Systematic Elucidation of the Mechanism of Genistein against Pulmonary Hypertension via Network Pharmacology Approach

: Numerous studies have shown that genistein has a good therapeutic effect on pulmonary hypertension (PH). However, there has been no systematic research performed yet to elucidate its exact mechanism of action in relation to PH. In this study, a systemic pharmacology approach was employed to analyze the anti-PH effect of genistein. Firstly, the preliminary predicted targets of genistein against PH were obtained through database mining, and then the correlation of these targets with PH was analyzed. After that, the protein-protein interaction network was constructed, and the functional annotation and cluster analysis were performed to obtain the core targets and key pathways involved in exerting the anti-PH effect of genistein. Finally, the mechanism was further analyzed via molecular docking of genistein with peroxisome proliferator-activated receptor γ (PPARγ). The results showed that the anti-PH effect of genistein may be closely related to PPARγ, apoptotic signaling pathway, and the nitric oxide synthesis process. This study not only provides new insights into the mechanism of genistein against PH, but also provides novel ideas for network approaches for PH-related research.

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Study of the Mechanism of the Reyanning Mixture Involved in Treating Novel Coronavirus Pneumonia Based on Network Pharmacology

Natural Product Communications ◽

10.1177/1934578x20954593 ◽

2020 ◽

Vol 15 (9) ◽

pp. 1934578X2095459

Author(s):

Anping Yang ◽

Hui Liu ◽

Fang Liu ◽

Lixia Fan ◽

Wanqin Liao ◽

...

Keyword(s):

Interaction Network ◽

Clinical Results ◽

Network Pharmacology ◽

Peroxisome Proliferator ◽

Peroxisome Proliferator Activated Receptor ◽

Inflammatory Reactions ◽

Kegg Pathways ◽

Target Network ◽

Bioactive Ingredients ◽

Novel Coronavirus

At present, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, and the best way to effectively carry out drug diagnosis and treatment presents difficulties for all medical staff. In China, some traditional Chinese medicines (TCMs) have been successfully applied to the treatment of SARS-CoV-2 and have achieved good clinical results, including the Reyanning mixture. In this study, we systematically analyzed the mechanism of the Reyanning mixture and its effects against SARS-CoV-2 based on the method of network pharmacology. Here, we used the TCM Systems Pharmacology database and employed a similarity algorithm to screen and identify the bioactive ingredients and potential targets of the Reyanning mixture. The GeneCards database was used to predict and screen the disease targets and build the active ingredient target network diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to construct the target signal pathway associations. The STRING tool was used to reconstruct the protein-protein interaction network. As a result, 27 candidate targets, such as tumor necrosis factor, interferon gamma, tumor protein P53, C-reactive protein, and peroxisome proliferator-activated receptor gamma, were identified among the 33 bioactive ingredients of the 4 TCMs in the Reyanning mixture with effects on treating SARS-CoV-2. These targets were significantly enriched in 20 KEGG pathways and associated with 48 diverse GO terms. All of these targets may play a role in inhibiting inflammatory reactions, regulating immune function, and reducing lung injury to achieve the purpose of treating SARS-CoV-2.

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Elucidating the Mechanisms of Action of Lianhua Qingwen decoction for the Treatment of COVID-19 via Systems Pharmacology Approaches

10.21203/rs.3.rs-21103/v2 ◽

2021 ◽

Author(s):

Xiting Wang ◽

Tao Lu

Keyword(s):

Molecular Docking ◽

Blood Circulation ◽

Interaction Network ◽

Enrichment Analysis ◽

Detection Algorithm ◽

Network Pharmacology ◽

Systems Pharmacology ◽

Protein Protein Interaction ◽

Further Development ◽

Protein Protein Interaction Network

Abstract Due to the severity of the COVID-19 epidemic, to identify a proper treatment for COVID-19 is of great significance. Traditional Chinese Medicine (TCM) has shown its great potential in the prevention and treatment of COVID-19. One of TCM decoction, Lianhua Qingwen decoction displayed promising treating efficacy. Nevertheless, the underlying molecular mechanism has not been explored for further development and treatment. Through systems pharmacology and network pharmacology approaches, we explored the potential mechanisms of Lianhua Qingwen treating COVID-19 and acting ingredients of Lianhua Qingwen decoction for COVID-19 treatment. Through this way, we generated an ingredients-targets database. We also used molecular docking to screen possible active ingredients. Also, we applied the protein-protein interaction network and detection algorithm to identify relevant protein groupings of Lianhua Qingwen. Totally, 605 ingredients and 1,089 targets were obtained. Molecular Docking analyses revealed that 35 components may be the promising acting ingredients, 7 of which were underlined according to the comprehensive analysis. Our enrichment analysis of the 7 highlighted ingredients showed relevant significant pathways that could be highly related to their potential mechanisms, e.g. oxidative stress response, inflammation, and blood circulation. In summary, this study suggests the promising mechanism of the Lianhua Qingwen decoction for COVID-19 treatment. Further experimental and clinical verifications are still needed.

