Rheumatoid arthritis (RA), a common chronic systemic autoimmune disease, is characterized by synovial hyperproliferation and inflammatory/immune cell infiltration [
The traditional Chinese medicine (TCM) is based on the theory of syndrome differentiation and has long been established as an effective treatment of RA. Huangqi Guizhi Wuwu Decoction (HGWD), a classical prescription described in
Network pharmacology is partly bioinformatics and was first proposed by Hopkins [
In the present study, network pharmacology was used to establish a compound-target-disease network for exploring the potential HGWD mechanism of action in RA treatment. This study provides a reference for future pharmacological studies and clinical applications. The flow diagram of the network is shown in Figure
The flow diagram of network pharmacology analysis.
Information on the HGWD compounds was retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP,
Target identification is an important aspect of drug exploration [
The differentially expressed genes in RA were retrieved from the GEO database (
The compound-target network of HGWD was constructed and visualized via Cytoscape 3.8.0 software [
Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) are important methods that describe the features of candidate targets. The two were performed in R software (Bioconductor, clusterProfiler) with the standard
The 3D protein structure of the three core proteins corresponding to the core targets, VCAM1, CTNNB1, and JUN, was downloaded from the UniProt database. Subsequently, the structure of the active ingredient in HGWD (the top 10 places in the number of targets) was downloaded from the PubChem database and saved in the PDB format. Using PyMOL software, the three proteins were virtually dehydrated and hydrogenated, the original ligands were extracted in each protein, and then were stored separately. AutoDockTools 1.5.6 was utilized to convert compounds, ligands, and proteins into the “pdbqt” format and to define if the location of each protein or its ligands was the active pocket of the protein. Finally, Vina 1.5.6 was run to assess molecular docking. At a binding energy value <0, the molecular proteins were considered to spontaneously bind and interact with each other. Accordingly, the lower the energy is, the more stable the molecular conformation is.
A total of 790 compounds of the five herb medicines in HGWD were retrieved from the TCMSP database. This included 87 compounds in Huangqi, 220 in Guizhi, 85 in Baishao, 265 in Shengjiang, and 133 in Dazao. Among them, 74 compounds passed OB ≥ 30% and DL ≥ 0.18 filtering. Specifically, the numbers of candidate compounds in Huangqi, Guizhi, Baishao, Shengjiang, and Dazao were 20, 7, 13, 5, and 29, respectively. The candidate compounds in HGWD used for further analysis are shown in Table
Basic information of the active compounds in the HGWD formula.
Molecule ID | Name | OB | DL | Source |
---|---|---|---|---|
MOL000211 | Mairin | 55.38 | 0.78 | Huangqi, Baishao, Dazao |
MOL000239 | Jaranol | 50.83 | 0.29 | Huangqi |
MOL000296 | Hederagenin | 36.91 | 0.75 | Huangqi |
MOL000033 | (3S,8S,9S,10R,13R,14S,17R)-10,13-Dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol | 36.23 | 0.78 | Huangqi |
MOL000354 | Isorhamnetin | 49.6 | 0.31 | Huangqi |
MOL000371 | 3,9-Di-O-methylnissolin | 53.74 | 0.48 | Huangqi |
MOL000374 | 5′-Hydroxyiso-muronulatol-2′,5′-di-O-glucoside | 41.72 | 0.69 | Huangqi |
MOL000378 | 7-O-Methylisomucronulatol | 74.69 | 0.3 | Huangqi |
