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.

项目摘要/摘要虽然多药耐药结核病的出现迫切需要使用新的抗菌剂行动方式，他们的发现仍然是一个重大挑战。揭示药物模式的许多技术行动依赖于低通量，耗时和针对目标的方法，这些方法可提供低通量对潜在药物的更广泛功能影响的维度观点。与Zampieri实验室一起瑞士Eth Zurich，通过利用CRISPR技术和非目标代谢组学，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作用，并允许进行高通量DE NROK功能注释大型复合库。阐明数千种药物或基因扰动的机理基础代谢产物提供丰富的多维信息，以互补与经典表型分析相互补可用于研究任何候选药物的作用和作用方式。本提案的总体目标是实现500种抗TB化合物的功能注释已知的效力但未知的MOA，这将为消除新的非常规策略铺平道路 TB。我们将建立MTB对基本基因敲低的代谢反应的汇编因子过表达和MTB生长抑制剂的独特选择。我们将使用我们的定制开发了计算工具，以根据代谢对药物作用和基因敲低进行分类轮廓，对非常规药物MOA做出可检验的假设，并将优先级化合物移动到遗传和生化命中验证。我们提议的工作的重要附带好处是在结核分枝杆菌中具有尚未功能的基因的功能注释，信息致密结核分枝杆菌中基因 - 药物代谢相互作用的数据库。