LingBing Zeng, Dongliang Wang, NiYa Hu, Qing Zhu, Kaishen Chen, Ke Dong, Yan Zhang, YuFeng Yao, XiaoKui Guo, Yung-Fu Chang, YongZhang Zhu
Abstract
Reverse vaccinology (RV) has been widely used for screening of surface-exposed proteins (PSEs) of important pathogens, including outer membrane proteins (OMPs), and extracellular proteins (ECPs) as potential vaccine candidates. In this study, we applied a novel RV negative strategy and a pan-genome analysis for screening of PSEs from 17 L. interrogans strains covering 11 predominately epidemic serovars and 17 multilocus typing (MLST) sequence types (STs) worldwide. Our results showed, for instance, out of a total of 633 predicted PSEs in strain 56601, 92.8% were OMPs or ECPs (588/633). Among the 17 strains, 190 core PSEs, 913 dispensable PSEs and 861 unique PSEs were identified. Of the 190 PSEs, 121 were further predicted to be highly antigenic and thus may serve as potential vaccine candidates against leptospirosis. With the exception of LipL45, OmpL1, and LigB, the majority of the 121 PSEs were newly identified antigens. For example, hypothetical proteins BatC, LipL71, and the OmpA family proteins sharing many common features, such as surface-exposed localization, universal conservation, and eliciting strong antibody responses in patients, are regarded as the most promising vaccine antigens. Additionally, a wide array of potential virulence factors among the predicted PSEs including TonB-dependent receptor, sphingomyelinase 2, leucine-rich repeat protein, and 4 neighboring hypothetical proteins were identified as potential antigenicity, and deserve further investigation. Our results can contribute to the prediction of suitable antigens as potential vaccine candidates against leptospirosis and also provide further insights into mechanisms of leptospiral pathogenicity. In addition, our novel negative-screening strategy combined with pan-genome analysis can be a routine RV method applied to numerous other pathogens.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 March 2017.
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#15,866,607
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