Antigen discovery by bioinformatics analysis and peptide microarray for the diagnosis of cystic echinococcosis

Author summaryCystic echinococcosis (CE) is a complex, neglected parasitic zoonosis. Tools and expertise in the diagnosis of CE are not widely available and the development of accurate and easily implementable diagnostic assays for CE has been highlighted as a priority also by the WHO. Antigen discovery for the immunodiagnosis of CE has relied so far on classical methods, which are impractical and poorly sensitive. Here, starting from the Echinococcus granulosus sensu stricto genome, we aimed to identify antigenic peptides having a potential for CE diagnosis based on a multiple-criteria strategy involving the bioinformatics selection of proteins, followed by screening by peptide microarray and validation by ELISA, using a panel of well-characterized sera from patients with hepatic CE in all development stages, and from clinically relevant controls. This methodology has virtually never been applied in the field of CE. Importantly, the developed database can be used to identify other E. granulosus s.s. antigenic candidates.BackgroundCystic echinococcosis (CE), caused by Echinococcus granulosus sensu lato, is a neglected zoonosis. Its diagnosis relies on imaging, supported by serology, while only imaging is useful for staging and follow-up. Since diagnostic tools and expertise are not widely available, new accurate and easily implementable assays for the diagnosis and follow-up of CE are highly needed. Methodology/Principal FindingsWe aimed to identify new E. granulosus antigens through a bioinformatics selection applied to the parasite genome, followed by peptide microarray screening and validation in ELISA, using independent panels of sera from patients with hepatic CE and clinically relevant controls.From 950 proteins selected in silico, 2,379 peptides were evaluated by microarray for IgG reactivity and eight candidates selected for validation. Reactivity to one peptide was significantly higher in the CE group (p = 0.044), but had suboptimal diagnostic accuracy. Conclusions/SignificanceHere we performed bioinformatics analysis and peptide microarray for antigen discovery, useful for the diagnosis of CE. Eight candidates were selected and validated. Reactivity to one peptide associated to CE but had suboptimal diagnostic accuracy. Importantly, the database developed in this study may be used to identify other antigenic candidates for CE diagnosis and follow-up.