Metabolic Pathway Extension Approach for Metabolomic Biomarker Identification

详细信息    查看全文

• 作者：Lin Wang ; Hui Ye ; Di Sun ; Tuo Meng ; Lijuan Cao ; Mengqiu Wu ; Min Zhao ; Yun Wang ; Baoqiang Chen ; Xiaowei Xu ; Guangji Wang ; Haiping Hao
• 刊名：Analytical Chemistry
• 出版年：2017
• 出版时间：January 17, 2017
• 年：2017
• 卷：89
• 期：2
• 页码：1229-1237
• 全文大小：469K
• ISSN：1520-6882

文摘

Discovery of metabolomic biomarkers represents an important task in disease diagnosis and therapy. Although the development of various analytical tools and online libraries facilitates the identification of biomarkers, the fast and reliable identification of new biomarkers that are not included in databases still represents a major bottleneck in the field of metabolomics. Here, we developed a metabolic pathway extension (MPE) approach to the fast characterization of metabolomic biomarkers. This approach was proposed based on a core concept that the whole metabolome is built from a limited number of initial metabolites via various kinds and multiple steps of metabolic reactions, and thus, theoretically, the whole metabolome might be mapped from the initial metabolites and metabolic reactions. Carnitine was used as an example of initial metabolites to validate this concept and the usefulness of MPE approach. The intragastric dosing of carnitine to mice induced a significant alternation of a total of 97 metabolites. Mass differences between each pair of metabolites were calculated and then matched with those of typical metabolic pathways automatically by an in-house developed program. Diagnostic ions and neutral losses were used for validating the matches. With this approach, 93 out of a total of 97 metabolites were putatively identified, while only half of them could be traced from the currently available online database. The MPE approach was further validated by applying to the identification of carnitine-associated biomarkers in a typical mice model of fasting, and extended to the development of bile acids submetabolome. Our study indicates that the MPE approach is highly useful for rapid and reliable identification of metabolically and structurally associated biomarkers.