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Aim To discuss the potential key mechanism of mogroside V in relieving pulmonary inflammation in asthmatic mice based on transcriptomics and proteomics. Methods Ovalbumin(OVA)was chosen to induce female BALB/C mouse asthma model, and the mice were treated with mogroside V to observe the pathological changes of lung tissues. Lung tissues in groups of natural control, ovalbumin control and mogroside V control were chosen for transcriptomic and proteomic analysis, and differential genes and proteins were screened for tendency analysis, followed by KEGG enrichment analysis for the potential genes and proteins. Results The results of lung morphological observation and HE revealed that mogroside V attenuated the OVA-induced pulmonary inflammation. Differential genes and proteins were selected from RNA-seq and DIA analysis. In the analysis of omics 454 genes increased in comparison between groups of natural control with ovalbumin control and decreased in comparison between groups of mogroside V control with ovalbumin control in 1 122 potential genes, and 111 genes were of opposite features. A total of 238 proteins increased in comparison between groups of natural control with ovalbumin control and decreased in comparison between groups of mogroside V control with ovalbumin control in 497 potential proteins, and 91 proteins were of opposite features. The PI3K/Akt signaling pathway was enriched from KEGG and tendency analysis of transcriptomics and proteomics. The key factors of Igha, Ighg1, PI3K and Akt increased in ovalbumin control group and decreased in mogroside V control group by the validation of molecualr biology experiments. Conclusions Transcriptomic and proteomic analysis exhibits that mogroside V relieves asthma in mice through inhibiting the activation of key factors including Lgha, Lgh1, PI3K and Akt, depressing the signaling pathway, attenuating pulmonary inflammation to reach the goal of moistening lung and relieve cough, which provides a reference for drug development of asthma.
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Objective:To explore the potential target and signaling pathway of Sijunzi Decoction in treating Colorectal Cancer (CRC) with network pharmacology.Methods:The Traditional Chinese Medicine System Platform (TCMSP) and Swiss Target Prediction database were adopted to establish the database of Sijunzi Decoction effective ingredients and targets. Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man, Drugbank and GeneCards were used to build the CRC target database. The common gene names of target proteins were checked in Uniprot database. The STRING database was applied to analyze the interactions between the targets and the DAVID was used for the enrichment analysis on gene ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The network topology results were analyzed by Cytoscape 3.7.1 software.Results:134 active compounds of Sijunzi Decoction were gained, and PPI includes 125 targets protein (TP53,AKT1,IL-6,et al.). The 516 cellular biological processes, 53 cellular component and 98 molecular function were obtained through GO biological process enrichment analysis. The result of KEGG pathway enrichment showed that PI3K-Akt signaling pathway, TNF signaling pathway and FoxO signaling pathway were the main pathways.Conclusion:Sijunzi Decoction is mainly used to treat CRC by regulating key proteins such as TP53,AKT1, IL-6 and interfering with signal pathways such as PI3K-Akt, TNF and FoxO, reflecting the characteristics of Sijunzi Decoction in the treatment of CRC with multi-component,multi-target and multi-pathway characters.
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Objective: To explore the potential material basis of Shengmai Injection for the treatment of coronavirus disease 2019 (COVID-19) through network pharmacology and molecular docking technology. Methods : The active compounds of Shengmai Injection were screened by TCMSP and BATMAN-TCM database. The action target was predicted by TCMSP and Targetnet online database, and the active component-action target network diagram was constructed by Cytoscape 3.7.1; Taking "coronavirus pneumonia" as the keyword, coronavirus-related disease targets were searched in GeneCards database and OMIM database. The common target was selected by intersection with the target of Shengmai Injection as the research target. The common target was imported into STRING database to obtain data, and then the protein-protein interaction network map was constructed in Cytoscape 3.7.1 software; The enrichment analysis of GO function and KEGG pathway was carried out by using R language to predict its action mechanism and construct the "component-target-pathway" network diagram; Molecular docking analysis of key targets was carried out by DiscoveryStudio 2.5 software. Results: A total of 22 active compounds were obtained from Shengmai Injection. They were DNOP, β-sitosterol, angeloylgomisin O, gomisin A, gomisin R, wuweizisu C, interiotherin B, changnanic acid, kadsulactone, kadsulignan B, neokadsuranic acid A, neokadsuranic acid B, neokadsuranic acid C, neokadsuranin, schisanlactone A, schisanlactone E, schizandronic acid, uridine, diosgenin, guanosine, N-trans-feruloyltyramine and stigmasterol. There were 224 corresponding targets and 16 common targets with COVID-19, namely CASP3, CASP8, PTGS2, BCL2, BAX, PRKCA, PTGS1, PIK3CG, F10, NOS3, DPP4, NOS2, TLR9, ACE, ICAM1 and PRKCE. The key targets were CASP3, PTGS2, NOS2, NOS3 and ICAM1. GO functional enrichment analysis showed that there were 771 entries for biological processes, 11 entries for cell composition and 79 items for molecular function. A total of 67 signal pathways were screened by KEGG pathway enrichment analysis, which were mainly related to AGE-RAGE signaling pathway in diabetic complications, apoptosis-multiple species, p53 signaling pathway, small cell lung cancer, and so on. The results of molecular docking showed that the components with better docking with the key targets were schisanlactone E, stigmasterol and N-trans-feruloyltyramine. Conclusion: The active compounds in Shengmai Injection, such as schisanlactone E, stigmasterol, N-trans-feruloyltyramine, can act on CASP3, PTGS2, NOS2, NOS3 and other targets to regulate multiple signaling pathways for anti-inflammatory, immune regulation, anti-shock and increasing blood oxygen saturation. This may play a role in the treatment of COVID-19.
