Genomic Analysis of Network Perturbations in Human Disease

人类疾病网络扰动的基因组分析

• 批准号: 8330450
• 负责人: Marc Vidal
• 金额: $ 5.49万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-20 至 2013-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8330450
• 关键词: Bioinformatics; Biological; Biological Models; Cancer Center Support Grant; Cells; Complex; Data; Data Set; Disease; Disease Outcome; Education and Outreach; Educational workshop; Environment; Exhibits; Gene Expression Profile; Genes; Genetic; Genetic Variation; Genomics; Genotype; Goals; Human; Human Genetics; Human Genome Project; Individual; Internships; Link; Maps; Mediating; Methods; Modeling; Network-based; Pathway Analysis; Phenotype; Predisposition; Property; Proteins; Proteomics; Resources; Software Tools; Structure; Therapeutic Intervention; Time; Underrepresented Minority; Variant; Viral; Viral Proteins; Virus; Virus Diseases; cancer genome; data integration; data management; genetic variant; genome sequencing; genome-wide; human disease; insight; meetings; member; open source; outreach program; pathogen; programs; protein protein interaction; tool development

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.

描述：个体之间的遗传差异可以极大地影响其对疾病的易感性。起源于人类基因组项目（HGP）的信息，包括基因组序列及其注释，以及HAPMAP和人类癌症基因组项目（HCGP）等项目，大大加速了我们找到具有广泛人类疾病的遗传变异和关联基因的能力。尽管有这些进展，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。即使已经确定了因果变体，必须先于治疗干预策略之前的生物学见解通常也很慢。这样做的主要原因是，功能序列变体的表型效应是由基因产物和代谢产物的动态网络介导的，这些网络表现出一个出现的特性，这些特性一次无法理解一个基因。我们的中心假设是，人类遗传变异和病原体（例如病毒）都会影响网络的局部和全球性质，以诱导“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来理解细胞网络的一般方法，并使用Interactome映射，蛋白质组学分析和转录分析对效果的读数进行了读数。我们选择了具有多种疾病结局的定义模型系统：病毒感染。我们将探讨这样一个概念，即人们必须了解复杂的蜂窝网络中的变化，以充分了解基因型，环境和表型之间的联系。我们将整合由特定病毒引起的网络级扰动的观察结果，以及针对相关人类疾病的全基因组人类变异数据集，目的是开发用于数据集成和网络预测的一般原理，在开放源代码软件工具中对这些疾病的实例化以及可用于评估我们方法价值的可测试假说的开发。 我们实现这些目标的计划总结在以下特定目的中：1。概述了与具有相关生物学特性的一组病毒的所有病毒宿主蛋白 - 蛋白质相互作用。 2。介绍了病毒蛋白诱导其宿主细胞转录组的扰动。 3。将所得的相互作用和扰动数据结合在一起，以得出基于蜂窝网络的模型。 4。使用开发的模型来解释在人类疾病中观察到的全基因组遗传变异，5。将各种CCSG成员开发的生物信息学资源整合在数据管理和传播的生物信息学核心中。 6.在现有的教育和外展计划的基础上，我们计划制定以基因组和网络为中心的教育计划，特别强调为代表性不足的少数群体提供实习，讲习班和科学会议的机会。