scholarly journals Potential mechanisms of Guizhi decoction against hypertension based on network pharmacology and Dahl salt-sensitive rat model

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jiye Chen ◽  
Yongjian Zhang ◽  
Yongcheng Wang ◽  
Ping Jiang ◽  
Guofeng Zhou ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Rat Model ◽  
Experimental Studies ◽  
Matrix Metalloproteinase 2 ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Active Compounds ◽  
Guizhi Decoction

Abstract Background Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore the potential mechanisms and therapeutic effects of GZD on hypertension by integrating network pharmacology and experimental validation. Methods The active ingredients and corresponding targets were collected from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from the CTD, GeneCards, OMIM and Drugbank databases. Multiple networks were constructed to identify the key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD against hypertension. Results A total of 112 active ingredients, 222 targets of GZD and 341 hypertension-related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets for experimental verification, including interleukin 6 (IL-6), C–C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2 (MMP-2), and MMP-9. Pathway enrichment analysis results indicated that 56 overlapping targets were mainly enriched in several inflammation pathways such as the tumor necrosis factor (TNF) signaling pathway, Toll-like receptor (TLR) signaling pathway and nuclear factor kappa-B (NF-κB) signaling pathway. Molecular docking confirmed that most active compounds of GZD could bind tightly to the key targets. Experimental studies revealed that the administration of GZD improved blood pressure, reduced the area of cardiac fibrosis, and inhibited the expression of IL-6, CCL2, IL-1β, MMP-2 and MMP-9 in rats. Conclusion The potential mechanisms and therapeutic effects of GZD on hypertension may be attributed to the regulation of cardiac inflammation and fibrosis.

scholarly journals Potential Mechanism of Guizhi Decoction in Hypertension Treatment Based on Network Pharmacology and Dahl Salt-Sensitive Rat Model

2020 ◽  
Author(s):  
Ji-ye Chen ◽  
Yong-jian Zhang ◽  
Yong-cheng Wang ◽  
Guo-feng Zhou ◽  
Jin-long Yang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Rat Model ◽  
Cardiac Fibrosis ◽  
Matrix Metalloproteinase 2 ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Potential Mechanism ◽  
Active Ingredients ◽  
Active Compounds ◽  
Guizhi Decoction

Abstract Introduction: Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore its therapeutic effects and potential mechanisms in the treatment of hypertension using network pharmacology and experimental validation.Methods: The active ingredients and corresponding targets were collected from Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from multiple databases, and multiple networks were constructed to identify key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD on hypertension. Results: A total of 112 active ingredients, 222 targets of GZD and 341 hypertension- related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets to perform experimental verification, including interleukin 6 (IL-6), C-C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2(MMP-2), and MMP9. Pathway enrichment results indicated that 56 overlapping targets mainly enriched in several inflammation pathways such as the tumor necrosis factor (TNF) signaling pathway, toll-like receptor (TLR) signaling pathway and nuclear factor kappa-B (NF-κB) signaling pathway. Molecular docking confirmed that most active compounds of GZD showed tight binding ability with the key targets. Experimental results demonstrated that compared with the group fed a high-salt diet in this study, the GZD improved blood pressure, reduced the area of cardiac fibrosis, and inhibited the expression of IL6, CCL2, IL1β, MMP2 and MMP9 in rats.Conclusions: The potential mechanism of the therapeutic effect of GZD on hypertension may be attributed to the regulation of cardiac inflammation and fibrosis.

scholarly journals Potential Mechanism of Guizhi Decoction in Hypertension Treatment Based on Network Pharmacology and Dahl Salt-Sensitive Rat Model

2020 ◽  
Author(s):  
Ji-ye Chen ◽  
Yong-jian Zhang ◽  
Yong-cheng Wang ◽  
Guo-feng Zhou ◽  
Xiao Li
Keyword(s):  
Rat Model ◽  
Matrix Metalloproteinase 2 ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Multiple Networks ◽  
Guizhi Decoction ◽  
Treatment Of Hypertension ◽  
Underlying Mechanisms ◽  
Analysis Platform

Abstract Background Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore its therapeutic effects and potential mechanisms in the treatment of hypertension using network pharmacology and experimental validation. Methods The active ingredients and corresponding targets were collected from Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from multiple databases, and multiple networks were constructed to identify key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD on hypertension. Results A total of 112 active ingredients, 222 targets of GZD and 341 hypertension- related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets to perform experimental verification, including interleukin 6 (IL-6), C-C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2(MMP-2), and MMP9. Pathway enrichment

scholarly journals Exploration of the mechanism of Ziyin Tongluo Formula on preventing and treating postmenopausal osteoporosis based on the network pharmacology and molecular docking