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Chinese medicine emphasizes the unity, integrity and connection of things itself. It is a subject of clinical treatment based on the theory of "holistic concept" and "dialectical treatment", and the principle of "treating the same disease with different treatments" and "treating different diseases with the same treatment". Network pharmacology was used to explore the correlation between drugs and diseases from the whole, and its integrity, systematicness, and the characteristics of paying attention to drug and target interaction are consistent with the basic characteristics of traditional Chinese medicine, so network pharmacology is a new subject from the system level to reveal the role of traditional Chinese medicine on the body network regulation. Network pharmacology used computer simulations and various databases to screen targets for drug molecules to predict their signaling pathways and the mechanism of action, and the related software was used to visualize the compound-target-pathway network, and the key nodes were screened by topological parameter analysis. It can solve the limitation of "single component-single target point-single pathway" in traditional Chinese medicine research to some extent. The thinking methods, innovation points, international application of traditional Chinese medicine and the challenges were summarized in this paper, in order to improve the application of network pharmacology in the field of traditional Chinese medicine, and provide a new direction for the research of traditional Chinese medicine.
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Objective: To investigate the anti-hepatoma active components of Rhei Radix et Rhizoma and their molecular mechanisms through GEO database, integrative pharmacology platform and molecular docking technology. Method: The active ingredients of Rhei Radix et Rhizoma were screened by TCMIP and the corresponding targets of these components were predicted through TCMIP and Swisstarget databases. The hepatoma gene chip database was downloaded from GEO databases, and the differentially expressed genes between hepatocellular carcinoma (HCC) and normal liver tissue were analyzed by GEO2R. Based on the matching results of potential targets of Rhei Radix et Rhizoma and the targets of hepatoma, the key targets of Rhei Radix et Rhizoma against hepatoma were screened, and GO function enrichment and KEGG pathway enrichment analysis of the key targets were performed. Main components and core targets of Rhei Radix et Rhizoma against hepatoma were analyzed and screened by constructing PPI network, component-target network and traditional Chinese medicine-component-target-pathway network. Furthermore, the molecular docking between the core targets and the main active components was performed by Schrodinger-Maestro software to virtually verify their binding ability and analyze their binding mode. Result: A total of 20 anti-hepatoma active components of Rhei Radix et Rhizoma were collected and related 86 targets were obtained, including CDK1, AKR1C3, PTGS2, AR and CCNB1, etc. The results of GO functional enrichment mainly focused on the cell cycle, G2/M transition of mitotic cell cycle, oxidation-reduction process, drug reactions and steroid metabolism processes, etc. The results of KEGG pathway enrichment mainly involved cell cycle, cell senescence, complement system, arachidonic acid metabolism and bile metabolism, and these metabolic pathways were related to cell apoptosis, metastasis, inflammation and immunity. The results of molecular docking showed that 92.2% of the active components had good binding ability with the 10 core proteins, and the main combination forms mainly were hydrogen bonds, hydrophobic bonds, π-π bonds and cation-π. Conclusion: The active components of Rhei Radix et Rhizoma including rhein, emodin, chrysophanol-8-O-β-D-glucopyranoside, chrysophanol-1-O-β-D-glucoside and rhapontigenin can act on multiple targets such as CDK1, CCNB1, CYP2C9, MMP9 and PTGS2, by regulating signaling pathways related to cell apoptosis, metastasis, inflammation and immunity to play an anti-hepatoma effect.
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Objective: In this study, network pharmacology techniques and methods were used to screen the material basis, targets and pathways of the interaction between the pungent-aromatic herbs (Citri Reticulatae Pericarpium, Caryophylli Flos, and Foeniculi Fructus) and olfactory receptor. To explain the molecular mechanism of the expression of pungent properties of the pungent-aromatic herb. Methods: TCMSP database was used to collect the chemical components of Citri Reticulatae Pericarpium, Caryophylli Flos, and Foeniculi Fructus, in order to screen out the effective components and predict the related targets. All the proteins related to olfactory receptors were collected from UniProt database. The selected targets and ORs proteins were uploaded to the online STRING database for protein interaction analysis. The interaction network was drawn by using the software of Cytoscape software, and the key targets was further analyzed by GO functional enrichment analysis and KEGG pathway enrichment analysis. Results: Five active components of Citri Reticulatae Pericarpium, five of Caryophylli Flos and two of Foeniculi Fructus, 224 targets and 515 olfactory receptors were obtained. Protein interaction network showed 25 targets (such as GNB2, CHRM2, CHRM3, GNGT2, ADRA2A) were the key targets for ORs of three pungent-aromatic herbs. The enrichment analysis of GO and KEGG identified 31 biological processes and 78 signaling pathways. Conclusion: Olfactory receptors are mainly activated by volatile oil components of the pungent-aromatic herb. And the classical cAMP signaling pathway may not be the only one after the olfactory receptor activated, which may be involved in the expression of pungent properties of the pungent-aromatic herb together with several other signaling pathways, so as to produce "pungent flavor".
