Pathway-based protein–protein association network to explore mechanism of α-glucosidase inhibitors from Scutellaria baicalensis Georgi against type 2 diabetes

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2021-04-26 DOI:10.1049/syb2.12019
Le Wang, Wenbo Diwu, Nana Tan, Huan Wang, Jingbo Hu, Bailu Xu, Xiaoling Wang
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引用次数: 2

Abstract

Natural products have been widely used in the treatment of type 2 diabetes (T2D). However, their mechanisms are often obscured due to multi-components and multi-targets. The authors constructed a pathway-based protein–protein association (PPA) network for target proteins of 13 α-glucosidase inhibitors (AGIs) identified from Scutellaria baicalensis Georgi (SBG), designed to explore the underlying mechanisms. This network contained 118 nodes and 1167 connections. An uneven degree distribution and small-world property were observed, characterised by high clustering coefficient and short average path length. The PPA network had an inherent hierarchy as C(k)∼k−0.71. It also exhibited potential weak disassortative mixing pattern, coupled with a decreased function Knn (k) and negative value of assortativity coefficient. These properties indicated that a few nodes were crucial to the network. PGH2, GNAS, MAPK1, MAPK3, PRKCA, and MAOA were then identified as key targets with the highest degree values and centrality indices. Additionally, a core subnetwork showed that chrysin, 5,8,2′-trihydroxy-7-methoxyflavone, and wogonin were the main active constituents of these AGIs, and that the serotonergic synapse pathway was the critical pathway for SBG against T2D. The application of a pathway-based protein–protein association network provides a novel strategy to explore the mechanisms of natural products on complex diseases.

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通路蛋白结合网络探讨黄芩α-葡萄糖苷酶抑制剂抗2型糖尿病的作用机制
天然产物已广泛应用于2型糖尿病(T2D)的治疗。然而,由于其多组分、多靶点的特点,其作用机制往往模糊不清。作者构建了黄芩(Scutellaria baicalensis Georgi, SBG)中13种α-葡萄糖苷酶抑制剂(AGIs)靶蛋白的pathway- protein association (PPA)网络,旨在探讨其作用机制。该网络包含118个节点和1167个连接。聚类系数高,平均路径长度短,分布程度不均匀,具有小世界特性。PPA网络具有固有的层次结构,C(k) ~ k−0.71。同时还表现出潜在的弱失配混合模式,并伴有函数Knn (k)的减小和配度系数的负值。这些特性表明,一些节点对网络至关重要。然后将PGH2、GNAS、MAPK1、MAPK3、PRKCA和MAOA确定为度值和中心性指数最高的关键靶点。此外,一个核心子网络显示,黄菊花素、5,8,2 ' -三羟基-7-甲氧基黄酮和黄酮素是这些AGIs的主要活性成分,血清素能突触途径是SBG抗T2D的关键途径。基于通路的蛋白-蛋白关联网络的应用为探索天然产物治疗复杂疾病的机制提供了一种新的策略。
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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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