Issue 38, 2021

Untargeted metabolomic and lipid metabolism-related gene expression analyses of the effects and mechanism of aged Liupao tea treatment in HFD-induced obese mice

Abstract

Liupao tea (LPT) has been demonstrated to have beneficial effects on obesity induced by a high-fat diet (HFD); however, the effects and mechanism of aged Liupao tea (different storage years) treatment on obesity have not yet been reported. In this study, mice were divided into four groups as follows: the control group fed a normal diet; the model group fed an HFD; and the LPT aged 1 year (1Y) and LPT aged 10 years (10Y) groups receiving an HFD and water extractions from LPTs of different ages for 5 weeks. Our results revealed that aged LPT significantly alleviated HFD-induced obesity symptoms, especially in the 10Y group. Additionally, metabolomic analysis identified 11 common differential metabolites that were partly recovered to normal levels after aged LPT treatment, involved mainly in the metabolic pathways of the citrate cycle, purine metabolism, fatty acid metabolism, and amino acid metabolism. Aged LPT treatment also regulated lipid metabolism-related gene expression in the liver, which decreased the mRNA levels of SREBP-1C/HMGR/FAS involved in de novo lipogenesis and increased the mRNA levels of PPARα, LDLR and LCAT. Our study demonstrated that aged LPT may be used as a potential dietary supplement for improving obesity-related diseases caused by an HFD.

Graphical abstract: Untargeted metabolomic and lipid metabolism-related gene expression analyses of the effects and mechanism of aged Liupao tea treatment in HFD-induced obese mice

Supplementary files

Article information

Article type
Paper
Submitted
08 Jun 2021
Accepted
30 Jun 2021
First published
06 Jul 2021
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2021,11, 23791-23800

Untargeted metabolomic and lipid metabolism-related gene expression analyses of the effects and mechanism of aged Liupao tea treatment in HFD-induced obese mice

W. Wu, Y. Hu, S. Zhang, D. Liu, Q. Li, Y. Lin and Z. Liu, RSC Adv., 2021, 11, 23791 DOI: 10.1039/D1RA04438A

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