MOL000379 | 9,10-Dimethoxypterocarpan-3-O- | 36.74 | 0.92 | Huangqi |
MOL000380 | (6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofuro[3,2-c]chromen-3-ol | 64.26 | 0.42 | Huangqi |
MOL000387 | Bifendate | 31.1 | 0.67 | Huangqi |
MOL000392 | Formononetin | 69.67 | 0.21 | Huangqi |
MOL000398 | Isoflavanone | 109.99 | 0.3 | Huangqi |
MOL000417 | Calycosin | 47.75 | 0.24 | Huangqi |
MOL000422 | Kaempferol | 41.88 | 0.24 | Huangqi, Baishao |
MOL000433 | FA | 68.96 | 0.71 | Huangqi |
MOL000438 | (3R)-3-(2-Hydroxy-3,4-dimethoxyphenyl)chroman-7-ol | 67.67 | 0.26 | Huangqi |
MOL000439 | Isomucronulatol-7,2′-di-O-glucosiole | 49.28 | 0.62 | Huangqi |
MOL000442 | 1,7-Dihydroxy-3,9-dimethoxy pterocarpene | 39.05 | 0.48 | Huangqi |
MOL000098 | Quercetin | 46.43 | 0.28 | Huangqi, Dazao |
MOL001736 | (-)-Taxifolin | 60.51 | 0.27 | Guizhi |
MOL000358 | Beta-sitosterol | 36.91 | 0.75 | Guizhi、Baishao、Shengjiang、Dazao |
MOL000359 | Sitosterol | 36.91 | 0.75 | Guizhi, Baishao |
MOL000492 | (+)-Catechin | 54.83 | 0.24 | Guizhi, Baishao, Dazao |
MOL000073 | ent-Epicatechin | 48.96 | 0.24 | Guizhi |
MOL004576 | Taxifolin | 57.84 | 0.27 | Guizhi |
MOL011169 | Peroxyergosterol | 44.39 | 0.82 | Guizhi |
MOL001928 | Albiflorin_qt | 66.64 | 0.33 | Baishao |
MOL001918 | Paeoniflorgenone | 87.59 | 0.37 | Baishao |
MOL001910 | 11alpha,12alpha-Epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-olide | 64.77 | 0.38 | Baishao |
MOL001925 | Paeoniflorin_qt | 68.18 | 0.4 | Baishao |
MOL001919 | (3S,5R,8R,9R,10S,14S)-3,17-Dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,9-hexahydro-1H-cyclopenta[a]phenanthrene-15,16-dione | 43.56 | 0.53 | Baishao |
MOL001930 | Benzoyl paeoniflorin | 31.27 | 0.75 | Baishao |
MOL001924 | Paeoniflorin | 53.87 | 0.79 | Baishao |
MOL001921 | Lactiflorin | 49.12 | 0.8 | Baishao |
MOL006129 | 6-Methylgingediacetate2 | 48.73 | 0.32 | Shengjiang |
MOL000449 | Stigmasterol | 43.83 | 0.76 | Shengjiang, Dazao |
MOL001771 | Poriferast-5-en-3beta-ol | 36.91 | 0.75 | Shengjiang |
MOL008698 | Dihydrocapsaicin | 47.07 | 0.19 | Shengjiang |
MOL012921 | Stepharine | 31.55 | 0.33 | Dazao |
MOL012940 | Spiradine A | 113.52 | 0.61 | Dazao |
MOL012946 | Zizyphus saponin I_qt | 32.69 | 0.62 | Dazao |
MOL012961 | Jujuboside A_qt | 36.67 | 0.62 | Dazao |
MOL012976 | Coumestrol | 32.49 | 0.34 | Dazao |
MOL012980 | Daechuine S6 | 46.48 | 0.79 | Dazao |
MOL012981 | Daechuine S7 | 44.82 | 0.83 | Dazao |
MOL012986 | Jujubasaponin V_qt | 36.99 | 0.63 | Dazao |
MOL012989 | Jujuboside C_qt | 40.26 | 0.62 | Dazao |
MOL012992 | Mauritine D | 89.13 | 0.45 | Dazao |
MOL001454 | Berberine | 36.86 | 0.78 | Dazao |
MOL001522 | (S)-Coclaurine | 42.35 | 0.24 | Dazao |
MOL003410 | Ziziphin_qt | 66.95 | 0.62 | Dazao |
MOL004350 | Ruvoside_qt | 36.12 | 0.76 | Dazao |
MOL005360 | Malkangunin | 57.71 | 0.63 | Dazao |
MOL000627 | Stepholidine | 33.11 | 0.54 | Dazao |
MOL007213 | Nuciferin | 34.43 | 0.4 | Dazao |
MOL000783 | Protoporphyrin | 30.86 | 0.56 | Dazao |
MOL000787 | Fumarine | 59.26 | 0.83 | Dazao |
MOL008034 | 21302-79-4 | 73.52 | 0.77 | Dazao |
MOL008647 | Moupinamide | 86.71 | 0.26 | Dazao |
MOL002773 | Beta-carotene | 37.18 | 0.58 | Dazao |
MOL000096 | (-)-Catechin | 49.68 | 0.24 | Dazao |
MOL013357 | (3S,6R,8S,9S,10R,13R,14S,17R)-17-[(1R,4R)-4-Ethyl-1,5-dimethylhexyl]-10,13-dimethyl-2,3,6,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthrene-3,6-diol | 34.37 | 0.78 | Dazao |
Volcano plot of differentially expressed genes. The red and green dots represent the significant differentially expressed genes.
