Metabolomics Core for the Dietary Biomarkers Development Center at Harvard University

项目总结-代谢组学核心 膳食生物标志物开发中心（DBDC）的首要目标是开发目标 饮食摄入量的生物标志物，可以补充目前的饮食摄入量评估方法。液体 基于色谱串联质谱（LC-MS）的分析技术提供了一种强有力的手段， 分析食物生物标记物，包括直接来自食物的营养和非营养分子，以及 它们的代谢物通过宿主和微生物组的代谢转化产生。我们提出 为哈佛大学的DBDC创建代谢组学核心， 非靶向LC-MS方法，以发现新的食物生物标志物在急性喂养研究进行的 干预核心，并在更大的人类队列中开发用于生物标志物验证研究的靶向测定， 与生物标志物项目合作。核心小组将利用联合国系统现有的专门知识和资源， 代谢组学平台在麻省理工学院和哈佛的布罗德研究所，并将使用能够高 用于已知身份化合物的混合分析的分辨率和准确质量（HRAM），使用 参比标准品和数千个尚待表征的分子峰。核心将使用 结构解析工作流程，结合了专用的Thermo ID-X LC-MS系统， 结合基于机器学习的结构预测的产物离子谱（MS/MS）的获取 工作流程，以有效地确定重要未知数的身份。一旦新的食物生物标志物被发现， 该中心将利用储存的血浆和尿液开发靶向三重四极杆MS检测方法 来自人类干预试验（OmniHeart中的164名参与者）和队列（LVS中的600名参与者和 SOLNAS）在生物标志物项目中。

PROJECT SUMMARY – METABOLOMICS CORE The overarching goal of the Dietary Biomarker Development Centers (DBDCs) is to develop objective biomarkers of dietary intake that can complement current dietary intake assessment methods. Liquid chromatography tandem mass spectrometry- (LC-MS-) based profiling technologies provide a powerful means to profile food biomarkers, including nutrient and non-nutrient molecules directly derived from food as well as their metabolites generated via metabolic transformation by both the host and the microbiome. We propose creating a Metabolomics Core for the DBDC at Harvard University that will use a suite of five complimentary nontargeted LC-MS methods to discover new food biomarkers in acute feeding studies conducted by the Intervention Core and to develop targeted assays for biomarker validation studies in larger human cohorts in conjunction with the Biomarkers Project. The Core will leverage existing expertise and resources of the Metabolomics Platform at the Broad Institute of MIT and Harvard and will use equipment capable of high resolution and accurate mass (HRAM) for hybrid analyses of compounds of known identity, confirmed using reference standards, and thousands of peaks from yet to be characterized molecules. The Core will use a structure elucidation workflow that incorporates a dedicated Thermo ID-X LC-MS system for extensive acquisition of product ion spectra (MS/MS) coupled with a machine learning-based structure prediction workflow to efficiently determine identities of important unknowns. Once new food biomarkers have been identified, the Core will develop targeted triple quadrupole MS-based assays using stored plasma and urine samples from a human intervention trial (164 participants in OmniHeart) and cohorts (600 in LVS and 450 in SOLNAS) in the Biomarkers Project.