Computational models of naturally acquired immunity to falciparum malaria

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: unknown

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Key facts

  • Disease

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

  • Research Location

    United States of America, Americas
  • Lead Research Institution

  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory


  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details


  • Broad Policy Alignment


  • Age Group


  • Vulnerable Population


  • Occupations of Interest



ABSTRACTSevere acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is a novel coronavirus that has spreadrapidly across the globe and caused unprecedented global health and economic threats. Emerging evidencesuggests 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 recentlybegun a randomized clinical trial of a Type III interferon, pegylated-lambda interferon (Lambda) for treatment ofSARS-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, withassessments for viral shedding in oropharyngeal and nasal swabs, daily symptom screening for 28 daysfollowing 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 immunologicinterrogation, 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 productionand cytokine production in response to toll-like receptor (TLR) ligands, predict duration of viral shedding and/orsymptoms in SARS-CoV-2 infected patients. We will also evaluate differences in immune trajectories based onthe presence or absence of clinical symptoms, participant sex, and age. We will broadly profile immuneresponses using parallel methodology to our U01, including transcriptional profiling, cellular phenotyping,plasma cytokine levels, antibody profiling and functional assays, and build flexible computational models tomodel interactions between different compartments of the immune system and to assess associations betweenimmune responses and virologic and clinical outcomes. In Aim 2, we will define the impact of Lambda on theadaptive immune response, including SARS-CoV-2 specific cellular and humoral immunity. We hypothesizethat treatment with Lambda reduces time to seroconversion and is associated with improved immunologicmemory to Lambda, including higher titers and duration of neutralizing antibodies and frequencies of Th2-typeT follicular helper cells. To perform these studies, we will leverage our computational immunology U01research 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 epidemiologyand 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, identifyingcorrelates of viral resolution, and analyzing the impact of a novel immunomodulatory drug on this immunity, ourresults will provide insight into mechanisms that can be exploited in the design of vaccines and othertherapeutics.