Identified the Synergistic Mechanism of Drynariae Rhizoma for Treating Fracture Based on Network Pharmacology

Background Drynariae Rhizoma (DR) has been widely used in the prevention and treatment of various fractures. However, the specific mechanisms of DR's active ingredients have not been elucidated. The purpose of this study was to explore the synergistic mechanisms of DR for treating fracture. Methods A network pharmacology approach integrating ingredient screening, target exploration, active ingredients-gene target network construction, protein-protein interaction network construction, molecular docking, gene-protein classification, gene ontology (GO) functional analysis, KEGG pathway enrichment analysis, and signaling pathway integration was used. Results This approach identified 17 active ingredients of DR, interacting with 144 common gene targets and 143 protein targets of DR and fracture. NCOA1, GSK3B, TTPA, and MAPK1 were identified as important gene targets. Five most important protein targets were also identified, including MAPK1, SRC, HRAS, RXRA, and NCOA1. Molecular docking found that DR has a good binding potential with common protein targets. GO functional analysis indicated that common genes involve multiple processes, parts and functions in biological process, cellular component, and molecular function, including positive regulation of transcription from RNA polymerase II promoter, signal transduction, cytosol, extracellular exosome, cytoplasm, and protein binding. The KEGG pathway enrichment analysis indicated that common gene targets play a role in repairing fractures in multiple signaling pathways, including MAPK, PI3K/AKT, Ras, and VEGF signaling pathways. MAPK and PI3K/AKT signaling pathways were involved in osteoblast formation, Ras signaling pathway was involved in enhancing mesenchymal stromal cell migration, and VEGF signaling pathway was involved in angiogenesis. Conclusion The study revealed the correlation between DR and fracture and the potential synergistic mechanism of different targets of DR in the treatment of fractures, which provides a reference for the development of new drugs.


Introduction
Fracture is a common and frequent disease that occurs in patients with various injuries or osteoporosis [1]. In China, the population-weighted incidence of traumatic fractures of the legs, arms, or trunk in 2014 was 3.21 per 1,000 people (95% CI 2.83-3.59) [2]. Osteoporotic fractures are estimated to account for half of all fractures by 2050, and the estimated cost of osteoporotic hip fractures worldwide may reach $131 billion [3]. erefore, the study of drugs for the prevention and treatment of fractures plays an important role in promoting patient health and reducing family economic pressure.
Recently, DR, one of the plants from Davalliaceae and Davallia Sm., has been widely used in the prevention and treatment of various fractures due to excellent treatment, low side effects, extensive use, and safety [4]. Animal experiments have confirmed that DR could alter the bone histomorphology and increase the number of trabeculae by 10% [5], and its osteogenesis is related to Runx2 and BMP-2 signaling pathways [6]. In addition, it is believed that the various ingredients contained in an herb could regulate multiple targets in different signaling pathways and produce synergistic therapeutic effects [7]. However, such research has not been carried out in the treatment of fractures with DR.
Network pharmacology based on systems biology and polypharmacology has achieved a paradigm shift from "one drug, one goal" to "multi-ingredient therapy, biological network," which has attracted the attention of Chinese medicine researchers and has been recognized as an effective tool for elucidating multiple components, targets, synergistic effects, and mechanisms of Chinese medicine [8][9][10]. It is reported that network pharmacology predicts the clinical efficacy, pathways, and side effects of drugs by constructing drug-drug networks, disease-drug networks, and diseasedisease networks, providing valuable information for improving the clinical efficacy, reducing toxicity, and elucidating multimechanisms of drugs [11]. For example, Wang Nani found that Er-Xian Decotion has 13 main components closely related to 65 osteoporosis-related targets by using network pharmacology, thereby constructing Er-Xian Decotion component-osteoporosis target network and potential antiosteoporosis mechanism [12]. Yueying et al. identified 108 compounds, 86 potential targets, and 47 signal transduction pathways that Danshiliuhao Granule regulates liver fibrosis by the network pharmacology method, which reflects the multicomponent, multitarget, and multichannel characteristics of Chinese herbal medicine in antiliver fibrosis [13]. erefore, in order to reveal the relationship between fracture and the active ingredients involved in the DR, we conducted network pharmacology to achieve this goal from protein and gene level. We collected the information of targets from active ingredients in DR and targets of fracture from several databases, respectively, and used network pharmacology to explore the potential synergistic mechanisms of DR for treating fracture.

