Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression

Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Mode...

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Autores principales:	Kexin Wang, Kai Li, Yupeng Chen, Genxia Wei, Hailang Yu, Yi Li, Wei Meng, Handuo Wang, Li Gao, Aiping Lu, Junxiang Peng, Daogang Guan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Chai-Hu-Shu-Gan-San depression network pharmacology model effect propagation space intervention-response proteins contribution index Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/ffab36f730c04b30ad89a90d9c434f4c
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spelling	oai:doaj.org-article:ffab36f730c04b30ad89a90d9c434f4c2021-11-12T05:35:47ZComputational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression1663-981210.3389/fphar.2021.782060https://doaj.org/article/ffab36f730c04b30ad89a90d9c434f4c2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphar.2021.782060/fullhttps://doaj.org/toc/1663-9812Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.Kexin WangKexin WangKexin WangKai LiYupeng ChenGenxia WeiHailang YuYi LiWei MengHanduo WangLi GaoAiping LuJunxiang PengDaogang GuanDaogang GuanFrontiers Media S.A.articleChai-Hu-Shu-Gan-Sandepressionnetwork pharmacology modeleffect propagation spaceintervention-response proteinscontribution indexTherapeutics. PharmacologyRM1-950ENFrontiers in Pharmacology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chai-Hu-Shu-Gan-San depression network pharmacology model effect propagation space intervention-response proteins contribution index Therapeutics. Pharmacology RM1-950
spellingShingle	Chai-Hu-Shu-Gan-San depression network pharmacology model effect propagation space intervention-response proteins contribution index Therapeutics. Pharmacology RM1-950 Kexin Wang Kexin Wang Kexin Wang Kai Li Yupeng Chen Genxia Wei Hailang Yu Yi Li Wei Meng Handuo Wang Li Gao Aiping Lu Junxiang Peng Daogang Guan Daogang Guan Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression
description	Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.
format	article
author	Kexin Wang Kexin Wang Kexin Wang Kai Li Yupeng Chen Genxia Wei Hailang Yu Yi Li Wei Meng Handuo Wang Li Gao Aiping Lu Junxiang Peng Daogang Guan Daogang Guan
author_facet	Kexin Wang Kexin Wang Kexin Wang Kai Li Yupeng Chen Genxia Wei Hailang Yu Yi Li Wei Meng Handuo Wang Li Gao Aiping Lu Junxiang Peng Daogang Guan Daogang Guan
author_sort	Kexin Wang
title	Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression
title_short	Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression
title_full	Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression
title_fullStr	Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression
title_full_unstemmed	Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression
title_sort	computational network pharmacology–based strategy to capture key functional components and decode the mechanism of chai-hu-shu-gan-san in treating depression
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/ffab36f730c04b30ad89a90d9c434f4c
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Computational Network Pharmacology–Based Strategy to Capture Key Functional Components and Decode the Mechanism of Chai-Hu-Shu-Gan-San in Treating Depression

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