Bioinformatics: how it is being used to identify bacterial vaccine candidates

Sifting through the core-genome to identify putative cross-protective antigens against Riemerella anatipestifer

Infectious serositis of ducks, caused by Riemerella anatipestifer, is one of the main infectious diseases that harm commercial ducks. Whole-strain-based vaccines with no or few cross-protection were observed between different serotypes of R. anatipestifer, and so far, control of infection is hampered by a lack of effective vaccines, especially subunit vaccines with cross-protection. Since the concept of reverse vaccinology was introduced, it has been widely used to screen for protective antigens in important pathogens. In this study, pan-genome binding reverse vaccinology, an emerging approach to vaccine candidate screening, was used to screen for cross-protective antigens against R. anatipestifer. Thirty proteins were identified from the core-genome as potential cross-protective antigens. Three of these proteins were recombinantly expressed, and their immunoreactivity with five antisera (anti-serotypes 1, 2, 6, 10, and 11) was demonstrated by Western blotting. Our study established a method for high-throughput screening of cross-protective antigens against R. anatipestifer in silico, which will lay the foundation for the development of a cross-protective subunit vaccine controlling R. anatipestifer infection. KEY POINTS: • Pan-genome binding reverse vaccine approach was first established in R. anatipestifer to screen for subunit vaccine candidates. • Thirty potential cross-protective antigens against R. anatipestifer were identified by this method. • The reliability of the method was verified preliminarily by the results of Western blotting of three of these potential antigens.

Multi-Omics Approach Reveals the Potential Core Vaccine Targets for the Emerging Foodborne Pathogen Campylobacter jejuni

Campylobacter jejuni is a leading cause of bacterial gastroenteritis in humans around the world. The emergence of bacterial resistance is becoming more serious; therefore, development of new vaccines is considered to be an alternative strategy against drug-resistant pathogen. In this study, we investigated the pangenome of 173 C. jejuni strains and analyzed the phylogenesis and the virulence factor genes. In order to acquire a high-quality pangenome, genomic relatedness was firstly performed with average nucleotide identity (ANI) analyses, and an open pangenome of 8,041 gene families was obtained with the correct taxonomy genomes. Subsequently, the virulence property of the core genome was analyzed and 145 core virulence factor (VF) genes were obtained. Upon functional genomics and immunological analyses, five core VF proteins with high antigenicity were selected as potential core vaccine targets for humans. Furthermore, functional annotations indicated that these proteins are involved in important molecular functions and biological processes, such as adhesion, regulation, and secretion. In addition, transcriptome analysis in human cells and pig intestinal loop proved that these vaccine target genes are important in the virulence of C. jejuni in different hosts. Comprehensive pangenome and relevant animal experiments will facilitate discovering the potential core vaccine targets with improved efficiency in reverse vaccinology. Likewise, this study provided some insights into the genetic polymorphism and phylogeny of C. jejuni and discovered potential vaccine candidates for humans. Prospective development of new vaccines using the targets will be an alternative to the use of antibiotics and prevent the development of multidrug-resistant C. jejuni in humans and even other animals.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

BACKGROUND

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

RESULTS

We have designed an integrative computational pipeline term as "PanRV" that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

CONCLUSION

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

Search for new compounds from Kitasato microbial library by physicochemical screening

The Ōmura research group of the Kitasato Institute has isolated multiple microorganisms over a period of five decades. The resulting collection comprises a broad spectrum of microbes, including strains producing novel and diverse compounds with biological activities. A bioassay-guided fractionation of microbial culture broths has been employed to screen the microbial collection for compounds with new biological activities. And numerous novel natural products have been discovered among the microbial metabolites produced by members of the collection. However, dereplication of already known compounds and their potential analogs is a vital part of the discovery process of new microbial natural products. Recently, it has become easy to acquire the ultraviolet (UV) and mass spectrometry (MS) spectra of many single components of microbial culture broths in combination with high-performance liquid chromatography. To achieve most effective utilization of our microbial library, new compounds from microbial culture broths were investigated by employing an approach based on the physico-chemical properties using spectral analyses such as UV and MS and color reaction, collectively designated as physicochemical (PC) screening. As a result of physicochemical screening, many new compounds were identified among the secondary metabolites of fresh isolated rare actinomycetes and Streptomyces spp. preserved for a long time as producer of biological compounds. In this review, we introduce the Kitasato microbial library and the new compounds discovered from the library by PC screening.

Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets

BACKGROUND

Acinetobacter baumannii has emerged as a significant nosocomial pathogen during the last few years, exhibiting resistance to almost all major classes of antibiotics. Alternative treatment options such as vaccines tend to be most promising and cost effective approaches against this resistant pathogen. In the current study, we have explored the pan-genome of A. baumannii followed by immune-proteomics and reverse vaccinology approaches to identify potential core vaccine targets.

RESULTS

The pan-genome of all available A. baumannii strains (30 complete genomes) is estimated to contain 7,606 gene families and the core genome consists of 2,445 gene families (~32 % of the pan-genome). Phylogenetic tree, comparative genomic and proteomic analysis revealed both intra- and inter genomic similarities and evolutionary relationships. Among the conserved core genome, thirteen proteins, including P pilus assembly protein, pili assembly chaperone, AdeK, PonA, OmpA, general secretion pathway protein D, FhuE receptor, Type VI secretion system OmpA/MotB, TonB dependent siderophore receptor, general secretion pathway protein D, outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase are identified as highly antigenic. Epitope mapping of the target proteins revealed the presence of antigenic surface exposed 9-mer T-cell epitopes. Protein-protein interaction and functional annotation have shown their involvement in significant biological and molecular processes. The pipeline is validated by predicting already known immunogenic targets against Gram negative pathogen Helicobacter pylori as a positive control.

CONCLUSION

The study, based upon combinatorial approach of pan-genomics, core genomics, proteomics and reverse vaccinology led us to find out potential vaccine candidates against A. baumannii. The comprehensive analysis of all the completely sequenced genomes revealed thirteen putative antigens which could elicit substantial immune response. The integration of computational vaccinology strategies would facilitate in tackling the rapid dissemination of resistant A.baumannii strains. The scarcity of effective antibiotics and the global expansion of sequencing data making this approach desirable in the development of effective vaccines against A. baumannii and other bacterial pathogens.

Prediction of IL4 inducing peptides

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.

Designing of interferon-gamma inducing MHC class-II binders

Background

The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4⁺ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides.

Results

It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC.

Conclusion

Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/).

Reviewers

This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai.

Identification of potential universal vaccine candidates against group A Streptococcus by using high throughput in silico and proteomics approach

Streptococcus pyogenes or group A Streptococcus (GAS) causes ~700 million human infections each year, resulting in over 500,000 deaths. The development of a commercial GAS vaccine is hampered due to high strain and serotype diversity in different geographical regions, and the generation of cross-reactive antibodies that may induce autoimmune disease. There is an urgent need to search for alternative vaccine candidates. High throughput multigenome data mining coupled with proteomics seems to be a promising approach to identify the universal vaccine candidates. In the present study, in silico analysis led to prediction of 147 proteins as universal vaccine candidates. Distribution pattern of these predicted candidates was explored in nonsequenced Indian GAS strains (n = 20) by using DNA array hybridization validating in silico analysis. High throughput analyses of surface proteins using 1D-SDS-PAGE coupled with ESI-LC-MS/MS was applied on highly (M49) and less (M1) invasive GAS strains of Indian origin. Comparative proteomics analysis revealed that highly invasive GAS M49 had metabolically more active membrane associated protein machinery than less invasive M1. Further, by overlapping proteomics data with in silico predicted vaccine candidate genes, 52 proteins were identified as probable universal vaccine candidates, which were expressed in these GAS serotypes. These proteins can further be investigated as universal vaccine candidates against GAS. Moreover, this robust approach may serve as a model that can be applied to identify the universal vaccine candidates in case of other pathogenic bacteria with high strain and genetic diversity.

The antigenome: from protein subunit vaccines to antibody treatments of bacterial infections?

New strategies are needed to master infectious diseases. The so-called "passive vaccination", i.e., prevention and treatment with specific antibodies, has a proven record and potential in the management of infections and entered the medical arena more than 100 years ago. Progress in the identification of specific antigens has become the hallmark in the development of novel subunit vaccines that often contain only a single immunogen, frequently proteins, derived from the microbe in order to induce protective immunity. On the other hand, the monoclonal antibody technology has enabled biotechnology to produce antibody species in unlimited quantities and at reasonable costs that are more or less identical to their human counterparts and bind with high affinity to only one specific site of a given antigen. Although, this technology has provided a robust platform for launching novel and successful treatments against a variety of devastating diseases, it is up till now only exceptionally employed in therapy of infectious diseases. Monoclonal antibodies engaged in the treatment of specific cancers seem to work by a dual mode; they mark the cancerous cells for decontamination by the immune system, but also block a function that intervenes with cell growth. The availability of the entire genome sequence of pathogens has strongly facilitated the identification of highly specific protein antigens that are suitable targets for neutralizing antibodies, but also often seem to play an important role in the microbe's life cycle. Thus, the growing repertoire of well-characterized protein antigens will open the perspective to develop monoclonal antibodies against bacterial infections, at least as last resort treatment, when vaccination and antibiotics are no options for prevention or therapy. In the following chapter we describe and compare various technologies regarding the identification of suitable target antigens and the foundation of cognate monoclonal antibodies and discuss their possible applications in the treatment of bacterial infections together with an overview of current efforts.

