Osteoporosis is a systemic bone disorder characterized by low bone density, poor bone quality, reduced bone strength, and an accompanying increased incidence of fractures [
Recently, traditional Chinese medicine (TCM) has attracted worldwide attention and has served as a main alternative treatment in East Asia, North America, and Europe due to its satisfactory curative effect, relatively low toxicity, and low cost [
Du Zhong (DZ), or Eucommiae Cortex, is one of the most commonly used TCM herbs in the treatment of osteoporosis. DZ has been found to facilitate osteogenesis through activating osteoblasts and to inhibit osteolysis by suppressing osteoclast activity [
In this network pharmacology work, we aim to comprehensively dissect the mechanisms of DZ in treating OPF. We collected related compounds of DZ from multiple databases and obtained the compounds’ potential targets via target fishing. Then, we matched these targets with OPF-related targets that were collected from a multisource database. Next, using overlapping targets obtained from the previous process, we built a protein-protein network to analyze their internal interactions and then screened out the hub genes. Furthermore, we used the clusterProfiler package in
The schematic map of the present study to investigate potential mechanisms of DZ in the OPF treatment.
To collect the active compounds of DZ, we utilized the Traditional Chinese Medicine System Pharmacology Database (TCMSP™,
Oral bioavailability (OB) refers to the percentage of an orally administered drug that reaches systemic circulation, and it is one of the most important pharmacokinetic profiles for drug screening. The TCMSP platform has adopted the OBioavail1.1 system, which integrates P450, 3A4, and P-glycoprotein information to obtain the OB value [
Drug-likeness (DL) is a qualitative index that represents the degree to which the target compound is “drug-like” and is used to remove chemically unsuitable compounds. TCMSP uses the Tanimoto similarity method to calculate the DL index by comparing the target compound to all 6511 molecules in the DrugBank database [
To collect candidate compounds, we set the potential TAR score cutoff ≥20 and
The compounds have effects on the targets that induce them to exert their biological functions. Thus, we used the TCMSP and BATMAN-TCM platforms to predict the targets of active compounds.
We used multiple databases to collect OPF-related targets, and the terms “Osteoporotic” and “Fracture” were used as the key words for the search. The databases in this step included the Comparative Toxicogenomics Database (CTD,
Two main networks were built in this process, including the compound-hub gene network and the hub gene-pathway network. The target information was obtained from the KEGG pathway enrichment results. Cytoscape 3.6.2 (
We used three parameters to describe and quantify the importance of nodes in these networks because nodes that bridge many edges with their neighbors are more likely to exert crucial mediating functions. (1) “Degree” means the number of edges shared with other nodes. Examining the node’s degree is the most straightforward method of quantifying its centrality [
We used the clusterProfiler package in
A total of 147 related compounds were found in DZ collected from the TCMSP database, and of these, 27 candidate compounds remained after the screening of ADME thresholds (OB ≥ 30%, DL ≥ 0.18). Using the BATMAN-TCM database, we obtained 73 active compounds that matched the filter criteria. In total, we collected 93 unique compounds.
For these unique compounds, we obtained 961 unique related targets, 104 from TCMSP and 857 from BATMAN-TCM. We integrated the OPF genes that were obtained from multisource databases, including the CTD, GeneCards, OMIM, HPO, GenCLiP 3 databases, and a total of 3,834 related genes were collected. After construction of the Venn diagram, three hundred twenty-five overlapping targets between the related targets of DZ and OPF were selected as the key targets on which DZ exerts its anti-OPF effects (Figure
The Venn diagram for the targets of DZ and OPF. The overlap targets are the potential therapeutic genes for DZ when treating OPF.
The data obtained from the String database were used to establish the PPI network for the 325 overlapping targets. In this network, there were 249 nodes and 1006 edges in total. Then, three main parameters, namely, “degree”, “betweenness”, and “closeness”, were used as filters to select key genes and to build the large hub nodes to determine the anti-OPF effect of DZ. The first screening threshold was degree ≥ 6, closeness ≥ 0.312, and betweenness ≥ 0.000, which resulted in 122 nodes and 745 edges. Then, these 72 key nodes were further screened with a second threshold consisting of degree ≥ 11, closeness ≥ 0.340, and betweenness ≥ 0.016, and 64 nodes and 414 edges remained after this screening (Figure
The whole screening process for the PPI network through a topological method. In these images, the bigger size and brighter color represent higher DC value.
Information of 64 hub targets.
