. These features indicate that φPsa17 may be suitable for formulation as a biocontrol agent of P. syringae pv. actinidiae.

Core and accessory genome architecture in a group of Pseudomonas aeruginosa Mu-like phages

Background

Bacteriophages that infect the opportunistic pathogen Pseudomonas aeruginosa have been classified into several groups. One of them, which includes temperate phage particles with icosahedral heads and long flexible tails, bears genomes whose architecture and replication mechanism, but not their nucleotide sequences, are like those of coliphage Mu. By comparing the genomic sequences of this group of P. aeruginosa phages one could draw conclusions about their ontogeny and evolution.

Results

Two newly isolated Mu-like phages of P. aeruginosa are described and their genomes sequenced and compared with those available in the public data banks. The genome sequences of the two phages are similar to each other and to those of a group of P. aeruginosa transposable phages. Comparing twelve of these genomes revealed a common genomic architecture in the group. Each phage genome had numerous genes with homologues in all the other genomes and a set of variable genes specific for each genome. The first group, which comprised most of the genes with assigned functions, was named “core genome”, and the second group, containing mostly short ORFs without assigned functions was called “accessory genome”. Like in other phage groups, variable genes are confined to specific regions in the genome.

Conclusion

Based on the known and inferred functions for some of the variable genes of the phages analyzed here, they appear to confer selective advantages for the phage survival under particular host conditions. We speculate that phages have developed a mechanism for horizontally acquiring genes to incorporate them at specific loci in the genome that help phage adaptation to the selective pressures imposed by the host.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1146) contains supplementary material, which is available to authorized users.

Progress in lactic acid bacterial phage research

Research on lactic acid bacteria (LAB) has advanced significantly over the past number of decades and these developments have been driven by the parallel advances in technologies such as genomics, bioinformatics, protein expression systems and structural biology, combined with the ever increasing commercial relevance of this group of microorganisms. Some of the more significant and impressive outputs have been in the domain of bacteriophage-host interactions which provides a prime example of the cutting-edge model systems represented by LAB research. Here, we present a retrospective overview of the key advances in LAB phage research including phage-host interactions and co-evolution. We describe how in many instances this knowledge can be pivotal in creating real improvements in the application of LAB cultures in commercial practice.

Genome Analysis of Pseudomonas aeruginosa Bacteriophage KPP23, Belonging to the Family Siphoviridae

Bacteriophage (phage) therapy is expected to become an alternative therapy for Pseudomonas aeruginosa infections. P. aeruginosa phage KPP23 is a newly isolated phage belonging to the family Siphoviridae and may be a therapeutic phage candidate. We report its complete genome, which comprises 62,774 bp of double-stranded DNA containing 95 open reading frames.

Characterization of a novel Pseudomonas aeruginosa bacteriophage, KPP25, of the family Podoviridae

Pseudomonas aeruginosa phages belonging to the family Podoviridae are one of the well-characterized phage groups. In this study, a novel P. aeruginosa phage, KPP25, was isolated and characterized. Phage KPP25's morphology was indicative of the family Podoviridae; however, analyses of the whole genome and the virion proteins suggested that it did not belong to any of the known podophage genera. Based on these analyses, phage KPP25 appears to be a novel podophage infecting P. aeruginosa.

Data-independent acquisition (MSE) with ion mobility provides a systematic method for analysis of a bacteriophage structural proteome

In this work, a method was developed to study the structural proteome of mycobacteriophage Marvin, a recent isolate from soil with 107 predicted coding sequences. This prototype method was applied for semi-quantitative analysis of the composition of this mycobacteriophage virion using ion mobility spectrometry and data-independent acquisition (MS(E)-IMS). MS(E)-IMS was compared to a more conventional proteomics technique employing mass spectrometry with a data-dependent acquisition strategy. MS(E)-IMS provided broad coverage of the virion proteome and high sequence coverage for individual proteins. This shotgun method does not depend on the limited sensitivity of visualization of protein bands by staining reagents inherent in gel-based methods. The method is comprehensive, provides high sequence coverage and is proposed as a particularly efficient method for the study of bacteriophage proteomes.

Naïve Bayes classifier with feature selection to identify phage virion proteins

Knowledge about the protein composition of phage virions is a key step to understand the functions of phage virion proteins. However, the experimental method to identify virion proteins is time consuming and expensive. Thus, it is highly desirable to develop novel computational methods for phage virion protein identification. In this study, a Naïve Bayes based method was proposed to predict phage virion proteins using amino acid composition and dipeptide composition. In order to remove redundant information, a novel feature selection technique was employed to single out optimized features. In the jackknife test, the proposed method achieved an accuracy of 79.15% for phage virion and nonvirion proteins classification, which are superior to that of other state-of-the-art classifiers. These results indicate that the proposed method could be as an effective and promising high-throughput method in phage proteomics research.

Escherichia coli O157:H7 typing phage V7 is a T4-like virus

The complete genome sequence of the Escherichia coli O157:H7 typing phage V7 was determined. Its double-stranded DNA genome is 166,452 bp long, encoding 273 proteins and including 11 tRNAs. This virus belongs to the genus T4-like viruses within the subfamily Tevenvirinae, family Myoviridae.

CIM(®) monolithic anion-exchange chromatography as a useful alternative to CsCl gradient purification of bacteriophage particles

The use of anion-exchange chromatography was investigated as an alternative method to concentrate and purify bacterial viruses, and parameters for different bacteriophages were compared. Chromatography was performed with Convective Interactive Media(®) monoliths, with three different volumes and two matrix chemistries. Eleven morphologically distinct phages were tested, infecting five different bacterial species. For each of the phages tested, a protocol was optimized, including the choice of column chemistry, loading, buffer and elution conditions. The capacity and recovery of the phages on the columns varied considerably between phages. We conclude that anion-exchange chromatography with monoliths is a valid alternative to the more traditional CsCl purification, has upscaling advantages, but it requires more extensive optimization.

Genomes and characterization of phages Bcep22 and BcepIL02, founders of a novel phage type in Burkholderia cenocepacia

Within the Burkholderia cepacia complex, B. cenocepacia is the most common species associated with aggressive infections in the lungs of cystic fibrosis patients, causing disease that is often refractive to treatment by antibiotics. Phage therapy may be a potential alternative form of treatment for these infections. Here we describe the genome of the previously described therapeutic B. cenocepacia podophage BcepIL02 and its close relative, Bcep22. Phage Bcep22 was found to contain a circularly permuted genome of 63,882 bp containing 77 genes; BcepIL02 was found to be 62,714 bp and contains 76 predicted genes. Major virion-associated proteins were identified by proteomic analysis. We propose that these phages comprise the founding members of a novel podophage lineage, the Bcep22-like phages. Among the interesting features of these phages are a series of tandemly repeated putative tail fiber genes that are similar to each other and also to one or more such genes in the other phages. Both phages also contain an extremely large (ca. 4,600-amino-acid), virion-associated, multidomain protein that accounts for over 20% of the phages' coding capacity, is widely distributed among other bacterial and phage genomes, and may be involved in facilitating DNA entry in both phage and other mobile DNA elements. The phages, which were previously presumed to be virulent, show evidence of a temperate lifestyle but are apparently unable to form stable lysogens in their hosts. This ambiguity complicates determination of a phage lifestyle, a key consideration in the selection of therapeutic phages.