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Mechanistic Investigation of Xuebijing for Treatment of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network Pharmacology
[Image: see text] After paraquat (PQ) poisoning, it is difficult to accurately diagnose patients’ condition by only measuring their blood PQ concentration. Therefore, it is important to establish an accurate method to assist in the diagnosis of PQ poisoning, especially in the early stages. In this s...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8340419/ https://www.ncbi.nlm.nih.gov/pubmed/34368559 http://dx.doi.org/10.1021/acsomega.1c02370 |
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author | Wang, Tongtong Li, Sha Wu, Yangke Yan, Xiao Zhu, Yiming Jiang, Yu Jiang, Feiya Liu, Wen |
author_facet | Wang, Tongtong Li, Sha Wu, Yangke Yan, Xiao Zhu, Yiming Jiang, Yu Jiang, Feiya Liu, Wen |
author_sort | Wang, Tongtong |
collection | PubMed |
description | [Image: see text] After paraquat (PQ) poisoning, it is difficult to accurately diagnose patients’ condition by only measuring their blood PQ concentration. Therefore, it is important to establish an accurate method to assist in the diagnosis of PQ poisoning, especially in the early stages. In this study, a gas chromatography–mass spectrometry (GC–MS) metabonomics strategy was established to obtain metabolite information. A random forest algorithm was used to search for potential biomarkers of PQ poisoning, and data mining and network pharmacological analysis were used to evaluate the active components, drug–disease targets, and key pathways of Xuebijing (XBJ) injection in the treatment of PQ-induced pulmonary fibrosis. Targets from the network pharmacology analysis and metabolites from plasma metabolomics were jointly analyzed to select crucial metabolic pathways. Finally, molecular docking technology and in vitro experiments were used to verify the pathway targets to further reveal the potential mechanisms underlying the antipulmonary fibrosis effect of XBJ. Metabonomics studies showed that l-valine, glycine, citric acid, d-mannose, d-galactose, maltose, l-tryptophan, and arachidonic acid contributed more to the differentiation of different groups than other metabolites. Compared with the control group, the PQ poisoning group had higher levels of l-valine, glycine, citric acid, l-tryptophan, and arachidonic acid, and lower levels of d-mannose, d-galactose, and maltose. After treatment with XBJ injection, the relative levels of these metabolites were reversed. The network pharmacological analysis screened a total of 180 targets, mainly involving multiple signaling pathways and metabolic pathways, which jointly played an antipulmonary fibrosis effect. Based on the combined analysis of 180 targets and 8 different metabolites, arachidonic acid metabolism was selected as the key metabolic pathway. Molecular docking analysis showed that the XBJ compound had strong binding activity with the target protein. Western blot results showed that XBJ injection could reduce the inflammatory response by downregulating the expressions of p-p65, p-IKBα, and p-IKKβ, thus inhibiting the development of PQ-induced pulmonary fibrosis. In summary, the combined results from metabolomics and network pharmacology studies showed that Xuebijing has the characteristics of multitarget, multichannel, and multicomponent action in the treatment of pulmonary fibrosis caused by PQ. |
format | Online Article Text |
id | pubmed-8340419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-83404192021-08-06 Mechanistic Investigation of Xuebijing for Treatment of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network Pharmacology Wang, Tongtong Li, Sha Wu, Yangke Yan, Xiao Zhu, Yiming Jiang, Yu Jiang, Feiya Liu, Wen ACS Omega [Image: see text] After paraquat (PQ) poisoning, it is difficult to accurately diagnose patients’ condition by only measuring their blood PQ concentration. Therefore, it is important to establish an accurate method to assist in the diagnosis of PQ poisoning, especially in the early stages. In this study, a gas chromatography–mass spectrometry (GC–MS) metabonomics strategy was established to obtain metabolite information. A random forest algorithm was used to search for potential biomarkers of PQ poisoning, and data mining and network pharmacological analysis were used to evaluate the active components, drug–disease targets, and key pathways of Xuebijing (XBJ) injection in the treatment of PQ-induced pulmonary fibrosis. Targets from the network pharmacology analysis and metabolites from plasma metabolomics were jointly analyzed to select crucial metabolic pathways. Finally, molecular docking technology and in vitro experiments were used to verify the pathway targets to further reveal the potential mechanisms underlying the antipulmonary fibrosis effect of XBJ. Metabonomics studies showed that l-valine, glycine, citric acid, d-mannose, d-galactose, maltose, l-tryptophan, and arachidonic acid contributed more to the differentiation of different groups than other metabolites. Compared with the control group, the PQ poisoning group had higher levels of l-valine, glycine, citric acid, l-tryptophan, and arachidonic acid, and lower levels of d-mannose, d-galactose, and maltose. After treatment with XBJ injection, the relative levels of these metabolites were reversed. The network pharmacological analysis screened a total of 180 targets, mainly involving multiple signaling pathways and metabolic pathways, which jointly played an antipulmonary fibrosis effect. Based on the combined analysis of 180 targets and 8 different metabolites, arachidonic acid metabolism was selected as the key metabolic pathway. Molecular docking analysis showed that the XBJ compound had strong binding activity with the target protein. Western blot results showed that XBJ injection could reduce the inflammatory response by downregulating the expressions of p-p65, p-IKBα, and p-IKKβ, thus inhibiting the development of PQ-induced pulmonary fibrosis. In summary, the combined results from metabolomics and network pharmacology studies showed that Xuebijing has the characteristics of multitarget, multichannel, and multicomponent action in the treatment of pulmonary fibrosis caused by PQ. American Chemical Society 2021-07-20 /pmc/articles/PMC8340419/ /pubmed/34368559 http://dx.doi.org/10.1021/acsomega.1c02370 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Wang, Tongtong Li, Sha Wu, Yangke Yan, Xiao Zhu, Yiming Jiang, Yu Jiang, Feiya Liu, Wen Mechanistic Investigation of Xuebijing for Treatment of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network Pharmacology |
title | Mechanistic Investigation of Xuebijing for Treatment
of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network
Pharmacology |
title_full | Mechanistic Investigation of Xuebijing for Treatment
of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network
Pharmacology |
title_fullStr | Mechanistic Investigation of Xuebijing for Treatment
of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network
Pharmacology |
title_full_unstemmed | Mechanistic Investigation of Xuebijing for Treatment
of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network
Pharmacology |
title_short | Mechanistic Investigation of Xuebijing for Treatment
of Paraquat-Induced Pulmonary Fibrosis by Metabolomics and Network
Pharmacology |
title_sort | mechanistic investigation of xuebijing for treatment
of paraquat-induced pulmonary fibrosis by metabolomics and network
pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8340419/ https://www.ncbi.nlm.nih.gov/pubmed/34368559 http://dx.doi.org/10.1021/acsomega.1c02370 |
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