Reagents and materials
1-Ethyl-3-(3-dimetylaminopropyl)-carbodiimide hydrochloride (EDC) was obtained from Shanghai Beinuo Biotechnology Co., Ltd. (Shanghai, China), and N-hydroxysuccinimide (NHS) was purchased from Shanghai Dibai Biotechnology Co., Ltd. Sodium acetate and dimethyl sulfoxide (DMSO) were purchased from Beijing Chemical Plant (Beijing, China). Ethanolamine hydrochloride was purchased from Shanghai Sanying Chemical Reagent Co., Ltd. (Shanghai, China), the protein TNF-a was provided by RD Biosciences (USA), artemisinin, scopoletin, arteannuin B and artemisinic acid were provided by Chengdu Ruifensi Biological Technology Co., Ltd. (Chengdu, China), and Dulbecco’s phosphate buffered saline PBS buffer (pH 4.7) was freshly prepared.
Plasma sample preparation
Approximately 40 mg of EDC and 10 mg of NHS were weighed, and 1 mL of solution was prepared with distilled water. The above solution was injected within 5 min into two channels that had been thoroughly rinsed with PBS buffer.
Fifty micrograms of TNF-a protein was dissolved in 100 µL of PBS, and 10 µL of the above solution was taken in three portions and diluted with sodium acetate solutions with pH values of 5.5, 6.0, and 6.5, respectively, and the final concentration was 50 µg/mL. The flow rate was reduced to 20 µL/min, and the left channel was rinsed for 10 min to determine the optimal pH of sodium acetate.
After determination of the optimal pH value, 1 M ethanolamine hydrochloride was injected into the two channels for 10 min to complete the sample fixation.
The four chemical compounds, scopoletin (A), artemisinin (B), artemisinic acid (C) and arteannuin B (D), were divided into 12 groups according to the combination rule of A, B, C, D, AB, AC, AD, ABC, ABD, ACD, BCD, and ABCD, and each group had 6 concentration levels. The control group was PBS buffer at pH 7.4.
Next, 19.2 mg of scutellaria lactone, 28.2 mg of artemisinin, 24.8 mg of artemisinin 2 and 23.4 mg of artemisinin were accurately weighed and dissolved in 1 mL DMSO (dimethyl sulfoxide). The DMSO solution in each group was gradient-diluted with PBS to final concentrations of 200 µM, 66.7 µM, 22.2 µM, 7.41 µM, and 2.47 µM.
Target fishing
Known therapeutic targets for the treatment of malaria were obtained from the DrugBank database (http://www.drugbank.ca/, version 4.3) [15]. The prediction of drug targets based on ligand structural features primarily includes chemical similarity searches and reverse pharmacophore searches. The theoretical basis of the chemical similarity search is that small molecular compounds with similar structural or physicochemical properties can act on targets with the same or similar properties: “antimalaria” was selected as the key word, as well as the drug-target interactions whose drugs are approved by the USA Food and Drug Administration (FDA) for treating menstrual disorders. All target gene/protein identifiers (IDs) were converted into the corresponding gene symbol/UniProtKB-Swiss-Prot IDs to facilitate further data analyses. After removing redundant entries, 25 target genes corresponding to 15 known antimalarial drugs were retrieved.
Protein‑protein interaction (PPI) data
PPI data were imported from the following PPI databases, including the Human Annotated and Predicted Protein Interaction Database (HAPPI, http://bio.informatics. iupui.edu/HAPPI/, Version 31.2) [16]. Based on the PPI network database, an interaction network of Artemisia annua candidate target groups and known antimalarial drug target groups was constructed, and the distribution of target nodes in metabolic pathways and the corresponding diseases was determined. As a result, a direct interaction network of key nodes was subsequently established and divided into different modules according to the functions of the nodes. According to the malaria pathway (ko05144: Malaria) in KEGG, molecules closely related to the malaria pathway were selected as candidates to be verified from the key nodes.
Network construction and topological analysis
Compound–target (C-T), target–pathway (T-P), and target–disease (T-D) networks of malaria were constructed using Cytoscape 3.2 software (https://cytoscape. org/download.html), a general bioinformatics software package for data integration and visualization of biological networks (Bindea et al., 2009; Smoot et al., 2011). An interaction network of Artemisia annua candidate target genes with known antimalarial drug target genes was established, consisting of 85 nodes and 298 pairs of interactions. The topological characteristic value of each node was calculated in the network, and the median of the topological characteristic value was used as the card value. A total of 32 key nodes were screened. A direct interaction network of key nodes was established and processed according to the node functions. The module was divided, the malaria pathway in Kyoto Encyclopedia of Genes and Genomes (KEGG) (ko05144: Malaria) was compared, and molecules closely related to the malaria pathway were selected as candidates to be verified from the key nodes.
Molecular docking
The molecular structures of CDK4, NFKB1, PIK3CG, MAPK1, TNF and ITGB2 protein targets (protein species is human) were searched in the database UniProt (http://www.uniprot.org/). The structures of scopolamine and artemisinic acid were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Chemical composition and protein structure were dehydrated and hydrotreated, respectively. Molecular docking and figures were generated using Discovery Studio Visualizer 2.5 software.
Probe Kd determination
Approximately 40 mg of EDC and 10 mg of NHS were weighed, and 1 mL of solution was prepared with distilled water. The above solution was injected within 5 min into two channels thoroughly rinsed with PBS buffer. Next, 19.2 mg of scopolamine, 28.2 mg of artemisinin, 24.8 mg of artemisinin, and 23.4 mg of artemisinic acid were precisely weighed in 1 mL DMSO (dimethyl sulfoxide) and mixed well. The TNF-α protein was immobilized on grafted sensor chips. The compound monomers and combinations are divided into 12 groups (Table 1). Each group of samples was injected from low to high concentrations, and a control group (PBS) was set at each concentration. Regression analysis when concentration curves are separated and approaching equilibrium. The dissociation constant (Kd) and its maximum value (Bmax) were then calculated by fitting the titration curve to the single-site saturation binding equation [Y = Bmax*X/(Kd + X)] using GraphPad Prism software (GraphPad software Incorporated, La Jolla, CA, USA).
Table 1
Compound monomers and combinations
Group | compounds |
A | Scopoletin |
B | Artemisinin |
C | Artemisinic acid |
D | Arteannuin B |
AB | Scopoletin; Artemisinin |
AC | Scopoletin; Artemisinic acid |
AD | Scopoletin; Arteannuin B |
ABC | Scopoletin; Artemisinin; Artemisinic acid |
ABD | Scopoletin; Artemisinin; Arteannuin B |
ACD | Scopoletin; Artemisinic acid; Arteannuin B |
BCD | Artemisinin; Artemisinic acid; Arteannuin B |
ABCD | Scopoletin; Artemisinin; Artemisinic acid; Arteannuin B |