The worldwide burden of malaria disease is profound. Infection with the Plasmodium falciparum species has the most devastating effect, causing the death of nearly one million African children each year. Even so, malaria control is a realistic goal, based on two lines of evidence: 1) natural immunity emerges with age in persons repeatedly exposed to the parasite;and 2) pre-erythrocytic vaccine candidates can reduce incidence of clinical disease. Currently, a major hindrance in achieving this goal is the lack of a deep understanding of the mechanisms of immune protection against malaria that can guide rational vaccine design. In this project we propose two Specific Aims that will use a comprehensive systems biology approach to broaden the immunologic knowledge base of malaria by investigating naturally acquired immunity and vaccine-induced protection in African populations living in malaria-endemic areas.
In Aim 1, we will determine the distinct immune signatures associated with control of parasitemia and acquired immunity in Ugandan children and adults.
In Aim 2, we will partner with investigators in the conduct of a phase III RTS,S/AS01E vaccine licensure trial to define the immunogenicity and correlates of vaccine protection in young children. As relatively new investigators in this exciting research field, we will contribute our collective expertise in the design and conduct of comprehensive immunologic studies in large-scale international vaccine studies in concert with advanced systems biology, bioinformatics and network analyses. Our Seattle colleagues with recognized leadership in the malaria field will guide our efforts, and we can efficiently build upon findings in the two interactive projects. We envision these investigations will lend significant insight into the innate and adaptive immune mechanisms that control malaria infection.
Forty percent of annual public health expenditures in sub-Saharan Africa is spent on the 300 million cases of acute malaria which result in one million deaths, mostly of children under five. The concomitant high costs exacted by this disease also restrain economic development. Our investigations will lend significant insight toward the development of a vaccine as part of a strategy to eradicate this scourge.
|Rothen, Julian; Murie, Carl; Carnes, Jason et al. (2018) Whole blood transcriptome changes following controlled human malaria infection in malaria pre-exposed volunteers correlate with parasite prepatent period. PLoS One 13:e0199392|
|Mpina, Maxmillian; Maurice, Nicholas J; Yajima, Masanao et al. (2017) Controlled Human Malaria Infection Leads to Long-Lasting Changes in Innate and Innate-like Lymphocyte Populations. J Immunol 199:107-118|
|HIPC-CHI Signatures Project Team; HIPC-I Consortium (2017) Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses. Sci Immunol 2:|
|Finak, Greg; Gottardo, Raphael (2016) Promises and Pitfalls of High-Throughput Biological Assays. Methods Mol Biol 1415:225-43|
|Finak, Greg; Langweiler, Marc; Jaimes, Maria et al. (2016) Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium. Sci Rep 6:20686|
|Malek, Mehrnoush; Taghiyar, Mohammad Jafar; Chong, Lauren et al. (2015) flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics 31:606-7|
|Lin, Lin; Frelinger, Jacob; Jiang, Wenxin et al. (2015) Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data. Cytometry A 87:675-82|
|Lin, Lin; Finak, Greg; Ushey, Kevin et al. (2015) COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat Biotechnol 33:610-6|
|Courtot, Mélanie; Meskas, Justin; Diehl, Alexander D et al. (2015) flowCL: ontology-based cell population labelling in flow cytometry. Bioinformatics 31:1337-9|
|Finak, Greg; Frelinger, Jacob; Jiang, Wenxin et al. (2014) OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput Biol 10:e1003806|
Showing the most recent 10 out of 15 publications