Screening of Active Ingredients of Drynariae Rhizoma.
Traditional Chinese Medicine Systems Pharmacology (TCMSP, http://lsp.nwu.edu.cn/, Version 2.3) Database and Analysis Platform includes chemicals, targets, and drugtarget-disease networks, as well as pharmacokinetic properties involving oral bioavailability, druglikeness, bloodbrain-barrier, and so on [14]. ere were 71 compounds of DR which were obtained from the TCMSP. e potential active ingredients of DR for treating fracture were screened according to their oral bioavailability (OB) ≥30% and druglikeness (DL) ≥0.18 recommended by TCMSP.

Obtaining the Chemical Structure of Active Ingredients.
e structure of the potential active ingredients of DR was downloaded from TCMSP and stored in mol2 format. If there was no chemical structure, the PubChem compound was put into the PubChem (https://pubchem.ncbi.nlm.nih. gov/) to download a chemical structure and save it in sdf format, or the PubChem compound was put into the Zinc database (https://zinc.docking.org/) to download a chemical structure and save it in mol2 format. e related SMILES of potential active ingredients was received from TCMSP or PubChem or Zinc database. en, the SMILES was put into the Swiss Target Prediction database (http://www. swisstargetprediction.ch/) to obtain the related drug target and save it.

Gene Targets of Drynariae Rhizoma.
e DRAR-CPI server (http://cpi.bio-x.cn/drar, update in 2017-7-26) has a collection of drug molecules and targetable human proteins [15]. When submitting a drug molecule, the server docks the drug uploaded by users with the three-dimensional structure of all protein targets in the database, scores, and ranks them with the affinity scoring function based on the protein-ligand interaction, thereby predicting the potential protein targets of human-targetable drugs [15,16]. is affinity score is called Z-score in the DRAR-CPI server [17]. Protein-ligand interaction with Z-score <− 0.5 was recommended by DRAR-CPI as a potential protein target for human-targetable drugs [16]. We uploaded the potential active ingredients of DR in mol2 or sdf format and used Z-score <− 0.5 to select potential protein targets for DR. A total of 1760 proteins with Z-score <− 0.5 and 355 protein targets were obtained after deletion of the duplicate data. e PDB ID of the protein targets were inputted into UniProt KB (http://www.uniprot. org/uniprot/) of the UniProt database, and the "popular organisms" was selected as human to obtain the gene targets associated with the potential active ingredient of DR.

Constructing the Ingredient-Target Network of Drynariae
Rhizoma.
e common gene targets of DR and fracture were introduced into the Cytoscape software (Version 3.4.0) to construct an ingredient-target network of Drynariae Rhizoma and analyze the topology properties of the network, including degree, betweenness centrality, and closeness centrality [18]. e degree describes the number of connections to a node in the network, indicating interaction with other nodes in the network. Betweenness centrality measures the proportion of a node between shortest paths among other nodes, suggesting the importance of nodes in maintaining network tightness. Closeness centrality indicates the degree of nodes close to the "center" of the network. A node with high degree, betweenness centrality, and closeness centrality values means that it plays a very important role in the network [18].

Constructing Protein-Protein Interaction (PPI) of Drynariae Rhizoma.
e String database (https://string-db.org/, Version 10.5) is a database containing known and predicted PPIs, which collect and integrate a large number of protein interactions involving 9,643,763 proteins and 1,380,838,440 interactions, including experimental data and interactive prediction data derived from bioinformatic methods [19]. Common gene targets of DR and fracture were imported into the STRING database, and the species were set to humans for PPIs. en, the highest confidence was set to 0.9 in the minimum required interaction score and the results were updated. e TSV format of the updated results were downloaded. en, node1, node2, and combined scores were extracted and imported into the Cytoscape software to create a PPI network, and the network was analyzed as follows: Step 1: analyze the topology properties of the network: cytoscape ⟶ tools ⟶ network analyzer ⟶ network analysis ⟶ analyze network, save the CSV format of the network result and extract the degree value.
Step 2: create a network map according to the degree: cytoscape ⟶ tool ⟶ network analyzer ⟶ network analysis ⟶ generate style from statistics ⟶ map node size to degree ⟶ map node color to degree and save the PPI network map.

