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Frontiers of Medicine

Front. Med.    2020, Vol. 14 Issue (5) : 651-663
High-throughput metabolomics reveals the perturbed metabolic pathways and biomarkers of Yang Huang syndrome as potential targets for evaluating the therapeutic effects and mechanism of geniposide
Heng Fang1, Aihua Zhang1, Xiaohang Zhou1, Jingbo Yu1, Qi Song1, Xijun Wang1,2()
1. National Chinmedomics Research Center, Sino-America Chinmedomics Technology Collaboration Center, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin 150040, China
2. State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau 519020, China
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High-throughput metabolomics can clarify the underlying molecular mechanism of diseases via the qualitative and quantitative analysis of metabolites. This study used the established Yang Huang syndrome (YHS) mouse model to evaluate the efficacy of geniposide (GEN). Urine metabolic data were quantified by ultra-performance liquid chromatography–tandem mass spectrometry. The non-target screening of the massive biological information dataset was performed, and a total of 33 metabolites, including tyramine glucuronide, aurine, and L-cysteine, were identified relating to YHS. These differential metabolites directly participated in the disturbance of phase I reaction and hydrophilic transformation of bilirubin. Interestingly, they were completely reversed by GEN. While, as the auxiliary technical means, we also focused on the molecular prediction and docking results in network pharmacological and integrated analysis part. We used integrated analysis to communicate the multiple results of metabolomics and network pharmacology. This study is the first to report that GEN indirectly regulates the metabolite “tyramine glucuronide” through its direct effect on the target heme oxygenase 1 in vivo. Meanwhile, heme oxygenase-1, a prediction of network pharmacology, was the confirmed metabolic enzyme of phase I reaction in hepatocytes. Our study indicated that the combination of high-throughput metabolomics and network pharmacology is a robust combination for deciphering the pathogenesis of the traditional Chinese medicine (TCM) syndrome.

Keywords metabolomics      liquid chromatography-mass spectrometry      metabolites      metabolic pathways     
Corresponding Author(s): Xijun Wang   
Just Accepted Date: 04 September 2019   Online First Date: 03 January 2020    Issue Date: 12 October 2020
 Cite this article:   
Heng Fang,Aihua Zhang,Xiaohang Zhou, et al. High-throughput metabolomics reveals the perturbed metabolic pathways and biomarkers of Yang Huang syndrome as potential targets for evaluating the therapeutic effects and mechanism of geniposide[J]. Front. Med., 2020, 14(5): 651-663.
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Heng Fang
Aihua Zhang
Xiaohang Zhou
Jingbo Yu
Qi Song
Xijun Wang
Fig.1  Comprehensive workflow of the metabolomics investigation for the perturbed metabolic analysis of YHS in a mouse model by using liquid chromatography-mass spectrometry.
Fig.2  Biochemical characteristics and H&E staining for liver histological evaluation. *Significant difference from control at P<0.05; **Significant difference from control at P<0.01; #Significant difference from model at P<0.05; ##Significant difference from model at P<0.01. The corresponding markers are indicated in the Supplementary Table S1.
Fig.3  Pattern recognition analysis of the urine profiling of YHS by using UPLC-Q/TOF-G2Si-HDMS. (A) Trajectory analysis of PCA score plots of YHS in positive and negative modes; (B) S-VIP plot from the supervised OPLS-DA analysis of YHS in positive and negative modes.
Fig.4  Dynamic changes of the urine metabolic biomarkers from YHS. (1) pyridoxal; (2) hydroxyphenyl acetylglycine; (3) 7,8-dihydropteroic acid; (4) 1,2-benzoquinone; (5) 5-methoxytryptophan; (6) kynurenic acid; (7) melatonin; (8) L-β-aspartyl-L-leucine; (9) 8-hydroxyguanosine; (10) epinephrine; (11) taurine; (12) 3b,16a-dihydroxyandrostenone sulfate; (13) spermidine; (14) L-cysteine; (15) 2-keto-6-acetamidocaproate; (16) L-β-aspartyl-L-phenylalanine; (17) 3-methylene-indolenine; (18) 5,6-dihydrouridine; (19) 10-formyltetrahydrofolate; (20) tyramine glucuronide; (21) riboflavin reduced; (22) phenylpyruvic acid; (23) isovaleryl alanine; (24) N-formyl-L-methionine; (25) L-γ-glutamyl-L-leucine; (26) L-gulonolactone; (27) gluconic acid; (28) vanillactic acid; (29) homogentisic acid; (30) dopaquinone; (31) N-acetylvanilalanine; (32) 2-isopropylmalic acid; (33) tyrosol.
Fig.5  Chemical structure and mass fragment information of kynurenic acid in the positive ionization mode for the visualization of metabolite identification.
Fig.6  Summary of pathway analysis with the MetaboAnalyst tool. Note: (1) Tyrosine metabolism; (2) taurine and hypotaurine metabolism; (3) ubiquinone and other terpenoid-quinone biosynthesis; (4) glutathione metabolism; (5) phenylalanine, tyrosine, and tryptophan biosynthesis; (6) thiamine metabolism; (7) ascobate and aldarate metabolism; (8) one carbon pool by folate; (9) vitamin B6 metabolism; (10) phenylalanine metabolism; (11) pantothenate and CoA biosynthesis; (12) folate biosynthesis; (13) pentose and glucuronate interconversions; (14) β-alanine metabolism; (15) glyoxylate and dicarboxylate metabolism; (16) starch and sucrose metabolism; (17) aminoacyl-tRNA biosynthesis; (18) cysteine and methionine metabolism; (19) glycine, serine, and threonine metabolism; (20) tryptophan metabolism; (21) arginine and proline metabolism; and (22) primary bile acid biosynthesis.
Fig.7  Perturbed metabolic network of GEN protection against YHS via the Ingenuity Pathway Analysis. Figure in red: potential biomarker of YHS mouse model influenced by GEN, and the relative content is represented by the column graph next to it.
Fig.8  Prediction network of GEN for potential protein targets. The green circles represent the compounds, red circles delineate the disease, and blue circles are the protein targets.
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