UniProt ID | Gene symbol | Description | Degree |
---|---|---|---|
Q499G7 | MAPK1 | Mitogen-activated protein kinase 1 | 41 |
P12931 | SRC | SRC proto-oncogene, non–receptor tyrosine kinase | 39 |
P27986 | PIK3R1 | Phosphoinositide-3-kinase regulatory subunit 1 | 36 |
P15692 | VEGFA | Vascular endothelial growth factor A | 31 |
Q53GA5 | TP53 | Tumor protein p53 | 29 |
Q504U8 | EGFR | Epidermal growth factor receptor | 29 |
P05412 | JUN | Jun proto-oncogene | 29 |
P01019 | AGT | Angiotensinogen | 29 |
Q75MH2 | IL6 | Interleukin 6 | 28 |
P01133 | EGF | Epidermal growth factor | 27 |
P45983 | MAPK8 | Mitogen-activated protein kinase 8 | 26 |
Q6FG41 | FOS | Fos proto-oncogene | 25 |
P00734 | F2 | Coagulation factor II, thrombin | 25 |
Q9UQS6 | FN1 | Fibronectin 1 | 24 |
Q6FH53 | EDN1 | Endothelin 1 | 24 |
Q5STB3 | TNF | Tumor necrosis factor | 24 |
P02775 | PPBP | Pro-platelet basic protein | 24 |
Q9UBT1 | ESR1 | Estrogen receptor 1 | 23 |
Q15788 | NCOA1 | Nuclear receptor coactivator 1 | 23 |
P35222 | CTNNB1 | Catenin beta 1 | 23 |
P04150 | NR3C1 | Nuclear receptor subfamily 3 group C member 1 | 23 |
P02768 | ALB | Albumin | 22 |
Q6P3U7 | RXRA | Retinoid | 21 |
P22681 | CBL | Cbl proto-oncogene | 21 |
P01308 | INS | Insulin | 20 |
P17612 | PRKACA | Protein kinase cAMP-activated catalytic subunit alpha | 18 |
P02765 | AHSG | Alpha 2-HS glycoprotein | 18 |
P01137 | TGFB1 | Transforming growth factor beta 1 | 18 |
V9HW22 | HSPA8 | Heat shock protein family A (Hsp70) member 8 | 17 |
Q9NUA2 | AR | Androgen receptor | 17 |
Q9GZV9 | FGF23 | Fibroblast growth factor 23 | 17 |
Q5JWF2 | GNAS | GNAS complex locus | 16 |
P19838 | NFKB1 | Nuclear factor kappa B subunit 1 | 16 |
Q14393 | GAS6 | Growth arrest specific 6 | 15 |
Q05655 | PRKCD | Protein kinase C delta | 15 |
P30518 | AVPR2 | Arginine vasopressin receptor 2 | 15 |
P02647 | APOA1 | Apolipoprotein A1 | 15 |
P01127 | PDGFB | Platelet-derived growth factor subunit B | 15 |
Q6PK50 | HSP90AB1 | Heat shock protein 90 alpha family class B member 1 | 14 |
Q5FC01 | IL4 | Interleukin 4 | 14 |
P0DMV9 | HSPA1A | Heat shock protein family A (Hsp70) member 1A | 14 |
P01344 | IGF2 | Insulin-like growth factor 2 | 14 |
Q96EB6 | SIRT1 | Sirtuin 1 | 13 |
Q4W448 | PPARG | Peroxisome proliferator activated receptor gamma | 13 |
Q07869 | PPARA | Peroxisome proliferator activated receptor alpha | 13 |
P60953 | CDC42 | Cell division cycle 42 | 13 |
P06401 | PGR | Progesterone receptor | 13 |
P01579 | IFNG | Interferon gamma | 13 |
P01034 | CST3 | Cystatin C | 13 |
Q9Y6Q9 | NCOA3 | Nuclear receptor coactivator 3 | 12 |
Q9Y494 | TAC1 | Tachykinin precursor 1 | 12 |
Q9NQ66 | PLCB1 | Phospholipase C beta 1 | 12 |
P29474 | NOS3 | Nitric oxide synthase 3 | 12 |
P13501 | CCL5 | C-C motif chemokine ligand 5 | 12 |
P09038 | FGF2 | Fibroblast growth factor 2 | 12 |
Q92731 | ESR2 | Estrogen receptor 2 | 11 |
Q59GY7 | STAT5A | Signal transducer and activator of transcription 5A | 11 |
P41221 | WNT5A | Wnt family member 5A | 11 |
P28335 | HTR2C | 5-Hydroxytryptamine receptor 2C | 11 |
P28223 | HTR2A | 5-Hydroxytryptamine receptor 2A | 11 |
P13500 | CCL2 | C-C motif chemokine ligand 2 | 11 |
P06850 | CRH | Corticotropin releasing hormone | 11 |
P02649 | APOE | Apolipoprotein E | 11 |
P00746 | CFD | Complement factor D | 11 |
Then, we further built the big hub nodes-compound network (Figure
The network for big hub genes-compounds connection. The diamond shape nodes are the hub genes and round ones represent compounds. And all nodes’ color changes according to their degree value.
After sorting the 343 biological process (BP) terms in ascending order of adjusted
Top 25 processes of the biological process enrichment.
We conducted further KEGG pathway enrichment analyses of the 325 overlapping genes to determine the potential therapeutic mechanism of DZ for OPF. Then, we sorted 25 pathways based on the adjusted
Top 25 pathways of the KEGG enrichment.
Information of 25 pathways.
