Potential Mechanisms of Triptolide against Diabetic Cardiomyopathy Based on Network Pharmacology Analysis and Molecular Docking

The incidence of heart failure was significantly increased in patients with diabetic cardiomyopathy (DCM). The therapeutic effect of triptolide on DCM has been reported, but the underlying mechanisms remain to be elucidated. This study is aimed at investigating the potential targets of triptolide as a therapeutic strategy for DCM using a network pharmacology approach. Triptolide and its targets were identified by the Traditional Chinese Medicine Systems Pharmacology database. DCM-associated protein targets were identified using the comparative toxicogenomics database and the GeneCards database. The networks of triptolide-target genes and DCM-associated target genes were created by Cytoscape. The common targets and enriched pathways were identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The gene-gene interaction network was analyzed by the GeneMANIA database. The drug-target-pathway network was constructed by Cytoscape. Six candidate protein targets were identified in both triptolide target network and DCM-associated network: STAT3, VEGFA, FOS, TNF, TP53, and TGFB1. The gene-gene interaction based on the targets of triptolide in DCM revealed the interaction of these targets. Additionally, five key targets that were linked to more than three genes were determined as crucial genes. The GO analysis identified 10 biological processes, 2 cellular components, and 10 molecular functions. The KEGG analysis identified 10 signaling pathways. The docking analysis showed that triptolide fits in the binding pockets of all six candidate targets. In conclusion, the present study explored the potential targets and signaling pathways of triptolide as a treatment for DCM. These results illustrate the mechanism of action of triptolide as an anti-DCM agent and contribute to a better understanding of triptolide as a transcriptional regulator of cytokine mRNA expression.

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Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/8872593 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Ping Yang ◽

Haifeng He ◽

Shangfu Xu ◽

Ping Liu ◽

Xinyu Bai

Keyword(s):

Molecular Docking ◽

Signaling Pathways ◽

Target Genes ◽

Target Prediction ◽

Interaction Network ◽

Network Pharmacology ◽

Core Network ◽

Active Ingredients ◽

Protein Protein Interaction ◽

Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

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Network Pharmacology‐Based Study on the Active Component and Mechanism of the Anti-Gastric-Cancer Effect of Herba Sarcandrae

Journal of Healthcare Engineering ◽

10.1155/2021/3001131 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Li Han ◽

Ying Han

Keyword(s):

Gastric Cancer ◽

Signaling Pathways ◽

Signaling Pathway ◽

Target Genes ◽

Scientific Basis ◽

Gene Expression Omnibus ◽

Network Pharmacology ◽

Chemical Components ◽

Active Components ◽

Pathway Network

Background. Herba Sarcandrae is used in the clinical practice of traditional Chinese medicine to deal with gastric cancer. However, there are few studies on its precise mechanism. Method. In this study, a network pharmacological approach was utilized to construct a molecular/target/pathway molecular regulatory network for the anti-gastric-cancer effect of Herba Sarcandrae. The active components of Herba Sarcandrae and their potential mechanisms were explored. Chemical components of the Herba Sarcandrae were identified through a database, and they were evaluated and screened based on oral bioavailability and drug similarity. Results. Genes related to gastric cancer were found in the Gene Expression Omnibus (GEO) database, and gene targets related to anti-gastric-cancer were chosen by comparison. Using annotation, visualization, and a comprehensive discovery database, the function and related pathways of target genes were analyzed and screened. Cytoscape software was utilized to construct a component/target/pathway network for the antitumor effect of Herba Sarcandrae. Finally, 6 drug ingredients and 29 target genes related to gastric cancer were detected. IL-17 signaling pathway, NF-kappa B signaling pathway, and other signaling pathways were significantly enriched. Many signaling pathways that directly act on tumors and indirect pathways inhibit the development of gastric cancer. Conclusion. This study provides a scientific basis for further elucidating the mechanism of the anti-gastric-cancer effect of Herba Sarcandrae.