Proteomic analysis and identification of Streptococcus pyogenes surface-associated proteins

Streptococcus pyogenes is a gram-positive human pathogen that causes a wide spectrum of disease, placing a significant burden on public health. Bacterial surface-associated proteins play crucial roles in host-pathogen interactions and pathogenesis and are important targets for the immune system. The identification of these proteins for vaccine development is an important goal of bacterial proteomics. Here we describe a method of proteolytic digestion of surface-exposed proteins to identify surface antigens of S. pyogenes. Peptides generated by trypsin digestion were analyzed by multidimensional tandem mass spectrometry. This approach allowed the identification of 79 proteins on the bacterial surface, including 14 proteins containing cell wall-anchoring motifs, 12 lipoproteins, 9 secreted proteins, 22 membrane-associated proteins, 1 bacteriophage-associated protein, and 21 proteins commonly identified as cytoplasmic. Thirty-three of these proteins have not been previously identified as cell surface associated in S. pyogenes. Several proteins were expressed in Escherichia coli, and the purified proteins were used to generate specific mouse antisera for use in a whole-cell enzyme-linked immunosorbent assay. The immunoreactivity of specific antisera to some of these antigens confirmed their surface localization. The data reported here will provide guidance in the development of a novel vaccine to prevent infections caused by S. pyogenes.

Bioinformatics in microbial biotechnology--a mini review

The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics--sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics--identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack of available gene-functionality from the wet-lab data, the lack of computer algorithms to explore vast amount of data with unknown functionality, limited availability of protein-protein and protein-DNA interactions, and the lack of knowledge of temporal and transient behavior of genes and pathways.

Neisseria meningitidis: an overview of the carriage state

During periods of endemic disease, about 10 % of the general population harbour Neisseria meningitidis in the nasopharynx. Since N. meningitidis is a strict human pathogen and most patients have not been in contact with other cases, asymptomatic carriers are presumably the major source of the pathogenic strains. Most carrier isolates are shown to lack capsule production. The capsule deficient state of meningococcal strains in the nasopharynx may aid evasion of the human immune defence and hence be selected to survive nasopharyngeal colonization. Carriage itself can be an immunizing process resulting in systemic protective antibody responses. Frequent nasopharyngeal colonization with related bacteria like Neisseria lactamica improves natural immunity to meningococci by the formation of cross-reacting antibodies. While most meningococcal strains recovered from patients belong to a limited number of clonal groups worldwide, strains isolated from carriers comprise numerous genotypes, with only a small proportion of the strains representing invasive clones. During the carriage state, co-colonization with other pathogenic and non-pathogenic bacteria may lead to genetic exchange, which may result in the emergence of new meningococcal clones. The high diversity of meningococcal carrier strains, compared with hypervirulent strains, supports the idea that transmissibility, not invasion, is essential in the life cycle of N. meningitidis.

Immunome-derived vaccines

Immune response to a subset of antigens and epitopes derived from an infectious pathogen may be sufficient for competent protection; immune recognition of every potential epitope derived from the pathogen's genome does not appear to be required. The pneumococcal and hepatitis vaccines, both of which are subunit vaccines, illustrate this premise. Similarly, 'immunome-derived vaccines' are based on the concept that response to the subset of antigens and epitopes that interface with the host immune system (the immunome) and not the whole organism (represented by the proteome or genome) can be sufficient for protection. Competent immune responses to cancer are also probably restricted to the neoplasm's 'immunome', although the set of antigens that drive successful immune response to cancer cells has proven more difficult to uncover. Researchers are now using bioinformatics sequence analysis tools, epitope mapping tools, microarrays and high-throughput immunology assays to discover the components of the immunome, which are then used to compose these new vaccines. At least one immunome-derived vaccine is in clinical trials and many others are in the vaccine pipeline. Due to the rapid improvement of immunoinformatics tools and immunological assays, the era of immunome-derived vaccines has begun.