Systems Biology Approaches to Viral Pathogenesis and Immunity

病毒发病机制和免疫的系统生物学方法

• 批准号: 8234065
• 负责人: MICHAEL G KATZE
• 金额: $ 96.05万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8234065
• 关键词: Animal Model; Animals; Antiviral Therapy; Bioinformatics; Biological; Biological Assay; Biological Markers; Biometry; Biostatistics Core; Cell Culture Techniques; Clinical Pathology; Collaborations; Data; Development; Diagnostic; Disease Outcome; Disease Progression; Ebola virus; Gene Expression; Gene Proteins; Genetic; Genetic Determinism; Genomics; Goals; Health Sciences; Immune response; Immunity; Immunization; Infection; Influenza; Investigation; Laboratories; Macaca; Maps; Modeling; Molecular Profiling; Mus; North Carolina; Oregon; Outcome; Pacific Northwest; Pathology; Positioning Attribute; Predisposition; Process; Proteomics; Quantitative Trait Loci; Regimen; Regulation; Research Personnel; SARS coronavirus; Sampling; Staging; System; Systems Biology; Universities; Vaccination; Vaccine Design; Vaccines; Viral; Viral Pathogenesis; Virulence; Virulent; Virus; Virus Diseases; Wisconsin; Work; adaptive immunity; base; data integration; drug development; experience; fighting; gene function; improved; influenzavirus; insight; metabolomics; network models; pathogen; prognostic; protein function; response; vaccination strategy; vaccine evaluation; virology; virus host interaction

This Project builds upon the strengths of the Katze laboratory in genomics, proteomics, and bioinformatics to develop systems level views of the virus-host interactions and viral and host genetic determinants that regulate and determine the outcome of infection. To achieve this goal, we will work closely with investigators from the University of North Carolina (Project 3.1) and the University of Wisconsin (Project 3.2) who will be using infection models of SARS-CoV, influenza, and Ebola viruses. In addition to the extensive amount of microarray data that we will be collecting on these infection models, we will integrate proteomics, metabolomics, and lipidomics data that will also be available for selected infection models. In Aim 1, we will use gene expression data to obtain expression quantitative trait loci (eQTL) mapping in screens of 400 genetically distinct mice, comparing the infection outcomes with SARS-CoV, influenza, and Ebola viruses. Aim 2 generates a systems level view of viral virulence and disease progression from more detailed animal models; data integration is a key aspect to make optimal use of genomic and proteomic data for better understanding gene and protein function, as well as discerning how gene expression and protein abundance changes correlate with innate and adaptive immune responses and eventual disease outcome. In Aim 3, we will use genomic approaches to furnish a comprehensive view of the changes in host gene expression that occur in response to the Ebola virus immunization regimens described in Project 3.2. These data may suggest genomic markers of protective immunity or indicate the predisposition of an animal to a particular response to immunization and subsequent challenge. Together, these aims provide an integrated approach that markedly enhances synergy among the collaborating projects by allowing us and our collaborators to place experimental findings in the context of a more comprehensive picture of the infection process. In addition, our high-throughput studies are likely to provide molecular signatures that predict protective immunity or pathology, candidate biomarkers for diagnostic or prognostic assays, and a rational basis for improvements to antiviral therapies or vaccine strategies.

该项目基于Katze实验室在基因组学，蛋白质组学和生物信息学方面的优势基础 开发有关病毒宿主相互作用以及病毒和宿主遗传决定因素的系统级别的视图 调节并确定感染的结果。为了实现这一目标，我们将与调查人员紧密合作 来自北卡罗来纳大学（项目3.1）和威斯康星大学（项目3.2） 使用SARS-COV，流感和埃博拉病毒的感染模型。除了大量 我们将在这些感染模型上收集的微阵列数据，我们将整合蛋白质组学， 代谢组学和脂质组学数据也将用于选定的感染模型。在AIM 1中，我们将 使用基因表达数据获得400屏幕中的表达定量性状基因座（EQTL）映射 遗传上不同的小鼠，将感染结果与SARS-COV，流感和埃博拉病毒进行比较。 AIM 2产生了更详细的动物的病毒毒力和疾病进展的系统级别。 模型；数据集成是最佳使用基因组和蛋白质组学数据的关键方面 了解基因和蛋白质功能，并辨别基因表达和蛋白质丰度 变化与先天和适应性免疫反应以及最终疾病结果相关。在AIM 3中，我们 将使用基因组方法来提供对宿主基因表达变化的全面看法 响应于项目3.2中描述的埃博拉病毒免疫方案。这些数据可能 建议保护性免疫的基因组标记或表明动物对特定的易感性 对免疫的反应和随后的挑战。这些目标共同提供了一种集成的方法 通过允许我们和我们的合作者来 在感染过程的更全面的情况下，将实验发现放置。在 此外，我们的高通量研究可能会提供可预测保护性的分子特征 免疫或病理学，诊断或预后测定法的候选生物标志物，以及合理的基础 改进抗病毒疗法或疫苗策略。