# Synthesizing immunoinformatics and genetic epidemiology to identify signatures of natural functional immunity to malaria parasites > **NIH NIH K01** · DUKE UNIVERSITY · 2024 · $123,780 ## Abstract 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 ... ## Key facts - **NIH application ID:** 10771298 - **Project number:** 5K01AI175527-02 - **Recipient organization:** DUKE UNIVERSITY - **Principal Investigator:** Christine Markwalter - **Activity code:** K01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $123,780 - **Award type:** 5 - **Project period:** 2023-03-01 → 2028-02-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10771298 ## Citation > US National Institutes of Health, RePORTER application 10771298, Synthesizing immunoinformatics and genetic epidemiology to identify signatures of natural functional immunity to malaria parasites (5K01AI175527-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10771298. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*