Metabolomics analysis of dandelions from different geographical regions in China

小桶 代谢组学 化学 主成分分析 代谢物 色谱法 生物化学 基因 转录组 计算机科学 基因表达 人工智能
作者
Songbao Zhang,Chao Li,Wei Gu,Ruijin Qiu,Chao Ji,Lingfeng Pei,Li Ma,Yangfang Guo,Rong Tian
出处
期刊:Phytochemical Analysis [Wiley]
卷期号:32 (6): 899-906 被引量:12
标识
DOI:10.1002/pca.3033
摘要

Abstract Introduction Dandelion ( Taraxacum mongolicum Hand.‐Mazz.) is a perennial herb with diverse pharmacological effects. The development and utilization of dandelion have attracted much attention. Objectives Our aims were to provide a reference basis for the identification of the origin of dandelions and to study the influence of their origin on their quality. Methods High‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry was used to analyze metabolites from dandelions from four different geographical regions in China, namely Gansu, Henan, Shanxi, and Jiangsu. Metabolite analysis was performed using orthogonal partial least‐squares discriminant analysis, and to identify potential metabolic pathways, MBRole was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Results Principal component analysis revealed that the chemical components of dandelions sampled from the four regions showed noticeable differences. Twenty‐six, six, six, eight, eight, and fifteen differentially produced metabolites were identified upon comparison between Gansu and Jiangsu, Gansu and Shanxi, Gansu and Henan, Henan and Shanxi, Henan and Jiangsu, and Shanxi and Jiangsu, respectively. These differentially produced metabolites were mainly phenolic compounds. Further, KEGG pathway enrichment analysis showed that the main metabolic pathways involved were biosynthesis of phenylpropanoids and flavonoids. Conclusion The methods reported herein can be used to identify the origin of dandelions; moreover, our results can serve as a reference basis for future studies.

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
rjx发布了新的文献求助10
刚刚
刚刚
liu920204完成签到,获得积分10
1秒前
左丘冬寒完成签到,获得积分10
2秒前
李串串完成签到 ,获得积分10
3秒前
3秒前
kaiX发布了新的文献求助10
3秒前
5秒前
快乐科学家完成签到,获得积分20
5秒前
5秒前
6秒前
Popping发布了新的文献求助10
6秒前
6秒前
新小pi发布了新的文献求助10
7秒前
薛薛薛杨完成签到 ,获得积分10
7秒前
动听衬衫应助沉默的金鱼采纳,获得10
8秒前
甜菜完成签到,获得积分10
8秒前
王敏发布了新的文献求助10
9秒前
9秒前
小贤完成签到,获得积分10
9秒前
bkagyin应助孤独梦安采纳,获得10
10秒前
liu920204发布了新的文献求助10
10秒前
赘婿应助张文远采纳,获得10
10秒前
正直的学姐完成签到 ,获得积分10
11秒前
高求发布了新的社区帖子
12秒前
Zoe完成签到,获得积分10
13秒前
凶狠的牛排完成签到 ,获得积分10
14秒前
CooLIT发布了新的文献求助30
15秒前
16秒前
zhouz完成签到,获得积分10
16秒前
科研通AI6应助wu采纳,获得10
18秒前
JamesPei应助Bo采纳,获得10
19秒前
所所应助雪白丸子采纳,获得10
20秒前
迷路寄容发布了新的文献求助10
20秒前
20秒前
我服有点黑应助zhouz采纳,获得50
20秒前
23秒前
25秒前
微凉完成签到 ,获得积分10
25秒前
25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
《机器学习——数据表示学习及应用》 600
Holistic Discourse Analysis 600
Vertébrés continentaux du Crétacé supérieur de Provence (Sud-Est de la France) 600
Fiction e non fiction: storia, teorie e forme 500
Routledge Handbook on Spaces of Mental Health and Wellbeing 500
Elle ou lui ? Histoire des transsexuels en France 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 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5322298
求助须知:如何正确求助?哪些是违规求助? 4463846
关于积分的说明 13891444
捐赠科研通 4355161
什么是DOI,文献DOI怎么找? 2392191
邀请新用户注册赠送积分活动 1385842
关于科研通互助平台的介绍 1355541