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.