An omic-informatic approach to narrow vaccine search space for infectious diseases

This project will test the potential of surface-exposed, invariant proteins as anti-trypanosome vaccines.
Background: where possible, disease elimination through vaccination is safe, effective and cheap, and the UK government has a significant interest in the development of new vaccines for infectious diseases, committing to invest £120m in vaccinology between 2016 and 2021. Pre-clinical vaccine testing starts with the identification of unique and exposed pathogen components capable of generating a protective immune response, and proceeds to the immunisation of a cohort of animals with a potential immunogen, followed by pathogen challenge and monitoring of disease. Mining a pathogen genome for genes encoding characteristics of membraine association is of limited predictive power, as a large proportion of predicted membraine proteins unlikely to be on the cell surface, and in-silico-generated datasets are often not amenable to validation studies. This reverse vaccinology approach makes the exploration of "vaccine space" through large-scale, protein-by-protein animal immunisation time-consuming and extremely costly.
Hypothesis: This project will use high-throughput proteomics and advanced informatics for the high-confidence identification of surface-exposed antigens of African trypanosomes - human parasites transmitted by tsetse fly bite, that threaten ~60 million people each year. Surface-exposed antigens were recently identified for the in vitro experimental model T.b.brucei [Gadelha et al, 2015], rendering confidence to the methodology and training proposed here. However, it remains unknown if those antigens are present in in vivo models, and whether vaccines against them would confer protection. Therefore, this project will use high-throughput proteomics to characterise the surface of host-derived parasites that cause the human chronic (T. b. gambiense) and acute (T. b. rhodesiense) trypanosomiasis.