小桶
代谢组学
化学
葡萄糖醛酸化
质谱法
色谱法
代谢途径
代谢物
代谢组
计算生物学
生物化学
新陈代谢
基因本体论
生物
酶
基因表达
基因
微粒体
作者
Sijia Zheng,Xiuqiong Zhang,Zaifang Li,Miriam Hoene,Louise Fritsche,Fujian Zheng,Qi Li,Andreas Fritsche,Andreas Peter,Rainer Lehmann,Xinjie Zhao,Guowang Xu
出处
期刊:Analytical Chemistry
[American Chemical Society]
日期:2021-07-30
卷期号:93 (31): 10916-10924
被引量:10
标识
DOI:10.1021/acs.analchem.1c01715
摘要
From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)–mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)–high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI