Genomic Analysis of Network Perturbations in Human Disease

描述：个体之间的遗传差异会极大地影响他们对疾病的易感性。来自人类基因组计划(HGP)的信息，包括基因组序列及其注释，以及HapMap和人类癌症基因组计划(HCGP)等计划，极大地提高了我们发现遗传变异并将基因与广泛的人类疾病联系起来的能力。尽管取得了这些进展，但将单个基因及其变异与疾病联系起来仍然是一个艰巨的挑战。即使在已经确定了因果变异的情况下，必须在治疗干预策略之前的生物学洞察力通常来得很慢。其主要原因是，功能序列变体的表型效应是由基因产物和代谢物的动态网络介导的，这些基因产物和代谢物表现出的紧急性质不能一次理解一个基因。我们的中心假设是，人类基因变异和病原体(如病毒)都会影响网络的局部和全球特性，从而导致“疾病状态”。因此，我们提出了一种基于网络结构的环境和遗传扰动并使用相互作用组图、蛋白质组分析和转录图谱来读出影响的一般方法来理解细胞网络。我们选择了一个明确的模型系统，该系统具有多种疾病结果：病毒感染。我们将探索这样一个概念，即一个人必须了解复杂细胞网络的变化，才能充分了解基因型、环境和表型之间的联系。我们将把来自特定病毒引起的网络水平扰动的观察结果与相关人类疾病的全基因组人类变异数据集整合在一起，目标是制定数据整合和网络预测的一般原则，在开源软件工具中实例化这些原则，并开发可用于评估我们方法价值的可测试假设。 我们实现这些目标的计划概括为以下具体目标：1.描述具有相关生物学特性的一组病毒的所有病毒-宿主蛋白质-蛋白质相互作用。2.描述病毒蛋白对宿主细胞转录组的干扰。3.组合产生的相互作用和扰动数据以推导基于蜂窝网络的模型。4.使用开发的模型解释在人类疾病中观察到的全基因组遗传变异；5.将CCSG各成员开发的生物信息学资源整合到一个生物信息学核心中，以进行数据管理和传播。6.在现有教育和推广计划的基础上，我们计划制定一个以基因组和网络为中心的教育计划，特别强调为代表不足的少数族裔提供实习、研讨会和科学会议的机会。

DESCRIPTION: Genetic differences between individuals can greatly influence their susceptibility to disease. The information originating from the Human Genome Project (HGP), including the genome sequence and its annotation, together with projects such as the HapMap and the Human Cancer Genome Project (HCGP) have greatly accelerated our ability to find genetic variants and associate genes with a wide range of human diseases. Despite these advances, linking individual genes and their variations to disease remains a daunting challenge. Even where a causal variant has been identified, the biological insight that must precede a strategy for therapeutic intervention has generally been slow in coming. The primary reason for this is that the phenotypic effects of functional sequence variants are mediated by a dynamic network of gene products and metabolites, which exhibit emergent properties that cannot be understood one gene at a time. Our central hypothesis is that both human genetic variations and pathogens such as viruses influence local and global properties of networks to induce "disease states." Therefore, we propose a general approach to understanding cellular networks based on environmental and genetic perturbations of network structure and readout of the effects using interactome mapping, proteomic analysis, and transcriptional profiling. We have chosen a defined model system with a variety of disease outcomes: viral infection. We will explore the concept that one must understand changes in complex cellular networks to fully understand the link between genotype, environment, and phenotype. We will integrate observations from network-level perturbations caused by particular viruses together with genome-wide human variation datasets for related human diseases with the goal of developing general principles for data integration and network prediction, instantiation of these in open-source software tools, and development of testable hypotheses that can be used to assess the value of our methods. Our plans to achieve these goals are summarized in the following specific aims: 1. Profile all viral-host protein-protein interactions for a group of viruses with related biological properties. 2. Profile the perturbations that viral proteins induce on the transcriptome of their host cells. 3. Combine the resulting interaction and perturbation data to derive cellular network-based models. 4. Use the developed models to interpret genome-wide genetic variations observed in human disease, 5. Integrate the bioinformatics resources developed by the various CCSG members within a Bioinformatics Core for data management and dissemination. 6. Building on existing education and outreach programs, we plan to develop a genomic and network centered educational program, with particular emphasis on providing access for underrepresented minorities to internships, workshop, and scientific meetings.