Date Published: August 2, 2017
Publisher: Public Library of Science
Author(s): Sapna Pahil, Neelam Taneja, Hifzur Rahman Ansari, G. P. S. Raghava, Niyaz Ahmed.
Shigellosis or bacillary dysentery is an important cause of diarrhea, with the majority of the cases occurring in developing countries. Considering the high disease burden, increasing antibiotic resistance, serotype-specific immunity and the post-infectious sequelae associated with shigellosis, there is a pressing need of an effective vaccine against multiple serotypes of the pathogen. In the present study, we used bio-informatics approach to identify antigens shared among multiple serotypes of Shigella spp. This approach led to the identification of many immunogenic peptides. The five most promising peptides based on MHC binding efficiency were a putative lipoprotein (EL PGI I), a putative heat shock protein (EL PGI II), Spa32 (EL PGI III), IcsB (EL PGI IV) and a hypothetical protein (EL PGI V). These peptides were synthesized and the immunogenicity was evaluated in BALB/c mice by ELISA and cytokine assays. The putative heat shock protein (HSP) and the hypothetical protein elicited good humoral response, whereas putative lipoprotein, Spa32 and IcsB elicited good T-cell response as revealed by increased IFN-γ and TNF-α cytokine levels. The patient sera from confirmed cases of shigellosis were also evaluated for the presence of peptide specific antibodies with significant IgG and IgA antibodies against the HSP and the hypothetical protein, bestowing them as potential future vaccine candidates. The antigens reported in this study are novel and have not been tested as vaccine candidates against Shigella. This study offers time and cost-effective way of identifying unprecedented immunogenic antigens to be used as potential vaccine candidates. Moreover, this approach should easily be extendable to find new potential vaccine candidates for other pathogenic bacteria.
Shigellosis is a highly infectious acute gastroenteritis and as few as 10 to 100 bacteria are capable of causing the disease . Around 90 million cases of the severe disease occur each year with 108,000 deaths, most of which occur in the developing countries. Children under 5 years of age are mainly affected [2,3]. The recent diagnosis studies using quantitative polymerase chain reaction (qPCR) have indicated that the traditional culture-based methods have under-estimated the global burden of shigellosis [4,5]. The disease also affects travelers to developing countries, military personnel, refugees and the institutionalized persons [2,6,7]. In the Indian subcontinent, the disease occurs endemically as well as many outbreaks are reported from time to time [8–13].
In the present study, we used bioinformatics approach to identify antigens conserved across major serotypes of Shigella. Many widely recommended prediction algorithms with high accuracy (up to 91.4%) for prokaryotes were used. We identified 48 novel immunogenic antigens by in-silico analysis and further evaluated the best five in wet-lab experiments.