Identification of candidate components in Angelicae Pubescentis Radix
The medicinal components of Angelicae Pubescentis Radix were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) .
Screening strategy for bioactive components of Angelicae Pubescentis Radix
Traditional Chinese medicines (TCMs) need to be distributed rapidly in the process of absorption, distribution, metabolism, and excretion (ADME). In ADME, oral bioavailability (OB) is one of the most representative pharmacokinetic parameters; thus, substances with OB ≥30% have higher druggability.
Druglikeness (DL) is used as a qualitative concept to estimate the drug properties of molecules in drug design. The DL index can be used to quickly screen active substances, and substances with DL ≥0.18 are considered to have druggability.
Therefore, the compounds of Angelicae Pubescentis Radix with OB ≥30% and DL ≥0.18 were chosen as the active ingredients in this experiment.
Prediction of active component targets of Angelicae Pubescentis Radix
All human protein and gene data were downloaded from the global protein resource database (Uniprot, http://www.Uniprot.org). Active component targets retrieved from the TCMSP database were compared with the Uniprot database to obtain standard gene names.
Collection and arrangement of RA targets
The human genes (GeneCard, http//www.GeneCard.org), Online Mendelian Inheritance in Man (OMIM, http://www.omim.org), and DrugBank (http://www.go.drugbank.com) databases were searched using the keyword “rheumatoid arthritis”  to obtain target genes of RA. The results were collated for screening and comparison to select the intersection. The data collated from the Uniprot database were matched with the intersection to obtain the targets for RA.
Construction of the protein-protein interaction network
The target genes of the components of Angelicae Pubescentis Radix and RA were intersected, and the intersected genes were input into the STRING online database (https://string-db.org) to construct a target network using the “organization” option. The minimum required interaction score was set to have medium reliability of 0.400. No maximum number of associates was set. The association importance and interaction of each node were determined using the difference in the degree values.
Gene ontology and genomes pathway enrichment analysis for RA-related targets of Angelicae Pubescentis Radix
The Metascape database and were used to conduct a functional enrichment analysis of the Gene Ontology (GO) and a pathway enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway with the background set as Homo sapiens to clarify the roles of the target proteins in the gene functions and signaling pathways [8,9,10,11]. Functional annotation and pathways of potential genes were visualized using biological processes (BP), molecular functions (MF), and cellular components (CC). An adjusted P-value of < 0.05 was set as the threshold value, and statistical significance was set at a P-value < 0.05.
Construction and analysis of network
To further visualize the molecular mechanisms of the active ingredients against RA, the obtained data were organized into network and type files that were imported into Cytoscape version 3.7.1 (NIH Biomedical Technology Research Center, Bethesda, MD, USA), an open-source software available at http://cytoscape.org . The software generated a network of active ingredients of the drugs, as well as target and disease pathways. In a graphical network, nodes represented components, targets, and pathways, while interactions between nodes were represented by edges. The “analyze network” function was used to calculate the degree between the active component and target. Larger degree values represented more important active components.
Molecular docking analysis
Molecular docking includes three steps: (a) preparation of ligands, (b) preparation of macromolecules (targets) and determination of their active sites, and (c) ligand-target docking.
Here, a two-dimensional structure of β-sitosterol was obtained using PubChem software (National Institutes of Health, Bethesda, MD, USA) and stored in the SDF format that was processed using ChemBio3D (CambridgeSoft, Waltham, MA, USA) to obtain a three-dimensional structure with minimum energy.
The target’s crystal structure was obtained from the Protein Data Bank, and the water molecules and hydrogenation were removed using PyMOL software (Schrodinger Company, New York, NY, USA).
Autodock Tools software (Scripps Research Institute, La Jolla, CA, USA) was used to convert the three-dimensional structure of β-sitosterol and crystal structure of the target into the pdbqt format. Molecular docking was conducted with β-sitosterol, a key active component in Angelicae Pubescentis Radix, and the 10 genes with the highest degree values in the protein-protein interaction (PPI) network. Molecular docking was assessed using Autodock Vina software (Scripps Research Institute) to evaluate the binding of β-sitosterol and the target based on the binding energy standards, and visual processing was performed using PyMOL software.
MH7A rheumatoid arthritis fibroblast cells (lot number 21112414) were purchased from Beina Bio (Hunan, China).
