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.     

Exploration of the mechanism of Zisheng Shenqi decoction against gout arthritis using network pharmacology

BackgroundIn this study, the network pharmacological methods were used to predict the target of effective components of compounds in Zisheng Shenqi Decoction (ZSD, or Nourishing Kidney Qi Decoction) in the treatment of gouty arthritis (GA).MethodThe main effective components and corresponding key targets of herbs in the ZSD were discerned through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis (TCMSP), Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine (BATMAN-TCM) database. UniProt database and Swiss Target Prediction (STP) database was used to rectify and unify the target names and supply the target information. The targets related to GA were obtained by using GeneCards database. After we discovered the potential common targets between ZSD and GA, the interaction network diagram of “ZSD-component-GA-target” was constructed by Cytoscape software (Version 3.7.1). Subsequently, the Protein-protein interaction (PPI) network of ZSD effective components-targets and GA-related targets was constructed by Search Tool for the Retrieval of Interacting Genes Database (STRING). Bioconductor package “org.Hs.eg.db” and “cluster profiler” package were installed in R software (Version 3.6.0) which used for Gene Ontology analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis.Results146 components and 613 targets of 11 herbal medicines in the ZSD were got from TCMSP database and BATMAN-TCM database. 987 targets of GA were obtained from GeneCards database. After intersected and removed duplications, 132 common targets between ZSD and GA were screened out by Cytoscape software (Version 3.7.1). These common targets derived from 81 effective components of 146 components, such as quercetin, stigmasterol and kaempferol. They were closely related to anti-inflammatory, analgesic and anti oxidative stress and the principal targets comprised of Purinergic receptor P2X, ligand-gated ion channel 7 (P2x7R), Nod-like receptor protein 3 (NLRP3) and IL-1β. GO enrichment analysis and KEGG pathway enrichment analysis by R software (Version 3.6.0) showed that the key target genes had close relationship with oxidative stress, reactive oxygen species (ROS) metabolic process and leukocyte migration in aspects of biological process, cell components and molecular function. It also indicated that ZSD could decrease inflammatory reaction, alleviate ROS accumulation and attenuate pain by regulating P2 × 7R and NOD like receptor signaling pathway of inflammatory reaction.ConclusionA total of 81 effective components and 132 common target genes between ZSD and GA were screened by network pharmacology. The PPI network, GO enrichment analysis and KEGG pathway enrichment analysis suggested that ZSD can exerte anti-inflammatory and analgesic effects on the treatment of GA by reducing decreasing inflammatory reaction, alleviating ROS accumulation, and attenuating pain. The possible molecular mechanism of it mainly involved multiple components, multiple targets and multiple signaling pathways, which provided a comprehensive understanding for further study. In general, the network pharmacological method applied in this study provides an alternative strategy for the mechanism of ZSD in the treatment of GA.     

Network Pharmacology-Based Study to Uncover Potential Pharmacological Mechanisms of Korean Thistle (Cirsium japonicum var. maackii (Maxim.) Matsum.) Flower against Cancer

Cirsium japonicum var. maackii (Maxim.) Matsum. or Korean thistle flower is a herbal plant used to treat tumors in Korean folk remedies, but its essential bioactives and pharmacological mechanisms against cancer have remained unexplored. This study identified the main compounds(s) and mechanism(s) of the C. maackii flower against cancer via network pharmacology. The bioactives from the C. maackii flower were revealed by gas chromatography-mass spectrum (GC-MS), and SwissADME evaluated their physicochemical properties. Next, target(s) associated with the obtained bioactives or cancer-related targets were retrieved by public databases, and the Venn diagram selected the overlapping targets. The networks between overlapping targets and bioactives were visualized, constructed, and analyzed by RPackage. Finally, we implemented a molecular docking test (MDT) to explore key target(s) and compound(s) on AutoDockVina and LigPlot+. GC-MS detected a total of 34 bioactives and all were accepted by Lipinski’s rules and therefore classified as drug-like compounds (DLCs). A total of 597 bioactive-related targets and 4245 cancer-related targets were identified from public databases. The final 51 overlapping targets were selected between the bioactive targets network and cancer-related targets. With Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, a total of 20 signaling pathways were manifested, and a hub signaling pathway (PI3K-Akt signaling pathway), a key target (Akt1), and a key compound (Urs-12-en-24-oic acid, 3-oxo, methyl ester) were selected among the 20 signaling pathways via MDT. Overall, Urs-12-en-24-oic acid, 3-oxo, methyl ester from the C. maackii flower has potent anti-cancer efficacy by inactivating Akt1 on the PI3K-Akt signaling pathway.     

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.     

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.     