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

10.21203/rs.3.rs-39351/v1 ◽

2020 ◽

Author(s):

Rong-Bin Chen ◽

Ying-Dong Yang ◽

Kai Sun ◽

Shan Liu ◽

Wei Guo ◽

...

Keyword(s):

Signaling Pathways ◽

Postmenopausal Osteoporosis ◽

Target Genes ◽

Interaction Network ◽

Network Pharmacology ◽

Regulation Of Transcription ◽

Active Ingredients ◽

Active Components ◽

Kegg Pathways ◽

Key Genes

Abstract Background Postmenopausal osteoporosis (PMOP) is a global chronic and metabolic bone disease that poses huge challenges to individuals and society. Previous studies have confirmed that Ziyin Tongluo Formula (ZYTLF) has a good clinical effect in the treatment of PMOP. However, the material basis and mechanism of ZYLTF against PMOP has not been thoroughly explained. Methods TCMSP, TCMID, and BATMAN-TCM databases were used to identify the active ingredients and their putative targets. Genes associated with PMOP were mined from GeneCards, OMIM, DisGeNET databases, and then mapped with the putative targets to obtain overlapping target genes. A network model of "herb-active ingredient-overlapping target genes" was constructed and a protein-protein interaction network of overlapping target genes was built and the key genes were selected based on the MCC algorithm. The key genes were imported to the DAVID database to performs GO and KEGG pathway enrichment analyses. Results Ninety-two active components of ZYTLF corresponded to 243 targets, with 129 target genes interacting with PMOP, and 50 key genes were selected. GO analysis results showed that biological process mainly included positive regulation of transcription, negative regulation of apoptosis, and cell components were mainly nucleus, cytoplasm, and molecular functions mainly included enzyme binding, protein binding and transcription factor binding. There were two main types of significant KEGG pathways in PMOP, hormone-related signaling pathways (estrogen, prolactin, thyroid hormone) and inflammation-related pathways (TNF, PI3K-Akt, MAPK ). Conclusions The underlying therapeutic mechanisms of ZYTLF action on PMOP maybe is that, the active ingredients such as quercetin, kaempferol, luteolin act on ESR1, TNF, IL6, MAPK8 and other key genes, which mainly enrich in estrogen, TNF, PI3K-Akt, MAPK and other signaling pathways.

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Network pharmacology approach to reveals therapeutic mech-anism of traditional plants formulation used by Malaysia in-digenous ethnics in coronaviruses infection

10.26434/chemrxiv-2022-rpm1s ◽

2022 ◽

Author(s):

Fui Fui Lem ◽

Dexter Jiunn Herng Lee ◽

Fong Tyng Chee ◽

Su Na Chin ◽

Kai Min Lin ◽

...

Keyword(s):

Bioactive Compounds ◽

Target Genes ◽

Interaction Network ◽

Bioactive Compound ◽

Functional Enrichment ◽

Network Pharmacology ◽

Pathway Network ◽

Pharmacological Mechanism ◽

Target Network ◽

Compound Target

Network pharmacology analysis can act as a strategy to identify the pharmacological effect of plant-based bioactive compounds against coronavirus diseases. This study aimed to investigate the potential pharmacological mechanism of a local ethnomedicine (Costus speciosus, Hibiscus rosa-sinensis and Phyllanthus niruri) of Northern Borneo against coronaviruses known as CHP. Compounds in CHP were extracted from databases and screened for their oral bioavailability and drug-likeness before a compound-target network was built. Furthermore, the protein-protein interaction network and pathway enrichment were constructed and analyzed. A compound-target network consisting of 48 putative bioactive compounds targeting 587 candidate genes was identified. A total of 186 coronavirus-related genes were extracted and TP53, STAT3, HSP90AA1, STAT1, and EP300 were predicted to be the key targets. Notably, mapping of these target genes into the target-pathway network illustrated that functional enrichment was on viral infection and regulation of inflammation pathways. Urinatetralin is predicted, for the first time, as a bioactive compound that solely targets STAT3. The results from this study indicate that compounds present in CHP employ STAT3 and its connected pathways as the mechanism of action against coronaviruses. In conclusion, urinatetralin should be further investigated for its potential application against coronavirus infections.

