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

描述（由申请人提供）：在过去的几十年里，由至少一种抗生素耐药的细菌引起的感染数量出现了惊人的增长。在同一时期，批准临床使用的新抗生素数量也出现了同样惊人的下降。这两种趋势背后的一个原因是，目前的基因组和其他分析在细菌中产生的新的单一药物靶点少得令人失望，这种情况要求重新努力开发新的药物靶点识别策略，例如开发联合疗法的合理方法。我们在这里提出了一种这样的方法：利用基因组尺度的计算机代谢模型和系统生物学的其他工具，在致病性肠杆菌的代谢网络中识别合成致死和合成致病基因对。然后，通过在大肠杆菌和肠炎沙门氏菌血清型鼠伤寒杆菌中构建假定的配对，对模型预测进行实验验证。经证实对两种微生物都具有合成致死作用的组合将进行虚拟和高通量筛选，以确定抑制生长或杀死多种肠杆菌成员的广谱双组分配方。因此，该项目将实现两个重要目标：建立基于系统生物学的代谢模型，作为合理、全面、公正地揭示组合药物开发目标的一种方式，并确定针对一类重要人类病原体的可能药物开发的特定小分子对。

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. PUBLIC HEALTH RELEVANCE: The number of infections caused by E. coli, Salmonella and similar bacteria has increased at an alarming rate over the past decade, a situation that calls for increased efforts to identify new drug targets shared among these pathogens and subsequent drug development. The research program proposed here seeks to employ computer models to find such targets and then identify chemical compounds that can block them. In this way, this program would improve public health by increasing the number of antibiotics that can be used to treat these infections.