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

基本信息

批准号：
7341401
负责人：
JAMES J COLLINS
金额：
$ 81.25万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-30 至 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7341401
关键词：
Accounting Address Affect Algorithms American Anti-Bacterial Agents Antibiotic Resistance Antibiotic Therapy Antibiotics Antitoxins Apoptosis Area Arrhythmia Attenuated Award Bacteria Bacterial Genes Bacterial Toxins Behavior Biochemical Biological Biological Phenomena Biological Process Biology Biomedical Engineering Biomedical Research Biotechnology Boston Brain Cardiac Categories Cell Communication Cell Death Cell Density Cell Surface Proteins Cell Therapy Cell Wall Cell physiology Cells Cessation of life Chelating Agents Chemicals Chronic Class Clinical Communication Communities Complex Computational Technique Computer Simulation Condition DNA DNA Damage DNA Gyrase DNA Replication Inhibition DNA biosynthesis Data Defense Mechanisms Detection Development Devices Diabetic Neuropathies Discipline Dissection Drug Delivery Systems Drug Design Elderly Engineering Enhancement Technology Environment Equilibrium Escherichia coli Event Facility Construction Funding Category Feedback Foundations Future Gene Expression Gene Expression Regulation Gene Proteins Gene Targeting Generations Genes Genetic Genetic Transcription Genome Genomics Global Change Goals Gram-Positive Bacteria Grant Growth Heart Hip region structure Human Human Characteristics Hydroxyl Radical Indium Infection Internet Iron Knock-out Knowledge Lead Lesion Logic Mammalian Cell Maps Measurement Mechanics Mediating Mediator of activation protein Medical Device Membrane Lipids Memory Metabolic Metabolic Control Methods Microbe Microbial Biofilms Microbiology Modeling Molecular Biology Molecular Profiling Nature Neurology Nobel Prize Noise Nonlinear Dynamics Norfloxacin Numbers Nutrient Nutritional Operon Organ Oryctolagus cuniculus Oxidation-Reduction Pathway interactions Patients Personal Satisfaction Pharmaceutical Preparations Physiological Pilot Projects Play Poisoning Population Population Control Population Heterogeneity Preparation Prevalence Probability Process Production Property Protein Biosynthesis Protein Synthesis Inhibition Proteins Quinolone Antibiotic Quinolones RNA Range Rate Reaction Reactive Oxygen Species Regulator Genes Regulatory Pathway Replication-Associated Process Repression Research Resistance Risk Role Route SOS Response Science Scientist Sensory Sequence Analysis Series Shapes Shotguns Signal Transduction Son of Sevenless Proteins Staging Stress Stroke Structure Study Section Sulfur Superoxides Synthetic Genes System Systems Biology Techniques Technology Testing Therapeutic Intervention Thinking Time Toxic effect Toxin Universities VAI-2 Work abstracting aminoacid biosynthesis antimicrobial bactericide base biocomputing biological adaptation to stress comparative concept design design and construction drug development drug discovery falls foot sole gene function genome sequencing improved in vivo inhibitor/antagonist innovation insight interest interfacial killings medical schools microbial model design mutant network models neurophysiology novel novel therapeutics prevent programs protein metabolite quorum sensing repaired research study resistance mechanism response sudden cardiac death synthetic biology tool transcription factor transmission process

项目摘要

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.

该项目的目标是利用创新的系统生物学和合成生物学方法定量表征和分析细胞底层的细菌基因调控网络对抗生素的反应、持续存在的形成和耐药性的出现。随着耐抗生素细菌菌株的惊人传播，更好地了解具体情况未来需要了解杀菌抗生素导致细胞死亡的一系列事件抗菌药物开发。因此，需要系统生物学和合成生物学方法来辨别基因、蛋白质和途径之间的相互作用，以进一步促进我们对细菌如何反应并防御抗生素的了解。这遗传网络的潜在逻辑的含义很难通过以下方式推断出来仅凭实验技术，在许多情况下，成功的方法将涉及联合新实验和计算建模技术。为了解决这个问题，我们有开发了能够构建定量模型的计算实验方法使用表达测量来构建基因、蛋白质和代谢调控网络，无需先验有关网络结构或功能的信息。在这个项目中，我们将使用这些方法对细胞对抗生素反应的细菌基因调控网络进行逆向工程，坚持者的形成和抵抗的出现。由此产生的网络和将分析途径，以深入了解相关生物的监管控制流程，网络模型将用于确定关键监管者和调解者各种表型反应。这项工作可能会对压力反应产生新的见解细菌和药物发现新靶点的识别，例如克服细菌保护机制或激活细菌程序性细胞死亡。该项目可能从而能够开发出能够解释和利用抗生素的新型抗生素遗传网络的复杂调控特性。