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

通过高通量代谢分析鉴定结核分枝杆菌基本功能的新抑制剂

基本信息

批准号：
10568482
负责人：
Michael Berney
金额：
$ 65.38万
依托单位：
ALBERT EINSTEIN COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-11-18 至 2027-10-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10568482
关键词：
Algorithms Anti-Bacterial Agents Antibiotics Antineoplastic Agents Antitubercular Agents Atlases Biochemical Biological Biological Assay Biology CRISPR interference CRISPR/Cas technology Cancer cell line Categories Cells Chemicals Communicable Diseases Computer Models Consumption Custom Data Databases Dimensions Droughts Drug Kinetics Drug Screening Drug Targeting Ensure Environment Escherichia coli Essential Genes Event Expression Library Fingerprint Genes Genetic Goals Growth Growth Inhibitors Lead Libraries Mass Spectrum Analysis Measures Metabolic Methodology Methods Molecular Multidrug-Resistant Tuberculosis Mycobacterium smegmatis Mycobacterium tuberculosis Nature Pharmaceutical Preparations Phenotype Property Proteolysis Regulator Genes Reproducibility Safety Switzerland System Techniques Technology Time Validation Work antimicrobial comparative computerized tools drug action drug candidate drug discovery emerging antibiotic resistance follow-up gene function in vivo inhibitor innovation insight knock-down lung cancer cell metabolic profile metabolome metabolomics meter novel novel strategies overexpression response small molecule small molecule libraries transcription factor tuberculosis drugs

项目摘要

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.

项目概要/摘要尽管多重耐药结核病的出现提出了对新抗菌药物的迫切需求行动模式，他们的发现仍然是一个重大挑战。许多解开药物模式的技术行动依赖于低吞吐量、耗时且针对特定目标的方法，这些方法提供低对潜在药物更广泛的功能影响的维度观点。与 Zampieri 实验室一起瑞士苏黎世联邦理工学院利用 CRISPR 技术和非靶向代谢组学，我们开发了一种基于遗传比较的组合计算/实验策略和药物诱导的代谢效应，并允许执行高通量从头功能注释大型化合物库。揭示数千种药物或基因扰动的机制基础代谢物提供了丰富的多维信息，与经典表型分析相补充可用于研究任何候选药物的效果和作用方式。本提案的总体目标是实现 500 种抗结核化合物的功能注释已知效力但未知 MoA，这将为新的非常规根除战略铺平道路结核病。我们将建立结核分枝杆菌对必需基因敲除、转录的代谢反应概要因子过度表达和 Mtb 生长抑制剂库的独特选择。我们将使用我们的定制- 开发了计算工具，根据代谢对药物作用和基因敲低进行分类配置文件，对非常规药物 MoA 做出可检验的假设，并将优先化合物转移到遗传和生化命中验证。我们提议的工作的一个重要的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。