Exploring the mechanism of action Xianlingubao Prescription in the treatment of osteoporosis by network pharmacology

In this study, the network pharmacology analysis method was used to explore the bioactive components and targets of Xianlinggubao (XLGB) and further elucidate its potential biological mechanisms of action in the treatment of osteoporosis (OP). The bioactive compounds and predictive targets of XLGB were collected from the traditional Chinese medicine systems pharmacology databases and analysis platform(TCMSP), the Encyclopeida of traditional Chinese medicine (ETCM), traditional Chinese medicine Databse@Taiwan, ChEMBL, STITCH, and SymMap database. The targets corresponding to OP were obtained by using Online Mendelian Inheritance in Man® (OMIM), GeneCards, the National Center for Biotechnology Information-Gene database. The XLGB-OP targets were obtained by intersecting with the targets of XLGB and OP. Protien-Protien interaciton (PPI) network was constructed using STRING online database and analyzed using Cytoscape 3.7.0 software to screen out hub genes. Gene ontology (GO) and KEGG enrichment analysis of the target in the PPI network was conducted using the ClusterProfiler package in R with adjusted p-value<0.05. A total of 65 XLGB bioactive compounds were screened corresponding to 776 XLGB targets and 2556 OP targets. The GO analysis and KEGG enrichment analyses suggested XLGB played a therapeutic roles in OP treatment via the interleukin-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, insulin resistance, Th-17 signaling pathway, etc. Five hub genes (AKT1, MAPK1, MAPK8, TP53, and STAT3) were screened using the degree algorithm, and molecular docking stimulation results showed that most bioactive compounds of XLGB had strong binding efficiency with hub genes. Overall, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical applications of XLGB.     

Uncovering the pharmacological mechanism of motherwort (Leonurus japonicus Houtt.) for treating menstrual disorders: A systems pharmacology approach

Leonurus japonicus (motherwort) is a traditional Chinese medicine that is widely used to treat menstrual disorders (MDs). However, the pharmacological mechanisms that underlie its clinical application remain unclear. In this study, a network pharmacology-based approach was used that integrated drug-likeness evaluation, oral bioavailability prediction, target exploration, network construction, bioinformatic annotation and molecular docking to investigate the mechanisms that underlie motherwort treatment for MDs. In total, 29 bioactive compounds were collected from 51 compounds in motherwort, which shared 17 common MDs-related targets. Network analysis indicated that motherwort played a therapeutic role in MDs treatment through multiple components that acted on multiple targets. Pathway enrichment analysis showed that the putative targets of motherwort were primarily involved in various pathways associated with the endocrine system, cancers, vascular system, and anti-inflammation process. Notably, five targets (i.e., AKT1, PTGS2, ESR1, AR and PPARG) were screened as hub genes based on a degree algorithm. Moreover, most of the bioactive components in motherwort had good binding ability with these genes, implying that motherwort could regulate their biological function. Collectively, this study elucidated the molecular mechanisms that underlay the efficiency of motherwort against MDs and demonstrated the potential of network pharmacology as an approach to uncover the action mechanism of herbal medicines.     

Anti-inflammatory or anti-SARS-CoV-2 ingredients in Huashi Baidu Decoction and their corresponding targets: Target screening and molecular docking study

Coronavirus disease 2019 (COVID-19) is a rapidly emerging infectious disease caused by SARS-CoV-2. Inflammatory factors may play essential roles in COVID-19 progression. Huashi Baidu Decoction (HSBD) is a traditional Chinese medicine (TCM) formula that can expel cold, dispel dampness, and reduce inflammation. HSBD has been widely used for the treatment of COVID-19. However, the active ingredients and potential targets for HSBD to exert anti-inflammatory or anti-SARS-CoV-2 effects remain unclear. In this paper, the active ingredients with anti-inflammatory or anti-viral effects in HSBD and their potential targets were screened using the Discovery Studio 2020 software. By overlapping the targets of HSBD and COVID-19, 8 common targets (FYN, SFTPD, P53, RBP4, IL1RN, TTR, SRPK1, and AKT1) were identified. We determined 2 key targets (P53 and AKT1) by network pharmacology. The main active ingredients in HSBD were evaluated using the key targets as receptor proteins for molecular docking. The results suggested that the best active ingredients Kaempferol2 and Kaempferol3 have the potential as supplements for the treatment of COVID-19.     

