A New Paradigm in Developing Microbial Libraries for Drug Discovery

开发用于药物发现的微生物库的新范例

• 批准号: 9759178
• 负责人: Chase Madison Clark
• 金额: $ 4.5万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-16 至 2021-05-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759178
• 关键词: 16S ribosomal RNA sequencing; Address; Agriculture; Algorithms; Bacteria; Botanicals; Chemicals; Collection; Complex; Computer software; Custom; Data; Databases; Development; Environment; Fingerprint; Fresh Water; Funding; Future; Generations; Geography; Grouping; Hour; Human; Human Microbiome; Imagery; Individual; Injections; Intelligence; Libraries; Location; Medicine; Metabolic; Metabolism; Methods; Mind; Morphology; Natural Product Drug; Natural Products; Pattern; Phylogenetic Analysis; Phylogeny; Plants; Porifera; Proteomics; Research; Research Personnel; Resources; Sampling; Science; Scientist; Seeds; Services; Source; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Taxonomy; Techniques; Technology; Testing; Variant; Work; base; design; drug discovery; innovation; microbial; microbiome; microbiome analysis; microbiome research; novel therapeutics; programs; sample collection; success; tool

Since the 1930s, scientists have collected bacteria from the environment and used their associated specialized metabolites (SM) as a source for drug discovery. However, phylogenetic and chemical redundancy in bacterial libraries resulted in a high re-discovery rate of known compounds, and consequently a large divestment in natural product drug discovery. The success of a discovery program is dependent on using libraries with a high diversity of taxa and natural products, however current practice relies entirely on colony morphology and/or 16S rRNA gene sequencing analysis to decide which isolated strains to retain for addition to a library. Importantly, this is not indicative of a strain’s ability to produce SMs. Therefore, the development of a semi-automated, open-access platform to rapidly organize unknown bacterial isolates based on phylogeny and SMs would greatly increase the efficiency of the front-end of drug discovery. This technology has broad future applications, given the interconnectedness of microbiomes, such as in humans and in botanical science. Our lab recently developed such a platform, called IDBac, which uses MALDI-TOF MS and custom software that allows fingerprinting of up to 384 bacterial colonies in four hours by a single user.15 However, while proven to work on relatively small subsets of strains, we have yet to establish precedence for this technology in building low-redundancy bacterial libraries from high numbers of samples (hundreds to thousands). The current proposal aims to develop a framework within IDBac for large, personalized database creation and spectra searching/matching. We will also enable smarter sample collection strategies by constructing IDBac tools to allow researchers the ability to a) build spectra databases of known “seed” strains for identifying unknown isolates, and b) visualize and study the interplay of bacterial phylogeny and specialized metabolism. Successful completion of these aims will contribute one of the few major advances to the front end of microbial library generation in nearly eight decades, and will enable researchers studying the microbiome of humans, plants, botanicals, and other sources to build custom microbial libraries for rapid in-house strain identification and characterization.27, 28

自 20 世纪 30 年代以来，科学家们从环境中收集细菌并利用其相关物质 专门代谢物（SM）作为药物发现的来源。然而，系统发育和化学 细菌文库的冗余导致已知化合物的重新发现率很高，因此 大规模撤资天然产物药物发现。发现计划的成功取决于 使用具有高度多样性的类群和天然产物的库，但是当前的实践完全依赖于 菌落形态和/或 16S rRNA 基因测序分析，以确定保留哪些分离菌株 除了图书馆。重要的是，这并不表明菌株产生 SM 的能力。因此， 开发半自动化、开放式平台来快速组织未知细菌 基于系统发育和SM的分离株将大大提高药物前端的效率 发现。鉴于互联性，这项技术具有广泛的未来应用 微生物组，例如人类和植物科学中的微生物组。我们实验室最近开发了这样一个平台， 称为 IDBac，它使用 MALDI-TOF MS 和定制软件，可对多达 384 种细菌进行指纹识别 单个用户在四小时内完成菌落。15 然而，虽然事实证明对相对较小的菌株子集有效， 我们尚未确立这项技术在构建低冗余细菌库方面的优先地位 大量样本（数百到数千）。目前的提案旨在制定一个框架 IDBac 用于大型、个性化数据库创建和光谱搜索/匹配。我们还将启用更智能的 通过构建 IDBac 工具来制定样本收集策略，使研究人员能够 a) 构建光谱 用于识别未知分离株的已知“种子”菌株数据库，以及 b) 可视化和研究相互作用 细菌系统发育和专门代谢。成功完成这些目标将有助于 近八年来微生物文库生成前端的少数重大进展之一， 并使研究人员能够研究人类、植物、植物药和其他物质的微生物组 建立定制微生物库的来源，用于快速内部菌株鉴定和 表征.27, 28