Chemistry, University of British Columbia, Kelowna, British Columbia, V1V1V7, Canada.
Agriculture, University of the Fraser Valley, Chilliwack, British Columbia, V2R 0N3, Canada.
F1000Res. 2024 Apr 8;11:1191. doi: 10.12688/f1000research.124194.2. eCollection 2022.
Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify.
An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography - mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation.
We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites.
HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.
代谢组学是对系统中所有代谢物的同时测定。尽管该领域取得了重大进展，但化合物鉴定仍然是一个挑战。对感兴趣的化合物类别的先验知识可以提高代谢物的鉴定。激素是一种小分子信号分子，它们协同作用，指导生物系统的所有发育、功能和繁殖方面，并且作为环境污染物也带来了挑战。激素在组织中天然存在于低水平，以多种形式储存，并在受到刺激时迅速动员，这使得它们难以测量、鉴定和定量。
对植物中的已知激素、其前体、代谢物和缀合物进行了深入的文献综述，以生成数据库，并开发了一个 RShiny 应用程序，以便能够针对数据库进行基于网络的搜索。还开发了一种带有保留时间预测的液相色谱-质谱 (LC-MS) 协议。对 14 项植物代谢组学研究进行了荟萃分析以进行验证。
我们开发了 HormonomicsDB，这是一种可以用来针对包含 200 多种已知激素、其前体和代谢物的数据库查询非靶向质谱 (MS) 数据集的工具。该方案包括样品制备、分析、数据处理和激素注释，旨在最大限度地减少不稳定激素的降解。该植物系统被用作模型来演示工作流程和数据采集和解释。分析条件通过使用通用溶剂系统标准化为 30 分钟的分析时间，以便具有基本 MS 知识的研究人员可以轻松转移。合成生物转化的结合使预测新的代谢物成为可能。
HormonomicsDB 适合任何具有兼容柱和缓冲系统的基于 LC-MS 的系统使用，能够在各种样品中表征已知的激素组，并生成假设以揭示对激素信号网络的新见解。