Network pharmacology analysis of Chaihu Lizhong Tang treating non-alcoholic fatty liver disease

BackgroundIn this study, the network pharmacological methods were used to predict the target of active components of Chaihu Lizhong Tang (CHLZT) in the treatment of non-alcoholic fatty liver disease (NAFLD).MethodThe active components of "CHLZT", their targets, and NAFLD related targets were screened by multiple databases, and the potential targets of "CHLZT" in the treatment of NAFLD were predicted. The active component-target network of "CHLZT" was constructed by Cytoscape software. The potential target of "CHLZT" for the treatment of NAFLD constructed protein-protein interaction (PPI) network in the Search Tool for the Retrieval of Interacting Genes Database (STRING). The hub genes of “CHLZT” in the treatment of NAFLD were screened by network topological parameters, and the results were verified by molecular docking. "ClusterProfiler" in R was used for Gene Ontology (GO) analysis and KEGG pathway enrichment analysis.ResultsOB ≥ 30 % and DL ≥ 0.18 were selected as the screening criteria of active components. A total of 83 active components and 456 targets were selected. Based on the evaluation of topological parameters of degree network, five hub genes for interaction with "CHLZT" therapy for NAFLD were screened, that is, AKT1, ALB, IL6, EGFR, and CASP3. The results of molecular docking showed that the active components in "CHLZT" had a good binding ability with the key targets. The enrichment analysis results showed that the treatment of NAFLD with "CHLZT" mainly involved in cofactor binding, protease binding, AGE-RAGE signaling pathway in diabetic complications, and IL-17 signaling pathway, which mediated the potential mechanism of "CHLZT" intervention in NAFLD.ConclusionThe molecular mechanism of "CHLZT" in the treatment of NAFLD indicated the synergistic features of multi-component, multi-target, and multi-pathway of traditional Chinese medicine, which provided an important scientific basis for further elucidating the mechanism of "CHLZT" in the treatment of NAFLD.     

A novel method for investigating the mechanism of the anti-rheumatoid arthritis activity of Angelicae pubescentis radix by integrating UHPLC–QTOF/MS and network pharmacology

The study aimed to establish a strategy to elucidate the in vivo constituents of Angelicae Pubescentis Radix (APR, also known as Duhuo) and reveal the probable mechanisms underlying its anti-rheumatoid arthritis activity. First recorded by Shennong Bencao Jing, APR is mainly used to treat Bi syndrome. Eleven absorbed components of APR were successfully identified using the rheumatoid arthritis (RA) rat model and the UHPLC–QTOF/MS technique. Two active ingredients (osthole and columbianadin) and five corresponding targets (PTGS1, PTGS2, RXRA, CCNA2 and ACHE) were found to construct a compound–protein interaction network in RA. In addition, a non-alcoholic fatty liver disease pathway, which was related to anti-RA activity, was eventually identified by KEGG analysis. Subsequently, molecular docking was performed by establishing a mixed matrix network, including the absorbed component, corresponding target and signaling pathway with two key compounds (osthole and columbianadin) and two important targets (PTGS2 and PTGS1). The result of molecular docking is in agreement with the network pharmacology.     

Exploring the mechanism of Cremastra Appendiculata (SUANPANQI) against breast cancer by network pharmacology and molecular docking

