Global Genetic Interaction Profiling in Prokaryotes

原核生物的全局遗传相互作用分析

• 批准号: 7875240
• 负责人: CAROL Anne GROSS
• 金额: $ 40.89万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-17 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7875240
• 关键词: Animal Model; Area; Bacteria; Bacterial Infections; Bacterial Proteins; Biology; Cells; Cellular biology; Communities; Complement; DNA; Data; Data Set; Databases; Drug Delivery Systems; Escherichia coli; Genes; Genetic; Genomics; Growth; Industry; Libraries; Link; Metabolism; Methodology; Methods; Nature; Organism; Pathway interactions; Plasmids; Procedures; Prokaryotic Cells; Proteins; Quantitative Genetics; Streptococcus pneumoniae; Time; Work; Yeasts; analytical tool; cell envelope; chemical genetics; comparative; computer program; design; functional genomics; improved; insight; interest; pathogen; protein function; success; tool

DESCRIPTION (provided by applicant): Functional genomics builds naturally on recent successes in comparative prokaryotic genomics. The power of these methods for interrogation of the pathways regulating growth has recently been demonstrated for yeast, but the field is less developed for bacteria, partly due to a lack of experimental tools. We propose to develop and exploit quantitative genetic approaches for prokaroytic organisms, opening up the power of these approaches initially to two organisms, E. coli, a gram negative model organism and S. pneumoniae, a gram positive pathogen. We will build the methodology we developed in E. coli to systematically introduce gene disruptions two at a time. We will carry out the procedure en masse, initially focusing on genes involved in cell envelope function and DNA metabolism, so that the effect on bacterial growth of thousands of combinations of pair-wise disruptions can be analyzed and compared. We will apply the analytical tools that had led to important insights into yeast cell biology to our data set and refine them for bacteria. These approaches have proven powerful for discovering the function of uncharacterized genes and the nature of protein pathways and networks within the cell. We will complement quantitative genetic interaction studies with chemical genetic initiatives, thus experimentally linking pharmacological targets to the genes involved in their biology. In this way, a more complete picture of how bacterial proteins function, and how different areas of bacterial cell biology are interconnected, will be assembled. Moreover, this work in E. coli and S. pneumoniae will confer more power on existing comparative genomic data for hundreds of bacteria. Great emphasis will be placed on dissemination of the results (which will be of wide interest) via searchable database that will link to other relevant websites (e.g. EcoliHub) so that diverse datasets can be integrated, as well as providing the community with the experimental tools (strains, plasmids, libraries, computer programs etc.) needed to extend this approach to other organisms. Bacteria are among the simplest organisms in nature. By removing genes two at a time and observing the effect, we will build the first comprehensive picture of how the E.coli bacterium's 4000 genes relate to each other. This type of work can help us understand how a bacterial cell works, and the information can be used to design useful organisms for industry, to identify drug targets, and improve therapy for bacterial disease.

描述(由申请人提供)：功能基因组学自然建立在比较原核生物基因组学最新成功的基础上。这些方法用于询问调控生长的途径的力量最近在酵母中得到了证明，但该领域对细菌的开发较少，部分原因是缺乏实验工具。我们建议开发和利用原核酸菌的定量遗传方法，将这些方法的力量最初开放给两种生物，革兰氏阴性模式生物大肠杆菌和革兰氏阳性病原体肺炎链球菌。我们将建立我们在大肠杆菌中开发的方法，一次系统地引入两个基因中断。我们将集体进行这一过程，最初重点关注与细胞膜功能和DNA新陈代谢有关的基因，以便分析和比较数千种成对干扰组合对细菌生长的影响。我们将把对酵母细胞生物学有重要见解的分析工具应用到我们的数据集，并对它们进行细菌提炼。事实证明，这些方法在发现未确定特征的基因的功能以及细胞内蛋白质通路和网络的性质方面是强大的。我们将用化学遗传举措来补充定量遗传相互作用研究，从而在实验上将药理靶标与参与其生物学的基因联系起来。通过这种方式，细菌蛋白质如何发挥作用以及细菌细胞生物学的不同领域是如何相互联系的更完整的图景将被组装在一起。此外，这项在大肠杆菌和肺炎链球菌上的工作将为数百种细菌的现有比较基因组数据提供更多的力量。将非常重视通过连接到其他相关网站(如EcoliHub)的可搜索数据库传播结果(这将引起广泛的兴趣)，以便整合不同的数据集，并向社区提供实验工具(菌株、质粒库、计算机程序等)。需要将这种方法推广到其他生物体。细菌是自然界中最简单的生物之一。通过一次移除两个基因并观察效果，我们将建立第一张全面的图景，了解大肠杆菌的4000个基因是如何相互联系的。这类工作可以帮助我们了解细菌细胞是如何工作的，这些信息可以用来设计工业用的有用生物体，确定药物靶点，并改进细菌疾病的治疗。