Dataset Information

Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19.

ABSTRACT:

Purpose

Huashi Baidu formula (HSBDF) was developed to treat the patients with severe COVID-19 in China. The purpose of this study was to explore its active compounds and demonstrate its mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through network pharmacology and molecular docking.

Methods

All the components of HSBDF were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using UniProt and GeneCards database. The herb-compound-target network was constructed by Cytoscape. The target protein-protein interaction network was built using STRING database. The core targets of HSBDF were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The main active compounds of HSBDF were docked with SARS-CoV-2 and angiotensin converting enzyme II (ACE2).

Results

Compound-target network mainly contained 178 compounds and 272 corresponding targets. Key targets contained MAPK3, MAPK8, TP53, CASP3, IL6, TNF, MAPK1, CCL2, PTGS2, etc. There were 522 GO items in GO enrichment analysis (p < .05) and 168 signaling pathways (p < .05) in KEGG, mainly including TNF signaling pathway, PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, MAPK signaling pathway, and HIF-1 signaling pathway. The results of molecular docking showed that baicalein and quercetin were the top two compounds of HSBDF, which had high affinity with ACE2.

Conclusion

Baicalein and quercetin in HSBDF may regulate multiple signaling pathways through ACE2, which might play a therapeutic role on COVID-19.

SUBMITTER: Tao Q

PROVIDER: S-EPMC7441778 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in millions of infections and raised global attention. Bitter almonds and licorice are both Traditional Chinese Medicines (TCM), often used in combination to treat lung diseases. Several prescriptions in the guidelines for the diagnosis and treatment of coronavirus disease 2019 (trial version ninth) contained bitter almond-licorice, which was effective in the treatment of COVID-19. However, the active ingredients, drug targets and therapeutic mechanisms of bitter almonds-licorice for the treatment of COVID-19 remain to be elucidated.MethodsThe active ingredients and targets were derived from the Traditional Chinese Medicine Systems Pharmacology (TCMSP). Meanwhile, targets associated with COVID-19 were obtained from the GeneCards database, PharmGkb database and DrugBank database. Then, the potential targets of bitter almond-licorice against COVID-19 were screened out. Protein-protein interaction (PPI) networks and core targets were analyzed through the String database and Cytoscape software. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed based on potential targets using R statistical software. Finally, molecular docking was used to validate the binding of the active ingredients to the core targets.ResultsThe results of the TCMSP database showed that the bitter almond-licorice had 89 active components against COVID-19, involving 102 targets. PPI network and core target analysis indicated that IL-6, TNF, MAPK1, and IL1B were the key targets against COVID-19. In addition, GO and KEGG enrichment analysis showed that the bitter almond-licorice were involved in various biological processes through inflammation-related pathways such as TNF signaling pathway and IL-17 signaling pathway. Finally, molecular docking approaches confirmed the affinity between the active components of the bitter almond-licorice and the therapeutic targets.ConclusionThe bitter almond-licorice could be used to treat COVID-19 by inhibiting inflammatory responses and regulating cellular stress. This work is based on data mining and molecular docking, and the findings need to be interpreted with caution.

Project description:BackgroundOsteoarthritis is a common degenerative disease with a high incidence, high disability rate, and poor prognosis. Clinical studies have shown that Bushen Huoxue formula can relieve joint swelling and pain and improve limb function and joint mobility, but there is a lack of high-quality scientific basis. Using network pharmacology and molecular docking technology to study the mechanism of Bushen Huoxue formula in the treatment of osteoarthritis.MethodsFirst, the active ingredients and corresponding target predictions of the formula were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the China National Knowledge Infrastructure. Meanwhile, the osteoarthritis disease targets were obtained through the genome annotation database platform (GeneCards) and the DrugBank database, and the target proteins obtained above were standardized using the Uniprot (https://www.uniprot.org) database standardization of names. Then, the Venn diagram was created by taking the intersection of the active ingredient and the target of the disease, and the "active ingredient-target" network was constructed and analyzed using Cytoscape 3.7.2 software. At the same time, the intersecting targets were imported into the Search Tool for the Retrieval of Interaction Gene/Proteins database to build a protein-protein interaction network and to screen the core targets; the intersecting targets were visualized by using the Database for Annotation, Visualization and Integrated Discovery 6.8 database for gene ontology functional analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and construct the "active ingredient-target-pathway" network. Finally, the main active ingredients of the formula for tonifying the kidney and invigorating the blood were validated by molecular docking with the core targets.ResultsA total of 194 active ingredients and 365 targets of the Bushen Huoxue formula were collected, 776 targets for osteoarthritis diseases and 96 targets for the intersection of active ingredients and diseases. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis yielded 104 relevant pathways, including tumor necrosis factor signaling pathways, cancer signaling pathways, nucleotide-binding oligomerization domain-like receptor signaling pathways, Toll-like receptors signaling pathways, and osteoclast differentiation, apoptosis, T-cell receptor signaling pathway, and other related pathways. The molecular docking results showed good binding of the main active ingredients to the core targets.ConclusionThis study shows that the treatment of osteoarthritis involves multicomponent, multitarget, and multipathway processes. The mechanism of anti-inflammatory, antioxidant, inhibition of cartilage matrix degradation, and reduction of subchondral bone destruction may be an important mechanism for the therapeutic effect.

