Wang Ning, Li Linman, Zhang Puyu, Mehmood Muhammad Aamer, Lan Chaohua, Gan Tian, Li Zaixin, Zhang Zhi, Xu Kewei, Mo Shan, Xia Gang, Wu Tao, Zhu Hui
College of Bioengineering, Sichuan University of Science and Engineering, Zigong 643000, China.
Luzhou LaoJiao Group Co. Ltd., Luzhou 646000, China.
Nutr Res Pract. 2023 Aug;17(4):682-697. doi: 10.4162/nrp.2023.17.4.682. Epub 2023 Mar 23.
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 ddHO (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.
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.
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.
背景/目的：藏茶是一种黑茶，由于天然产物固有的复杂性，藏茶的化学成分和有益作用尚未完全明确。本研究的目的是利用知识引导的多层网络（KGMN）技术解析藏茶的成分，并探讨其在小鼠体内潜在的抗氧化和降血脂机制。
材料/方法：将C57BL/6J小鼠连续灌胃藏茶提取物（T组）、绿茶提取物（G组）和双蒸水（H组）15天。检测小鼠的总抗氧化能力（T-AOC）和超氧化物歧化酶（SOD）活性。采用转录组测序技术研究藏茶在小鼠体内抗氧化和降脂作用的分子机制。此外，通过实时定量聚合酶链反应（qPCR）方法检测各组肝脏抗氧化和脂质代谢相关基因的表达水平。
结果显示，利用KGMN策略在藏茶中初步注释出42种黄酮类化合物。与H组相比，藏茶显著降低了小鼠的体重增加，并提高了T-AOC和SOD活性。基于转录组和qPCR结果，证实藏茶可通过调节氧化应激和脂质代谢相关途径（如胰岛素抵抗、P53信号通路、胰岛素信号通路、脂肪酸延长和脂肪酸代谢）在抗氧化和降脂方面发挥关键作用。
本研究首次使用计算工具深入探究藏茶的成分，并揭示其潜在的抗氧化和降血脂机制，为藏茶的成分和生物活性提供了新的见解。
