Malaria remains one of the most deadly infectious diseases in the developing world. The absence of a vaccine and the development of parasite resistance to commonly used antimalarial drugs underscore the urgent need for new therapeutic approaches. The goal of this project is to generate insights into the mechanisms whereby Plas- modium falciparum, the parasite responsible for the most virulent form of malaria, regulates its genes'expression throughout its life cycle. Mechanisms controlling gene expression in the parasite are still poorly understood. Increasing evidence indicates that control of gene expression in Plasmodium occurs at multiple levels, via local protein-DNA binding events, patterns of histone modifications, local chromatin structure and nucleosome occupancy, and large-scale chromatin structure. A variety of existing genome-wide data sets, including the genomic DNA sequence as well as measurements of RNA expression levels and nucleosome occupancy, provide insight into many aspects of this regulatory machinery. However, a global picture of the structure of DNA in the nucleus of the parasite is not yet available. This project will apply a recently developed technology to map in Plasmodium all intra- and inter-chromosomal interactions at kilobase resolution throughout the parasite life cycle. These data will be used to build a dynamic three-dimensional model of the Plasmodium genome in vivo. The project will also generate a series of maps of histone modifications genome-wide. Finally, these two new data sets, along with existing data sets, will be integrated using machine learning methods to produce a predictive model of gene expression across the Plasmodium eryrthrocytic cycle. Rational drug design requires a detailed understanding of the molecular basis of disease. By providing fundamental insight into the regulatory mechanisms of the malaria parasite, this project will improve our ability to design new drugs and novel lines of defense against malaria.
To develop effective strategies for combating malaria, we must understand the biology of Plasmodium falciparum, the parasite that causes the most lethal form of the disease. This project will use a recently developed technique to discover the spatial organization of the Plasmodium genome during the parasite infectious cycle. The resulting data will be integrated with newly generated patterns of histone modifications and will be used in combination with published genome-wide data sets to develop a computational model that will yield insights into transcriptional regulation in the human malaria parasite.
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