Molecular
Docking. SystemsDock (http://systemsdock. unit.oist.jp, Version 2.0) is a web server for network pharmacology-based prediction and analysis that could be used to illustrate the role of ligands on a complex molecular network [20]. It evaluates the protein-ligand binding potential of molecular docking by combining docking with the intelligence (dock-IN) score. e dock-IN score is the negative logarithm of the experimental dissociation/inhibition constant (pKd/pKi), which ranges from 0 to 10, indicating weak to strong binding [20]. It is believed that the docK-IN score above 4.25 indicates a slight binding potential between the protein and ligand; a value greater than 5.0 indicates a moderate binding potential, and a value greater than 7.0 indicates a strong binding potential [16]. We extracted the top 5 proteins with the highest degree value in the PPI network. e proteins that were recognized by systemsDock docked with the potential active ingredients of DR to receive the dock-IN score. e results were saved, and their dock-IN score was analyzed to assess the binding potentials between the potential active ingredients of DR and protein targets.

GO Functional Analysis and KEGG Pathway Enrichment
Analysis. GO (http://www.geneontology.org) is widely used for annotation of gene function, providing detailed annotations of gene function in terms of biological process (BP), cellular component (CC), and molecular function (MF), respectively [21]. Database for annotation, visualization, and integrated discovery (David, https://david.ncifcrf.gov/, Version 6.8) is a functional genomic annotation database that provides bioinformatics annotation for genes or proteins based on the gene annotation function of the GO database and the signaling pathway information of the KEGG database [22]. We performed GO functional analysis and KEGG pathway enrichment analysis in the David database.
e procedure was as follows: Step 1: paste the common gene targets of DR and fracture list.
Step 6: download the results of BP, CC, and MF in the gene ontology.
Step 7: download the results of KEGG pathway in the pathways.
Step 8: targets with P < 0.05 were screened and sorted by count (number of targets), and the top-ranked biological processes or KEGG pathways were extracted.
Step 9: BP, CC, and MF were designed using GraphPad Prism 5.0 software. e KEGG pathways were designed by the advanced bubble chart of the omicshare tool (http://omicshare.com/tools/ Home/Soft/getsoft/type/index).

Collect Protein Class Corresponding to Common Gene
Targets. DisGeNET (http://www.disgenet.org/web/ DisGeNET/menu, Version 5.0) is a discovery platform that contains one of the largest publicly available genes and variants associated with human disease. It could be used to analyze the properties of disease genes and investigate the molecular basis of specific diseases and their comorbidities, as well as adverse drug reactions [23]. We used the search function of the DisGeNET platform to retrieve the protein class corresponding to common gene targets.

Pathway Integration.
We used the KEGG Mapper tool in the KEGG database (http://www.kegg.jp/) to retrieve some pathways of DR for fractures and then integrate into a final pathway map. e procedure was as follows: Step 1: used the UniProt KB search function of the UniProt database to retrieve the UniprotID of the common gene targets.
Step 2: import the UniProt ID of the common gene targets.
Step 3: set the parameters: search against: hsa, primary ID: NCBI-UniProt ID, and examples: Homo sapiens pathway.
Step 5: integrate the signal path.

Gene Target Prediction.
A total of 303 gene targets associated with the potential active ingredients of DR were retrieved in the UniProt database. A total of 3,173 fracturerelated genes were received, and 3,054 genes remained after deletion of the duplicate and false-positive genes. Common gene target screening for fracture and DR are shown in Figure 1. A total of 144 common gene targets of DR and fracture were received, indicating the potential targets for DR to treat fractures, as shown in Table 2.    e active ingredients and gene targets of DR was inputted into Cytoscape software to construct the ingredient-target network, as shown in Figure 2. In the network, the pink oval nodes represent the main active ingredients of DR, and the light green rectangle nodes represent the potential gene targets for DR to treat fractures. e line represents the correlation between the active ingredients of DR and the gene targets.
ere are 161 nodes and 774 lines in the network. An active ingredient could be linked to different gene targets, and a gene target could be linked to different active ingredients, suggesting the multicomponent and multitarget characteristics of DR. e topology properties of active ingredients of DR are shown in Supplementary Table 1. Both cyclolaudenol and cycloartenone were linked to a maximum number of gene targets, with 70 (48.61%) different gene targets. Cyclolaudenol acetate was linked to 54 (37.5%) different gene targets. Xanthogalenol was linked to a minimum number of gene targets for a total of 29 (20.14%). In addition, there were four gene targets with the top four degree values, betweenness centrality, and closeness centrality at the same time (Table 3), which were NCOA1, GSK3B, TTPA, and MAPK1.

