Identification of New Features from Known Bacterial Protective Vaccine Antigens Enhances Rational Vaccine Design

With many protective vaccine antigens reported in the literature and verified experimentally, how to use the knowledge mined from these antigens to support rational vaccine design and study underlying design mechanism remains unclear. In order to address the problem, a systematic bioinformatics analysis was performed on 291 Gram-positive and Gram-negative bacterial protective antigens with experimental evidence manually curated in the Protegen database. The bioinformatics analyses evaluated included subcellular localization, adhesin probability, peptide signaling, transmembrane α-helix and β-barrel, conserved domain, Clusters of Orthologous Groups, and Gene Ontology functional annotations. Here we showed the critical role of adhesins, along with subcellular localization, peptide signaling, in predicting secreted extracellular or surface-exposed protective antigens, with mechanistic explanations supported by functional analysis. We also found a significant negative correlation of transmembrane α-helix to antigen protectiveness in Gram-positive and Gram-negative pathogens, while a positive correlation of transmembrane β-barrel was observed in Gram-negative pathogens. The commonly less-focused cytoplasmic and cytoplasmic membrane proteins could be potentially predicted with the help of other selection criteria such as adhesin probability and functional analysis. The significant findings in this study can support rational vaccine design and enhance our understanding of vaccine design mechanisms.

[1]  William C. Wimley,et al.  The versatile β-barrel membrane protein , 2003 .

[2]  T. Fletcher,et al.  Ebola virus disease and critical illness , 2016, Critical Care.

[3]  J. Saiz,et al.  Zika Virus: the Latest Newcomer , 2016, Front. Microbiol..

[4]  G. Delogu,et al.  Immunogenicity of DNA Vaccines Expressing Tuberculosis Proteins Fused to Tissue Plasminogen Activator Signal Sequences , 1999, Infection and Immunity.

[5]  Paul J. Kennedy,et al.  A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms , 2013, BMC Bioinformatics.

[6]  Shawn T. Brown,et al.  Contagious diseases in the United States from 1888 to the present. , 2013, The New England journal of medicine.

[7]  Juancarlos Chan,et al.  Gene Ontology Consortium: going forward , 2014, Nucleic Acids Res..

[8]  R. Rappuoli,et al.  Vaccines, new opportunities for a new society , 2014, Proceedings of the National Academy of Sciences.

[9]  Ankit Gupta,et al.  Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions , 2013, BMC Bioinformatics.

[10]  J. Venter,et al.  Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. , 2000, Science.

[11]  Y. He,et al.  Antibiotic Resistance Determinant-Focused Acinetobacter baumannii Vaccine Designed Using Reverse Vaccinology , 2017, International journal of molecular sciences.

[12]  D. Medini,et al.  Bexsero® chronicle , 2014, Pathogens and global health.

[13]  Brenda S. Collins,et al.  Gram-negative outer membrane vesicles in vaccine development. , 2011, Discovery medicine.

[14]  Henry R. Bigelow,et al.  Predicting transmembrane beta-barrels in proteomes. , 2004, Nucleic acids research.

[15]  Martin Ester,et al.  PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes , 2010, Bioinform..

[16]  A. Goyal,et al.  Bacterial adhesins, the pathogenic weapons to trick host defense arsenal. , 2017, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[17]  Yongqun He,et al.  Bioinformatics analysis of bacterial protective antigens in manually curated Protegen database , 2012 .

[18]  M. Bounpheng,et al.  Safety and immunogenicity of mammalian cell derived and Modified Vaccinia Ankara vectored African swine fever subunit antigens in swine , 2017, Veterinary Immunology and Immunopathology.

[19]  M. Webber,et al.  Molecular mechanisms of antibiotic resistance , 2014, Nature Reviews Microbiology.

[20]  Yongqun He,et al.  Protegen: a web-based protective antigen database and analysis system , 2010, Nucleic Acids Res..

