A comparative 'omics' approach for prediction of candidate Strongyloides stercoralis diagnostic coproantigens

Human infection with the intestinal nematode Strongyloides stercoralis is persistent unless effectively treated, and potentially fatal in immunosuppressed individuals. Epidemiological data are lacking due to inadequate diagnosis. A rapid antigen detection test is a priority for population surveillance, validating cure after treatment, and for screening prior to immunosuppression. We analysed open access omics data sets and used online predictors to identify S. stercoralis proteins that are likely to be present in infected stool, Strongyloides-specific, and antigenic. Transcriptomic data from gut and non-gut dwelling life cycle stages of S. stercoralis revealed 328 proteins that are differentially expressed. Strongyloides ratti proteomic data for excreted and secreted (E/S) proteins were matched to S. stercoralis, giving 1,057 orthologues. Five parasitism-associated protein families (SCP/TAPS, prolyl oligopeptidase, transthyretin-like, aspartic peptidase, acetylcholinesterase) were compared phylogenetically between S. stercoralis and outgroups, and proteins with least homology to the outgroups were selected. Proteins that overlapped between the transcriptomic and proteomic datasets were analysed by multiple sequence alignment, epitope prediction and 3D structure modelling to reveal S. stercoralis candidate peptide/protein coproantigens. We describe 22 candidates from seven genes, across all five protein families for further investigation as potential S. stercoralis diagnostic coproantigens, identified using open access data and freely-available protein analysis tools. This powerful approach can be applied to many parasitic infections with omic data to accelerate development of specific diagnostic assays for laboratory or point-of-care field application. Author summaryThe worm Strongyloides stercoralis causes infectious disease in people throughout tropical and sub-tropical regions, leading to an extensive reduction in quality of life and even death. Millions of people are at risk of infection with this parasite and improved diagnostic and control methods and technologies are urgently required. Currently, most diagnosis is carried out through methods involving visual inspection of patients faeces, which has a number of drawbacks, particularly its poor sensitivity. This paper presents a new method to develop improved diagnostic tests for S. stercoralis, by computational analysis of publicly available gene and protein sequences to predict proteins that may be detectable in faeces. This would enable the development of rapid diagnostic tests in the form of lateral flows or dipsticks, with better predictive ability and fewer drawbacks than current diagnostic methods. A number of potential proteins, predicted to have all the desired characteristics for use in such tests were found through the new method and have been presented in this paper. With validation, new diagnostic tests for S. stercoralis could be developed from these results and the computational approach could be used to target other parasitic diseases.