Computational Prediction of Antiangiogenesis Synergistic Mechanisms of Total Saponins of Panax japonicus Against Rheumatoid Arthritis - Guo, Xiang; Ji, Jinyu; Jose Kumar Sreena, Goutham Sanker; Hou, Xiaoqiang; Luo, Yanan; Fu, Xianyun; Mei, Zhigang; Feng, Zhitao (Frontiers in Pharmacology, 2020)
Objective: To investigate the anti-angiogenesis mechanisms and key targets of total saponins of Panax japonicus (TSPJ) in the treatment of rheumatoid arthritis (RA). Methods: RStudio3.6.1 software was used to obtain differentially expressed genes (DEGs) by analyzing the differences in gene expression in the synovial tissue of RA and to predict the potential targets of active compounds from TSPJ by the PharmMapper and SwissTargetPrediction databases. We evaluated the overlapping genes by intersectional analysis of DEGs and drug targets. Based on the overlapping genes, we used Cytoscape 3.7.2 software to construct a protein–protein interactions (PPI) network and applied Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to determine the mechanisms of the treatment. Finally, the correlations with angiogenesis-related genes were explored. Collagen-induced arthritis (CIA) model was established and treated with different doses of TSPJ. The manifestations of CIA were determined by evaluation of arthritis index and histology score. Serum levels of vascular endothelial growth factor (VEGF) and the hypoxia-inducible factor 1 (HIF-1) were tested by ELISA. The mRNA levels of IL-1β and IL-17A were detected by real time-quantitative PCR. Results: Altogether, 2670 DEGs were obtained by differential analysis, and 371 drug targets were predicted for four active components (Araloside A, Chikusetsusaponin IVa, Ginsenoside Rg2, and Ginsenoside Ro). A total of 52 overlapping genes were included in the PPI network and the KEGG analysis. However, only 41 genes in the PPI network had protein interactions. The results of the KEGG enrichment analysis were all related to angiogenesis, including VEGF and HIF-1 signaling pathways. Seven genes with negative correlations and 16 genes with positive correlations were obtained by correlational analysis of DEGs in the VEGF and HIF-1 signaling pathways. SRC proto-oncogene, nonreceptor tyrosine kinase (SRC), and the signal transducer and activator of transcription 3 (STAT 3) had a higher value of degree and showed a significant correlation in the pathways; they were regarded as key targets. Compared with the model group, TSPJ significantly relieved the symptoms and decreased the expression of VEGFA, HIF-1α, IL-1β, and IL-17A in serum or spleens of CIA mice. Conclusion: In the current study, we found that antiangiogenesis is one of the effective strategies of TSPJ against RA; SRC and STAT 3 may be the key targets of TSPJ acting on the VEGF and HIF-1 signaling pathways, which will provide new insight into the treatment of RA by inhibiting inflammation and angiogenesis.