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A Network Pharmacology Approach to Investigate the Underlying Mechanisms of Alpinia Katsumadai Hayata on Acne Vulgaris

E3S Web of Conferences ◽

10.1051/e3sconf/202125102060 ◽

2021 ◽

Vol 251 ◽

pp. 02060

Author(s):

Li Wang ◽

Yang Nie ◽

Huifang Chen ◽

Jun Sun ◽

Mingyue Hu ◽

...

Keyword(s):

Signaling Pathways ◽

Acne Vulgaris ◽

Therapeutic Targets ◽

Network Pharmacology ◽

Stress Regulation ◽

Pathway Network ◽

Vegf Signaling ◽

Anti Inflammatory ◽

Underlying Mechanisms ◽

Key Pathways

The Alpinia katsumadai Hayata Doukou, DK, is a traditional Chinese medicine that has shown superior anti-inflammatory property, which is widely used in the food and commodity industry. A network pharmacology analysis was performed to identify the potential anti-acne compounds, hub therapeutic targets, and the key pathways via TCMSP, BATMAN, CTD, PDB and PubChem databases. Finally, the “compoundtarget- pathway” network was constructed. The study found total 7 active compounds, including quercetin, (2R)-5,7-dihydroxy-2-phenylchroman-4-one, dehydrodiisoeugenol, (2R)-7-hydroxy-5-methoxy-2- phenylchroman-4-one, Pinocembrin, and 1,7-diphenyl-5-hydroxy-6-hepten-3-one alpinolide peroxide. In addition, 30 therapeutic targets, and 4 hub therapeutic targets of the DK were identified. The biological processes were primarily related to inflammatory response, response to oxidative stress, regulation of insulin secretion, etc. Which was significantly associated with ten pathways including the PI3K-Akt signaling pathways, VEGF signaling pathways, etc. Furtherly, the 4 hub targets AKT1, F2, AR, and PTGS2 with higher connectivity in PPI network were verificated though molecular docking, which once again proved that these targets are potential targets of their corresponding chemical molecules. Therefore, DK might have a synergistic effect on the anti-inflammatory effects via the various active compositions, targets and signaling pathways.

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Investigation of Anti-SARS, MERS, and COVID-19 Effect of Jinhua Qinggan Granules Based on a Network Pharmacology and Molecular Docking Approach

Natural Product Communications ◽

10.1177/1934578x211020619 ◽

2021 ◽

Vol 16 (5) ◽

pp. 1934578X2110206

Author(s):

Ying Zhang ◽

Yunfeng Yao ◽

Yanfang Yang ◽

Hezhen Wu

Keyword(s):

Molecular Docking ◽

Signaling Pathways ◽

Interaction Network ◽

Antiviral Effect ◽

Network Pharmacology ◽

Active Compounds ◽

Discovery Studio ◽

Pathway Network ◽

Protein Protein Interaction ◽

The Common

Objective Jinhua Qinggan Granules (JQGs) have achieved certain results in the prevention and treatment of COVID-19 in China during this coronavirus storm. In this study, we aimed to analyze the common mechanisms of JQG in the treatment of coronavirus-induced diseases, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19 via network pharmacology and molecular docking. Methods The active compounds of JQG were collected through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The common targets associated with these 3 diseases were screened from GeneCards database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of JQG’s core targets were analyzed using The Database for Annotation, Visualization, and Integrated Discovery and KOBAS 3.0 system. Further, the protein-protein interaction network was built using STRING database. The compound-target- signaling pathway network was constructed using Cytoscape 3.7.2. The core components of JQG were docked with core targets, COVID-19 coronavirus 3 Cl hydrolase, and angiotensin-converting enzyme 2 (ACE2) via Discovery Studio 2016 software. Results A total of 139 active compounds, 50 core targets, and 122 signaling pathways were screened out. The results of molecular docking showed that arctiin and linarin had a higher docking score with 3 Cl, ACE2, and core targets of JQH for antiviral effect. Conclusion The potential mechanism of action of JHQ in the treatment of MERS, SARS, and COVID-19 may be associated with the regulation of genes co-expressed with ACE2 and immune- related signaling pathways.