ID | Description | Count | |
---|---|---|---|
hsa04933 | AGE-RAGE signaling pathway in diabetic complications | 4.29750564649423e-15 | 27 |
hsa04915 | Estrogen signaling pathway | 4.29750564649423e-15 | 31 |
hsa05205 | Proteoglycans in cancer | 7.02978144058047e-12 | 33 |
hsa05418 | Fluid shear stress and atherosclerosis | 3.50540676797616e-09 | 24 |
hsa04928 | Parathyroid hormone synthesis, secretion, and action | 3.50540676797616e-09 | 21 |
hsa05224 | Breast cancer | 8.65129662473866e-09 | 24 |
hsa04934 | Cushing syndrome | 2.25061036469472e-08 | 24 |
hsa05215 | Prostate cancer | 2.25061036469472e-08 | 19 |
hsa04024 | cAMP signaling pathway | 4.25537215261066e-08 | 28 |
hsa05032 | Morphine addiction | 4.35290458036493e-08 | 18 |
hsa05143 | African trypanosomiasis | 5.25038311520481e-08 | 12 |
hsa04912 | GnRH signaling pathway | 5.25038311520481e-08 | 18 |
hsa01522 | Endocrine resistance | 1.16942061208876e-07 | 18 |
hsa05226 | Gastric cancer | 1.53049773606446e-07 | 22 |
hsa04080 | Neuroactive ligand-receptor interaction | 3.92997114777747e-07 | 34 |
hsa04010 | MAPK signaling pathway | 4.75554213381079e-07 | 31 |
hsa04020 | Calcium signaling pathway | 8.10557748077706e-07 | 24 |
hsa04921 | Oxytocin signaling pathway | 1.09530352609987e-06 | 21 |
hsa05144 | Malaria | 1.3497422679709e-06 | 12 |
hsa04659 | Th17 cell differentiation | 1.90497387620456e-06 | 17 |
hsa04066 | HIF-1 signaling pathway | 2.39525818923615e-06 | 17 |
hsa05210 | Colorectal cancer | 2.67545458381123e-06 | 15 |
hsa04929 | GnRH secretion | 2.67545458381123e-06 | 13 |
hsa04927 | Cortisol synthesis and secretion | 3.01425725456316e-06 | 13 |
hsa04668 | TNF signaling pathway | 3.01425725456316e-06 | 17 |
The targets-pathway network of DZ for treating OPF. The circle nodes represent big hub genes and the V-shape nodes represent the top 50 pathways. The nodes’ size and colors are dependent on DC value.
Osteoporosis is becoming a serious health burden due to the aging population. Therefore, prevention of osteoporosis and of osteoporotic fracture has considerable social and economic significance [
DZ has been found to alleviate improved osteoblast activity and reverse bone loss in ovariectomized (OVX) mice, which may result from the interaction between its active compounds and related targets. Thus, we investigated the anti-OPF mechanism of DZ through a network pharmacology method. Based on the big hub genes-compounds network (Figure
In the present study, 325 common gene targets of DZ and OPF were identified and 64 hub genes were identified that may play critical roles in this treatment. Some anti-OPF effects of these genes have been confirmed by clinical trials or animal experiments. Chow et al. found that low-magnitude high-frequency vibration stimulation is able to promote fracture healing by positively regulating the impaired innate immune response (lower expression of TNF-
We conducted GO biological process and KEGG enrichment analyses of the overlapping genes to identify their functions. We found 44 (13.6%) genes involved in reactive oxygen species metabolic processes. Reactive oxygen species play key roles in regulating cell proliferation, apoptosis, migration, and differentiation [
Forty-five (13.9%) genes were involved in the regulation of the inflammatory response. In vitro and in vivo studies have shown that the proinflammatory cytokines IL-6 and TNF-
By utilizing network pharmacology, we investigated the potential targets of DZ and the underlying mechanism of its anti-OPF effects, which may be based on the regulation of ROS and the inflammatory response. According to the KEGG pathway enrichment results, we found that the PI3K-Akt signaling pathway, MAPK signaling pathway, and TNF signaling pathway may be the main pathways in treating OPF. Thus, we believe that the anti-OPF effect of DZ is mainly based on its direct or indirect regulation of the abovementioned potential targets and pathways and that DZ provides promising directions for future research, which is essential to reveal its exact regulatory mechanisms.
Du Zhong
Osteoporotic fractures
Traditional Chinese medicine
Traditional Chinese Medicine System Pharmacology Database
Oral bioavailability
Drug-likeness
Comparative Toxicogenomics Database
Online Mendelian Inheritance in Man
Human Phenotype Ontology
Protein-protein interaction
Kyoto Encyclopedia of Genes and Genomes
Gene Ontology
Biological process
Ovariectomized
Reactive oxygen species.
All our main data used to support the findings of this study have been deposited in the Figshare repository (
The authors declare that they have no conflicts of interest regarding the publication of this paper.
Yongming Shuai and Fanhui Zeng conceived the idea of this article and supervised the research. Yongming Shuai performed the research, analyzed the data, and wrote the manuscript. Zhili Jiang and Qiuwen Yuan performed Target prediction and analysis as well as related enrichment processes. Shuqiang Tu and Fanhui Zeng participated in revising the data and improving manuscript writing. All authors reviewed the manuscript. And all authors read and approved the final version of the manuscript.