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    Bile acids (BAs), as crucial endogenous metabolites, are closely related to cholestasis, metabolic disorders, and cancer. To better understand their function and disease pathogenesis, global profiling of BAs is necessary. Here, multidimensional data mining was developed for the discovery and identification of potentially unknown BAs in cholestasis rats. Based on an in-house theoretical BA database and using a newly established liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS/MS) method, four-dimensional (4D) data including the retention times (RT), abundances, HRMS, and HRMS/MS spectra were acquired and elucidated. And 491 BAs were totally profiled. Then, the relationships between RT with different conjugation types, different positions and configurations of hydroxyl/ketone groups as well as fragmentation rules of hydroxyl, ortho-hydroxyl, ketone, and conjugated groups of BAs were summarized to assist BA identification for the first time. Finally, 292 BAs were assigned with molecular formulas, 201 of which were putatively identified by integrating the 4D data, applying structure-driven relative retention time rules, and a comparison with synthetic BAs. The estimated concentrations of 201 BAs, including 93 reported and 108 newly identified BAs, were quantified by using surrogate standards with similar structure. Among 201 BAs, 38 BAs were detected in both humans and rats for the first time. Our strategy has expanded the scope of BAs and provides a way to identify a class of metabolites. Compared to normal rats, the significantly increased sulfated and glucuronide conjugated BAs in urine and feces from experimentally cholestatic rats may reveal a way to diagnose intrahepatic cholestasis. Copyright © 2020 Elsevier B.V. All rights reserved.


    Miao Lin, Xiong Chen, Zhe Wang, Dongmei Wang, Jin-Lan Zhang. Global profiling and identification of bile acids by multi-dimensional data mining to reveal a way of eliminating abnormal bile acids. Analytica chimica acta. 2020 Oct 02;1132:74-82

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    PMID: 32980113

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