The compound-target network of HGWD was established with the collected compounds and their targets as shown in Figure
Compound-target network of the HGWD formula. The blue triangles represent the targets, and the ellipses represent active compounds. The green, yellow, amaranth, wathet, and red colors represent compounds from Baishao, Dazao, Guizhi, Huangqi, and multidrug, respectively.
To elucidate the mechanism by which HGWD ameliorates RA, the potential target network was merged with the RA-related target network to form a core PPI network of 2284 nodes and 53,119 edges (Figure
Protein interaction network of the HGWD formula.
During the second screening, since the number of genes was limited, only BC average value was used. The candidate targets were further identified, and 111 targets with BC ≥ 348.07 (BC average value) were chosen (Figure
To further confirm the biological responses from RA treatment with HGWD, GO analysis of the 49 RA-related potential therapeutic target genes was performed based on BP, CC, and MF. The analysis results revealed a total of 739 entries. In BP enrichment analysis, 666 entries were obtained including response to antibiotic, response to alcohol, and response to steroid hormone. In CC enrichment analysis, 34 entries involved in membrane raft, membrane microdomain, membrane region, etc. were obtained. The MF enrichment analysis revealed 39 entries, including kinase regulator activity, protein kinase regulator activity, and serine hydrolase activity. The top 20 terms are shown in Figure
Gene ontology terms of the candidate targets of the HGWD formula against RA. (a) Biological process. (b) Cellular component. (c) Molecular function.
To further show the biological processes of these targets, the KEGG pathway analysis was performed. The analysis results showed that these targets were enriched in 79 pathways with an adjusted
KEGG pathway enrichment of the candidate targets of the HGWD formula against RA.
The gene-pathway network analysis was constructed according to the significantly enriched pathways and genes which regulate these pathways as shown in Figure
Gene-pathway network of the HGWD formula against RA. The yellow squares represent the target genes, and the red v-shapes represent pathways. The large size represents the larger betweenness centrality.
Normally, high connectivity compounds are associated with more targets. According to the number of related targets, the top 10 active compounds in HGWD with high connectivity in the compound-target network were selected for molecular docking (Table
The binding energy of the main potential active ingredients in the HGWD formula.
Compound | Molecular formula | Binding energy (VCAM1) | Binding energy (CTNNB1) | Binding energy (JUN) |
---|---|---|---|---|
Quercetin | C15H10O7 | −7.1 | −7.3 | −7.3 |
Kaempferol | C15H10O6 | −7.1 | −6.5 | −7.4 |
Beta-sitosterol | C29H50O | −7.3 | −7.1 | −7.8 |
Formononetin | C16H12O4 | −7 | −6.3 | −6.9 |
7-O-Methylisomucronulatol | C18H20O5 | −6.7 | −6 | −6.9 |
Isorhamnetin | C16H12O7 | −6.9 | −6.5 | −7.5 |
Beta-carotene | C40H56 | −6.5 | −7.9 | −8.1 |
Stepholidine | C19H21NO4 | −7.5 | −6.7 | −6.9 |
(S)-Coclaurine | C17H19NO3 | −7.1 | −6.3 | −7.1 |
Stigmasterol | C29H48O | −7.6 | −7 | −8.1 |
Molecular docking diagram. (a) Stepholidine-VCAM1. (b) Stigmasterol-VCAM1. (c) Beta-carotene-CTNNB1. (d) Quercetin-CTNB1. (e) Stigmasterol-JUN. (f) Beta-carotene-JUN.
TCM, characterized by multicompound and multitarget medicines, cures diseases via multiple targets, multiple pathways, and multiple links. Due to the complex chemical ingredients of TCM, conventional research methods such as clinical and pharmacological research are not capable of fully elucidating the mechanism of action of TCM. Fortunately, network pharmacology provides a solution to this challenge since it is suitable for multicompound and multitarget research. In the present study, the mechanism of action of HGWD on RA was explored via network pharmacology methods. This provided a clear direction for further basic and clinical research.