BackgroundSUANPANQI, the pseudo phosphorous stem of Cremastra appendiculata, is one of the most well-known traditional Chinese medicine, which has been shown to inhibit tumorigenesis in various human cancers. However, the underlying mechanism of SUANPANQI treatment against breast cancer (BRCA) remains unclear. In this study. we aim to investigate the bioactive compounds and mechanisms of SUANPANQI in the treatment of BRCA based on network pharmacology and molecular docking.MethodsThe compounds were collected from previous research. SwissADME was used to screen bioactive compounds. The targets corresponding to SUANPANQI and BRCA were obtained using MalaCards and SwissTargetPrediction. SUANPANQI-related and BRCA-related targets were found and then overlapped to get intersections, which represented potential anti-BRCA targets of SUANPANQI. The Cytoscape software was used to construct bioactive compounds targeting the BRCA network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the targets was extracted from the metascape database, then conducted using the Cluster Profiler package in R software. Protein-Protein interaction (PPI) network was constructed using the STRING online database and analyzed using Cytoscape software. Pivotal genes were screened using the topological analysis, survival analysis, and pathological stage analysis. Molecular docking analysis was used to verify whether the bioactive compounds had a definite affinity with the pivotal targets.ResultsSixty-five bioactive compounds of SUANPANQI were involved with 225 predicted BRCA targets. Then, a compound-target network and a PPI network were constructed. The GO analysis and KEGG enrichment analysis suggested that SUANPANQI worked against BRCA via PI3K-Akt, Ras, FoxO, Rap1, and ErbB signaling pathways, etc. After topological analysis, survival analysis, and pathological stage analysis of the SUANPANQI potential targets against BRCA, 6 pivotal target genes (AR, HSP90AA1, MMP9, PGR, PTGS2, TNF) that were highly responsible for the therapeutic effects of SUANPANQI against BRCA were obtained. Molecular docking results showed that 6 bioactive compounds of SUANPANQI had strong binding efficiency with the 6 pivotal genes.ConclusionsThe present study clarifies the mechanism of SUANPANQI against BRCA through multiple targets and pathways, and provides evidence to support its clinical use.     

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BackgroundThe underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).Material and methodsWe obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the “limma” package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.ResultsThe preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.ConclusionsWe identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.     

An integrated approach based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry,network pharmacology,and molecular docking to study the key effective compounds and mechanism of action of Platycodi Radix in the treatment of chronic obstructive pulmonary disease

Platycodi Radix (PR) is a valuable herb that is widely used in the treatment of chronic obstructive pulmonary disease in clinics. However, the mechanism of action for the treatment of chronic obstructive pulmonary disease remains unclear due to the lack of in vivo studies. Our study established a novel integrated strategy based on ultra-performance liquid chromatography coupled with time-of-flight mass spectrometry, network pharmacology, and molecular docking to systematically analyze the tissue distribution and active compounds of PR in vivo and the therapeutic mechanism of chronic obstructive pulmonary disease. First, tissue distribution studies have shown that the lung is the organ with the highest distribution of PR compounds. Subsequently, network pharmacology results showed that the tumor necrosis factor signaling pathway, interleukin-17 signaling pathway, and mitogen-activated protein kinase signaling pathway were the critical mechanisms of PR against chronic obstructive pulmonary disease. Ultimately, molecular docking results showed that the key targets were stably bound to the corresponding active compounds of PR. Our study is of great significance for the screening of the key effective compounds and the study of the mechanism of action in traditional Chinese medicine and provides data to support the further development and utilization of PR.     

An integrated approach of high-performance liquid chromatography–mass spectrometry-based chemical profiling,network pharmacology,and molecular docking to reveal the potential mechanisms of Qishen Gubiao granules for treating coronavirus disease 2019

Qishen Gubiao granules, a traditional Chinese medicine preparation composed of nine herbs, have been widely used to prevent and treat coronavirus disease 2019 with good clinical efficacy. In the present study, an integrated strategy based on chemical profiling followed by network pharmacology and molecular docking was employed, to explore the active components and potential molecular mechanisms of Qishen Gubiao granules in the therapy of coronavirus disease 2019. Using the ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry technique, a total of 186 ingredients corresponding to eight structure types in Qishen Gubiao preparation were identified or structurally annotated with the elucidation of the fragmentation pathways in the typical compounds. The network pharmacology analysis screened 28 key compounds including quercetin, apigenin, scutellarein, luteolin and naringenin acting on 31 key targets, which possibly modulated signal pathways associated with immune and inflammatory responses in the treatment of coronavirus disease 2019. The molecular docking results observed that the top 5 core compounds had a high affinity for angiotensin-converting enzyme 2 and 3-chymotrypsin-like protease. This study proposed a reliable and feasible approach for elucidating the multi-components, multi-targets, and multi-pathways intervention mechanism of Qishen Gubiao granules against coronavirus disease 2019, providing a scientific basis for its further quality evaluation and clinical application.     

