Metabolomics Core for the Dietary Biomarkers Development Center at Harvard University

项目摘要-代谢组学核心 膳食生物标志物发展中心(DBDC)的首要目标是制定目标 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)的图谱技术提供了强有力的手段 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群的代谢转化而产生。我们建议 为哈佛大学的DBDC创建代谢组学核心，将使用一套五个免费的 非靶向LC-MS方法在急性喂养研究中发现新的食品生物标记物 干预核心和开发有针对性的分析方法，用于在更大的人类队列中进行生物标记物验证研究 与生物标记物项目合作。核心将利用现有的专业知识和资源 在麻省理工学院和哈佛大学的代谢组学平台，并将使用能够 用于已知身份的化合物的混合分析的分辨率和准确质量(HRAM)，确认使用 参比标准，以及数千个尚未表征的峰的分子。核心将使用 结构鉴定工作流程，结合了专门的Thermo ID-X LC-MS系统，用于广泛的 产物离子谱(MS/MS)的获取与基于机器学习的结构预测 高效确定重要未知因素身份的工作流程。一旦新的食品生物标志物被 确定后，Core将利用储存的血浆和尿液开发基于MS的有针对性的三重四极杆分析 来自人类干预试验的样本(OmniHeart164名参与者)和队列(LVS组600人，LV组450人 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.