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

翻译后摘要：生物活性代谢物的精确作用模式（MOA）的确定仍然是一个 植物天然产品社区面临的主要挑战。因为技术上的挑战 与此相关的问题，以及植物和天然产品提取物的复杂性，MOA 通常直到发现过程的后期才进行确定，导致高冗余率 和药物发现应用的磨损。这个TRD项目旨在将传统的天然产品 通过开发用于预测化合物特性和作用模式的新平台， 直接从复杂混合物的初级筛选中获得。这种方法利用了最近开发的 表型图像为基础的筛选，在我们的实验室开发的评估生物活性的天然 产品提取物，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。通过利用这两个信息丰富的整合 正交分析方法，我们已经能够成功地证明从头预测 化合物MO，并通过使用该方法分离的纯化合物的下游评价来验证这些化合物。 方法论该TRD项目旨在1）通过纳入生物测定来提高生物测定的分辨率 额外的细胞系，染色组和参考化合物，2）开发下一代非靶向 代谢组学平台专门为天然产物混合物的分析进行了优化，以及3）创建新的 信息学工具来整合和查询这两种信息丰富的分析方法。结束时 项目预计，我们将创造一种独特的工具，在植物自然界中具有广泛的实用性。 products字段，可通过Web界面访问，并配置为轻松包含样本， 摘录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.