A Network Biology Approach to Antibiotic Action and Bacterial Defense Mechanisms

抗生素作用和细菌防御机制的网络生物学方法

• 批准号: 8128715
• 负责人: JAMES J COLLINS
• 金额: $ 80.44万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-30 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8128715
• 关键词: Accounting; Address; Anti-Bacterial Agents; Antibiotic Resistance; Antibiotics; Apoptosis; Bacteria; Bacterial Genes; Biological Process; Biology; Cell Death; Complex; Computer Simulation; Defense Mechanisms; Development; Engineering; Event; Future; Gene Proteins; Genetic; Goals; Lead; Logic; Measurement; Mediator of activation protein; Methods; Modeling; Pathway interactions; Property; Resistance; Structure; Systems Biology; Techniques; Work; bactericide; biological adaptation to stress; drug development; drug discovery; innovation; insight; network models; novel; protein metabolite; research study; resistant strain; response; synthetic biology

The goal of this project is to use innovative systems biology and synthetic biology approaches to quantitatively characterize and analyze bacterial gene regulatory networks underlying cellular responses to antibiotics, the formation of persisters and the emergence of resistance. With the alarming spread of antibiotic-resistant strains of bacteria, a better understanding of the specific sequences of events leading to cell death from bactericidal antibiotics is needed for future antibacterial drug development. Accordingly, there is a need for systems biology and synthetic biology approaches to discern the interplay between genes, proteins and pathways in furthering our understanding of how bacteria respond and defend themselves against antibiotics. The implications of the underlying logic of genetic networks are difficult to deduce through experimental techniques alone, and successful approaches will in many cases, involve the union of new experiments and computational modeling techniques. To address this problem, we have developed computational-experimental methods that enable construction of quantitative models of gene, protein and metabolite regulatory networks using expression measurements and no prior information on the network structure or function. In this project, we will use these approaches to reverse engineer bacterial gene regulatory networks underlying cellular responses to antibiotics, the formation of persisters and the emergence of resistance. The resulting networks and pathways will be analyzed to gain insight into the regulatory control of the associated biological processes, and the network models will be used to identify key regulators and mediators for a variety of phenotypic responses. This work could lead to new insights into the stress response of bacteria and the identification of novel targets for drug discovery, e.g., ones that overcome bacterial protective mechanisms or activate bacterial programmed cell death. This project may thus enable the development of novel classes of antibiotics that account for and utilize the complex regulatory properties of genetic networks.

这个项目的目标是利用创新的系统生物学和合成生物学的方法来

项目成果

Synthetic gene networks that count.

• DOI: 10.1126/science.1172005
• 发表时间: 2009-05-29
• 期刊: Science (New York, N.Y.)
• 作者: Friedland AE;Lu TK;Wang X;Shi D;Church G;Collins JJ
• 通讯作者: Collins JJ

How antibiotics kill bacteria: from targets to networks.

• DOI: 10.1038/nrmicro2333
• 发表时间: 2010-06
• 期刊: Nature reviews. Microbiology

Role of reactive oxygen species in antibiotic action and resistance.

• DOI: 10.1016/j.mib.2009.06.018
• 发表时间: 2009-10
• 期刊: CURRENT OPINION IN MICROBIOLOGY
• 影响因子: 5.4
• 作者: Dwyer, Daniel J.;Kohanski, Michael A.;Collins, James J.
• 通讯作者: Collins, James J.

Microbial environments confound antibiotic efficacy.

• DOI: 10.1038/nchembio.740
• 发表时间: 2011-12-15
• 期刊: NATURE CHEMICAL BIOLOGY
• 影响因子: 14.8
• 作者: Lee, Henry H.;Collins, James J.
• 通讯作者: Collins, James J.

Potentiating antibacterial activity by predictably enhancing endogenous microbial ROS production.

• DOI: 10.1038/nbt.2458
• 发表时间: 2013-02
• 期刊: Nature biotechnology
• 影响因子: 46.9