Urinary metabolomics analysis reveals the effect of volatile oil from Angelica sinensis on LPS‐induced inflammation rats

Lipopolysaccharide (LPS)‐induced inflammation occurs commonly and volatile oil from Angelica sinensis (VOAS) can be used as an anti‐inflammatory agent. The molecular mechanisms that allow the anti‐inflammatory factors to be expressed are still unknown. In this paper, we applied gas chromatography–ma...

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Bibliographic Details
Published in:Biomedical chromatography 2019-02, Vol.33 (2), p.e4402-n/a
Main Authors: Hua, Yong‐li, Ma, Qi, Zhang, Xiao‐song, Yao, Wan‐ling, Ji, Peng, Hu, Jun‐jie, Wei, Yan‐ming
Format: Article
Language:English
Subjects:
Angelica sinensis
Angelica sinensis - chemistry
Animals
Anti-Inflammatory Agents - pharmacology
Biomarkers - urine
correlation network
inflammation
Inflammation - chemically induced
Inflammation - metabolism
Inflammation - urine
Lipopolysaccharides - adverse effects
Liver - drug effects
Liver - pathology
Lung - drug effects
Lung - pathology
Male
Metabolic Networks and Pathways - drug effects
Metabolome - drug effects
metabolomics
Oils, Volatile - pharmacology
Plant Oils - pharmacology
Rats
Rats, Wistar
Succinic Acid - metabolism
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Summary:Lipopolysaccharide (LPS)‐induced inflammation occurs commonly and volatile oil from Angelica sinensis (VOAS) can be used as an anti‐inflammatory agent. The molecular mechanisms that allow the anti‐inflammatory factors to be expressed are still unknown. In this paper, we applied gas chromatography–mass spectrometry (GC–MS) and high‐performance liquid chromatography–time‐of‐flight mass spectrometry (LC‐Q/TOF–MS) based on a metabolomics platform coupled with a network approach to analyze urine samples in three groups of rats: one with LPS‐induced inflammation (MI); one with intervention with VOAS; and normal controls (NC). Our study found definite metabolic footprints of inflammation and showed that all three groups of rats, MI, intervention with VOAS and NC have distinct metabolic profiles in urine. The concentrations of 48 metabolites differed significantly among the three groups. The metabolites in urine were screened by the GC–MS and LC‐Q/TOF–MS methods. The significantly changed metabolites (p  1.5) between MI, NC and VOAS were included in the metabolic networks. Finally, hub metabolites were screened, including glycine, arachidonic acid, l‐glutamate, pyruvate and succinate, which have high values of degree (k). the Results suggest that disorders of glycine, arachidonic acid, l‐glutamate, pyruvate and succinate metabolism might play an important part in the predisposition and development of LPS‐induced inflammation. By applying metabolomics with network methods, the mechanisms of diseases are clearly elucidated.
ISSN:0269-3879
1099-0801
DOI:10.1002/bmc.4402