Omics combined with network pharmacology reveal the neuroprotective mechanism of Sophora tonkinensis based on the biolabel research pattern: The treatment of Parkinson’s disease against oxidative stress and neuroexcitatory toxicity

药理学 机制(生物学) 神经保护 槐花 帕金森病 氧化应激 化学 生物化学 毒性 传统医学 疾病 生物 中医药 医学 内科学 有机化学 病理 哲学 替代医学 认识论
作者
Shuai‐nan Zhang,Hongmei Li,Qi Liu,Xu‐zhao Li,Wude Yang,Ying Zhou
出处
期刊:Biomedical Chromatography [Wiley]
卷期号:37 (3) 被引量:3
标识
DOI:10.1002/bmc.5557
摘要

Abstract Based on the biolabel research pattern, omics and network pharmacology were used for exploring the neuroprotection of Sophora tonkinensis (ST) in the treatment of brain diseases. Multi‐omics were applied to investigate biolabels for ST intervention in brain tissue. Based on biolabels, the therapeutic potential, mechanism and material basis of ST for treating brain diseases were topologically analyzed by network pharmacology. A Parkinson’s disease (PD) mouse model was used to validate biolabel analysis results. Four proteins and three metabolites were involved in two key pathways (alanine, aspartate and glutamate metabolism and arginine biosynthesis) and considered as biolabels. Network pharmacology showed that ST has the potential to treat some brain diseases, especially PD. Eight compounds (including caffeic acid, gallic acid and cinnamic acid) may serve as the material basis of ST treating brain diseases via the mediation of three biolabels. In the PD model, ST and its active compounds (caffeic acid and gallic acid) may protect dopaminergic neurons (maximum recovery rate for dopamine, 49.5%) from oxidative stress (E3 ubiquitin‐protein ligase parkin, reactive oxygen species, nitric oxide, etc.) and neuroexcitatory toxicity (glutamate dehydrogenase, glutamine, glutamic acid, etc.). These findings indicated that omics and network pharmacology may contribute to the achievement of the objectives of this study based on the biolabel research pattern.
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
闪闪的以山完成签到 ,获得积分10
刚刚
杨杨杨发布了新的文献求助10
刚刚
1秒前
苗芸发布了新的文献求助10
2秒前
yang发布了新的文献求助10
2秒前
kelite发布了新的文献求助10
3秒前
Hello应助奋斗的元珊采纳,获得10
3秒前
4秒前
7秒前
cherry完成签到,获得积分10
8秒前
9秒前
9秒前
LYH关注了科研通微信公众号
10秒前
10秒前
科研通AI5应助millie采纳,获得10
12秒前
妩媚的强炫完成签到,获得积分10
12秒前
Amor完成签到,获得积分10
14秒前
77完成签到 ,获得积分10
14秒前
朱湋帆完成签到 ,获得积分10
16秒前
高高应助懦弱的乐蕊采纳,获得10
17秒前
17秒前
18秒前
舒适的平蓝完成签到 ,获得积分10
19秒前
BCS完成签到,获得积分10
20秒前
良辰应助帅气的黑猫采纳,获得10
22秒前
FFZ发布了新的文献求助10
22秒前
22秒前
布鲁盖发布了新的文献求助10
22秒前
orixero应助科研小小小白采纳,获得10
22秒前
26秒前
Ava应助Tong采纳,获得30
28秒前
单薄归尘完成签到 ,获得积分10
29秒前
30秒前
32秒前
einuo完成签到,获得积分10
32秒前
龚成明完成签到,获得积分10
33秒前
34秒前
34秒前
研友_8DAv0L发布了新的文献求助10
35秒前
35秒前
高分求助中
Continuum Thermodynamics and Material Modelling 4000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
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 3522937
求助须知:如何正确求助?哪些是违规求助? 3103910
关于积分的说明 9267916
捐赠科研通 2800665
什么是DOI,文献DOI怎么找? 1537075
邀请新用户注册赠送积分活动 715371
科研通“疑难数据库(出版商)”最低求助积分说明 708759