Homotherapy for heteropathy active components and mechanisms of Qiang-Huo-Sheng-Shi decoction for treatment of rheumatoid arthritis and osteoarthritis

Qiang-Huo-Sheng-Shi decoction (QHSSD), a classic traditional Chinese herbal formula, which has been reported to be effective in rheumatoid arthritis (RA) and osteoarthritis (OA). However, the concurrent targeting mechanism of how the aforementioned formula is valid in the two distinct diseases OA and RA, which represents the homotherapy-for-heteropathy principle in traditional Chinese medicine (TCM), have not yet been clarified. In the present study, network pharmacology was adopted to analyze the potential molecular mechanism, and therapeutic effective components of QHSSD on both OA and RA. A total of 153 active ingredients in QHSSD were identified, 142 of which associated with 59 potential targets for the two diseases were identified. By constructing the protein-protein interaction network and the compound-target-disease network, 72 compounds and 10 proteins were obtained as the hub targets of QHSSD against OA and RA. The hub genes of ESR1, PTGS2, PPARG, IL1B, TNF, MMP2, IL6, CYP3A4, MAPK8, and ALB were mainly involved in osteoclast differentiation, the NF-κB and TNF signaling pathways. Moreover, molecular docking results showed that the screened active compounds had a high affinity for the hub genes. This study provides new insight into the molecular mechanisms behind how QHSSD presents homotherapy-for-heteropathy therapeutic efficacy in both OA and RA. For the first time, a two-disease model was linked with a TCM formula using network pharmacology to identify the key active components and understand the common mechanisms of its multi-pathway regulation. This study will inspire more innovative and important studies on the modern research of TCM formulas.     

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.     

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.     

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.     

Defining the Role of Isoeugenol from Ocimum tenuiflorum against Diabetes Mellitus-Linked Alzheimer’s Disease through Network Pharmacology and Computational Methods

The present study involves the integrated network pharmacology and phytoinformatics-based investigation of phytocompounds from Ocimum tenuiflorum against diabetes mellitus-linked Alzheimer’s disease. It aims to investigate the mechanism of the Ocimum tenuiflorum phytocompounds in the amelioration of diabetes mellitus-linked Alzheimer’s disease through network pharmacology, druglikeness and pharmacokinetics, molecular docking simulations, GO analysis, molecular dynamics simulations, and binding free energy analyses. A total of 14 predicted genes of the 26 orally bioactive compounds were identified. Among these 14 genes, GAPDH and AKT1 were the most significant. The network analysis revealed the AGE-RAGE signaling pathway to be a prominent pathway linked to GAPDH with 50.53% probability. Upon the molecular docking simulation with GAPDH, isoeugenol was found to possess the most significant binding affinity (−6.0 kcal/mol). The molecular dynamics simulation and binding free energy calculation results also predicted that isoeugenol forms a stable protein–ligand complex with GAPDH, where the phytocompound is predicted to chiefly use van der Waal’s binding energy (−159.277 kj/mol). On the basis of these results, it can be concluded that isoeugenol from Ocimum tenuiflorum could be taken for further in vitro and in vivo analysis, targeting GAPDH inhibition for the amelioration of diabetes mellitus-linked Alzheimer’s disease.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.     

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BackgroundIt is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.MethodsTwo expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.Results1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.ConclusionsIn summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.     

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.     

Down regulation of lactotransferrin enhanced radio-sensitivity of nasopharyngeal carcinoma

IntroductionIt is reported that LTF had a radiation resistance effect, and its expression in nasopharyngeal carcinoma (NPC) was significantly down-regulated. However, the mechanism of down-regulated LTF affecting the sensitivity of radiotherapy has remained elusive.MethodsWe re-analyzed the microarray data GSE36972 and GSE48503 to find differentially expressed genes (DEGs) in NPC cell line 5−8 F transfected with LTF or vector control, and the DEGs between radio-resistant and radio-sensitive NPC cell lines. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and protein-protein interaction network (PPI) analysis of DEGs were performed to obtain the node genes. The target genes of miR-214 were also predicted to complement the mechanism associated with radiotherapy resistance because it could directly target LTF.ResultsThis study identified 1190 and 1279 DEGs, respectively. GO and KEGG analysis showed that apoptotic process and proliferation, PI3K-Akt signaling pathway were significantly enriched pathways. Four nodes (DUSP1, PPARGC1A, FOS and SMARCA1) associated with LTF were screened. And 42 target genes of miR-214 were cross-linked to radiotherapy sensitivity.ConclusionsThe present study demonstrates the possible molecular mechanism that the down-regulated LTF enhances the radiosensitivity of NPC cells through interaction with DUSP1, PPARGC1A, FOS and SMARCA1, and miR-214 as its superior negative regulator may play a role in regulating the radiotherapy effect.     

A network-based pharmacology study of active compounds and targets of Fritillaria thunbergii against influenza

Seasonal and pandemic influenza infections are serious threats to public health and the global economy. Since antigenic drift reduces the effectiveness of conventional therapies against the virus, herbal medicine has been proposed as an alternative. Fritillaria thunbergii (FT) have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses. Herein, we used a network pharmacology-based strategy to predict potential compounds from Fritillaria thunbergii (FT), target genes, and cellular pathways to better combat influenza and influenza-associated diseases. We identified five compounds, and 47 target genes using a compound-target network (C-T). Two compounds (beta-sitosterol and pelargonidin) and nine target genes (BCL2, CASP3, HSP90AA1, ICAM1, JUN, NOS2, PPARG, PTGS1, PTGS2) were identified using a compound-influenza disease target network (C-D). Protein-protein interaction (PPI) network was constructed and we identified eight proteins from nine target genes formed a network. The compound-disease-pathway network (C-D-P) revealed three classes of pathways linked to influenza: cancer, viral diseases, and inflammation. Taken together, our systems biology data from C-T, C-D, PPI and C-D-P networks predicted potent compounds from FT and new therapeutic targets and pathways involved in influenza.     

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.     

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.