Omics-Based Systems Vaccinology for Vaccine Target Identification

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Omics technologies include genomics, transcriptomics, proteomics, metabolomics, and immunomics. These technologies have been used in vaccine research, which can be summarized using the term “vaccinomics.” These omics technologies combined with advanced bioinformatics analysis form the core of “systems vaccinology.” Omics technologies provide powerful methods in vaccine target identification. The genomics-based reverse vaccinology starts with predicting vaccine protein candidates through in silico bioinformatics analysis of genome sequences. The VIOLIN Vaxign vaccine design program is the first web-based vaccine target prediction software based on the reverse vaccinology strategy. Systematic transcriptomics and proteomics analyses facilitate rational vaccine target identification by detesting genome-wide gene expression profiles. Immunomics is the study of the set of antigens recognized by host immune systems and has also been used for efficient vaccine target prediction. With the large amount of omics data available, it is necessary to integrate various vaccine data using ontologies, including the Gene Ontology (GO) and Vaccine Ontology (VO), for more efficient vaccine target prediction and assessment. All these omics technologies combined with advanced bioinformatics analysis methods for a systems biology-based vaccine target prediction strategy. This article reviews the various omics technologies and how they can be used in vaccine target identification. INTRODUCTION Vaccination is one of the most effective tools to prevent against infectious diseases, cancer, allergy, and autoimmune diseases. Infectious diseases are a major source of mortality, contributing to 26% of global human mortality in 2001. Cancer, allergy, and autoimmune diseases also cause significant mortality and morbidity in human and animal victims. Vaccine immunization induces strong host immune responses to the administrated antigen and provides a long-term protection against a disease. However, an effective and safe vaccine for many deadly diseases, including acquired immunodeficiency syndrome, tuberculosis, and malaria, still does not exist. New approaches toward efficient vaccine target identification and vaccine development are desired. A new era of vaccine research began in 1995 when the complete genome of Haemophilus influenzae (a pathogenic bacterium) was published. Since then, thousands of pathogen genomes have been sequenced. Various host (such as human and mouse) genomes are also available. With the availability of large amounts of genome sequences, high-throughput-omics technologies—genomics, transcriptomics, proteomics, metabolomics, immunomics, and other omics approaches—have been invented. Advance bioinformatics approaches have also been developed to support the analysis of large amounts of omics data at differing levels, ranging from gene annotation, data normalization, significant gene expression detection, function enrichment, to pathway analysis. These omic and bioinformatic technologies enable the testing and screening of millions of possible vaccine candidates and vaccine-induced host immune targets in real time. This article reviews how omics technologies combined with advanced bioinformatics data analyses have been used in vaccine target identifications. GENOMICS-BASED REVERSE VACCINOLOGY FOR VACCINE TARGET IDENTIFICATION Reverse vaccinology is an emerging and revolutionary vaccine development approach that starts with the prediction of vaccine targets by bioinformatics analysis of genome sequences. Predicted proteins are selected based on desirable attributes. Reverse vaccinology was first applied to the development of a vaccine against serogroup B Neisseria meningitidis (MenB), the major cause of sepsis and meningitis in children and young adults. The complete MenB genome was screened using bioinformatics algorithms for open reading frames coding for putative surfaceexposed or secreted proteins, which are susceptible to antibody recognition and therefore the most suitable vaccine candidates. Out of approximately 600 novel vaccine candidates, 350 were expressed in Escherichia coli, and 28 were found to elicit protective antibody response. It took less than 18 months to identify more vaccine candidates in MenB than had been discovered over the previous 40 years by conventional methods. Derived from the first reverse vaccinology attempt, Bexsero, a multicomponent, broadcoverage MenB vaccine, was developed. Following the generation of comprehensive clinical and epidemiological data on Bexsero, Novartis submitted a Marketing Authorization Application in late 2010 to the European Medicines Agency for the use of Bexsero in humans. This milestone occurred approximately 10 years after the first reverse vaccinology publication, representing a huge success in vaccine research and development (R&D). Besides identifying secreted or outer membrane proteins, many more reverse vaccinology criteria have been developed since its first application report in 2000 described above. For example, when an outer membrane protein contains more than one transmembrane helix, the recombinant protein is often difficult to clone and purify. Therefore, the number of transmembrane domains of a protein can be used as a filtering criterion. Another criterion is the selection of bacterial adhesins that are responsible for adherence, colonization, and invasion of microbes to host cells. With the availability of multiple genomes sequenced for pathogens, it is also possible to run comparative genomics analyses to identify vaccine targets shared by many pathogenic organisms. These conserved proteins can be used to induce protection against multiple pathogenic strains. It is also important to compare sequence similarities between vaccine protein candidates and host proteome. A pathogen protein homologous to a host protein may induce an autoimmune disease or immune tolerance. The traditional immunoinformatics approaches for prediction immune epitopes can also be used for screening protective antigens. Vaxign is the first web-based vaccine design program utilizing the reverse vaccinology strategy. Predicted features in the Vaxign pipeline include protein subcellular location, transmembrane helices, adhesin probability, conservation among pathogen genomes, conservation to human and/or mouse proteins, sequence exclusion from genome(s) of nonpathogenic strain(s), and epitope binding to major histocompatibility complex (MHC) class I and class II. Vaxign has been demonstrated to successfully predict vaccine targets for Brucella spp., uropathogenic E. coli, and human herpesvirus 1 virus. Currently, more than 200 genomes have been precomputed using the Vaxign pipeline and available for query in the Vaxign website. Vaxign also performs dynamic vaccine target prediction based on input sequences. The concept of reverse vaccinology has been successfully applied to many other pathogens.