Protein-Protein Interaction of Drynariae Rhizoma.
e PPI network of DR is shown in Figure 3. In the network, the node represents the protein, and the size and color of the node represent the value of the degree. e larger the node and the brighter the color (yellow to blue), the greater the value of the degree. e line indicates the association between proteins. Results showed that there were 143 nodes and 315 lines.
Degree in the network indicates the number of proteins that a protein has interacting with. In other words, topdegree protein targets screened in PPI plays a pivotal role in the treatment of fractures with DR. Five important protein targets with top degree of DR were identified in the PPI network and are shown in Table 4. ey were MAPK1, SRC, HRAS, RXRA, and NCOA1.

Molecular Docking.
ree important protein targets with top degree of DR were identified by SystemsDock, including SRC, RXRA, and NCOA1. Dock-IN score of these three proteins docked with 17 active ingredients of DR are shown in Table 5

Protein Class Corresponding to Common Gene
Targets. e protein class corresponding to potential targets for treating fracture from the main active ingredients of DR is presented in Table 6. e results showed that DR treatment

Signaling Pathway Integration.
Four pathways associated with the potential targets of DR main active ingredients for treating fracture are presented in Figure 6. e arrow (⟶) indicates the promoting effect, the T-arrows (⊣) indicate the inhibition, and the arrows of different colors represent different signaling pathways. e targets of the signaling pathway were marked as light blue, and the potential targets of DR main active ingredients for treating fracture were marked as dark blue.
ere were 21 (14.58%) potential targets of main active ingredients of DR for treating fracture in the PI3K-AKT, MAPK, Ras, and VEGF signaling pathways, indicating that the fracture targets play a role in these signaling pathways. In addition, some targets play a role in a variety of signaling pathways, such as Ras, RafB, AKT/PKB, PI3K, ERK, and JNK.

