Multi-Omic and multi-scale data integration for the characterization of malaria infection in non-human primates
Yan, Yi Heng
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Plasmodium parasites were identified as the cause of malaria more than 200 years ago. However, malaria remains a public health burden responsible for approximately 400,000 (236,000 ~ 635,000) death in 2015. Severe malaria is responsible for the majority of malaria mortality, yet the understanding of mechanisms of host responses underlying severe malaria pathology remains incomplete. The objective of this project is to identify and characterize host transcriptomic, cellular and cytokine responses that are associated with malaria severity. To quantify the removal of healthy red blood cells (hRBCs) by the host, we created a novel mathematical model that could capture the various outcomes of malaria infection. This model was fitted to the Malaria Host-Pathogen Interaction Center (MaHPIC) time series data set of five Macaca mulatta infected with Plasmodium cynomolgi. Using the fitted model, our group discovered association of the loss of healthy red blood cells and pro-inflammatory cytokines and CD-8 T cell population. Furthermore, our group also created novel statistical tools for the identification of differential networks. Through the application of both traditional bioinformatics analysis tools and differential network analysis, our group characterized severe malaria infection with differential transcriptional up-regulation of genes linked with response to the pathogen-associated molecular pattern (PAMP) and pro-inflammatory cytokines. Through a combined approach of mathematical modeling, differential network analysis and traditional bioinformatics analysis, we were able to identify host transcriptomic, cellular and cytokine responses that are associated with both malaria severity and host removal of healthy red blood cells. This project provides novel insight into the molecular and cellular basis for the development of severe malaria.