A temporal view of the Plasmodium-red blood cell interactome

描述(申请人提供)：细胞蛋白质与其他蛋白质形成复杂的相互作用网络。尽管很明显，这些网络是动态的结构，会随着环境信号的变化而变化，并在疾病状态下发生变化，但我们评估网络中的时间变化的能力仍处于早期阶段。感染疟疾的红细胞是研究蛋白质相互作用网络随时间变化的一个简单模型。红细胞是一种高度分化的细胞，缺乏细胞器，含有有限的蛋白质。在感染期间，疟疾寄生虫将蛋白质输出到红细胞中，从而显著改变宿主细胞的特性。由于红细胞缺乏细胞核，因此不能通过改变其蛋白质含量来应对寄生虫感染，因此红细胞表型的变化一定是由于大约300种输出到红细胞中的疟疾蛋白所致。出口的疟疾蛋白被认为与红细胞蛋白结合，并以这种方式作为损害，导致细胞蛋白相互作用网络的扰动。由于有关大多数疟疾基因表达时间的详细信息已经可用，我们可以模拟细胞蛋白质相互作用网络随时间的变化，并将疟疾-红细胞相互作用组中的这些时间变化与细胞特性的变化联系起来。然而，关于这些疟疾蛋白的功能信息很少，而且只报道了出口的疟疾蛋白与红细胞蛋白之间的相互作用的数量非常有限。这项研究的总体目标是阐明、验证和在时间上模拟疟疾寄生虫恶性疟原虫和红细胞蛋白之间的蛋白质-蛋白质相互作用网络。新的酵母菌株、改进的酵母双杂交筛选方法和两种互补酵母双杂交筛选方法的组合将用于创建高质量的疟疾-红细胞蛋白相互作用网络。相互作用将通过GST下拉和分裂荧光素酶试验来验证，这是我们在这个项目中开发的，用于高通量分析酵母中的蛋白质-蛋白质相互作用。根据这些数据，我们将为每个互动开发一个置信度分数。然后，疟疾-红细胞蛋白的相互作用将与现有的基因表达数据相结合，以开发疟疾-红细胞相互作用组的时间依赖视图。以我们模型的结果为指导，我们将使用重新密封的RBC幽灵和红细胞计数法来研究疟疾-RBC蛋白在时间上不同相互作用的功能。该项目在研究疟疾-红细胞蛋白相互作用网络的应用和技术开发方面具有创新性。该项目产生的数据不仅与我们对相互作用组动力学的理解有关，而且也与理解疟疾寄生虫如何致病有关。 与公共健康相关：蛋白质通过复杂的相互作用网络相互联系。为了更好地了解这些网络如何随着时间和疾病状态的变化而变化，我们将开发一个感染疟疾的红细胞中蛋白质-蛋白质相互作用的时间模型。这些数据通常与我们对细胞过程的理解有关，特别是与疟疾寄生虫如何致病有关。

DESCRIPTION (provided by applicant): Cellular proteins form complex networks of interactions with other proteins. Although it is clear that these networks are dynamic structures that change in response to environmental signals and that are altered in disease states, our ability to assess the temporal changes in the networks is still at an early stage. Malaria-infected RBCs are a simple model in which to study changes in protein interaction networks over time. RBCs are highly differentiated cells that lack organelles and that contain a limited set of proteins. During infection, malaria parasites export proteins into the RBC that dramatically alter the properties of the host cell. Because RBCs lack nuclei, and thus cannot respond to parasite infection by altering their protein content, the changes that occur in the RBC phenotype must be due to the approximately 300 malaria proteins that are exported into the RBC. The exported malaria proteins are thought to bind to RBC proteins and, in this way, act as lesions that cause perturbations in the cellular protein interaction network. Since detailed information about the timing of expression of most malaria genes is already available, we can model changes in the cellular protein interaction network over time and correlate those temporal changes in the malaria-RBC interactome with changes in cellular properties. However, functional information about these malaria proteins is scarce and only a very limited number of interactions between exported malaria proteins and RBC proteins have been reported. The overall goal of this research is to elucidate, validate, and temporally model the network of protein-protein interactions between the malaria parasite Plasmodium falciparum and RBC proteins. A combination of new yeast strains, improved yeast two-hybrid screening methodology, and two complementary yeast two-hybrid screening approaches will be used to create a high-quality malaria-RBC protein interaction network. Interactions will be validated by GST pull downs and the split-luciferase assay, which we develop in this project for high-throughput analysis of protein-protein interactions in yeast. From this data we will develop a confidence score for each interaction. The malaria-RBC protein interactions will then be integrated with existing gene expression data to develop a time-dependent view of the malaria-RBC interactome. Using the results of our model as a guide, we will investigate the function of temporally distinct malaria-RBC protein interactions using the resealed RBC ghosts and ektacytometry. This project is innovative in its application and development of technologies to study the malaria-RBC protein interaction network. The data generated from this project is relevant not only to our understanding of interactome dynamics but also for understanding how malaria parasites cause disease. PUBLIC HEALTH RELEVANCE: Proteins are linked to each other by a complex network of interactions. To better understand how these networks change over time and in disease states, we will develop a temporal model of protein- protein interactions in malaria-infected red blood cells. This data is generally relevant to our understanding of cellular processes and specifically relevant to how malaria parasites cause disease.