Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Our research includes serial experimental technique and the protocol has these details which are necessary for readers to understand our methods. Our integrated method compensate for the disadvantage of network pharmacology and metabolomics, and can be used for the therapeutic meta-analysis of natural medicine. This method is used to screen the active compound from the company's ingredient and the suit for joggers with massive chemical composition, such as a traditional Chinese medicine.

To begin, select the active ingredients and key targets by searching the keyword phyllanthi fructus on the traditional Chinese medicine systems pharmacology database. To obtain the list of the candidate active ingredients and targets of fructus phyllanthi, or FP, search the keyword hyperlipidemia in the gene cards database, the online Mendelian inheritance in man database, and the therapeutic target database to obtain the respective candidate targets of hyperlipidemia. Download the spreadsheets of disease targets and save them to a folder and delete the repeated targets to obtain the hyperlipidemia targets list.

Then copy the lists of FP active ingredients, FP targets, and hyperlipidemia targets into a new spreadsheet. Use the data identify duplicates function in the toolbar to get intersection targets. Import the intersection target list into UniProt knowledge base to standardize the gene and protein names.

To construct a protein protein interaction network, paste the intersection target list of FP against hyperlipidemia in the list of names dialogue box of String database 11.5. Select homo sapiens in organisms. Click search and then continue.

Once the results are available, enable the high disconnected nodes in the network in advanced settings. Set the highest confidence to 0.900 in the minimum required interaction score. Then click the update button.

Click on exports in the title bar and download the short tabular text of the protein protein interaction network in PNG and TSV format. To construct a drug component disease target network, open Cytoscape 3.9.1 and import the TSV format file. Optimize the color, font, and side of the network nodes through the style bar in the control panel.

Using the analyze network function for network topology analysis, obtain hub genes in CytoHubba in Cytoscape, and establish the drug ingredient target disease network. To perform GO and KEGG enrichment analysis, open David bioinformatics resources. Click on start analysis and paste the target list into the left dialogue box.

Select official gene symbol in select identifier and homo sapiens in select species. Then enable gene list in list type and click on submit list. Next, click functional annotation clustering under analyze above gene list with one of David tools.

Enable GO term BP direct, GO term CC direct, GO term MF direct, and gene ontology for GO function enrichment analysis. Enable KEGG pathway in pathways for KEGG pathway enrichment analysis. Click on the functional annotation chart to display the results.

Use SIMCA P software for multivariate statistical analysis of the integral values obtained from LCMS findings. Use orthogonal partial least squares discriminant analysis, or OPLSDA, for the mean center data and the modeling of sample classes. After the OPLSDA test, consider the metabolites with integral variable importance in the projection, or VIP values, of greater than one and a P value of less than 0.05 from the student's T test as the potential differential metabolites.

Identify the disturbed metabolites and metabolic pathways by open database sources, including human metabolome, Kyoto encyclopedia of genes and genomes, and MetaboAnalyst 5.0. Visualize the result views by MetaboAnalyst 5.0 and the Wukong platform. Download the selected FP ingredients 3D structure from the traditional Chinese medicine systems pharmacology database.

Search the ingredient names in the chemical names search box and download the corresponding 3D structure files in MOL two format. Next, download the crystal structures of the key targets from the alpha fold protein structure database. Search the target names in the search box and download the corresponding crystal structure files in PDB format.

Then import ingredients and target structure files into AutoDock tools software. Click on edit and delete water to delete water molecules. Then, click edit hydrogens and add to add hydrogens.

Set the ingredients as the ligand and perform blind docking by selecting the whole targets as the receptor. Enter a value in the box behind center and size to adjust the newly developed space, making it possible to encompass the ligand and the receptor fully. Save the ligand and receptor files in PDBQT format.

Use AutoDock Vina to perform molecular docking. Set the receptor bar to the name of the receptor dot PDBQT and the ligand bar to the name of the ligand dot PDBQT. Obtain the optimal location for ligand binding to the receptor and record the binding energy value at the optimal position.

Import the docking files into the protein ligand interaction profiler to get the visual system model. Download the model files in PSC format and import them into PyMOL software to construct further visualization. Through network pharmacology, a total of 19 ingredients and 134 ingredient related targets of FP were found.

After matching the FP related targets with the hyperlipidemia related targets, 62 were identified as potential targets for FP against hyperlipidemia. The protein protein interaction network was constructed by String and Cytoscape. The GO enrichment results suggested that FP's biological processes and molecular function against hyperlipidemia were mainly related to gene expression and protein binding.

The KEGG enrichment proved that FP could intervene in lipid metabolism and atherosclerosis. OPLSDA analysis was used to explore the metabolite separation among the negative control, high fat diet and high dose FP mice groups, which showed that the same group samples clustered together and different group samples distinguished well. A total of 16 and six metabolites were identified as differential metabolites in FP affecting HFD mice in the plasma and liver.

Heat maps plotted by MetaboAnalyst 5.0 showed that all the differential metabolites in the plasma and liver were changed in the high fat diet group and most were reversed in the FP group. Tryptophan metabolism was affected significantly in the plasma. Taurine and hypotaurine metabolism was affected significantly in the liver.

Differential metabolites were imported into the MetScape plugin in Cytoscape and matched the hub genes identified in network pharmacology to collect the compound reaction enzyme gene networks. Further, the ingredients targets metabolites pathways network has been constructed. Molecular docking was used to analyze their ligand active site interactions.

This technology is a comprehensive method of starting the pharmacological genetical ingredients of traditional Chinese medicine. It provides a new idea for research involving drugs with complete ingredients.