The spatial organization of the Plasmodium genome throughout its infectious cycle

描述(申请人提供)：疟疾仍然是发展中国家最致命的传染病之一。缺乏疫苗和对常用抗疟疾药物产生寄生虫抗药性突出表明迫切需要新的治疗方法。该项目的目标是深入了解恶性疟原虫在其整个生命周期中调节其基因表达的机制。恶性疟原虫是造成最致命形式疟疾的寄生虫。控制寄生虫基因表达的机制仍然知之甚少。越来越多的证据表明，疟原虫的基因表达调控发生在多个水平上，通过局部蛋白质-DNA结合事件、组蛋白修饰模式、局部染色质结构和核小体占据以及大规模染色质结构。现有的各种全基因组数据集，包括基因组DNA序列以及RNA表达水平和核小体占有率的测量，提供了对这一调控机制的许多方面的洞察。然而，目前还没有关于这种寄生虫细胞核内DNA结构的全球图片。该项目将应用最近开发的一项技术，在疟原虫整个生命周期中以千碱基的分辨率绘制出所有染色体内和染色体间的相互作用图。这些数据将被用来建立体内疟原虫基因组的动态三维模型。该项目还将在全基因组范围内生成一系列组蛋白修饰图谱。最后，这两个新的数据集，以及现有的数据集，将使用机器学习方法进行整合，以产生跨疟原虫红细胞周期的基因表达的预测模型。合理的药物设计需要对疾病的分子基础有详细的了解。通过提供对疟疾寄生虫调控机制的基本见解，该项目将提高我们设计抗击疟疾的新药和新防线的能力。

DESCRIPTION (provided by applicant): 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.