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.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
hh发布了新的文献求助10
1秒前
羊羊完成签到,获得积分10
1秒前
卡卡罗特先森完成签到 ,获得积分10
2秒前
朽木完成签到 ,获得积分10
4秒前
4秒前
fanyueyue应助111采纳,获得10
6秒前
6秒前
6秒前
kcmat发布了新的文献求助10
7秒前
hh完成签到,获得积分10
8秒前
Philadelphus发布了新的文献求助10
9秒前
einuo完成签到,获得积分10
9秒前
AKYDXS完成签到,获得积分10
12秒前
昏睡的蟠桃应助Llllll采纳,获得200
12秒前
科研通AI2S应助hao采纳,获得10
12秒前
13秒前
13秒前
香蕉觅云应助阿湫采纳,获得10
14秒前
星辰大海应助星辰采纳,获得10
14秒前
阿卡宁完成签到,获得积分10
14秒前
lzw完成签到 ,获得积分10
14秒前
沉静烧仙草完成签到,获得积分20
15秒前
烟花应助嘉嘉琦采纳,获得10
15秒前
隐形曼青应助科研通管家采纳,获得10
15秒前
Hello应助科研通管家采纳,获得10
16秒前
Ava应助科研通管家采纳,获得10
16秒前
上官若男应助科研通管家采纳,获得10
16秒前
16秒前
赘婿应助科研通管家采纳,获得10
16秒前
烟花应助科研通管家采纳,获得10
16秒前
FashionBoy应助科研通管家采纳,获得10
16秒前
英俊的铭应助科研通管家采纳,获得10
16秒前
在水一方应助科研通管家采纳,获得10
16秒前
accepted应助科研通管家采纳,获得10
16秒前
脑洞疼应助科研通管家采纳,获得10
16秒前
16秒前
cdh1994应助kcmat采纳,获得10
16秒前
我是老大应助科研通管家采纳,获得10
16秒前
乐乐应助科研通管家采纳,获得10
16秒前
FashionBoy应助科研通管家采纳,获得10
16秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038524
求助须知:如何正确求助?哪些是违规求助? 3576221
关于积分的说明 11374737
捐赠科研通 3305912
什么是DOI,文献DOI怎么找? 1819354
邀请新用户注册赠送积分活动 892688
科研通“疑难数据库(出版商)”最低求助积分说明 815048