Discussion
In order to reveal the relationship between fracture and the active ingredients involved in the DR, we predicted the mechanism of DR treatment fractures by constructing a biological network of interactions between active ingredients and common gene targets and common protein targets from a molecular level. A total of 17 active ingredients of DR were received in our study, including (2R)-5,7-dihydroxy-2-(4- Note. e size and the color of the node represents the value of the degree (yellow ⟶ orange ⟶ blue indicates that the degree value is from low to high, and the small circle represents a low degree value).  [24]. In addition, Kang Suk-Nam finds that the total phenolics and flavonoids of DR are better extracted with 70% ethanol instead of water, and this ethanol extraction method also makes these extracts have higher antioxidant activity and in vitro antiosteoporosis effect [25].
In the ingredient-target network, all active ingredients were also identified to bind well to the fracture gene targets, binding to at least 29 (20.14%) different gene targets. erefore, the 17 active ingredients of DR may have the effect of reducing bone loss and promoting fracture healing.
In our study, 144 common gene targets of DR and fracture were received, and 774 interactions between the active ingredients of DR and common gene targets were found. Some gene targets have been confirmed by clinical trials or animal experiments. For example, Guimarães et al. found that polymorphisms in the FGFR1 and BMP4 genes were associated with fracture nonunion in patients [26]. And our team's previous study also found that the total flavonoids of DP could promote osteogenesis and mineralization in rats with tibial defects by increasing the gene expression of BMP2, BMP4, BMPR1A, and Smadl [27]. In the ingredient-target network, NCOA1, GSK3B, TTPA, and MAPK1 were identified as important gene targets based on degree values, betweenness centrality, and closeness centrality. Qin et al. found   [31]. ese gene targets are involved in vascularization, osteoblast differentiation, and osteoclastogenesis in fracture repair. Besides, we found that one active ingredient can interact with different gene targets, and one gene target can interact with different active ingredients, which is consistent with the modern drug theory of "multi-ingredient, multitarget" [9].
To identify the interactions of proteins corresponding to common genes, we conducted a PPI network. A total of 143  Figure 6: Antifracture pathways of potential targets for treating fracture from main active ingredients of Drynariae Rhizoma. Note. e arrow (⟶) indicates the promoting effect, the T-arrows (⊣) indicate the inhibition, and the arrows of different colors represent different signaling pathways. e targets of the signaling pathway were marked as light blue, and the potential targets of main active ingredients of DR for treating fracture were marked as dark blue.
common protein targets for DR and fracture were received, with 315 PPIs. In addition, MAPK1, SRC, HRAS, RXRA, and NCOA1 were identified as the five most important target proteins. Previous studies have found that MAPK1 and SRC could promote proliferation and differentiation of myeloid cells and inhibit apoptosis [32,33]. Clinical cases have found that elevated levels of fibroblast growth factor 23 in patients with dysplasia are associated with HRAS mutations [34]. RXRA is an essential cofactor in the action of 1,25-dihydroxyvitamin D, and umbilical cord RXRA methylation was inversely related to offspring bone mineral content [35]. Coronnello et al. found that NCOA1 modulate the estrogen effects in bone, and miR-488-5p overexpression reduces NCOA1 protein levels, thereby reducing bone mineral density [36]. ese protein targets are associated with bone growth and angiogenesis in fracture repair. At the same time, we docked SRC, RXRA, and NCOA1 with 17 potential active ingredients of DR and found that 41 (80.39%) had moderate binding potential, suggesting that DR could bind well to fracture-related protein targets. In order to identify the function of the common gene, we performed GO functional analysis on these genes. e results showed that the common gene involves multiple processes, parts and functions in BP, CC, and MF, which was consistent with existing studies about DR and fracture repair. For example, in the BP, 33 (22.92%) gene targets were involved in positive regulation of transcription from the RNA polymerase II promoter, and 30 (20.84%) gene targets were involved in signal transduction. Previous studies have shown that the promoter activates the polymerase to bind precisely to the template DNA and has the specificity of transcription initiation [37]. e RNA polymerase II promoter responsible for mRNA transcription is the largest and most important class of promoters [37].
is provides conditions for DR to initiate osteogenic targets. Besides, some signal transduction genes have been found in experiments. Song Nan found that VEGFR-2 may play a signal transduction role for naringin, one ingredient of DR, to stimulate angiogenesis and promote fracture healing [38]. In the CC, 63 (43.75%) gene targets were involved in cytosol, 60 (41.67%) gene targets were involved in extracellular exosome, and 58 (40.28%) gene targets were involved in cytoplasm.
is indicates that the recovery of the fracture requires the support of various components in the cell, which is consistent with previous studies [39]. In the MF, 110 (76.39%) gene targets were involved in protein binding, suggesting that mutual recognition between proteins has good gene regulation conditions. is is consistent with the protein class corresponding to the potential target. ese results were further validated in the protein class corresponding to the common gene. In the protein class, all of these common genes have been found to regulate a variety of fracture-related molecules, such as transcription factors, receptors, enzyme regulators, molecular chaperones, cell adhesion molecules, enzyme, and so on.
In order to identify the synergistic mechanism of DR for fracture, we performed KEGG pathway enrichment analysis and summarized some important signaling pathways, which provides direction for future research. In the KEGG pathway enrichment analysis, 17 (11.81%) gene targets were involved in MAPK signaling pathway, 17 (11.81%) gene targets were involved in PI3K-Akt signaling pathway, 14 (9.72%) gene targets were involved in Ras signaling pathway, and 6 (4.17%) gene targets were involved in VEGF signaling pathway, which suggest that common gene targets play a role in repairing fractures in multiple signaling pathways. MAPK and PI3K/AKT signaling pathways have been demonstrated to promote osteoblastic bone formation [40]. Zhang et al. confirmed that total flavonoids from DR promote the osteogentic differentiation of ciliary neurotrophic factormodified myoblasts by activating p38 MAPK signaling pathway [41]. Moreover, total flavonoids of DR could promote osteogenic differentiation of rat dental pulp stem cells via the PI3K/Akt pathway [42]. Lin et al. found that the effect of naringin on the healing of fracture may be related to the promotion of the synthesis and secretion of cellular chemokines (CXCL5, CXCL6) and enhancement of mesenchymal stromal cell migration through Ras signaling pathway [43]. In addition, naringin stimulates angiogenesis by regulating the VEGF/VEGFR-2 signaling pathway in rats, thereby promoting fracture healing [38]. However, the mechanism of some active ingredients of DR in the treatment of fractures has not yet been verified. erefore, we integrated MAPK, PI3K/AKT, Ras, and VEGF signaling pathways to provide a reference for researchers to verify the mechanism of other DR active ingredients in the treatment of fractures.

Conclusion
We collected the gene and protein targets of fractures and active ingredients of DR and then used network pharmacology to reveal the correlation between drugs and diseases and the potential synergistic mechanism of different targets of DR in the treatment of fractures, which provides a reference for the development of new drugs.

Data Availability
All data are available from the corresponding author upon reasonable request.
Evidence-Based Complementary and Alternative Medicine