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Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury
Astragali Radix (AR) has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. However, little is known about the effects of AR on acetaminophen (APAP)-induced liver injury (ALI). In the current study, a network pharmacology–based approach was applied to...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289209/ https://www.ncbi.nlm.nih.gov/pubmed/35860733 http://dx.doi.org/10.3389/fmed.2022.697644 |
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author | Peng, Yuan Zhu, Gerui Ma, Yuanyuan Huang, Kai Chen, Gaofeng Liu, Chenghai Tao, Yanyan |
author_facet | Peng, Yuan Zhu, Gerui Ma, Yuanyuan Huang, Kai Chen, Gaofeng Liu, Chenghai Tao, Yanyan |
author_sort | Peng, Yuan |
collection | PubMed |
description | Astragali Radix (AR) has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. However, little is known about the effects of AR on acetaminophen (APAP)-induced liver injury (ALI). In the current study, a network pharmacology–based approach was applied to characterize the action mechanism of AR on ALI. All compounds of AR were obtained from the corresponding databases, and active compounds were selected according to its oral bioavailability and drug-likeness index. The potential genes of AR were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and PubChem, whereas the potential genes related to ALI were obtained from Online databases (GeneCards and Online Mendelian Inheritance in Man) and Gene Expression Omnibus profiles. The enriched processes, pathways, and target genes of the diseases were analyzed by referring to the Search Tool for the Retrieval of Interacting Genes/Proteins database. A network constructed through Cytoscape software was used to identify the target proteins that connected the compounds in AR with the differential genes of ALI. Subsequently, the potential underlying action mechanisms of AR on ALI predicted by the network pharmacology analyses were experimentally validated in APAP-induced liver injury in mice and HL7702 cells incubated with APAP. The compound-target network included 181 targets, whereas the potential genes related to ALI were 4,621. A total of 49 AR–ALI crossover proteins, corresponding to 49 genes, were filtered into a protein–protein interaction network complex and designated as the potential targets of AR on ALI. Among the genes, the three highest-scoring genes, MYC, MAPK8, and CXCL8 were highly associated with apoptosis in ALI. Then in vitro and in vivo experiments confirmed that AR exhibited its prominent therapeutic effects on ALI mainly via regulating hepatocyte apoptosis related to inhibiting the expressions of MYC (c-Myc), MAPK8 (JNK1), and CXCL8 (IL-8). In conclusion, our study suggested that the combination of network pharmacology prediction with experimental validation might offer a useful tool to characterize the molecular mechanism of AR on ALI. |
format | Online Article Text |
id | pubmed-9289209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92892092022-07-19 Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury Peng, Yuan Zhu, Gerui Ma, Yuanyuan Huang, Kai Chen, Gaofeng Liu, Chenghai Tao, Yanyan Front Med (Lausanne) Medicine Astragali Radix (AR) has been widely used in traditional Chinese medicine prescriptions for acute and chronic liver injury. However, little is known about the effects of AR on acetaminophen (APAP)-induced liver injury (ALI). In the current study, a network pharmacology–based approach was applied to characterize the action mechanism of AR on ALI. All compounds of AR were obtained from the corresponding databases, and active compounds were selected according to its oral bioavailability and drug-likeness index. The potential genes of AR were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and PubChem, whereas the potential genes related to ALI were obtained from Online databases (GeneCards and Online Mendelian Inheritance in Man) and Gene Expression Omnibus profiles. The enriched processes, pathways, and target genes of the diseases were analyzed by referring to the Search Tool for the Retrieval of Interacting Genes/Proteins database. A network constructed through Cytoscape software was used to identify the target proteins that connected the compounds in AR with the differential genes of ALI. Subsequently, the potential underlying action mechanisms of AR on ALI predicted by the network pharmacology analyses were experimentally validated in APAP-induced liver injury in mice and HL7702 cells incubated with APAP. The compound-target network included 181 targets, whereas the potential genes related to ALI were 4,621. A total of 49 AR–ALI crossover proteins, corresponding to 49 genes, were filtered into a protein–protein interaction network complex and designated as the potential targets of AR on ALI. Among the genes, the three highest-scoring genes, MYC, MAPK8, and CXCL8 were highly associated with apoptosis in ALI. Then in vitro and in vivo experiments confirmed that AR exhibited its prominent therapeutic effects on ALI mainly via regulating hepatocyte apoptosis related to inhibiting the expressions of MYC (c-Myc), MAPK8 (JNK1), and CXCL8 (IL-8). In conclusion, our study suggested that the combination of network pharmacology prediction with experimental validation might offer a useful tool to characterize the molecular mechanism of AR on ALI. Frontiers Media S.A. 2022-07-04 /pmc/articles/PMC9289209/ /pubmed/35860733 http://dx.doi.org/10.3389/fmed.2022.697644 Text en Copyright © 2022 Peng, Zhu, Ma, Huang, Chen, Liu and Tao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Peng, Yuan Zhu, Gerui Ma, Yuanyuan Huang, Kai Chen, Gaofeng Liu, Chenghai Tao, Yanyan Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury |
title | Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury |
title_full | Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury |
title_fullStr | Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury |
title_full_unstemmed | Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury |
title_short | Network Pharmacology–Based Prediction and Pharmacological Validation of Effects of Astragali Radix on Acetaminophen-Induced Liver Injury |
title_sort | network pharmacology–based prediction and pharmacological validation of effects of astragali radix on acetaminophen-induced liver injury |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289209/ https://www.ncbi.nlm.nih.gov/pubmed/35860733 http://dx.doi.org/10.3389/fmed.2022.697644 |
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