2020 ◽  
Author(s):  
Rong-Bin Chen ◽  
Ying-Dong Yang ◽  
Kai Sun ◽  
Shan Liu ◽  
Wei Guo ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Postmenopausal Osteoporosis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Active Compounds ◽  
Core Proteins ◽  
Key Genes

Abstract Background: Postmenopausal osteoporosis (PMOP) is a global chronic and metabolic bone disease, which poses huge challenges to individuals and society. Ziyin Tongluo Formula (ZYTLF) has been proved effective in the treatment of PMOP. However, the material basis and mechanism of ZYLTF against PMOP have not been thoroughly elucidated.Methods: Online databases were used to identify the active ingredients of ZYTLF and corresponding putative targets. Genes associated with PMOP were mined, and then mapped with the putative targets to obtain overlapping genes. Multiple networks were constructed and analyzed, from which the key genes were selected. The key genes were imported to the DAVID database to performs GO and KEGG pathway enrichment analysis. Finally, AutoDock Tools and other software were used for molecular docking of core compounds and key proteins. Results: Ninety-two active compounds of ZYTLF corresponded to 243 targets, with 129 target genes interacting with PMOP, and 50 key genes were selected. Network analysis showed the top 5 active ingredients including quercetin, kaempferol, luteolin, scutellarein, and formononetin., and the top 50 key genes such as VEGFA, MAPK8, AKT1, TNF, ESR1. Enrichment analysis uncovered two significant types of KEGG pathways in PMOP, hormone-related signaling pathways (estrogen , prolactin, and thyroid hormone signaling pathway) and inflammation-related pathways (TNF, PI3K-Akt, and MAPK signaling pathway). Moreover, molecular docking analysis verified that the main active compounds were tightly bound to the core proteins, further confirming the anti-PMOP effects. Conclusions: Based on network pharmacology and molecular docking technology, this study initially revealed the mechanisms of ZYTLF on PMOP, which involves multiple targets and multiple pathways.

scholarly journals Elucidation of the active compounds and molecular mechanisms of Smilacis Chinae Rhizoma for treatment of pelvic inflammatory disease based on network pharmacology and MMGBSA-docking

2020 ◽  
Author(s):  
Yunsen Zhang ◽  
Zikuang Zhao ◽  
Wenxiang Wang ◽  
Qi Li ◽  
Huimin Chen ◽  
...  
Keyword(s):  
Pelvic Inflammatory Disease ◽  
Signaling Pathway ◽  
Inflammatory Disease ◽  
Molecular Mechanisms ◽  
Binding Free Energy ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Active Compounds ◽  
Bidirectional Regulation

Abstract Background Smilacis Chinae Rhizoma (SCR) is widely used in the treatment of pelvic inflammatory disease (PID). However, its active ingredients and the mechanisms against PID remain elusive. This study aimed to clarify the active ingredients and explore their molecular mechanisms on PID. Method Network pharmacology and MMGBSA-docking exploited the active compounds and mechanisms against PID, as well as validating the binding mode of candidate targets.Results Network pharmacology revealed 32 active compounds and 718 compound-related targets mapped to 91 pathways which were clustered 7 genres (e.g., immunoregulation). C-T-P network and PPI analysis illustrated 17 PID-related targets, indicating that SCR may decrease inflammation, ameliorate fibrosis, and inhibit microorganisms via bidirectionally regulating IL-17 signaling pathway. Furthermore, active compounds were uncovered that bound to prostaglandin-endoperoxide synthase 2, matrix metalloprotein-9, lipocalin, signal transducer and activator of transcription 3, myeloperoxidase, and tumor necrosis factor. 19 active compounds (e.g., rutin (-66.43 kcal/mol), moracin M (-37.01 kcal/mol) and oxyresveratrol (-38.84 kcal/mol)) were found to show excellent binding free energy, demonstrating that H-bond, Pi electron cloud and electrostatic potential as the main binding ability to these targets. Conclusion Approach of network pharmacology and MMGBSA-docking revealed the active ingredients, such as rutin, moracin M, and oxyresveratrol, in SCR and dissected it exhibits the therapeutic effects (e.g., decrease inflammation, ameliorate fibrosis, and inhibit microorganisms) of PID by the bidirectional regulation of IL-17 signaling pathway.

scholarly journals Potential mechanism of Ziyin Tongluo Formula in the treatment of postmenopausal osteoporosis: based on network pharmacology and ovariectomized rat model

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Rong-Bin Chen ◽  
Ying-Dong Yang ◽  
Kai Sun ◽  
Shan Liu ◽  
Wei Guo ◽  
...  
Keyword(s):  
Postmenopausal Osteoporosis ◽  
Signaling Pathway ◽  
Rat Model ◽  
Experimental Studies ◽  
Enrichment Analysis ◽  
Ovariectomized Rat ◽  
Active Ingredients ◽  
Active Compounds ◽  
Osteoclast Activity ◽  
Key Genes

