Nutr Res Pract.  2023 Aug;17(4):682-697. 10.4162/nrp.2023.17.4.682.

In-silico annotation of the chemical composition of Tibetan tea and its mechanism on antioxidant and lipidlowering in mice

• Wang N
• Li L
• Zhang P
• Mehmood MA
• Lan C
• Gan T
• Li Z
• Zhang Z
• Xu K
• Mo S
• Xia G
• Wu T
• Zhu H

• KMID: 2545185
• DOI: http://doi.org/10.4162/nrp.2023.17.4.682

Abstract

BACKGROUND/OBJECTIVES
Tibetan tea is a kind of dark tea, due to the inherent complexity of natural products, the chemical composition and beneficial effects of Tibetan tea are not fully understood. The objective of this study was to unravel the composition of Tibetan tea using knowledge-guided multilayer network (KGMN) techniques and explore its potential antioxidant and hypolipidemic mechanisms in mice.
MATERIALS/METHODS
The C57BL/6J mice were continuously gavaged with Tibetan tea extract (T group), green tea extract (G group) and ddH 2 O (H group) for 15 days. The activity of total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) in mice was detected. Transcriptome sequencing technology was used to investigate the molecular mechanisms underlying the antioxidant and lipid-lowering effects of Tibetan tea in mice. Furthermore, the expression levels of liver antioxidant and lipid metabolism related genes in various groups were detected by the real-time quantitative polymerase chain reaction (qPCR) method.
RESULTS
The results showed that a total of 42 flavonoids are provisionally annotated in Tibetan tea using KGMN strategies. Tibetan tea significantly reduced body weight gain and increased T-AOC and SOD activities in mice compared with the H group. Based on the results of transcriptome and qPCR, it was confirmed that Tibetan tea could play a key role in antioxidant and lipid lowering by regulating oxidative stress and lipid metabolism related pathways such as insulin resistance, P53 signaling pathway, insulin signaling pathway, fatty acid elongation and fatty acid metabolism.
CONCLUSIONS
This study was the first to use computational tools to deeply explore the composition of Tibetan tea and revealed its potential antioxidant and hypolipidemic mechanisms, and it provides new insights into the composition and bioactivity of Tibetan tea.

Keyword

Tea; lipid metabolism; antioxidant; transcriptome

Figure

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  Fig. 1 Annotation of unknown metabolites using recursive network algorithm. The compounds in Tibetan tea were identified by non-targeted UPLC-Q-TOF mass spectrometry combined with KGMN analysis, and a total of 17 recursive steps were performed.KGMN, knowledge-guided multilayer network.

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  Fig. 2 Effects of green tea and Tibetan tea on the body parameters of mice. Mice were randomly divided into 3 experimental groups: ddH2O group (control; H), Tibetan tea extract group (T), Green tea extract group (G). (A) Body weights. (B) The activity of T-AOC in serum and liver (serum: U/mL and liver: U/g). (C) SOD activity in serum and liver (serum: U/mL and liver: U/g). The data are expressed as the mean ± SD.T-AOC, total antioxidant capacity; SOD, superoxide dismutase.*P < 0.05, **P < 0.01, ***P < 0.005 vs. the H group.

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  Fig. 3 Transcriptome analysis of mice liver. Mice liver type were divided into 3 groups: ddH2O group mice liver (LH), Tibetan tea extract group mice liver (LT), and green tea extract mice liver (LG). (A) Principal component analysis results. The abscissa is the first principal component, and the ordinate is the second principal component. (B) Venn diagram of coexpression. (C) Statistical histogram of the number of differentially compared genes in combination. Grey and blue represent up-regulated and down-regulated differential genes, respectively, and the numbers on the bars represent the number of differential genes.

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  Fig. 4 GO enrichment analysis. The abscissa is the GO term, and the ordinate is the significance level of GO term enrichment. Mice liver type were divided into 3 groups: ddH2O group mice liver (LH), Tibetan tea extract group mice liver (LT), and green tea extract group mice liver (LG). (A) LG vs. LH. (B) LT vs. LH.GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

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  Fig. 5 KEGG pathway enrichment analysis. The abscissa is the ratio of the number of differential genes annotated in the KEGG pathway to the total number of differential genes, and the ordinate is the KEGG pathway. Mice liver type were divided into 3 groups: ddH2O group mice liver (LH), Tibetan tea extract group mice liver (LT), and green tea extract group mice liver (LG). (A) LT vs. LH. (B) LG vs. LH.KEGG, Kyoto Encyclopedia of Genes and Genomes.

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  Fig. 6 The relative mRNA expression levels of Cu/Zn-SOD (A), Mn-SOD (B), GSH-Px (C), PPAR-α (D), LDLR (E), CPT-1a (F), C/EBP-α (G), FAS (H), SREBP-1c (I) in liver tissues. Mice were randomly divided into 3 experimental groups: ddH2O group (H), Tibetan tea extract group (T), and green tea extract group (G). Bar graphs depicting the mean ± standard deviation mRNA expression levels of antioxidant genes and lipid metabolism related genes in liver tissue of male C57BL/6J mice. Antioxidant genes: Cu/Zn-SOD (A), Mn-SOD (B), GSH-Px (C); lipid metabolism related genes: PPAR-α (D), LDLR (E), CPT-1a (F), C/EBP-α (G), FAS (H), SREBP-1c (I). Cu/Zn-SOD, copper/zinc superoxide dismutase; Mn-SOD, manganese superoxide dismutase; GSH-Px, Plasma glutathione peroxidase; PPAR-α, peroxisome proliferator-activated receptor alpha; LDLR, low-density lipoprotein receptor; CPT-1a, carnitine palmitoyltransferase-1a; C/EBP-α, CCAAT/enhancer binding proteins alpha; FAS, fatty acid synthase; SREBP-1c, sterol regulatory element-binding protein-1c.*P < 0.05 vs. H group.

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