Network pharmacology analysis of pharmacological mechanisms underlying the anti-type 2 diabetes mellitus effect of guava leaf

The current study aimed to explore the anti-type 2 diabetes mellitus (T2DM) mechanism of guava leaf based on network pharmacology. The compounds contained in guava leaf was summarized from the literature, and a series of databases was used to identify the active components and corresponding potential targets. The intersection between diabetes-associated genes searched in the GeneCard database and the predicted targets of guava leaf active components was defined as target genes, which were then used to construct a “compound-active components-target genes” pharmacological network. The biological functions and pathway enrichment analyses of target genes were performed in KOBAS 3.0. The differential expression analysis of GSE76894 was performed to obtain the differential expressed genes (DEGs) in T2DM patients by comparing non-diabetic controls. Finally, the intersection between DEGs and target genes were named key genes, and the representative pathways in which these genes were involved were drawn through KEGG Mapper. We found that the active components of guava leaf may regulate the PI3K-AKT signaling pathway, T2DM regulation process, and insulin resistance pathway, which was evidenced by KEGG pathway analysis of key genes. These results implied that guava leaf has a potential anti-T2DM property and its mode of action may be exerted via regulating insulin secretion and reducing blood sugar level.     

中药补气与活血分子作用机理的计算机模拟

采用分子相似性分析、分子对接和生物网络技术等计算机模拟方法对治疗冠心病的中药活血与补气的分子作用机理进行研究. 结果表明, 计算机模拟方法可以体现化合物的结构差异, 并提示中药中化学成分与相关靶标相互作用的分子机理, 而生物网络的构建和分析可以将化学成分的聚类、差异, 以及有效成分与相关靶标的复杂分子作用机理可视化.     

Antitumor effect of guava leaves on lung cancer: A network pharmacology study

Guava is known for its hypoglycemic, antivirus, antibacterial, anti-inflammatory, antioxidant, and antitumor properties. In this study, triterpenoids, sesquiterpenes, and flavonoids were examined as potential targets of constituents of guava leaves. Our study was aimed to reveal the antitumor mechanism and construct the network pharmacology network of guava leaf constituents and lung cancer. The potential targets of guava leaf constituents were searched in target databases, while the disease genes were searched in the GeneCards database. The common targets of drugs and diseases were screened out. A network map was constructed by the Cytoscape software, and the GO and KEGG pathways were analyzed. The existing cases were studied by SystemsDock molecular docking and cBioPortal tumor database study. Among the 66 chemical constituents of guava leaves, 153 of their targets were the lung cancer genes involved in many signaling pathways, such as the PI3K-Akt signaling pathway, in small cell lung cancer and non-small cell lung cancer. There was a binding activity between ligand compounds and receptor proteins. Guava leaves inhibited tumor through a gene regulatory network, and may play an important role in gene-targeting therapy. Through network pharmacology, we found that guava leaves had potential targets that interacted with various tumors, regulating the signaling pathways of cancers. This study preliminarily verified the pharmacological basis and the mechanism of the antitumor effect of guava leaves, providing a foundation for further research.     

Classification of diterpenoid alkaloids from Aconitum kusnezoffii Reichb. by liquid chromatography-tandem mass spectrometry-based on molecular networking

Trace amounts of components in traditional Chinese medicine are considered pharmacological active substances used for treating many serious diseases. However, purifying all the trace substances and making clear their structures are not easy. In this context, high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry based molecular networking was applied to investigate the chemical constituents of the roots of Aconitum kusnezoffii Reichb., which led to the identification of 33 nodes in different groups ( N1 – N33 ). Based on the excremental fragmentation pathway of known diterpenoid alkaloids ( 1 – 9 ) and comparisons of characteristic ions and characteristic loss of analogs in literature, the structures of unknown ions were deduced. This work lays a foundation for the evaluation of the clinical basis and mechanism of traditional Chinese medicine from the aspects of chemistry. In this paper, the method speculation of unknown natural products by means of molecular network method is expected to be applied in the discovery and change law of relevant active components in clinical pharmacology and the change of complex systems caused by trace active compounds.     