Abstract Background Amending from ancient classic, Ziyin Tongluo Formula (ZYTLF) has been prescribed to treat postmenopausal osteoporosis (PMOP) for decades with good curative effect. However, the possible mechanisms of it are still unknown. Methods Ovariectomized rat model was established to validate the therapeutic effect of ZYTLF on PMOP by Micro-CT bone analysis and pathological observation. Subsequently, active ingredients of ZYTLF and corresponding putative targets were identified by online databases. Overlapping genes were first obtained from mining genes associated with PMOP and then overlapped them with the putative targets. Key genes were selected from the multiple constructed and analyzed networks. GO and KEGG pathway enrichment analysis were performed by importing the key genes to the DAVID database. Moreover, validation of the binding association between key targets and their corresponding active compounds were accomplished by AutoDock Tools and other software. Lastly, Enzyme linked immunosorbent assay (Elisa) detection and Western blot analysis were utilized to further explore the possible mechanism of ZYTLF on PMOP. Results With 129 target genes interacting with PMOP, 92 active compounds of ZYTLF corresponded to 243 targets, and 50 key genes were chosen. Network analysis revealed the top 10 active ingredients, such as quercetin and kaempferol and the top 50 key genes, such as ERα, p38 MAPK, p-AKT and TGF-β1. Enrichment analysis uncovered multiple signaling pathways, including estrogen signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway and MAPK signaling pathway. Furthermore, our finding of the foremost active compounds was tightly bound to the core proteins, which were verified by molecular docking analysis. Through experimental studies, we confirmed that the prescription of ZYTLF could ameliorate the OVX-induced bone loss, suppress the osteoclast activity and boost osteoblast ability through experimental studies. Conclusion The potential mechanisms and therapeutic effects of ZYTLF against PMOP may be ascribed to inhibition of osteoclast activity, boost of osteoblast activity and enhancement of the expression of ERα.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology.

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanism ◽  
Effector Mechanisms

Abstract Objective: To examine the potential effector mechanisms of Xuebijing (XBJ) on coronavirus disease 2019 (COVID-19) based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets—glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14—had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin–angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)–protein kinase B (Akt)–vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “ingredient-target-pathway” effector network. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Interleukin 2 ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanisms

Abstract Objective To examine the potential effector mechanisms of Xuebijing (XBJ) on coronavirus disease 2019 (COVID-19) based on network pharmacology.Methods We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound–potential-effector target” network and protein–protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) Biological Process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets.Results We obtained 147 potential COVID-19 effector targets of XBJ. Fourteen of these targets—glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), Estrogen Receptor 1 (ESR1), and MAPK14—had degree values > 40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide (LPS), and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin–angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)–protein kinase B (Akt)–vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “ingredient–target–pathway” effector network. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. The results of this study validated current pharmacological mechanistic studies of XBJ in the treatment of sepsis and severe pneumonia and could better explain XBJ’s effector mechanisms in the clinical treatment of COVID-19.

scholarly journals Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xinmiao Wang ◽  
Haoyu Yang ◽  
Lili Zhang ◽  
Lin Han ◽  
Sha Di ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Signaling Pathway ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Go Analysis

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanism ◽  
Effector Mechanisms

Abstract Background: Chinese medicine Xuebijing (XBJ) has proven to be effective in the treatment of mild coronavirus disease 2019 (COVID-19) cases. But the bioactive compounds and potential mechanisms of XBJ for COVID-19 prevention and treatment are unclear. This study aimed to examine the potential effector mechanisms of XBJ on COVID-19 based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. We used AutoDock vina and PyMOL software for molecular docking. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets-glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14 had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin-angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)-protein kinase B (Akt)-vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “drug-ingredient-target-pathway” effector network. The molecular docking results showed that the core 18 effective ingredients had a docking score of less than -4.0 with those top 10 targets. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that key ingredients and their targets have potential binding activity, the existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanism ◽  
Effector Mechanisms

Abstract Background: Chinese medicine Xuebijing (XBJ) has proven to be effective in the treatment of mild coronavirus disease 2019 (COVID-19) cases. But the bioactive compounds and potential mechanisms of XBJ for COVID-19 prevention and treatment are unclear. This study aimed to examine the potential effector mechanisms of XBJ onCOVID-19 based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. We used AutoDock vina and PyMOL software for molecular docking. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets-glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14 had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin-angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)-protein kinase B (Akt)-vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “drug-ingredient-target-pathway” effector network. The molecular docking results showed that the core 18 effective ingredients had a docking score of less than -4.0 with those top 10 targets. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that key ingredients and their targets have potential binding activity, the existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

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