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Investigating drug–target interactions in frontotemporal dementia using a network pharmacology approach

Beni-Suef University Journal of Basic and Applied Sciences ◽

10.1186/s43088-021-00145-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Archana Balasubramanian ◽

Raksha Sudarshan ◽

Jhinuk Chatterjee

Keyword(s):

Substance Abuse ◽

Targeted Therapy ◽

Drug Interactions ◽

Protein Interaction Network ◽

Interaction Network ◽

Network Pharmacology ◽

Specific Drug ◽

Protein Protein Interaction ◽

App Gene ◽

Protein Protein Interaction Network

Abstract Background Frontotemporal dementia (FTD) is the second most common type of dementia in individuals aged below 65 years with no current cure. Current treatment plan is the administration of multiple medications. This has the issue of causing adverse effects due to unintentional drug–drug interactions. Therefore, there exists an urgent need to propose a novel targeted therapy that can maximize the benefits of FTD-specific drugs while minimizing its associated adverse side effects. In this study, we implemented the concept of network pharmacology to understand the mechanism underlying FTD and highlight specific drug–gene and drug–drug interactions that can provide an interesting perspective in proposing a targeted therapy against FTD. Results We constructed protein–protein, drug–gene and drug–drug interaction networks to identify highly connected nodes and analysed their importance in associated enriched pathways. We also performed a historeceptomics analysis to determine tissue-specific drug interactions. Through this study, we were able to shed light on the APP gene involved in FTD. The APP gene which was previously known to cause FTD cases in a small percentage is now being extensively studied owing to new reports claiming its participation in neurodegeneration. Our findings strengthen this hypothesis as the APP gene was found to have the highest node degree and betweenness centrality in our protein–protein interaction network and formed an essential hub node between disease susceptibility genes and neuroactive ligand–receptors. Our findings also support the study of FTD being presented as a case of substance abuse. Our protein–protein interaction network highlights the target genes common to substance abuse (nicotine, morphine and cocaine addiction) and neuroactive ligand–receptor interaction pathways, therefore validating the cognitive impairment caused by substance abuse as a symptom of FTD. Conclusions Our study abandons the one-target one-drug approach and uses networks to define the disease mechanism underlying FTD. We were able to highlight important genes and pathways involved in FTD and analyse their relation with existing drugs that can provide an insight into effective medication management.

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Screening of Prognostic Factors in Early-Onset Breast Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033819893670 ◽

2020 ◽

Vol 19 ◽

pp. 153303381989367 ◽

Cited By ~ 1

Author(s):

Zhun Yu ◽

Qi He ◽

Guoping Xu

Keyword(s):

Breast Cancer ◽

Transcription Factor ◽

Differentially Expressed Genes ◽

Early Onset ◽

Differentially Expressed ◽

Peroxisome Proliferator ◽

Early Onset Breast Cancer ◽

Microrna Target ◽

Peroxisome Proliferator Activated Receptor ◽

Protein Protein Interaction

Background: Gene expression profiles from early-onset breast cancer and normal tissues were analyzed to explore the genes and prognostic factors associated with breast cancer. Methods: GSE109169 and GSE89116 were obtained from the database of Gene Expression Omnibus. We firstly screened the differentially expressed genes between tumor samples and normal samples from patients with early-onset breast cancer. Based on database for annotation, visualization and intergrated discovery (DAVID) tool, functional analysis was calculated. Transcription factor-target regulation and microRNA-target gene network were constructed using the tool of transcriptional regulatory relatitionships unraveled by sentence-based text mining (TRRUST) and miRWalk2.0, respectively. The prognosis-related survival information was compiled based on The Cancer Genome Atlas breast cancer clinical data. Results: A total of 708 differentially expressed genes from GSE109169 data sets and 358 differentially expressed genes from GSE89116 data sets were obtained, of which 122 common differentially expressed genes including 102 uniformly downregulated genes and 20 uniformly upregulated genes were screened. Protein–protein interaction network with a total of 83 nodes and 157 relationship pairs was obtained, and genes in protein–protein interaction, such as peroxisome proliferator-activated receptor γ, FGF2, adiponectin, and PCK1, were recognized as key nodes in protein–protein interaction. In total, 66 transcription factor–target relationship pairs were obtained, and peroxisome proliferator-activated receptor γ was the only one downregulated transcription factor. MicroRNA-target gene network contained 368 microRNA-target relationship pairs. Moreover, 16 differentially expressed genes, including 2 upregulations and 14 downregulations, were related to a significant correlation with the prognosis, including SQLE and peroxisome proliferator-activated receptor γ. Conclusions: SQLE and peroxisome proliferator-activated receptor γ might be important prognostic factors in breast cancers, and adiponectin might be important in breast cancer pathogenesis regulated by peroxisome proliferator-activated receptor γ.