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Objective::To predict Xiao Xianxiongtang's treatment of coronary heart disease (CHD) targets and analyze their function by the network pharmacology method, and build ingredients-targets-channel network pharmacological model, in order to reveal potential pathways and mechanisms of Xiao Xianxiongtang for CHD treatment. Method::Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to obtain components, and CHD targets over Xiao Xianxiongtang were predicted by using Swiss Target Prediction reverse pharmacophore matching method. CHD targets which Food and Drug Administration (FDA) approved were collected from Therapeutic Target Database (TTD), Drugbank and Disease-gene Net databases (DisGeNET). Wenn diagram was used to obtain the correlation intersection.Target characteristics were analyzed with GEO2R online, Reactome FI was used to analyze the enrichment of target pathways, and Cytoscape software was used to construct the " component-target-pathway" network. Result::Network analysis showed that Xiao Xianxiongtang treated CHD by regulating 24 target proteins through 25 therapeutic components, and acting on 21 specific pathways and 4 biological processes.According to the multiple gene chip analysis of GEO2R online, there were up-down-regulated differences in the targets, including 11 up targets and 13 down targets. Conclusion::Xiao Xianxiongtang treats CHD by involving the biological processes through berberine and flavonoid groups of Coptidis Rhizoma, nucleosides and organic acids of Arum ternatum Thunb, stigmasterols and flavonoids of Trichosanthes kirilowii Maxim, such as gene expression, metabolism and protein metabolism, adjusting the gene expressions of relevant target proteins, regulating gene transcription pathways, such as biological oxidation reaction and lipid and lipoprotein metabolism, insulin-like growth factor binding protein (IGFBPs) of insulin-like growth factor (IGF) transshipment and intake, and the degradation of extracellular matrix signaling pathways.
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Objective: To explore the potential Q-markers between crude Schisandrae Chinensis Fructus (SCF) and vinegar-processed Schisandrae Chinensis Fructus (VSCF) based on multivariate statistical analysis and network pharmacology. Methods: UPLC-Q/TOF-MS was used to analyze the main lignans in SCF and VSCF, and the potential differences of chemical components (Q-markers) between SCF and VSCF were screened out by using multiple statistical methods. Furthermore, through network pharmacology and bioinformatics, the main action targets and pathways related to significantly different components were analyzed to construct the “component-target-pathway” network relationship and predict the potential quality markers between SCF and VSCF. Results: In this study, 40 different constituents of Schisandra chinensis between SCF and VSCF were screened, among which eight chemical markers had significant differences between SCF and VSCF. Five chemical constituents were identified and confirmed, namely 5-HMF, deoxyschizandrin and its isomer, schisandrin B, and schisantherin D. The other three chemical markers were speculated to be lignans by analyzing the first-and second-order mass spectrometry information. The results of network pharmacological analysis showed that the five potential quality markers identified were highly related to the main pharmacological effects of SCF. Finally, schisandrin B and 5-hydroxymethyl furfural were identified as the most representative potential quality markers. Conclusion: The results showed that the chemical composition of SCF had a series of complex changes. It was determined that schisandrin B and 5-hydroxymethyl furfural could be used as representative potential quality markers between SCF and VSCF. It is speculated that lignans may be the basis of the important effect of VSCF on liver protection.
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This paper aimed to predict and explore the mechanism of multiple components, targets and pathways of Siwu decoction for treatment of primary dysmenorrhea, and to establish a network pharmacological model of "compound-target-pathway-disease". According to the active ingredients in Siwu Decoction, Swiss Target Prediction server was used to predict the active component targets based on the reverse pharmacodynamic group matching method, and the primary dysmenorrhea targets approved by FDA were selected by database including DrugBank, OMIM and TTD. According to the enrichment analysis of the target pathways by using KEGG, the Cytoscape software was used to construct the network of "compound-target-pathway-disease" of Siwu Decoction. Network analysis showed that there were 20 active components involved in 114 pathways. And 16 components, 16 target proteins and 24 pathways were related to primary dysmenorrhea. Siwu Decoction may play a role in treating primary dysmenorrhea by acting on protein targets and pathways related to hormone regulation, central analgesia, spasmolysis,inflammation and immunity. This study revealed the potential active compounds and possible mechanism of Siwu Decoction for treatment of primary dysmenorrhea, providing theoretical references for further systematic laboratory experiments on effective compounds and action mechanism of Siwu Decoction.