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

该项目将测试表面暴露的不变蛋白作为抗锥虫疫苗的潜力。背景：在可能的情况下，通过接种疫苗消除疾病是安全、有效和廉价的，英国政府对开发新的传染病疫苗非常感兴趣，承诺在2016年至2021年期间在疫苗方面投资1.2亿GB。临床前疫苗测试从识别能够产生保护性免疫反应的独特和暴露的病原体成分开始，然后用潜在的免疫原对一群动物进行免疫接种，然后是病原体挑战和疾病监测。挖掘病原体基因组中编码膜相关特征的基因的预测能力有限，因为很大一部分预测的膜蛋白不太可能在细胞表面，而且电子产生的数据集通常不适合进行验证研究。这种反向疫苗学方法使得通过大规模、逐个蛋白质的动物免疫来探索“疫苗空间”既耗时又极其昂贵。假设：该项目将使用高通量蛋白质组学和先进的信息学来高度可信地鉴定非洲锥虫表面暴露的抗原-通过采采蝇叮咬传播的人类寄生虫，这些寄生虫每年威胁到约6000万人。最近为体外实验模型T.b.brucei确定了表面暴露的抗原[Gadelha等人，2015]，为这里提出的方法和培训提供了信心。然而，目前还不清楚这些抗原是否存在于活体模型中，以及针对它们的疫苗是否会提供保护。因此，该项目将使用高通量蛋白质组学来表征宿主衍生寄生虫的表面，这些寄生虫导致人类慢性锥虫病(T.B.gbiense)和急性锥虫病(T.B.rodesiense)。

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.