In total of 47 active components of Wuling Powder were obtained from TCMSP. Then, the component-related targets were predicted by using PharmMapper, and 85 targets were obtained after removing the repetitive and unhuman ones. Using CTD and GeneCards, 795 targets were gathered after removing duplicates. Hence, in total of 14 common targets between Wuling Powder and osteoporosis were screened and identified, including CREBBP, ADAM17, GOT1, GAPDH, USP8, ERBB2, EEF1A1, MTOR, RAC1, ETS1, DDX58, GCK, EGF and S100A8 (Fig. 1).
The DAVID database was used to perform GO analysis on above these mentioned 14 potential targets. The results showed that GO terms were mainly divided into three parts: BP, CC and MF (Fig. 2, Table 1). The BP was associated with the activation of positive regulation of cell proliferation, positive regulation of mitogen-activated protein kinase activity, epidermal growth factor (EGF) receptor signaling pathway, positive regulation of translation, positive regulation of actin filament polymerization, gluconeogenesis, positive regulation of stress fiber assembly, cell proliferation, glycolytic process, cellular response to EGF stimulus, regulation of angiogenesis, regulation of cell motility, canonical glycolysis, positive regulation of dendritic spine development, positive regulation of gene expression, positive regulation of lamellipodium assembly, ruffle organization, positive regulation of transcription from RNA polymerase III promoter, positive regulation of cellular component movement, response to hypoxia, positive regulation of EGF-activated receptor activity, Notch signaling pathway, phosphatidylinositol-mediated signaling, positive regulation of cell growth, wound healing, ErbB2 signaling pathway, cell motility and positive regulation of protein phosphorylation. The CC was associated with the extracellular exosome, extrinsic component of plasma membrane, nucleus, membrane, cytosol, cytoplasm and ruffle membrane. Meanwhile, MF was related to phosphatidylinositol-4,5-bisphosphate 3-kinase activity, protein kinase binding, protein binding and identical protein binding.
GO analysis Term −lgP GO analysis Term −lgP MF Phosphatidylinositol-4,5-bisphosphate
1.330 458 942 BP Cellular response to epidermal growth factor stimulus 1.597 605 914 MF Protein kinase binding 1.483 890 136 BP Regulation of angiogenesis 1.624 448 483 MF Protein binding 2.575 547 180 BP Regulation of cell motility 1.668 187 537 MF Identical protein binding 2.700 355 758 BP canonical glycolysis 1.700 062 419 CC Extracellular exosome 1.420 012 191 BP Positive regulation of dendritic spine development 1.771 993 335 CC Extrinsic component of plasma membrane 1.752 189 855 BP Positive regulation of gene expression 1.772 401 258 CC Nucleus 1.766 251 423 BP positive regulation of lamellipodium assembly 1.909 366 035 CC Membrane 1.846 409 675 BP Ruffle organization 1.967 047 883 CC Cytosol 3.194 830 627 BP Positive regulation of transcription from RNA polymerase III promoter 2.071 317 993 CC Cytoplasm 3.365 585 256 BP Positive regulation of cellular component movement 2.112 555 573 CC Ruffle membrane 4.614 238 777 BP Response to hypoxia 2.121 784 446 BP Positive regulation of cell proliferation 1.310 036 708 BP Positive regulation of epidermal growth factor-activated receptor activity 2.158 157 947 BP Positive regulation of MAP kinase activity 1.349 289 116 BP Notch signaling pathway 2.461 908 377 BP Epidermal growth factor receptor signaling pathway 1.371 489 538 BP Phosphatidylinositol-mediated signaling 2.531 310 317 BP Positive regulation of translation 1.394 938 020 BP Positive regulation of cell growth 2.730 278 166 BP Positive regulation of actin filament polymerization 1.464 764 359 BP Wound healing 2.772 160 186 BP Gluconeogenesis 1.474 369 513 BP ERBB2 signaling pathway 3.416 928 041 BP Positive regulation of stress fiber assembly 1.494 263 496 BP Cell motility 3.820 318 868 BP Cell proliferation 1.501 227 493 BP Positive regulation of protein phosphorylation 3.941 867 898 BP Glycolytic process 1.584 795 757 BP MF for molecular function; CC for cellular component; BP for biological process
Table 1. The terms for biological process, cell component and molecular function with P < 0.05
Figure 2. The terms for biological process, cell component and molecular function with P < 0.05 were shown.
