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 [MDPI AG]
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.
最长约 10秒,即可获得该文献文件

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
1秒前
1秒前
魏伯安发布了新的文献求助10
1秒前
1秒前
zhouleiwang完成签到,获得积分10
2秒前
李爱国应助aiming采纳,获得10
3秒前
无奈傲菡完成签到,获得积分10
4秒前
TT发布了新的文献求助10
4秒前
啦啦啦发布了新的文献求助10
5秒前
sun发布了新的文献求助10
6秒前
荣荣完成签到,获得积分10
6秒前
7秒前
小安完成签到,获得积分10
8秒前
Spencer完成签到 ,获得积分10
8秒前
PengHu完成签到,获得积分10
9秒前
9秒前
11秒前
13秒前
13秒前
13秒前
ywang发布了新的文献求助10
14秒前
失眠虔纹完成签到,获得积分10
14秒前
斯文败类应助nextconnie采纳,获得10
14秒前
药学牛马发布了新的文献求助10
18秒前
18秒前
19秒前
22秒前
张无缺完成签到,获得积分10
25秒前
27秒前
CodeCraft应助MES采纳,获得10
28秒前
笨笨乘风完成签到,获得积分10
29秒前
田様应助axunQAQ采纳,获得10
31秒前
完美秋烟发布了新的文献求助10
31秒前
无花果应助糊涂的小伙采纳,获得10
31秒前
白betty完成签到,获得积分10
31秒前
MQ&FF完成签到,获得积分0
32秒前
啦啦啦完成签到,获得积分10
33秒前
34秒前
35秒前
英俊的铭应助小安采纳,获得10
36秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527998
求助须知:如何正确求助?哪些是违规求助? 3108225
关于积分的说明 9288086
捐赠科研通 2805889
什么是DOI,文献DOI怎么找? 1540195
邀请新用户注册赠送积分活动 716950
科研通“疑难数据库(出版商)”最低求助积分说明 709849