Synthesizing immunoinformatics and genetic epidemiology to identify signatures of natural functional immunity to malaria parasites

项目总结/摘要 一种有效的疟疾疫苗将对消除疟疾运动产生变革性影响。的一大挑战 疟疾疫苗开发的一个重要问题是，大多数免疫原性寄生虫抗原也表现出极高的免疫原性， 多态因此，单价疫苗对错配变体的效力较低， 不完全交叉保护免疫此外，自然获得的保护性免疫的特征， 告知疫苗设计，在大多数实地研究中并不清晰可辨，其中寄生虫多样性的背景 并且累积的终生暴露可以掩盖暴露的生物标志物之间的功能反应。 了解自然暴露于蛋白质变体如何提供保护对于设计疫苗至关重要 可以克服寄生虫多样性并提供强大的保护。此外，将感染与寄生虫联系起来 携带变异单倍型以随后针对那些特异性变异表位的免疫应答将 支持这一结论，并可以确定交叉反应性或交叉保护模式，并告知多价 疫苗靶点筛选和设计。寄生虫抗原变异与变异体特异性宿主的平行分析 在一项一致队列的多年纵向研究中， 对多态性疟疾抗原内的变体位置和表位进行三角测量， 免疫力我将利用密集采样的纵向寄生虫基因型数据（36个月的观察， 500名参与者）和样本收集作为一个正在进行的，NIH资助的队列研究的一部分，并结合联合收割机这丰富的 具有高维血清学测量、分子流行病学和数据科学的采样结构 来开发用于表位筛选的计算机模拟方法。具体而言，我将：（1）将保护性临床再感染 表型与恶性疟原虫CSP C-末端氨基酸位置和表位的计算机比较，（2） 累积寄生虫单倍型暴露概况的位置和表位特异性血清反应性， 衍生的CSP序列，和（3）测量和比较由非CSP抗原候选物赋予的保护 和变异体。在完成这些目标后，我将开发新的数据 用于筛选表位和疫苗靶点的多态性抗原的科学驱动的方法， 为消灭疟疾运动设计合理疫苗提供信息。拟议的工作建立在PI的基础上， 在疟疾分子流行病学和血清学的优势，并作为一个桥梁，在电脑疫苗。它建立 现有的合作，资源和支持性的制度环境。拟议的项目和 职业发展计划提供流行病学，免疫学，信息学和 转化研究，这将使PI能够开展旨在减轻负担的独立职业生涯 疟疾的研究和培训下一代血清流行病学和分子流行病学交叉领域的科学家， 生物分析化学和数据科学。

PROJECT SUMMARY/ABSTRACT An effective malaria vaccine would be transformative for malaria elimination campaigns. A major challenge to malaria vaccine development is that most immunogenic parasite antigens also exhibit extremely high polymorphism. As a consequence, monovalent vaccines have lower efficacy against mismatched variants due to imperfect cross-protective immunity. Additionally, signatures of naturally-acquired protective immunity, which inform vaccine design, are not clearly legible in most field studies, where the background of parasite diversity and accumulated lifetime exposure can bury functional responses among biomarkers of exposure. Understanding how natural exposure to protein variants confers protection is essential for designing vaccines that can overcome parasite diversity and provide robust protection. Additionally, linking infections with parasites harboring variant haplotypes to subsequent immune responses against those specific variant epitopes would support this conclusion and could identify cross-reactivity or cross-protection patterns and inform multivalent vaccine target screening and design. Parallel analysis of parasite antigenic variation and variant-specific host antibody responses in a multi-year longitudinal study of a consistent cohort offers an unprecedented opportunity to triangulate variant positions and epitopes within polymorphic malaria antigens that contribute to protective immunity. I will leverage densely-sampled longitudinal parasite genotype data (36 months of observation in over 500 participants) and samples collected as part of an ongoing, NIH-funded cohort study and combine this rich sampling structure with high-dimensional serological measurements, molecular epidemiology, and data science to develop in silico approaches for epitope screening. Specifically, I will: (1) correlate protective clinical reinfection phenotypes with P. falciparum CSP C-terminal amino acid positions and epitopes in silico, (2) compare cumulative parasite haplotype exposure profiles to position- and epitope-specific seroreactivity against field- derived CSP sequences, and (3) measure and compare protection conferred by non-CSP antigen candidates and variants in a naturally-exposed population. Upon completion of these aims, I will have developed new data science-driven approaches for screening polymorphic antigens for epitopes and vaccine targets, which could inform rational vaccine design for malaria elimination campaigns. The proposed work builds upon the PI’s strengths in malaria molecular epidemiology and serology and serves as a bridge to in silico vaccinology. It builds on existing collaborations, resources, and a supportive institutional environment. The proposed projects and career development plan offer extensive training opportunities in epidemiology, immunology, informatics, and translational research, which will position the PI to launch an independent career aimed at reducing the burden of malaria and training the next generation of scientists at the intersection of sero- and molecular epidemiology, bioanalytical chemistry, and data science.