基于气相色谱-质谱和网络药理学探讨紫斑罂粟壳挥发油镇咳祛痰平喘的作用机制

为研究紫斑罂粟壳挥发油镇咳化痰平喘的活性成分及作用机制,采用气相色谱-质谱（GC-MS)联用法分析罂粟壳挥发油成分,并结合Pubchem和Swiss Target Prediction数据库筛选活性成分靶点. 其中,在GeneCards数据库中检索镇咳、祛痰、平喘相关的靶点,利用在线Venn取交集基因,Cytoscap 3.7.1软件构建成分-靶点-疾病网络图筛选关键成分,String数据库构建蛋白互作网络筛选核心作用靶点,DAVID数据库进行GO功能和KEGG通路富集分析. 结果表明,GC-MS鉴别出紫斑罂粟壳挥发油中28个化学成分,虚拟筛选获得20个活性成分对应的259个靶点. 网络药理学预测紫斑罂粟壳挥发油通过肿瘤坏死因子（TNF）、磷酸化蛋白激酶（AKT1）、SRC蛋白激酶（SRC）、表皮生长因子受体（EGFR）和丝裂原活化蛋白激酶 1（MAPK1）等关键靶点,进而协同调控肿瘤通路,神经配体-受体相互作用、PI3K-Akt信号通路等多条信号通路发挥镇咳祛痰、平喘的治疗作用. 研究为后续试验研究罂粟壳挥发油的药效物质及作用机制提供参考.     

Uncovering the anticancer mechanism of petroleum extracts of Farfarae Flos against Lewis lung cancer by metabolomics and network pharmacology analysis

Lung cancer shows the highest incidence rate in the world. Thus, it has become increasingly important to find therapeutic drugs to treat lung cancer. Farfarae Flos (FF) has been used in traditional Chinese medicine to treat pulmonary diseases such as cough, bronchitis and asthmatic disorders. In this study, the anti-proliferation effects of petroleum extracts of FF (PEFF) on Lewis lung cancer cells and the corresponding mechanisms were studied using cell metabolomics. Fifteen differential metabolites in the cell extracts and the corresponding medium related to the anti-proliferation effect of PEFF were identified, which were probably involved in pyruvate metabolism and glycine, serine and threonine metabolism. For the cellular uptake compounds in PEFF, six metabolites derived from two prototype compounds were also tentatively identified by UHPLC-Q-Orbitrap high-resolution MS. Network pharmacology analysis demonstrated that the anti-proliferation mechanism of PEFF was also probably related to the target genes, including, Aurora-A, glutathione S-transferase Mu 1 (GSTM1), glutathione S-transferase P 1 (GSTP1), progesterone receptor and heme oxygenase-1 (HO-1), and further associated with the proteoglycans and PI3K/Akt signaling pathway. Cell metabolomics and network pharmacology analysis provided a holistic method to investigate the anti-proliferation mechanisms of PEFF. However, further studies were still needed to validate the potential target genes, pathways and active compounds in PEFF.     

Investigation Driven by Network Pharmacology on Potential Components and Mechanism of DGS,a Natural Vasoprotective Combination,for the Phytotherapy of Coronary Artery Disease

Phytotherapy offers obvious advantages in the intervention of Coronary Artery Disease (CAD), but it is difficult to clarify the working mechanisms of the medicinal materials it uses. DGS is a natural vasoprotective combination that was screened out in our previous research, yet its potential components and mechanisms are unknown. Therefore, in this study, HPLC-MS and network pharmacology were employed to identify the active components and key signaling pathways of DGS. Transgenic zebrafish and HUVECs cell assays were used to evaluate the effectiveness of DGS. A total of 37 potentially active compounds were identified that interacted with 112 potential targets of CAD. Furthermore, PI3K-Akt, MAPK, relaxin, VEGF, and other signal pathways were determined to be the most promising DGS-mediated pathways. NO kit, ELISA, and Western blot results showed that DGS significantly promoted NO and VEGFA secretion via the upregulation of VEGFR2 expression and the phosphorylation of Akt, Erk1/2, and eNOS to cause angiogenesis and vasodilation. The result of dynamics molecular docking indicated that Salvianolic acid C may be a key active component of DGS in the treatment of CAD. In conclusion, this study has shed light on the network molecular mechanism of DGS for the intervention of CAD using a network pharmacology-driven strategy for the first time to aid in the intervention of CAD.