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Bortezomib alleviates experimental pulmonary hypertension by regulating intracellular calcium homeostasis in PASMCs

AJP Cell Physiology ◽

10.1152/ajpcell.00324.2015 ◽

2016 ◽

Vol 311 (3) ◽

pp. C482-C497 ◽

Cited By ~ 9

Author(s):

Jun Zhang ◽

Wenju Lu ◽

Yuqin Chen ◽

Qian Jiang ◽

Kai Yang ◽

...

Keyword(s):

Pulmonary Hypertension ◽

Vascular Remodeling ◽

Transient Receptor Potential ◽

Hypoxia Inducible Factor ◽

Pulmonary Arteries ◽

Receptor Potential ◽

Peroxisome Proliferator ◽

Peroxisome Proliferator Activated Receptor ◽

Canonical Transient Receptor Potential ◽

Transient Receptor

The ubiquitin-proteasome system is considered to be the key regulator of protein degradation. Bortezomib (BTZ) is the first proteasome inhibitor approved by the US Food and Drug Administration for treatment of relapsed multiple myeloma and mantle cell lymphoma. Recently, BTZ treatment was reported to inhibit right ventricular hypertrophy and vascular remodeling in hypoxia-exposed and monocrotaline-injected rats. However, the underlying mechanisms remain poorly understood. We previously confirmed that hypoxia-elevated basal intracellular Ca2+ concentration ([Ca2+]i) and store-operated Ca2+ entry (SOCE) in pulmonary artery smooth muscle cells (PASMCs) are involved in pulmonary vascular remodeling. In this study we aim to determine whether BTZ attenuates the hypoxia-induced elevation of [Ca2+] in PASMCs and the signaling pathway involved in this mechanism. Our results showed that 1) in hypoxia- and monocrotaline-induced rat pulmonary hypertension (PH) models, BTZ markedly attenuated the development and progression of PH, 2) BTZ inhibited the hypoxia-induced increase in cell proliferation, basal [Ca2+]i, and SOCE in PASMCs, and 3) BTZ significantly normalized the hypoxia-upregulated expression of hypoxia-inducible factor-1α, bone morphogenetic protein 4, canonical transient receptor potential isoforms 1 and 6, and the hypoxia-downregulated expression of peroxisome proliferator-activated receptor-γ in rat distal pulmonary arteries and PASMCs. These results indicate that BTZ exerts its protective role in the development of PH potentially by inhibiting the canonical transient receptor potential-SOCE-[Ca2+]i signaling axis in PASMCs.

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Investigation of the Potential Mechanism Governing the Effect of the Shen Zhu San on COVID-19 by Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/8468303 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23 ◽

Cited By ~ 1

Author(s):

Yuxuan Wang ◽

Yuhua Ru ◽

Guowei Zhuo ◽

Maozheng Sheng ◽

Shuangqiu Wang ◽

...

Keyword(s):

Combined Therapy ◽

Target Prediction ◽

Interaction Network ◽

Modern Medicine ◽

Network Pharmacology ◽

Target Tissue ◽

Protein Protein Interaction ◽

Compound Screening ◽

Therapeutic Mechanisms ◽

Microarray Analyses

Background. Since December 2019, coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 infection has emerged in Wuhan and rapidly spread throughout China and even to other countries. Combined therapy with modern medicine and traditional Chinese medicine has been proposed, in which Shen Zhu San (SZS) was regarded as one of the basic prescriptions. Methods. Network pharmacological approaches along with candidate compound screening, target prediction, target tissue location, protein-protein interaction network, gene ontology (GO), KEGG enrichment analyses, and gene microarray analyses were applied. Results. A total of 627 targets of the 116 active ingredients of SZS were identified. Targets in immune cells and tissues were much more abundant than those in other tissues. A total of 597 targets were enriched in the GO biological cellular process, while 153 signaling pathways were enriched according to the KEGG analysis. A total of 450 SARS-related targets were integrated and intersected with the targets of SZS to identify 40 common targets that were significantly enriched in five immune function aspects of the immune system process during GO analysis. Several inflammation-related pathways were found to be significantly enriched throughout the study. Conclusions. The therapeutic mechanisms of the effects of SZS on COVID-19 potentially involve four effects: suppressing cytokine storms, protecting the pulmonary alveolar-capillary barrier, regulating the immune response, and mediating cell death and survival.

