Identification of new inhibitors of essential functions in M. tuberculosis by high-throughput metabolic profiling

项目摘要/摘要 虽然耐多药结核病的出现增加了对新的抗菌素的迫切需求 行动模式，他们的发现仍然是一个重大挑战。破解毒品模式的许多技术 行动方案依赖于低吞吐量、耗时和针对特定目标的方法，这些方法提供了低吞吐量、低成本 对潜在药物更广泛的功能影响的多维视角。与赞比埃里实验室一起在 瑞士苏黎世工业大学，通过利用CRISPR技术和非靶向代谢组学，我们 开发了一种基于基因比较的计算/实验相结合的策略 和药物诱导的代谢效应，并允许执行高通量从头开始的功能注释 大型复合库。解开数千种药物或基因扰动的机制基础 代谢产物提供了丰富的多维信息，补充了经典的表型图谱 并可用于考察任何候选药物的作用效果和作用方式。 本提案的总体目标是实现500个抗结核化合物的功能注释 已知的效力但未知的MOA，这将为消除 结核病。我们将建立一个结核分枝杆菌对必需基因敲除、转录的代谢反应纲要。 因子过度表达和一系列独特的结核分枝杆菌生长抑制物库。我们将用我们的习俗- 开发了计算工具，根据新陈代谢对药物作用和基因敲除进行分类 简介，提出关于非常规药物MOA的可检验假设，并将优先化合物转移到 基因和生化检测确认。我们提议的工作的一个重要附带好处是 结核分枝杆菌中功能未知的基因的功能注释和信息密集 结核分枝杆菌基因-药物-代谢相互作用数据库。

Project Summary/Abstract While the emergence of multi-drug resistant tuberculosis raises an urgent need for antimicrobials with new Modes of Action, their discovery remains a major challenge. Many of the techniques to unravel drug Modes of Action rely on low-throughput, time-consuming and target-specific approaches that provide low- dimensional views into the broader functional impact of potential drugs. Together with the Zampieri lab at ETH Zurich, Switzerland, by leveraging CRISPR technology and non-targeted metabolomics, we developed a combined computational/experimental strategy that is based on the comparison of genetic and drug induced metabolic effects and allows to perform high-throughput de novo functional annotations of large compound libraries. Unraveling the mechanistic basis of drug or gene perturbations of thousands of metabolites provides rich multidimensional information complementary to classical phenotypic profiling and can be used to investigate the effect and mode of action of any drug candidate. The overall goal of the present proposal is to achieve functional annotation of 500 anti-TB compounds with known potency but unknown MoA, which will pave the way to new unconventional strategies to eradicate TB. We will build a compendium of metabolic responses of Mtb to essential gene knockdown, transcription factor overexpression and a unique selection of libraries of Mtb growth inhibitors. We will use our custom- developed computational tools to categorize drug action and gene knockdowns according to metabolic profiles, make testable hypothesis about unconventional drug MoA and move prioritized compounds to genetic and biochemical hit validation. An important collateral benefit of our proposed work will be functional annotation of genes with yet unknown function in M. tuberculosis, and an information dense database on gene-drug-metabolic interactions in M. tuberculosis.