Model-guided Identification of Synthetic Lethal Genes for Drug Target Development

模型引导的药物靶标开发合成致死基因鉴定

基本信息

批准号：
8286210
负责人：
BERNHARD O PALSSON
金额：
$ 59.25万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-07-01 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8286210
关键词：
Anti-Bacterial Agents Antibiotics Bacteria Biochemical Pathway Biological Assay Chemicals Clinical Combined Modality Therapy Computer Simulation Databases Development Drug Delivery Systems Drug Formulations Enterobacteriaceae Enzymes Escherichia coli Escherichia coli O157 Genes Genome Genomics Goals Growth Human Infection Klebsiella pneumonia bacterium Lead Lethal Genes Libraries Metabolic Metabolism Methods Modeling Molecular Organism Outcome Pathogenesis Pharmacotherapy Proteins Protocols documentation PubChem Public Health Research Salmonella Salmonella enterica Salmonella typhimurium Screening procedure Set protein Shigella flexneri Structure Systems Biology Testing Time Update Validation Work Yersinia pestis bacterial resistance base combinatorial drug development high throughput screening improved killings member method development mutant network models novel pathogen pathogenic Escherichia coli programs small molecule stem theories tool trend virtual

项目摘要

DESCRIPTION (provided by applicant): The past several decades have seen an alarming rise in the number of infections caused by bacteria resistant to at least one antibiotic. During the same period, there has been an equally alarming decline in the number of new antibiotics receiving approval for clinical use. One reason underlying both trends is that current genomic and other analyses have produced disappointingly few new single drug targets within bacteria, a situation that calls for renewed efforts to develop novel drug target identification strategies such as rational ways to develop combination therapies. We propose one such method here: to deploy genome-scale in silico models of metabolism and other tools from systems biology to identify synthetic lethal and synthetic sick gene pairs within the metabolic networks of pathogenic Enterobacteria. Model predictions would then be tested experimentally by constructing putative pairs in Escherichia coli and Salmonella enterica serovar Typhimurium. Pairs confirmed to be synthetically lethal in both organisms would then be subjected to virtual and high-throughput screening to identify broad-spectrum two-component formulations which inhibit growth or kill multiple members of Enterobacteria. This program would achieve two important goals as a result: to establish systems biology-based metabolic models as one way to uncover - rationally, comprehensively, and in an unbiased manner - targets for combinatorial drug development, and to identify specific pairs of small molecules for possible drug development against an important class of human pathogens.

描述（由申请人提供）：过去几十年来，由于细菌对至少一种抗生素的耐药性引起的感染数量令人震惊。在同一时期，新的抗生素数量同样令人震惊，获得了临床用途的批准。这两种趋势的基本原因之一是，当前的基因组和其他分析在细菌中产生了令人失望的新单一药物靶标，这种情况需要采取新的努力来制定新的药物靶标识别策略，例如开发合并疗法的理性方法。我们在这里提出了一种这样的方法：在代谢和其他工具的其他工具的硅模型中部署基因组规模，以在致病性肠杆菌的代谢网络中识别合成的致死性和合成生病基因对。然后，将通过在大肠杆菌和肠道沙门氏菌鼠伤寒沙门氏菌中构建推定的对通过构建假定的对来测试模型预测。对两种生物的综合性均具有致命性的对，然后将进行虚拟和高通量筛选，以识别宽光谱的两组分制，以抑制生长或杀死肠杆菌的多个成员。该计划将实现两个重要的目标：建立基于系统生物学的代谢模型，作为一种合理，全面，公正的方式来揭示组合药物开发的目标，并确定针对人类病原体的重要药物开发的特定小分子。