Modern pharmacology has shown that HGWD has a specific therapeutic effect on RA, where it elicits obvious anti-inflammatory and analgesic effects [
In this study, the compound-target network of HGWD was constructed using 28 compounds and their 49 targets. The network diagram demonstrated that most of the HGWD compounds affected multiple targets. For instance, quercetin, kaempferol, and beta-sitosterol acted on 33, 14, and 8 targets, respectively. Therefore, they were the likely key active compounds for HGWD. Quercetin is a common active component of Huangqi and Dazao. Kaempferol is a common active component of Huangqi and Baishao. Beta-sitosterol is a common active component of Guizhi, Baishao, Shengjiang, and Dazao. These drugs have been mainly used together in Chinese medical history. It indicates that the compatibility between these drugs has a synergistic effect and increases their efficacy.
Quercetin is a flavonol that has unique therapeutic biological properties including anticarcinogenic, anti-inflammatory, antioxidant, antiviral, and psychostimulant activities [
The PPI networks of HGWD targets and RA-related targets were constructed and merged to identify the candidate targets of HGWD against RA. To accurately obtain the targets, two parameters including DC and BC were set to construct a new network. Through PPI analysis, VCAM1, CTNNB1, and JUN were established as the important targets in RA treatment. VCAM-1 is a glycoprotein expressed in vascular endothelial cells, whose serum is positively correlated with RA [
The targets of HGWD against RA were enriched in BP, CC, and MF through GO analysis. Results suggested that HGWD mainly regulated response to antibiotics, response to steroid hormones, and response to radiation. Furthermore, it was shown to affect some cellular components and molecular functions including membrane raft, membrane microdomain, membrane region, and kinase regulator activity. In the present study, 79 KEGG pathways including TNF signaling pathway and IL-17 signaling pathway were significantly enriched. IL-17 and TNF-
TNF-
The gene-pathway network was constructed to explore the main target genes of HGWD against RA. The results revealed that JUN, FOS, CCND1, IL6, E2F2, and ICAM1 are important target genes. A study found that the ERK-JNK-P38 signaling pathways in autoimmune disease are activated, leading to high levels of downstream JUN and Fos protein. Subsequently, the two combine to form dimer transcription factor-activating protein 1 (AP-1), which is involved in the occurrence and development of RA. It regulates the transformation of initial T cells into effector T cells, hence regulating the immune response and inflammatory process [
Our study systematically elucidated the mechanisms of action and molecular targets of HGWD against RA via the network pharmacology approach. Quercetin, kaempferol, and beta-sitosterol regulated most of the targets related to RA. The HGWD can regulate gene function through their related pathways including TNF signaling pathway and IL-17 signaling pathway. The key target genes in the gene-pathway network of HGWD against RA were JUN, FOS, CCND1, IL6, E2F2, and ICAM1. Furthermore, according to molecular docking analysis, important compounds such as stepholidine, stigmasterol, beta-carotene, quercetin, and the core protein CTNNB1, VCAM1, and JUN all have good binding ability. The network pharmacology is a promising suitable approach for the study of TCM formulations.
Rheumatoid arthritis
Disease-modifying antirheumatic drugs
Methotrexate
Traditional Chinese medicine
Huangqi Guizhi Wuwu Decoction
Traditional Chinese Medicine Systems Pharmacology Database
Absorption, distribution, metabolism, and excretion
Oral bioavailability
Drug-like properties
Protein-protein interaction
Degree centrality
Betweenness centrality
Closeness centrality
Eigenvector centrality
Local average connectivity-based method
Network centrality
Gene ontology
Kyoto Encyclopedia of Genes and Genomes
Biological process
Cellular component
Molecular function
Interleukin-6
Disease Activity Score-28
Granulocyte-macrophage colony-stimulating factor
Nuclear factor-kappa B
Activating protein-1
Intercellular adhesion molecule-1
Lymphocyte function antigen-1.
The data supporting the findings of this study are available from the corresponding author upon request.
Wei Liu and Yihua Fan are the co-first authors.
The authors declare that they have no conflicts of interest.
Wei Liu and Yihua Fan contributed equally to this work.
The authors would like to thank FreeScience (