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Bioinformatics analysis of different candidate genes involved in hepatocellular carcinoma induced by HepG2 cells or tumor cells of patients

Journal of International Medical Research ◽

10.1177/0300060520932112 ◽

2020 ◽

Vol 48 (6) ◽

pp. 030006052093211

Author(s):

Xiang Zhang ◽

Songna Yin ◽

Ke Ma

Keyword(s):

Hepatocellular Carcinoma ◽

Hepg2 Cells ◽

Liver Parenchyma ◽

Preclinical Study ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Peroxisome Proliferator ◽

Ppi Network ◽

Peroxisome Proliferator Activated Receptor ◽

Protein Protein Interaction

Objective Hepatocellular carcinoma (HCC) is a common cancer with a high mortality rate; the molecular mechanism involved in HCC remain unclear. We aimed to provide insight into HCC induced with HepG2 cells and identify genes and pathways associated with HCC, as well as potential therapeutic targets. Methods Dataset GSE72581 was downloaded from the Gene Expression Omnibus, including samples from mice injected in liver parenchyma with HepG2 cells, and from mice injected with cells from patient tumor explants. Differentially expressed genes (DEGs) between the two groups of mice were analyzed. Then, gene ontology and Kyoto Encyclopedia of Gene and Genomes pathway enrichment analyses were performed. The MCODE plug-in in Cytoscape was applied to create a protein–protein interaction (PPI) network of DEGs. Results We identified 1,405 DEGs (479 upregulated and 926 downregulated genes), which were enriched in complement and coagulation cascades, peroxisome proliferator-activated receptor signaling pathway, and extracellular matrix–receptor interaction. The top 4 modules and top 20 hub genes were identified from the PPI network, and associations with overall survival were determined using Kaplan–Meier analysis. Conclusion This preclinical study provided data on molecular targets in HCC that could be useful in the clinical treatment of HCC.

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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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A Network Pharmacology Approach to Uncover the Mechanisms of Shen-Qi-Di-Huang Decoction against Diabetic Nephropathy

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2018/7043402 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 9

Author(s):

Sha Di ◽

Lin Han ◽

Qing Wang ◽

Xinkui Liu ◽

Yingying Yang ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Signaling Pathway ◽

Molecular Mechanisms ◽

Interaction Network ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Protein Protein Interaction ◽

Regulation Of Blood Pressure ◽

Protein Protein Interaction Network

Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

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Identification of Candidate Genes and Pathways in Nonsegmental Vitiligo Using Integrated Bioinformatics Methods

Dermatology ◽

10.1159/000511893 ◽

2020 ◽

pp. 1-9

Author(s):

Baoyi Liu ◽

Yongyi Xie ◽

Zhouwei Wu

Keyword(s):

Gene Expression ◽

Interaction Network ◽

Unknown Origin ◽

Cell Receptor ◽

R Package ◽

Peroxisome Proliferator ◽

Hub Genes ◽

Peroxisome Proliferator Activated Receptor ◽

Lesional Skin ◽

Functional Changes

Background: Nonsegmental vitiligo (NSV) is an acquired depigmentation disorder of unknown origin. Enormous interests focus on finding novel biomarkers and pathways responsible for NSV. Methods: The gene expression level was obtained by integrating microarray datasets (GSE65127 and GSE75819) from the Gene Expression Omnibus database using the sva R package. Differentially expressed genes (DEGs) between each group were identified by the limma R package. The interaction network was constructed using STRING, and significant modules coupled with hub genes were identified by cytoHubba and molecular complex detection. Pathway analyses were conducted using generally applicable gene set enrichment and further visualized in R environment. Results: A total of 102 DEGs between vitiligo lesional skin and healthy skin, 14 lesion-specific genes, and 29 predisposing genes were identified from the integrated dataset. Except for the anticipated decrease in melanogenesis, three major functional changes were identified, including oxidative phosphorylation, p53, and peroxisome proliferator-activated receptor (PPAR) signaling in lesional skin. PPARG, MUC1, S100A8, and S100A9 were identified as key hub genes involved in the pathogenesis of vitiligo. Besides, upregulation of the T cell receptor signaling pathway was considered to be associated with susceptibility of the skin in NSV patients. Conclusion: Our study reveals several potential pathways and related genes involved in NSV using integrated bioinformatics methods. It might provide references for targeted strategies for NSV.

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