Computational models of naturally acquired immunity to falciparum malaria

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ABSTRACT Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has spread rapidly across the globe and caused unprecedented global health and economic threats. Emerging evidence suggests that SARS-CoV-2 infection is associated with an impaired Type I and Type III interferon response, and that this reduced response may play a critical role in immunopathogenesis. Our collaboration has recently begun a randomized clinical trial of a Type III interferon, pegylated-lambda interferon (Lambda) for treatment of SARS-CoV-2 infected patients at Stanford University. In the parent study, 120 SARS-CoV-2 infected patients (both symptomatic and asymptomatic) are being randomized to receive Lambda vs. placebo, with assessments for viral shedding in oropharyngeal and nasal swabs, daily symptom screening for 28 days following treatment, and peripheral blood collected at multiple timepoints, including 4, 7, and 10 months post- infection. In this proposal, we will leverage samples collected from this trial, comprehensive immunologic interrogation, and computational analysis to elucidate the dynamics of the host immune response to SARS- CoV-2. In Aim 1, we will determine whether specific immune features, including endogenous IFN-λ production and cytokine production in response to toll-like receptor (TLR) ligands, predict duration of viral shedding and/or symptoms in SARS-CoV-2 infected patients. We will also evaluate differences in immune trajectories based on the presence or absence of clinical symptoms, participant sex, and age. We will broadly profile immune responses using parallel methodology to our U01, including transcriptional profiling, cellular phenotyping, plasma cytokine levels, antibody profiling and functional assays, and build flexible computational models to model interactions between different compartments of the immune system and to assess associations between immune responses and virologic and clinical outcomes. In Aim 2, we will define the impact of Lambda on the adaptive immune response, including SARS-CoV-2 specific cellular and humoral immunity. We hypothesize that treatment with Lambda reduces time to seroconversion and is associated with improved immunologic memory to Lambda, including higher titers and duration of neutralizing antibodies and frequencies of Th2-type T follicular helper cells. To perform these studies, we will leverage our computational immunology U01 research team at Stanford and UCSF including experts in clinical trials and cellular immunity (Dr. Jagannathan), antibody profiling and function (Drs. Greenhouse and Wang), infectious diseases epidemiology and biostatistics (Dr. Rodriguez-Barraquer), and biomedical informatics and computational biology (Dr. Butte). By improving our understanding of the host immune response to natural SARS-CoV2 infection, identifying correlates of viral resolution, and analyzing the impact of a novel immunomodulatory drug on this immunity, our results will provide insight into mechanisms that can be exploited in the design of vaccines and other therapeutics.