Exploring the active ingredients and potential mechanisms of action of sinomenium acutum in the treatment of rheumatoid arthritis based on systems biology and network pharmacology

Abstract

Objective: To investigate and predict the targets and signaling pathways of sinomenium acutum (SA) in the treatment of rheumatoid arthritis (RA) through systems biology and network pharmacology, and to elucidate its possible mechanisms of action. Methods: We screened the active ingredients and corresponding target proteins of SA in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID) and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN); and obtained the targets of rheumatoid arthritis diseases in a database of gene-disease associations (DisGeNET), Online Mendelian Inheritance in Man (OMIM) database. The two targets were mapped by Venn diagram and the intersection was taken. The intersecting targets were used to construct protein-protein interaction (PPI) network maps in the String database, and Metascape was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Finally, the molecular docking technique was applied to validate and further clarify the core target of SA for the treatment of rheumatoid arthritis. Results: A total of six active ingredients and 217 potential targets were obtained after screening; 2,752 rheumatoid arthritis-related targets and 66 targets common to RA and SA. GO function and KEGG pathway enrichment analysis yielded 751 GO function entries (652 GO biological processes, 59 GO molecular functions and 40 GO cellular components) and 77 KEGG signaling pathways. It mainly involves pathways related to neural activity ligand-receptor interaction pathways, cancer pathways, calcium signaling channels, Th17 cell differentiation and others, which are mainly classified into four categories, including regulation of immunity, anti-inflammation, regulation of cell growth and apoptosis, and signaling. The molecular docking results showed that the binding energy of PTGS2, CASP3, JUN and PPARG to the key components beta-sitosterol, 16-epi-Isositsirikine, Sinomenine and Stepholidine were ≤ -6.5 kcal/mol, suggesting the existence of molecular binding sites. Conclusion: SA acts on key targets such as PTGS2, CASP3, JUN, and PPARG to modulate signaling pathways such as neural activity ligand-receptor interaction, cancer, calcium ion, NF-κB, and Th17 cell differentiation to regulate immunity, anti-inflammation, modulation of cell cycle, bone metabolism, and signaling for the treatment of RA. It was also confirmed that the treatment of RA with SA has multi-component, multi-target, multi-pathway and multi-mechanism characteristics.

Keywords: molecular docking; network pharmacology; rheumatoid arthritis; sinomenium acutum; target.

FIGURE 1

The workflow of the analysis for this study. TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform), TCMID (Traditional Chinese Medicines Integrated Database), BATAMN (Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine), DisGeNET (a database of gene-disease associations), OMIM (Online Mendelian Inheritance in Man), OB (oral bioavailability), DL (drug-likeness), RA (Rheumatoid arthritis), SA (Sinomenium acutum), PPI (protein-protein interaction network), GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes).

FIGURE 2

Network diagram of SA—Active ingredient—Target. The green color represents the drug SA, the orange diamond represents the active ingredient of SA, and the purple arrow represents the target protein. The lines represent the relationship between drug, active ingredient, and target.

FIGURE 3

Venn diagram of the intersection target of SA and RA.

FIGURE 4

Network diagram of SA-Components-Potential targets of action-RA. The orange oval represents the drug (SA), the orange circle represents RA, the middle cyan rectangle represents the active ingredient, and the yellow represents the potential target of action between the two. The lines represent the relationship between SA, active ingredient, potential target of action, and RA.

FIGURE 5

PPI network of SA for RA. A total of 56 nodes and 160 lines are included. The larger the node, the redder the color, the larger the degree. The more lines between nodes, the larger the between centrality. The larger the degree and between centrality, the more important the node is.

FIGURE 6

Key targets for the treatment of RA with SA. the y-axis displays significant top 21 genes, and the x-axis shows degree counts of these genes.

FIGURE 7

Bar graph of GO gene function annotation. (A) BP, (B) MF; (C) CC. horizontal coordinates represent p-values taken at log base 10, vertical coordinates represent GO entry names, and colors from light to dark represent the degree of significance.

FIGURE 8

Bar graph of KEGG enrichment pathways. The horizontal coordinates represent the p-values with log base 10 and the vertical coordinates represent the pathway names. The color represents the enrichment significance, the darker the color, the higher the enrichment. Figure (A) show KEGG PATHWAY information via intersecting targets, Figure (B) show KEGG PATHWAY information via key targets.

FIGURE 9

Molecular docking of the active ingredients of SA with key targets. (A) JUN target is hydrogen-bonded to the active ingredient of SA; (B) PTGS2 target is hydrogen-bonded to the active ingredient of SA; (C) PPARG target is hydrogen-bonded to the active ingredient of SA; (D) CASP3 target is hydrogen-bonded to the active ingredient of SA.

FIGURE 10

Possible targets and signaling pathways for the treatment of RA with SA.