Drugs and reagents
Dulbecco’s modified eagle medium (Art. No.10–013-CVRC, Corning, New York, NY, USA); β-sitosterol (Batch No. Y22A10C85758, Shanghai Yuanye Biotechnology Co., Ltd., Shanghai, China), fetal bovine serum (Art. No.04–007-1a, Biological Industries, Kibbutz Beit Haemek, Israel), phosphate buffer saline (PBS; Art.No. WH0112201 911 XP, Procell, Wuhan, China), pancreatin (Art. No.143188, Biosharp, Hefei, China), DMSO (Tianjin Fuyu Fine Chemical Co., Ltd., Wuching District, China), MTT (Art. No. M8180, Beijing Suleibao Technology Co., Ltd., Beijing, China), a BCA kit (Lot No.20210922, Bio-Swamp Life Science Lab, Wuhan, China), an ECL high-sensitivity chemiluminescent solution kit (Batch No.: GC 10AA0033, Biological Engineering Co., Ltd., Shanghai, China), β-actin (Batch No.: F200040, Abways Technology, Shanghai, China), VEGFA (Batch No. 83 m8093, Affinity Biosciences, Beijing, China), PTGS2 (Batch No. 86F4760, Affinity Biosciences), VEGFR2 (Batch No. 84 g5912, Affinity Biosciences), and horseradish peroxidase-labeled goat anti-rabbit IgG secondary antibody (Batch No. F300405, Abways Technology) were used in this study.
A CO2 incubator (Wiggins WCI-180; Beijing Sanyi Experimental Instrument Institute, Beijing, China), clean bench (SW-CJ-2FD; SDT Scientific Instrument Co., Ltd., Shanghai, China), centrifuge (TD5; Shanghai Lu Xiangyi Centrifuge Instrument Co., Ltd., Shanghai, China), microplate reader (SPARK 10 M; TECAN, Männedorf, Switzerland), electrophoresis instrument (DYCZ-2DN; Beijing Liuyi Biotechnology Co., Ltd., Beijing, China), decoloring shaker (WD-9405F; Beijing Liuyi Biotechnology Co., Ltd), and chemiluminescence analyzer (WD-9423B; Beijing Liuyi Biotechnology Co., Ltd) were used in this study.
MTT cell proliferation assay
The proliferation of MH7A cells was detected using the MTT cell proliferation assay. Cells in the logarithmic phase were inoculated on culture plates and incubated for 24 h and 48 h at 37 °C with 5% CO2. The cells were divided into blank, control, and medication groups. When the cell density reached 80%, β-sitosterol was added to the medication group at gradient concentrations (2 μg/mL, 4 μg/mL, 6 μg/mL, 8 μg/mL, and 10 μg/mL). After 24 h, 5 μg/mL MTT solution was added, and the cells were cultured for 4 h, after which the supernatant in the wells was discarded, and 150 μL DMSO was added to each well. The solution was shaken on a shaking table in the dark for 10 min until the crystals were completely dissolved. The optical densities (ODs) of the well plates were measured using a microplate reader at 570 nm and compared with the OD of the control group to determine the relative cell activity.
Detection of cell cycle and apoptosis rate via flow cytometry
The effects of β-sitosterol on the cell cycle and apoptosis were detected using flow cytometry. MH7A cells in the logarithmic phase were inoculated into 96-well plates and were incubated with gradient concentrations of β-sitosterol for 24 h. Then, the cells were digested with trypsin, centrifuged at 1500 r/min for 5 min, and washed with PBS. The supernatant was discarded after centrifugation, and the cells were fixed with 2-mL 75% ethanol at 4 °C for 12 h. The cells were centrifuged, the supernatant was removed before two washes with PBS were conducted, and 500 μL of propidium iodide and ribonuclease mixture was added. For the apoptosis analysis, the cell concentration was adjusted to 1 × 105/L, and 100 μL of binding buffer, 5 μL of annexin V-phycoerythrin, and 5 μL of 7-amino-actin D were added.
Western blot assay to determine protein content
Cells were lysed with radioimmunoprecipitation assay lysis buffer on ice for 30 min and centrifuged at 1200 r/min at 4 °C. The supernatant was used for protein quantitative detection via the BCA method. The protein samples were denatured and stored at − 20 °C. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis was conducted, followed by membrane transfer via blocking with TBST containing 5% defatted milk powder for 2 h. The membrane was incubated with antibodies against VEGFA, VEGFR2, or PTGS2 for 12 h at 4 °C and then washed with TBST (three washes, 15 min each). The membranes were then incubated with secondary antibodies (horseradish peroxidase-labeled) for 2 h at room temperature and were washed again with TBST (three washes, 15 min each). The membrane was developed using the ECL chemiluminescent color method and ImageJ software (National Institutes of Health, Maryland, USA).
Experimental data are shown as means±standard deviations. SPSS 22.0 (IBM Corp., Armonk, NY, USA) software was used for statistical analysis. One-way analysis of variance was used for multi-group comparison, and the t-test was used for inter-group comparison. P < 0.05 was considered to indicate a statistically significant difference.