MacMillan: Project 1; Technology Development and Research Project #1: Cytological Profiling (Linington)

摘要：生物活性代谢物的精确作用模式(MOA)的测定仍然是 植物学天然产品社区面临的主要挑战。因为技术上的挑战 与这一问题有关，以及植物和天然产品提取物的复杂性，农业部 通常直到发现过程的后期才处理确定问题，从而导致高冗余率 以及用于药物发现应用的自然减员。这个TRD项目旨在颠覆传统的天然产品 通过开发预测化合物身份和行为模式的新平台来实现发现过程 直接从复杂混合物的初步筛选开始。这种方法利用了最近开发的 我们实验室开发的基于图像的表型筛选技术用于评估天然药物的生物活性 产品提取，并将其与高分辨率UPLC-MS分析相结合，以连接化学成分 具有独特的但不是预定义的生物表型。通过使用这两个信息丰富的集成 我们已经成功地用正交剖面法证明了从头开始的预测 化合物MOS，并通过对使用此方法分离的纯化合物的下游评估来验证这些 方法论。这个TRD项目的目的是：1)通过包合提高生物测定的分辨率 更多的细胞系、染色集和参考化合物，2)开发下一代非靶向 代谢组学平台专门针对天然产品混合物的分析进行优化，以及3)创建新的 信息学工具集成和查询这两种信息丰富的剖析方法。在结束时， 该项目预计，我们将创造一个独特的工具与广泛的用途，在植物自然 Products字段，可通过Web界面访问，并配置为方便地包含样本和 摘自国内和国际上所有感兴趣的研究人员。

Abstract: Determination of the precise mode of action (MOA) for bioactive metabolites remains one of the central challenges facing the botanical natural products community. Because of the technical challenges associated with this issue, and the complex nature of botanicals and natural products extracts, MOA determination is often not addressed until late in the discovery process, leading to a high rate of redundancy and attrition for drug discovery applications. This TRD project aims to invert the traditional natural product discovery process by developing a new platform for the prediction of compound identities and modes of action directly from primary screening of complex mixtures. This approach takes advantage of recently developed phenotypic image-based screening developed in our laboratories for assessing biological activities of natural product extracts, and combines this with high-resolution uPLC-MS analyses to connect chemical constituents with unique but not predefined biological phenotypes. By using the integration of these two information-rich orthogonal profiling methods we have been able to successfully demonstrate the de novo prediction of compound MOs, and validate these by downstream evaluation of pure compounds isolated using this methodology. This TRD project aims to 1) increase the resolution of the biological assay through the inclusion of additional cell lines, stain sets and reference compounds, 2) Develop a next-generation untargeted metabolomics platform specifically optimized for the analysis of natural product mixtures, and 3) Create new informatics tools to integrate and query these two information-rich profiling methods. At the conclusion of the project it is anticipated that we will have created a unique tool with broad utility within the botanical natural products field, which is accessible via a web interface and is configured for facile inclusion of samples and extracts from all interested researchers both nationally and internationally.