Project description:Background:Yizhiqingxin formula (YZQX) is a promising formula for the treatment of Alzheimer's disease (AD) with significant clinical effects. Here, we coupled a network pharmacology approach with the Gene Expression Omnibus (GEO) database to illustrate comprehensive mechanisms and screen for molecular targets of YZQX for AD treatment. Methods:First, active ingredients of YZQX were screened for the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database with the absorption, distribution, metabolism, and excretion (ADME) parameters. Subsequently, putative targets of active ingredients were predicted using the DrugBank database. AD-related targets were retrieved by analyzing published microarray data (accession number GSE5281). Protein-protein interaction (PPI) networks of YZQX putative targets and AD-related targets were constructed visually and merged to identify candidate targets for YZQX against AD using Cytoscape 3.7.2 software. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to further clarify the biological functions of the candidate targets. The gene-pathway network was established to filter for key target genes. Results:Forty-three active ingredients were identified, and 193 putative target genes were predicted. Seven hundred and ten targets related to AD were screened with |log2 FC| > 1 and P < 0.05. Based on the PPI network, 110 target genes of YZQX against AD were identified. Moreover, 32 related pathways including the PI3K-Akt signaling pathway, MAPK signaling pathway, ubiquitin-mediated proteolysis, apoptosis and the NF-kappa B signaling pathway were significantly enriched. In the gene-pathway network, MAPK1, AKT1, TP53, MDM2, EGFR, RELA, SRC, GRB2, CUL1, and MYC targets are putative core genes for YZQX in AD treatment. Conclusion:YZQX against AD may exert its neuroprotective effect via the PI3K-Akt signaling pathway, MAPK signaling pathway, and ubiquitin-mediated proteolysis. YZQX may be a promising drug that can be used in the treatment of AD.

Project description:Weibing Formula 1, a classic traditional formula, has been widely used clinically to treat gastritis in recent years. However, the potential pharmacological mechanism of Weibing Formula 1 is still unclear to date. A network pharmacology-based strategy was performed to uncover the underlying mechanisms of Weibing Formula 1 against gastritis. Furthermore, we structured the drug-active ingredients-genes-disease network and PPI network of shared targets, and function enrichment analysis of these targets was carried out. Ultimately, Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR were used to verify the related genes. We found 251 potential targets corresponding to 135 bioactive components of Weibing Formula 1. Then, 327 gastritis-related targets were known gastritis-related targets. Among which, 60 common targets were shared between potential targets of Weibing Formula 1 and known gastritis-related targets. The results of pathway enrichment analysis displayed that 60 common targets mostly participated in various pathways related to Toll-like receptor signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction pathway, chemokine signaling pathway, and apoptosis. Based on the GSE60427 dataset, 15 common genes were shared between differentially expressed genes and 60 candidate targets. The verification results of the GSE5081 dataset showed that except for DUOX2 and VCAM1, the other 13 genes were significantly upregulated in gastritis, which was consistent with the results in the GSE60427 dataset. More importantly, real-time quantitative PCR results showed that the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly upregulated and NOS2, EGFR, and IL-10 were downregulated in gastritis patients, while the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly downregulated and NOS2, EGFR, and IL-10 were upregulated after the treatment of Weibing Formula 1. PTGS2, NOS2, EGFR, MMP9, CXCL2, CXCL8, and IL-10 may be the important direct targets of Weibing Formula 1 in gastritis treatment. Our study revealed the mechanism of Weibing Formula 1 in gastritis from an overall and systematic perspective, providing a theoretical basis for further knowing and application of this formula in the future.