[21]  William C Wimley,et al.  The versatile beta-barrel membrane protein. , 2003, Current opinion in structural biology.

[22]  R. Exley,et al.  Identification of novel antigens that protect against systemic meningococcal infection. , 2005, Vaccine.

[23]  G. Schulz.  The structure of bacterial outer membrane proteins. , 2002, Biochimica et biophysica acta.

[24]  E. K. Jagusztyn-Krynicka,et al.  Evaluation of a protective effect of in ovo delivered Campylobacter jejuni OMVs , 2016, Applied Microbiology and Biotechnology.

[25]  J. Sirard,et al.  Bacterial flagellins: mediators of pathogenicity and host immune responses in mucosa. , 2004, Trends in microbiology.

[26]  Michael Y. Galperin,et al.  The COG database: a tool for genome-scale analysis of protein functions and evolution , 2000, Nucleic Acids Res..

[27]  Timo K. Korhonen,et al.  The Pla surface protease/adhesin of Yersinia pestis mediates bacterial invasion into human endothelial cells , 2001, FEBS letters.

[28]  Ning Ma,et al.  BLAST+: architecture and applications , 2009, BMC Bioinformatics.

[29]  S. Hagius,et al.  Identification of Brucella melitensis 16M genes required for bacterial survival in the caprine host. , 2006, Microbes and infection.

[30]  R. Rappuoli.  Reverse vaccinology : Genomics , 2000 .

[31]  R. Rappuoli,et al.  Two years into reverse vaccinology. , 2003, Vaccine.

[32]  Davide Heller,et al.  eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences , 2015, Nucleic Acids Res..

[33]  Yongqun He,et al.  Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development , 2010, Journal of biomedicine & biotechnology.

[34]  S. Brunak,et al.  SignalP 4.0: discriminating signal peptides from transmembrane regions , 2011, Nature Methods.

[35]  J. Flynn,et al.  Challenges and future in vaccines, drug development, and immunomodulatory therapy. , 2014, Annals of the American Thoracic Society.

[36]  L. Rubin,et al.  Use of Serogroup B Meningococcal Vaccines in Persons Aged ≥10 Years at Increased Risk for Serogroup B Meningococcal Disease: Recommendations of the Advisory Committee on Immunization Practices, 2015 , 2015, MMWR. Morbidity and mortality weekly report.

[37]  Yongqun He,et al.  GOfox: Semantics-based simplified hierarchical classification and interactive visualization to support GO enrichment analysis , 2015, ICBO.

[38]  R. Coler,et al.  Protection and Long-Lived Immunity Induced by the ID93/GLA-SE Vaccine Candidate against a Clinical Mycobacterium tuberculosis Isolate , 2015, Clinical and Vaccine Immunology.

[39]  Vasant Honavar,et al.  Predicting protective bacterial antigens using random forest classifiers , 2012, BCB.

[40]  Faramarz Valafar,et al.  Improving reverse vaccinology with a machine learning approach. , 2011, Vaccine.

[41]  P. Cossart,et al.  How bacterial pathogens colonize their hosts and invade deeper tissues. , 2015, Microbes and infection.

[42]  Paolo Fontana,et al.  Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms , 2012, BMC Bioinformatics.

[43]  J. Mckinney,et al.  Role of KatG catalase‐peroxidase in mycobacterial pathogenesis: countering the phagocyte oxidative burst , 2004, Molecular microbiology.

[44]  Rino Rappuoli,et al.  Reverse vaccinology. , 2000, Current opinion in microbiology.

[45]  Robert T. Chen,et al.  Emerging Vaccine Informatics , 2011, Journal of biomedicine & biotechnology.

[46]  Francesco Filippini,et al.  NERVE: New Enhanced Reverse Vaccinology Environment , 2006, BMC biotechnology.

[47]  Irini A. Doytchinova,et al.  BMC Bioinformatics BioMed Central Methodology article VaxiJen: a server for prediction of protective antigens, tumour , 2007 .

[48]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.