Through comprehensive analysis, first 12 Wuling Powder-related KEGG signal pathways were obtained, and the KEGG signal pathway for Wuling Powder was constructed according to the P value on a bubble plot (Table 2). Subsequently, based on the systems-level image, an important signal pathway, hypoxia-inducible factor-1 (HIF-1) signal pathway, was selected for the further analysis (Fig. 3).
Term Gene Number P Value HIF-1 signaling pathway 5 0.000 016 Pathways in cancer 6 0.000 336 Prostate cancer 4 0.000 409 Central carbon metabolism in cancer 3 0.005 296 Pancreatic cancer 3 0.005 459 Renal cell carcinoma 3 0.005 624 Adherens junction 3 0.006 484 ErbB signaling pathway 3 0.009 612 Choline metabolism in cancer 3 0.012 8 Carbon metabolism 3 0.015 854 Proteoglycans in cancer 3 0.045 833 Focal adhesion 3 0.048 35
Table 2. The KEGG signal pathway for Wuling Powder
Figure 3. The “Rich factor” represented the ratio of the number of target genes belonging to this pathway and the number of the annotated genes located in a pathway. A high rich factor represented a high level of enrichment. The size of the dot meant the number of target genes in the pathway, and the color of the dot reflected the P value.
After obtaining the active components-related targets in Wuling Powder, a PPI network for these targets was established as shown in Fig. 4, which reveals the significant relationships among EGF, MTOR, EIF4E and RPS3. In order to show these complicated relationships, the PPI network between the active components and their related targets was also constructed (Fig. 5). Finally, 6 ones out of 14 common targets between Wuling Powder and osteoporosis were screened by regulating minimum required interaction score to the highest confidence (0.900), and then the PPI network was constructed (Fig. 6). It showed the significant connections among EGF, ERBB2, ADAM17, RAC1, MTOR and USP8.
Figure 4. The construction of PPI network of proteins expressed by Wuling Powder. The 36 nodes represented 36 proteins, and 39 lines represented 39 pairs of interaction among these proteins. The node size and color represented the degree, while line size and color represented the combined score. All data were from STRING.
Figure 5. The construction of PPI network between active compounds and their related targets in Wuling Powder. Hexagons represented active compounds of Wuling Powder. Roundness represented gene symbols of these targets.
Figure 6. The construction of PPI network of proteins expressed by common targets. The 6 nodes represented 6 proteins, and 6 lines represented 6 pairs of interaction among proteins. The node size and color represented degree, while edge size and color represented the combined score. All data were from STRING.
Molecular docking analysis demonstrated the potential binding relationships between these active components and their related target genes. A docking score of > 4.25 was considered fair, 5 ≤ docking score < 7 was considered good, and docking score ≥ 7 was considered excellent; this scoring was conventionally used to classify ligand binding activity . Among them, Wuling Powder was successfully combined with three key genes including EGF, MTOR and RAC1, and the docking score was between 4.777 and 7.982, indicating that EGF, MTOR and RAC1 may have the strong binding ability to Wuling Powder (Fig.7).
Study on the action mechanism of Wuling Powder on treating osteoporosis based on network pharmacology
- Received Date: 2020-03-11
- Available Online: 2021-01-20
Abstract: Osteoporosis is a health problem to cause global concern. A lot of methods have been used to prevent and treat osteoporosis, but there is still a lack of effective treatment for osteoporosis owing to limited understanding of its mechanism. Therefore, the aim of this present study is to explore the underlying mechanism of Wuling Powder, a traditional Chinese medicine on treating osteoporosis. In this study, we firstly screened and identified the common targets between Wuling Powder and osteoporosis through the related databases, and then explored the relationships among these targets, Wuling Powder and osteoporosis by using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and network analyses. Subsequently, the molecular docking was performed by using systemsDock to evaluate the potential binding relationships between the active components of Wuling Powder and their related targets. The results showed that in total of 14 common targets including CREBBP, ADAM17, GOT1, GAPDH, USP8, ERBB2, EEF1A1, MTOR, RAC1, ETS1, DDX58, GCK, EGF and S100A8 were screened. EGF, ERBB2, MTOR and HIF-1 were the potential therapeutic targets for osteoporosis, and they were also the related targets for predicting active components in Wuling Powder. Taken together, we concluded that Wuling Powder might be used to treat osteoporosis through above these targets.