Diagnostics development for the neglected parasitic diseases strongyloidiasis and visceral leishmaniasis

Strongyloidiasis, caused by the gut nematode Strongyloides stercoralis, and visceral leishmaniasis (VL), caused by the protozoa Leishmania donovani and L. infantum, are two potentially fatal parasitic diseases with wide global distribution and close association with poverty. Although both infections are treatable, it is imperative to validate cure after treatment. Available diagnostics for both infections have reasonable to high sensitivity for current infection but cannot easily distinguish cure. There is a need for diagnostic tests that are rapid, simple to use and deployable in field conditions to diagnose infection and validate cure. This project aimed to identify candidate coproantigens of S. stercoralis and urine antigens of L. donovani, and to investigate the utility of IgG1 serology for determining cure versus relapse after treatment for VL. For Strongyloides coproantigen discovery, open access ‘omic’ data were analysed using computational tools. For Leishmania urine antigen discovery, antibodies were used to capture parasite antigens from VL urine, which were then identified by mass spectrometry. For Leishmania IgG1 serology, paired sera from preand post-treatment (cured) VL were compared with relapse sera in ELISA and with novel IgG1 specific rapid diagnostic tests (RDTs). This work identified over 40 candidate coproantigens of Strongyloides that satisfied the required characteristics. Seven L. donovani proteins were identified in VL urine, within which 22 protein sequences were indicated as having high epitope potential and specificity to L. donovani. In VL serology, IgG1 was able to differentiate between cure and relapse of VL in both ELISA and RDT assays. With development and optimisation, the candidate antigens and IgG1 assays presented here have potential to contribute to disease control for these parasitic infections. The computational method of antigen selection used here for S. stercoralis can be applied to multiple parasitic helminth infections using the wealth of open access ‘omic’ data