Project description:BackgroundThe incidence of depression has been increasing globally, which has brought a serious burden to society. Sinisan Formula (SNSF), a well-known formula of traditional Chinese medicine (TCM), has been found to demonstrate an antidepressant effect. However, the therapeutic mechanism of this formula remains unclear. Thus, the present study aimed to explore the mechanism of SNSF in depression through network pharmacology combined with molecular docking methods.Materials and methodsBioactive compounds, potential targets of SNSF, and related genes of depression were obtained from public databases. Essential ingredients, potential targets, and signaling pathways were identified using bioinformatics analysis, including protein-protein interaction (PPI), the Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, Autodock software was further performed for conducting molecular docking to verify the binding ability of active ingredients to targets.ResultsA total of 91 active compounds were successfully identified in SNSF with the use of the comprehensive network pharmacology approach, and they were found to be closely connected to 112 depression-related targets, among which CREB1, NOS3, CASP3, TP53, ESR1, and SLC6A4 might be the main potential targets for the treatment of depression. GO analysis revealed 801 biological processes, 123 molecular functions, and 67 cellular components. KEGG pathway enrichment analysis indicated that neuroactive ligand-receptor interaction, serotonergic synapse pathways, dopaminergic synapse pathways, and GABAergic synapse pathways might have played a role in treating depression. Molecular docking suggested that beta-sitosterol, nobiletin, and 7-methoxy-2-methyl isoflavone bound well to the main potential targets.ConclusionThis study comprehensively illuminated the active ingredients, potential targets, primary pharmacological effects, and relevant mechanism of the SNSF in the treatment of depression. SNSF might exert its antidepressant effects by regulating the signaling pathway of 5-hydroxytryptamine, dopamine, GABA, and neuroactive ligand receptor interactions. Still, more pharmacological experiments are needed for verification.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) is a new global public health emergency. The therapeutic benefits of Cold‒Damp Plague Formula (CDPF) against COVID-19, which was used to treat "cold‒dampness stagnation in the lung" in Trial Versions 6 and 7 of the "Diagnosis and Treatment Protocol for COVID-19", have been demonstrated, but the effective components and their mechanism of action remain unclear.MethodsIn this study, a network pharmacology approach was employed, including drug-likeness evaluation, oral bioavailability prediction, protein‒protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, and virtual docking, to predict the bioactive components, potential targets, and molecular mechanism of CDPF for COVID-19 treatment.ResultsThe active compound of herbs in CDPF and their candidate targets were obtained through database mining, and an herbs-ingredients-targets network was constructed. Subsequently, the candidate targets of the active compounds were compared to those relevant to COVID-19, to identify the potential targets of CDPF for COVID-19 treatment. Subsequently, the PPI network was constructed, which provided a basis for cluster analysis and hub gene screening. The seed targets in the most significant module were selected for further functional annotation. GO enrichment analysis identified four main areas: (1) cellular responses to external stimuli, (2) regulation of blood production and circulation, (3) free radical regulation, (4) immune regulation and anti-inflammatory effects. KEGG pathway analysis also revealed that CDPF could play pharmacological roles against COVID-19 through "multi components‒multi targets‒multi pathways" at the molecular level, mainly involving anti-viral, immune-regulatory, and anti-inflammatory pathways; consequently, a "CDPF-herbs-ingredients-targets-pathways-COVID-19" network was constructed. In hub target analysis, the top hub target IL6, and ACE2, the receptor via which SARS-CoV-2 typically enters host cells, were selected for molecular docking analyses, and revealed good binding activities.ConclusionsThis study revealed the active ingredients and potential molecular mechanism by which CDPF treatment is effective against COVID-19, and provides a reference basis for the wider application and further mechanistic investigations of CDPF in the fight against COVID-19.

Project description:Objectives: Network pharmacological methods were used to investigate the underlying molecular mechanisms of LianXia NingXin (LXNX) formula, a Chinese prescription, to treat coronary heart disease (CHD) and disease phenotypes (CHD related diseases and symptoms). Methods: The different seed gene lists associated with the herbs of LXNX formula, the CHD co-morbid diseases and symptoms which were relieved by the LXNX formula (co-morbid diseases and symptoms) were curated manually from biomedical databases and published biomedical literatures. Module enrichment analysis was used to identify CHD-related disease modules in the protein-protein interaction (PPI) network which were also associated to the targets of LXNX formula (LXNX formula's CHD modules). The molecular characteristics of LXNX formula's CHD modules were investigated via functional enrichment analysis in terms of gene ontology and pathways. We performed shortest path analysis to explore the interactions between the drug targets of LXNX formula and CHD related disease phenotypes (e.g., co-morbid diseases and symptoms). Results: We identified two significant CHD related disease modules (i.e., M146 and M203), which were targeted by the herbs of LXNX formula. Pathway and GO term functional analysis results indicated that G-protein coupled receptor signaling pathways (GPCR) of M146 and cellular protein metabolic process of M203 are important functional pathways for the respective module. This is further confirmed by the shortest path analysis between the drug targets of LXNX formula and the aforementioned disease modules. In addition, corticotropin releasing hormone (CRH) and natriuretic peptide precursor A (NPPA) are the only two LXNX formula target proteins with the low shortest path length (on average shorter than 3) to their respective CHD module and co-morbid disease and symptom gene groups. Conclusion: G-protein coupled receptor signaling pathway and cellular protein metabolic process are the key LXNX formula's pathways to treat CHD disease phenotypes, in which CRH and NPPA are the two key drug targets of LXNX formula. Further evidences from Chinese herb pharmacological databases indicate that Pinellia ternata (Banxia) has relatively strong adjustive functions on the two key targets.