Host-Pathogen Interaction Network Learning from In Vivo Gene Co-Expression

宿主-病原体相互作用网络从体内基因共表达中学习

• 批准号: 7744949
• 负责人: Kenneth L Drake
• 金额: $ 13.44万
• 依托单位: SERALOGIX
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7744949
• 关键词: Algorithms; Animal Model; Animals; Biological; Biological Markers; Categories; Cattle; Communities; Complex; Computational Technique; Computer software; Computing Methodologies; Data; Databases; Disease; Gene Expression; Gene Proteins; Genes; Genetic; Genomics; Goals; Immunologics; Immunotherapeutic agent; Infection; Knowledge; Lead; Learning; Literature; Measurement; Measures; Methodology; Methods; Molecular; Molecular Profiling; Mutate; National Institute of Allergy and Infectious Disease; Organism; Pathway Analysis; Pathway interactions; Pattern; Pattern Recognition; Pharmaceutical Preparations; Phase; Positioning Attribute; Proteins; Proteomics; Publishing; RNA; Salmonella enterica; Salmonella typhimurium; Services; Software Tools; Structure; Systems Biology; Testing; Texas; Time; Vaccine Adjuvant; base; computer based statistical methods; data modeling; gene interaction; improved; in vivo; innovation; mathematical model; method development; mutant; novel; numb protein; pathogen; protein expression; protein protein interaction; prototype; public health relevance; research study; tool

DESCRIPTION (provided by applicant): The Phase I objective is to prototype a new systems biology software tool to identifying probable host-pathogen protein-protein interactive (PPI) relationships and networks from in vivo experiments where host-pathogen gene expressions are measured simultaneously. Computational methods for identifying such interactions are an important unsolved problem. New methods are emerging for simultaneous host-pathogen measurement of gene and protein expression. Tremendous amounts of data are generated from these methods and represent an increased analysis complexity that cannot be dealt with manually. New system biology computational techniques will be essential to aid in its analysis. Having new tools for identifying host-pathogen PPI relationships will significantly advance our understanding of immunologic mechanisms and has great implications in identifying potential targets/biomarkers for new immunotherapeutic drugs, adjuvants, and vaccines. This is extremely important knowledge for NIAID Category A, B and C priority pathogens and other common diseases of high concern. Proof-of-feasibility will be done using host and pathogen microarray and proteomic data from a bovine animal model challenged by Salmonella enterica Serovar Typhimurium (wild type S. Typhimurium) and an isogenic sipA, sopABDE2 mutant. Data will be provided to Seralogix by collaborators at Texas A&M National Center for Foreign Animal and Zoonotic Disease Defense. PUBLIC HEALTH RELEVANCE: The new system biology computational techniques, proposed herein, will be essential to aid in the analysis of complex host-pathogen interactions. Having new tools for identifying host-pathogen protein- protein relationships will significantly advance our understanding of immunologic mechanisms and has great implications in identifying potential targets/biomarkers for new immunotherapeutic drugs, adjuvants, and vaccines.

描述(申请人提供)：第一阶段的目标是原型一个新的系统生物学软件工具，以识别可能的宿主-病原体蛋白-蛋白质交互作用(PPI)关系和网络，从体内实验中同时测量宿主-病原体基因的表达。识别这种相互作用的计算方法是一个重要的悬而未决的问题。新的方法正在出现，用于同时测量寄主-病原体的基因和蛋白质表达。这些方法产生了大量的数据，增加了无法手动处理的分析复杂性。新的系统生物学计算技术将对其分析起到至关重要的作用。拥有识别宿主-病原体PPI关系的新工具将极大地促进我们对免疫学机制的理解，并对识别新的免疫治疗药物、佐剂和疫苗的潜在靶点/生物标记物具有重要意义。这对于NIAID A、B和C类优先病原体和其他高度关注的常见疾病来说是极其重要的知识。可行性的证明将使用宿主和病原体微阵列以及来自受鼠伤寒沙门氏菌(野生型鼠伤寒沙门氏菌)和等基因SipA，sopABDE2突变体挑战的牛动物模型的蛋白质组数据。数据将由德克萨斯州A&M国家外来动物和人畜共患病防御中心的合作者提供给Seralogix。公共卫生相关性：在此提出的新的系统生物学计算技术将是帮助分析复杂的宿主-病原体相互作用的关键。拥有识别宿主-病原体蛋白质-蛋白质关系的新工具将极大地促进我们对免疫学机制的理解，并对识别新的免疫治疗药物、佐剂和疫苗的潜在靶点/生物标志物具有重要意义。