Protectome analysis: a new selective bioinformatics tool for bacterial vaccine candidate discovery

Abstract

New generation vaccines are in demand to include only the key antigens sufficient to confer protective immunity among the plethora of pathogen molecules. In the last decade, large-scale genomics-based technologies have emerged. Among them, the Reverse Vaccinology approach was successfully applied to the development of an innovative vaccine against Neisseria meningitidis serogroup B, now available on the market with the commercial name BEXSERO® (Novartis Vaccines). The limiting step of such approaches is the number of antigens to be tested in in vivo models. Several laboratories have been trying to refine the original approach in order to get to the identification of the relevant antigens straight from the genome. Here we report a new bioinformatics tool that moves a first step in this direction. The tool has been developed by identifying structural/functional features recurring in known bacterial protective antigens, the so called "Protectome space," and using such "protective signatures" for protective antigen discovery. In particular, we applied this new approach to Staphylococcus aureus and Group B Streptococcus and we show that not only already known protective antigens were re-discovered, but also two new protective antigens were identified.

Fig. 1.

The operative steps of Protectome analysis. The Protectome approach is based on mining the conserved pangenome a bacterial specie by using specific features. Different bioinformatics tools are used to scan for “protective signatures” i.e. (1) the identified Pfam domains, (2) a protein architecture organized in multiple internal repeats, and (3) a species- or genus-specificity of the corresponding genes with either no, species/genus-specific or DUF domains. Based on the currently defined molecular features, the Protectome approach selects less than 5% of the total predicted ORFs.

Fig. 2.

Signal peptide prediction. Predicted localization of Protectome antigens compared with the total number of proteins in each sub-cellular fraction in GBS and S. aureus, respectively.

Fig. 3.

In vitro enzymatic activity of SAG1333. A commercial kit was used to prove the enzymatic activity of SAG1333 to produce adenosine from AMP by releasing the phosphate. The figure shows the measurement of free phosphate (pmol) determined in different compounds. Kit reagents and the enzyme alone were phosphate-free, AMP gave negligible amounts of phosphate. On the other hand, recombinant SAG1333 releases 2354 pmol of free phosphate by releasing the phosphate generated in the dephosphorylation reaction of the AMP.

Fig. 4.

Real-time analysis of the effect of recombinant SAG1333 on macrophage integrity by xCELLigence system. Graphs report the measurement of cellular Trans-Electric Resistance by xCELLigence. SAG1333 was added at a concentration of 25 and 10 μg/ml; SLO at 20 μg/ml. The following samples were tested: SAG1333 was tested alone or plus 5 mm AMP on Raw cells (high panel) and A549 cells (low panel); Data represent the mean ± S.D. of three independent wells.

Fig. 5.

Protection studies in relevant in vivo models. Protection conferred by the two antigens were assessed by active maternal immunization/neonatal pup challenge model for GBS (panel A), and the peritonitis (panel B) and kidney abscess model (panel C) for S. aureus antigen.