Integrated Analysis of Metabolomics Combined with Network Pharmacology and Molecular Docking Reveals the Effects of Processing on Metabolites of Dendrobium officinale

代谢组学 代谢物 石斛 化学 计算生物学 药理学 传统医学 生物 生物化学 植物 色谱法 医学
作者
Lilan Xu,Si-Min Zuo,Mei Liu,Tao Wang,Zizheng Li,Yong‐Huan Yun,Weimin Zhang
出处
期刊:Metabolites [Multidisciplinary Digital Publishing Institute]
卷期号:13 (8): 886-886 被引量:1
标识
DOI:10.3390/metabo13080886
摘要

Dendrobium officinale (D. officinale) is a precious medicinal species of Dendrobium Orchidaceae, and the product obtained by hot processing is called “Fengdou”. At present, the research on the processing quality of D. officinale mainly focuses on the chemical composition indicators such as polysaccharides and flavonoids content. However, the changes in metabolites during D. officinale processing are still unclear. In this study, the process was divided into two stages and three important conditions including fresh stems, semiproducts and “Fengdou” products. To investigate the effect of processing on metabolites of D. officinale in different processing stages, an approach of combining metabolomics with network pharmacology and molecular docking was employed. Through UPLC-MS/MS analysis, a total of 628 metabolites were detected, and 109 of them were identified as differential metabolites (VIP ≥ 1, |log2 (FC)| ≥ 1). Next, the differential metabolites were analyzed using the network pharmacology method, resulting in the selection of 29 differential metabolites as they have a potential pharmacological activity. Combining seven diseases, 14 key metabolites and nine important targets were screened by constructing a metabolite–target–disease network. The results showed that seven metabolites with potential anticoagulant, hypoglycemic and tumor-inhibiting activities increased in relative abundance in the “Fengdou” product. Molecular docking results indicated that seven metabolites may act on five important targets. In general, processing can increase the content of some active metabolites of D. officinale and improve its medicinal quality to a certain extent.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
暖暖的禾日完成签到,获得积分10
刚刚
ronnie发布了新的文献求助10
2秒前
思源应助唐盼烟采纳,获得10
2秒前
mandala关注了科研通微信公众号
2秒前
2秒前
郑皓文完成签到,获得积分10
5秒前
6秒前
yushiolo完成签到 ,获得积分10
7秒前
Lucas应助松林采纳,获得10
7秒前
爆米花应助123采纳,获得10
7秒前
思源应助whoops采纳,获得10
8秒前
Elizabeth12138完成签到 ,获得积分10
8秒前
9秒前
上官若男应助zhang采纳,获得10
9秒前
9秒前
yuncong323完成签到,获得积分10
10秒前
10秒前
协和_子鱼完成签到,获得积分10
10秒前
10秒前
开朗的巧凡完成签到 ,获得积分10
11秒前
yuqin发布了新的文献求助10
12秒前
13秒前
疯友完成签到,获得积分10
13秒前
那一天发布了新的文献求助10
13秒前
14秒前
避橙发布了新的文献求助10
15秒前
lsl发布了新的文献求助30
15秒前
16秒前
凌儿响叮当完成签到 ,获得积分10
16秒前
荣荣发布了新的文献求助10
16秒前
研友_nPxP9n发布了新的文献求助10
16秒前
17秒前
完美世界应助Literaturecome采纳,获得10
18秒前
无限尔曼完成签到 ,获得积分10
18秒前
小通通完成签到 ,获得积分10
18秒前
19秒前
zhang完成签到,获得积分20
20秒前
123发布了新的文献求助10
20秒前
yuqin完成签到,获得积分10
20秒前
lkk发布了新的文献求助10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6355960
求助须知:如何正确求助?哪些是违规求助? 8170826
关于积分的说明 17202157
捐赠科研通 5412016
什么是DOI,文献DOI怎么找? 2864441
邀请新用户注册赠送积分活动 1841945
关于科研通互助平台的介绍 1690226