Bacterial Detection Using Unlabeled Phage Amplification and Mass Spectrometry through Structural and Nonstructural Phage Markers

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.

Pred-BVP-Unb: Fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination

Bacteriophage virion proteins (BVPs) are bacterial viruses that have a great impact on different biological functions of bacteria. They are significantly used in genetic engineering and phage therapy applications. Correct identification of BVP through conventional pathogen methods are slow and expensive. Thus, designing a Bioinformatics predictor is urgently desirable to accelerate correct identification of BVPs within a huge volume of proteins. However, available prediction tools performance is inadequate due to the lack of useful feature representation and severe imbalance issue. In the present study, we propose an intelligent model, called Pred-BVP-Unb for discrimination of BVPs that employed three nominal sequences-driven descriptors, i.e. Bi-PSSM evolutionary information, composition & translation, and split amino acid composition. The imbalance phenomena between classes were coped with the help of a synthetic minority oversampling technique. The essential attributes are selected by a robust algorithm called recursive feature elimination. Finally, the optimal feature space is provided to support vector machine classifier using a radial base kernel in order to train the model. Our predictor remarkably outperforms than existing approaches in the literature by achieving the highest accuracy of 92.54% and 83.06% respectively on the benchmark and independent datasets. We expect that Pred-BVP-Unb tool can provide useful hints for designing antibacterial drugs and also helpful to expedite large scale discovery of new bacteriophage virion proteins. The source code and all datasets are publicly available at https://github.com/Muhammad-Arif-NUST/BVP_Pred_Unb.

Detection of methicillin-resistant Staphylococcus aureus using phage amplification combined with matrix-assisted laser desorption/ionization mass spectrometry

Antibiotic resistance continues to contribute significantly to morbidity and mortality across the world. Developing new tests for antibiotic-resistant bacteria is a core action to combat resistant infections. We describe a method that uses phage amplification detection (PAD) combined with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to rapidly identify Staphylococcus aureus and determine phenotypic susceptibility to cefoxitin. Samples tested for S. aureus are incubated together with bacteriophage in the presence and absence of cefoxitin and subjected to rapid trypsin digestion followed by MALDI-MS analysis. Tryptic peptides derived from amplified phage proteins can be detected by MALDI-MS, as validated by time-of-flight (TOF)/TOF analysis of each peptide combined with database searching. Methicillin-resistant S. aureus show significant phage amplification in the presence of cefoxitin, while methicillin-sensitive S. aureus show no phage amplification relative to a no-antibiotic control. We also show that PAD methodology can be implemented on an FDA-approved commercial MALDI-MS bacterial identification system to identify S. aureus and determine antibiotic susceptibility. The novelty of this assay includes the use of phage-derived tryptic peptides as detected by MALDI-MS to monitor the results of PAD on an instrument common to many modern microbiology laboratories.

Advance in phage display technology for bioanalysis

Phage display technology has emerged as a powerful tool for target gene expression and target-specific ligand selection. It is widely used to screen peptides, proteins and antibodies with the advantages of simplicity, high efficiency and low cost. A variety of targets, including ions, small molecules, inorganic materials, natural and biological polymers, nanostructures, cells, bacteria, and even tissues, have been demonstrated to generate specific binding ligands by phage display. Phages and target-specific ligands screened by phage display have been widely used as affinity reagents in therapeutics, diagnostics and biosensors. In this review, comparisons of different types of phage display systems are first presented. Particularly, microfluidic-based phage display, which enables screening with high throughput, high efficiency and integration, is highlighted. More importantly, we emphasize the advances in biosensors based on phages or phage-derived probes, including nonlytic phages, lytic phages, peptides or proteins screened by phage display, phage assemblies and phage-nanomaterial complexes. However, more efficient and higher throughput phage display methods are still needed to meet an explosion in demand for bioanalysis. Furthermore, screening of cyclic peptides and functional peptides will be the hotspot in bioanalysis.

