Computational models of naturally acquired immunity to falciparum malaria

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PROJECT SUMMARY/ABSTRACT Immunity to malaria is complex, involving a fine interplay between immune compartments over time. Most prior efforts to understand the development of immunity have been limited to a narrow set of measurements or reductionist animal or human challenge models that fail to capture the complexity of repeated infection in naturally exposed individuals. We propose to comprehensively evaluate and model the innate and adaptive immune response to repeated P. falciparum (Pf) infections over time. This project takes advantage of a unique malaria cohort study in Uganda, with participants seen in our clinic monthly and for all illnesses, allowing us to capture both symptomatic and asymptomatic infections. By leveraging our well-characterized cohort, detailed immunological characterization of host responses, and state-of-the-art computational models of immunity, we will 1) Comprehensively characterize the immune response to symptomatic and asymptomatic P. falciparum infections. We hypothesize that symptomatic – but not asymptomatic – infections will be characterized by an attenuation of the innate and adaptive inflammatory response. We will profile the innate and adaptive immune response to symptomatic and asymptomatic infections in children at multiple time points in the weeks following Pf infection. Data from transcriptional profiling, deep cellular phenotyping, antibody profiling, and stimulation assays will be used to build flexible computational models, capturing interactions between different compartments of the immune system and the trajectory of the immune response after a single infection. 2) Determine how the immune state evolves in response to recurrent P. falciparum infections. We hypothesize that recurrent infection will result in a shift of the immune state from one biased towards dynamic, inflammatory immune responses to one characterized by a more stable, regulatory state and the acquisition of functional antibodies. We will model the evolution of key immunological parameters identified in Aim 1, along with assays of anti-parasitic humoral and cellular function, over years of repeated infection and across ages by generating longitudinal data over a period of 2 years. This aim complements Aim 1 in providing important information to define emergent properties of the immune response from cumulative infections over longer time scales, spanning the period of immune acquisition. 3) Identify key aspects of the immune state leading to anti-parasite and anti-disease immunity to P. falciparum infection. We hypothesize that functional antibody responses will be most strongly associated with anti-parasite immunity, and that attenuation of innate responses will be most strongly associated with anti-disease immunity. Guided by findings from Aims 1 and 2, we will develop computational models to identify the key determinants of clinical immune phenotypes, obtained by evaluating the clinical outcomes of infection over the subsequent year. All models will be validated and iteratively refined using data from independent individuals, external data and laboratory-based experiments. Data and models will be made available and findable through appropriate public repositories.