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

描述(申请人提供)：在过去的几十年里，由至少对一种抗生素产生抗药性的细菌引起的感染数量出现了惊人的增长。在同一时期，获得批准用于临床的新抗生素的数量也出现了同样令人震惊的下降。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：在代谢的电子模型中部署基因组规模，并使用系统生物学的其他工具来识别致病肠杆菌代谢网络中的合成致死和合成致病基因对。然后，将通过在大肠杆菌和鼠伤寒沙门氏菌中构建假定的对来验证模型预测。被证实在两种生物中都是合成致死的对，然后将接受虚拟和高通量筛选，以识别抑制生长或杀死多个肠杆菌成员的广谱双组分配方。该计划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、全面和不偏不倚地发现组合药物开发靶点的一种方式，以及识别特定的小分子对，用于针对一类重要的人类病原体进行可能的药物开发。 与公共卫生相关：在过去十年中，由大肠杆菌、沙门氏菌和类似细菌引起的感染数量以惊人的速度增加，这种情况要求加大努力，以确定这些病原体共有的新药物靶点，并随后进行药物开发。这里提出的研究计划寻求使用计算机模型来寻找这样的目标，然后识别可以阻止它们的化合物。通过这种方式，该计划将通过增加可用于治疗这些感染的抗生素的数量来改善公共健康。

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.