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DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

Bioinformatics ◽

10.1093/bioinformatics/btaa428 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4466-4472 ◽

Cited By ~ 5

Author(s):

Tianyi Zhao ◽

Yang Hu ◽

Jiajie Peng ◽

Liang Cheng

Keyword(s):

Target Genes ◽

Interaction Network ◽

Gene Interaction ◽

Biological Data ◽

Supplementary Information ◽

Gene Interaction Network ◽

Convolutional Network ◽

Protein Coding ◽

Lncrna Gene ◽

Novel Target

Abstract Motivation Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data are increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA. Results In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues and miRNA-mediated lncRNA–gene pairs. Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high area under curves (0.90–0.98) and area under precision-recall curves (0.91–0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs. Availability and implementation https://github.com/zty2009/LncRNA-target-gene. Supplementary information Supplementary data are available at Bioinformatics online.

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Identification of the Active Constituents and Significant Pathways of Shen-qi-Yi-zhu Decoction on Antigastric Cancer: A Network Pharmacology Research and Experimental Validation

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6642171 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Shuhong Zeng ◽

Zhibao Yu ◽

Xintian Xu ◽

Yuanjie Liu ◽

Jiepin Li ◽

...

Keyword(s):

Experimental Validation ◽

Target Genes ◽

Cell Model ◽

Network Pharmacology ◽

Disease Network ◽

Systematic Method ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Underlying Mechanisms

Shen-qi-Yi-zhu decoction (SQYZD) is an empirical prescription with antigastric cancer (GC) property created by Xu Jing-fan, a National Chinese Medical Master. However, its underlying mechanisms are still unclear. Based on network pharmacology and experimental verification, this study puts forward a systematic method to clarify the anti-GC mechanism of SQYZD. The active ingredients of SQYZD and their potential targets were acquired from the TCMSP database. The target genes related to GC gathered from GeneCards, DisGeNET, OMIM, TTD, and DrugBank databases were imported to establish protein-protein interaction (PPI) networks in GeneMANIA. Cytoscape was used to establish the drug-ingredients-targets-disease network. The hub target genes collected from the SQYZD and GC were parsed via GO and KEGG analysis. Our findings from network pharmacology were successfully validated using an in vitro HGC27 cell model experiment. In a word, this study proves that the combination of network pharmacology and in vitro experiments is effective in clarifying the potential molecular mechanism of traditional Chinese medicine (TCM).

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Community Detection in Large-Scale Bipartite Biological Networks

Frontiers in Genetics ◽

10.3389/fgene.2021.649440 ◽

2021 ◽

Vol 12 ◽

Author(s):

Genís Calderer ◽

Marieke L. Kuijjer

Keyword(s):

Biological Networks ◽

Large Scale ◽

Interaction Network ◽

Gene Interaction ◽

Community Structures ◽

Gene Interaction Network ◽

Structure Detection ◽

Wide Range ◽

Disease Associations ◽

Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

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Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5190808 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Minglong Guan ◽

Lan Guo ◽

Hengli Ma ◽

Huimei Wu ◽

Xiaoyun Fan

Keyword(s):

Biological Activity ◽

Molecular Docking ◽

Protein Interaction ◽

Protein Interaction Network ◽

Rosmarinic Acid ◽

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

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