Integrating network pharmacology and bioinformatics analysis to explore the mechanism of Yupingfengsan in treating lung adenocarcinoma

Identification of genetic biomarkers, drug targets and agents for respiratory diseases utilising integrated bioinformatics approaches

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Identifying absorbable bioactive constituents of yupingfeng powder acting on COVID-19 through integration of UPLC-Q/TOF-MS and network pharmacology analysis

Objective

Yupingfeng Powder (YPF), a kind of preventative patent medicine, is chosen for treatment of coronavirus disease 2019 (COVID-19) due to its high frequency application in respiratory tract diseases, such as chronic obstructive pulmonary disease, asthma, respiratory tract infections, and pneumonia, with the advantage of reducing the relapse rate and the severity. However, the active components of YPF and the mechanisms of components affecting COVID-19 are unclear. This study aimed to determine active constituents and elucidate its potential mechanisms.

Methods

Ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q/TOF-MS) and liquid chromatography-triple quadrupole mass spectrometry (LC-QQQ-MS) were used to determine the components and absorbable constituents of YPF. Secondly, TCMSP, Drugbank, Swiss and PharmMapper were used to search the targets of absorbable bioactive constituents of YPF, and the targets of COVID-19 were identified based on GeneCards and OMIM databases. STRING database was used to filter the possible inter-protein interactions. Thirdly, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed to identify molecular function and systemic involvement of target genes.

Results

A total of 61 components of YPF and 36 absorbable constituents were identified through UPLC-Q/TOF-MS. Wogonin, prim-O-glucosylcimifugin, 5-O-methylvisamminol, astragaloside IV and 5-O-methylvisamminol (hydroxylation) were vital constituents for the treatment of COVID-19, and RELA, TNF, IL-6, MAPK14 and MAPK8ere recognized as key targets of YPF. The major metabolic reactions of the absorbed constituents of YPF were demethylation, hydroxylation, sulfation and glucuronidation. GO and KEGG pathway analysis further showed that the most important functions of YPF were T cell activation, response to molecule of bacterial origin, cytokine receptor binding, receptor ligand activity, cytokine activity, IL-17 signaling pathway, Chagas disease, lipid and atherosclerosis, etc.

Conclusion

The approach of combining UPLC-Q/TOF-MS with network pharmacology is an effective tool to identify potentially bioactive constituents of YPF and its key targets on treatment of COVID-19.

Network Pharmacology Study to Interpret Signaling Pathways of Ilex cornuta Leaves against Obesity

Ilex cornuta Leaves (ICLs) are a representative and traditional prescription for controlling obesity. Nevertheless, the corresponding therapeutic compounds and related pharmacological mechanisms of such medication remain undocumented. The compounds from ICLs were identified by gas chromatography-mass spectrum (GC-MS), and SwissADME confirmed their physicochemical properties. Next, the target proteins related to compounds or obesity-associated proteins were retrieved from public databases. RPackage constructed the protein–protein interaction (PPI) network, a bubble chart, and signaling pathways–target proteins–compounds (STC) network. Lastly, a molecular docking test (MDT) was performed to evaluate the affinity between target proteins and ligands from ICLs. GC-MS detected a total of 51 compounds from ICLs. The public databases identified 219 target proteins associated with selective compounds, 3028 obesity-related target proteins, and 118 overlapping target proteins. Moreover, the STC network revealed 42 target proteins, 22 signaling pathways, and 39 compounds, which were viewed to be remedially significant. The NOD-like receptor (NLR) signaling pathway was considered a key signaling pathway from the bubble chart. In parallel, the MDT identified three target proteins (IL6, MAPK1, and CASP1) on the NLR signaling pathway and four compounds against obesity. Overall, four compounds from ICLs might show anti-obesity synergistic efficacy by inactivating the NLR signaling pathway.

Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of Buyang Huanwu Decoction in the Treatment of Ischemic Stroke

Methods

The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SwissTargetPrediction, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-target network, and herb-target-pathway network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification.

Results

A total of 235 putative targets were obtained from 59 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved in TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-sitosterol, kaempferol, stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets.

Conclusions

Our study demonstrated that BHD exerted the effect of treating IS by regulating multitargets and multichannels with multicomponents through the method of network pharmacology and molecular docking.

Exploration in the mechanism of rhubarb for the treatment of hyperviscosity syndrome based on network pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Hyperviscosity syndrome (HVS) is a major risk factor for thrombotic diseases. Rhubarb, well-known as a traditional Chinese medicine, exhibits multiple pharmacological activities, especially for promoting blood circulation to remove blood stasis (PBRB), which has been become a functional health food for decreasing the risk of cardiovascular diseases. However, due to the complexity of rhubarb components, it is still difficult to clarify the specific targets of effective substances in PBRB, and the pharmacodynamic mechanism needs to be further probed.

MATERIALS AND METHODS

The "compound-target-cell-disease" network analysis was initially used to predict potential targets and bioactive compounds. The effect of rhubarb for the treatment of HVS was examined by histopathology and biochemical assays based on the HVS rat model.

RESULTS

Through the "compound-target-cell-disease" network analysis, eight potential therapeutic targets were eventually screened out, and platelets were predicted as the main effector cells of rhubarb in PBRB. Among targets coagulation factor II (prothrombin, F2) and fibrinogen gamma chain (FGG) were closely related to platelets, and five compounds associated with F2 and FGG were predicted including emodin-8-O-beta-D-glucopyranoside (Emo), physcion-8-O-beta-D-glucopyranoside (Phy), procyanidin B-5,3'-O-gallate, torachrysone-8-O-beta-D-(6'-oxayl)-glucoside and epicatechin. Furthermore, thoracic aorta histopathology and biochemical examinations showed middle dose of rhubarb (0.42 g/kg/day) significantly ameliorated pathological changes, hemorheology parameters, as well as levels of representative biomarkers such as plasma P-selectin (P-sel) and thromboxane (TXB2) in platelet activation compared to HVS rat model, whose effects were comparable to the positive drug aspirin or even better. Finally, it was further validated F2 and FGG as the major effective targets of rhubarb as well as its two active ingredients Emo and Phy in PBRB.

CONCLUSIONS

This study may provide an innovative way and scientific information to further understand the main effective components of rhubarb and its mechanisms about targets of F2 and FGG in PBRB, especially the new therapeutic target FGG, which also provide a basis for establishing a quality control for rhubarb by bioassays that could correlate the clinical efficacy and its mechanism.