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一种基于 LC-MS 的新型靶向代谢组学方法,用于研究饮食摄入的生物标志物。

A Novel LC-MS Based Targeted Metabolomic Approach to Study the Biomarkers of Food Intake.

机构信息

Laboratory for Functional Foods and Human Health, Center for Excellence in Post-Harvest Technologies, North Carolina Agricultural and Technical State University, North Carolina Research Campus, 500 Laureate Way, Kannapolis, NC, 28081, USA.

出版信息

Mol Nutr Food Res. 2020 Nov;64(22):e2000615. doi: 10.1002/mnfr.202000615. Epub 2020 Oct 12.

Abstract

SCOPE

In this work, an integrated strategy is developed for rapid discovery, precise identification, and automated quantification for the biomarkers of food intake (BFIs) for specific food exposure using an ultra-high-pressure liquid chromatography-high-resolution mass spectrometry (MS) based targeted metabolomics approach.

METHODS AND RESULTS

Using whole grain (WG) wheat intake as an example, the combination of paired mass distance networking and parallel reaction monitoring analysis is applied to selectively extract and identify WG metabolites in human urine samples. As a result, a total of 76 wheat phytochemical-derived metabolites, including 17 alkylresorcinol metabolites, 20 benzoxazinoid derivatives, and 39 phenolic acid metabolites are identified. Subsequently, a MS spectral database consisting of the identified metabolites is created by mzVault. The characteristics of identified metabolites from the database are incorporated into the TraceFinder software to establish a quantification platform. Using a standardized urine sample, the authors are able to simultaneously quantify both free and conjugated (sulfate and glucuronide) WG wheat metabolites in real samples without further enzymatic hydrolysis, which is validated by using authentic standards to quantify these metabolites.

CONCLUSION

This novel strategy opens the window to study the biomarkers of specific food intake and make it feasible to validate the BFIs in large-scale human studies.

摘要

范围

本工作开发了一种综合策略,用于使用基于超高压液相色谱-高分辨率质谱(MS)的靶向代谢组学方法,快速发现、精确鉴定和自动定量特定食物暴露的食物摄入生物标志物(BFIs)。

方法和结果

以全谷物(WG)小麦摄入为例,将配对质量距离网络和并行反应监测分析相结合,用于选择性地提取和鉴定人尿液样本中的 WG 代谢物。结果,总共鉴定出 76 种源自小麦植物化学物质的代谢物,包括 17 种烷基间苯二酚代谢物、20 种苯并恶嗪衍生物和 39 种酚酸代谢物。随后,通过 mzVault 创建了一个包含鉴定出的代谢物的 MS 光谱数据库。将数据库中鉴定出的代谢物的特征纳入 TraceFinder 软件中,建立了一个定量平台。使用标准化尿液样本,作者能够在无需进一步酶解的情况下,同时定量测定真实样本中游离和结合(硫酸盐和葡萄糖醛酸)WG 小麦代谢物,通过使用真实标准品来定量这些代谢物对其进行验证。

结论

这种新策略为研究特定食物摄入的生物标志物打开了窗口,并使在大规模人体研究中验证 BFIs 成为可能。

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