Mass spectrometry for the detection of bioterrorism agents: from environmental to clinical applications

In the current context of international conflicts and localized terrorist actions, there is unfortunately a permanent threat of attacks with unconventional warfare agents. Among these, biological agents such as toxins, microorganisms, and viruses deserve particular attention owing to their ease of production and dissemination. Mass spectrometry (MS)-based techniques for the detection and quantification of biological agents have a decisive role to play for countermeasures in a scenario of biological attacks. The application of MS to every field of both organic and macromolecular species has in recent years been revolutionized by the development of soft ionization techniques (MALDI and ESI), and by the continuous development of MS technologies (high resolution, accurate mass HR/AM instruments, novel analyzers, hybrid configurations). New possibilities have emerged for exquisite specific and sensitive detection of biological warfare agents. MS-based strategies for clinical application can now address a wide range of analytical questions mainly including issues related to the complexity of biological samples and their available volume. Multiplexed toxin detection, discovery of new markers through omics approaches, and identification of untargeted microbiological or of novel molecular targets are examples of applications. In this paper, we will present these technological advances along with the novel perspectives offered by omics approaches to clinical detection and follow-up.

Rapid Detection of Listeria by Bacteriophage Amplification and SERS-Lateral Flow Immunochromatography

A rapid Listeria detection method was developed utilizing A511 bacteriophage amplification combined with surface-enhanced Raman spectroscopy (SERS) and lateral flow immunochromatography (LFI). Anti-A511 antibodies were covalently linked to SERS nanoparticles and printed onto nitrocellulose membranes. Antibody-conjugated SERS nanoparticles were used as quantifiable reporters. In the presence of A511, phage-SERS nanoparticle complexes were arrested and concentrated as a visible test line, which was interrogated quantitatively by Raman spectroscopy. An increase in SERS intensity correlated to an increase in captured phage-reporter complexes. SERS limit of detection was 6 × 10(6) pfu·mL(-1), offering detection below that obtainable by the naked eye (LOD 6 × 10(7) pfu·mL(-1)). Phage amplification experiments were carried out at a multiplicity of infection (MOI) of 0.1 with 4 different starting phage concentrations monitored over time using SERS-LFI and validated by spot titer assay. Detection of L. monocytogenes concentrations of 1 × 10(7) colony forming units (cfu)·mL(-1), 5 × 10(6) cfu·mL(-1), 5 × 10(5) cfu·mL(-1) and 5 × 10(4) cfu·mL(-1) was achieved in 2, 2, 6, and 8 h, respectively. Similar experiments were conducted at a constant starting phage concentration (5 × 10(5) pfu·mL(-1)) with MOIs of 1, 2.5, and 5 and were detected in 2, 4, and 5 h, respectively.

Rapid quantification of Escherichia coli in food and media using bacteriophage T7 amplification and liquid chromatography-multiple reaction monitoring tandem mass spectrometry

Conventional microbiological assays have been a valuable tool for specific enumeration of indicative bacteria of relevance to food and public health, but these culture-based methods are time-consuming and require tedious biochemical and morphological identification. In this work, we exploit the ability of bacteriophage T7 to specifically infect Escherichia coli and amplify nearly a 100-fold in 1–2 h. Bacteriophage amplification is integrated with liquid chromatography-multiple reaction monitoring tandem mass spectrometry (LC-MRM–MS/MS) for quantitation of phage-specific peptides. Heavy isotopic 15N labeled T7 is introduced as the inoculum phage and internal standard. Quantification is performed by determining the ratio of phage-specific peptides over the internal standard which value is proportional to E. coli numbers. A broad dynamic range of 6-log orders ranging from 3.0 × 10(3) to 3.0 × 10(9) CFU/ml is attained in LB, while between 4.1 × 10(4)–2.7 × 10(9) CFU/ml and 1.9 × 10(3)–3.0 × 10(7) CFU/ml was enumerated respectively in coconut water and apple juice. With this method, viable E. coli are quantified in 4 h with a detection limit of 3.0 × 10(3) CFU/ml, 4.1 × 10(4) CFU/ml and 1.9 × 10(3) CFU/ml in LB, coconut water and apple juice, respectively. This method has potential as a rapid tool for detection of fecal contamination during food bioprocessing and distribution to safeguard public health.

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics

Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.