A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Rhizoma Dioscoreae Nipponicae in the Treatment of Asthma

Background In this study, network pharmacological methods were used to analyze the targets of Rhizoma Dioscoreae Nipponicae (RDN) and investigate the potential underlying mechanism of RDN in the treatment of asthma. Methods Asthma-related targets were obtained from the GeneCards and DisGeNET databases. The bioactive components of RDN were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database, and the targets of these compounds were predicted using the BATMAN-TCM database. The network of RDN component targets was constructed using Cytoscape. A protein-protein interaction (PPI) network was constructed in Cytoscape to determine the potential targets of RDN for the treatment of asthma. The hub genes of RDN in the treatment of asthma were screened using network topological parameters. Gene ontology (GO) and the KEGG pathways were analyzed. Molecular docking and in vivo experiments were performed to validate the network pharmacology results. Results A total of four bioactive components and 55 targets were identified. The results of the enrichment analysis suggested that the treatment of asthma with RDN involved signaling pathways, such as those related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, the cell cycle, prostate cancer, transcriptional misregulation in cancer, hepatitis B, thyroid hormone signaling, and PI3K-AKT signaling, as well as other signaling pathways. Molecular docking showed that the active components of RDN could stably bind to the predicted target. In vivo experiments showed that RDN could regulate the expression of target genes and inhibit the activation of the PI3K-AKT signaling pathway. Conclusion To a certain extent, this study reveals the potential bioactive components and molecular mechanisms of RDN in the treatment of asthma and provides new insights for the development of new drugs for asthma.


Introduction
Asthma is a common chronic condition characterized by chronic airway inflammation, which leads to increased bronchial hyperresponsiveness and mucus hypersecretion [1]. Asthma can be caused by a variety of stimuli, including viral infections, exposure to allergens and/or pollutants, smoking, sudden temperature variations, stress, and exercise [2,3]. e global incidence of asthma was 43.12 million cases/year in 2017, with asthma incidence and mortality rates of 272.68 million and 490,000 cases, respectively, in the same year [4]. Asthma has a definite impact on the national economies of countries worldwide [5]. Presently, some patients with asthma cannot be effectively treated with corticosteroids. erefore, it is necessary to develop new and significant treatments for asthma. Clinical studies have found that traditional Chinese medicine (TCM), a type of alternative medicine, has good clinical effects on asthma [6][7][8]. As a supplementary medicine, TCM is widely used in the treatment of asthma with good results and few side effects [9].
TCM can act on multiple biological processes through multiple targets and bioactive components to treat diseases. Network pharmacology is a branch of pharmacology that is based on systems biology and multiple pharmacology theories and primarily focuses on biomolecular networks [19]. At present, network pharmacology is widely used in TCM research to reveal the mechanisms of complex herbal formulae by discovering bioactive ingredients and biomarkers. Furthermore, molecular docking is an important process in structural molecular biology and in computer-aided drug design for the development of new medicines [20].
Here, we analyzed the role of RDN in asthma treatment using a network pharmacology approach, and the results were validated by molecular docking and in vivo experiments.

Screening and Obtaining the Bioactive Components and
Targets of RDN. In this study, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://ibts.hkbu.edu.hk/LSP/tcmsp. php) was used to determine the components of RDN [21]. TCM is mostly administered orally and exerts a variety of biological effects through its action on organs and tissues after absorption, distribution, metabolism, and excretion (ADME) in vivo, known as the pharmacokinetics of TCM. Oral bioavailability (OB) and drug-like (DL) are important parameters of ADME [22][23][24]. Components with OB ≥ 30% and DL ≥ 0.18 were selected as the main candidate bioactive components. In addition, more active components of RDN with significant pharmacological effects were supplemented based on previous studies [25].
BATMAN-TCM [26] was used to retrieve and predict the targets of the main bioactive components in RDN. In addition, components without potential targets were excluded. e targets were collected with a score cutoff of ≥40. e obtained targets, including the name, gene ID, and organism, were confirmed using the UniProt protein database (https://www.uniprot.org) [27]. e duplicate targets were removed and then uploaded to the Cytoscape platform to construct the herb-component-target network.

Identification of Asthma-Related Targets.
e keyword "asthma" was used as the search term to collect disease targets based on the GeneCards (https://www.genecards. org) [28] and DisGeNET (https://www.disgenet.org) [29] databases. e asthma-related targets obtained from the last search were uploaded to the STRING database (https://string-db. org/) to analyze the relational value and were then imported to the Cytoscape platform to construct the asthma target network.  [30]. PPI networks of RDN targets and asthma-related targets were constructed using Cytoscape.

Construction of the
Central network evaluation is considered a primary method for screening core proteins in PPI networks. First, the PPI networks of RDN targets and asthma-related targets were merged into an intersection. Next, the Cytoscape plugin CytoNCA was applied to assess the intersection from the six centrality measures: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC) [31]. In addition, CytoHubba, a Cytoscape plugin, was used to select the hub genes [32].

GO and KEGG Pathway Enrichment Analysis.
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the DAVID Bioinformatics Resources 6.8 database. GO analysis included three aspects: molecular function (MF), cellular component (CC), and biological process (BP).

Chemical Analysis of RDN.
e RDN test sample was prepared according to a previous study [33]. Standard diosgenin was purchased from the Tauto Biological Technology Company (China). e chemical constituents of RDN were determined using an Agilent 1260 high-performance liquid chromatography (HPLC) system (Agilent, CA, USA). Samples (10 μL) were separated using an Agilent ZORBAX Eclipse Plus C18 column (4.6 mm × 250 mm, 5 μm) at 30°C, with mobile phases consisting of water (A) and acetonitrile (B). e following gradient conditions were used: 0-5 min, 15% B; 5-30 min, 15-40% B; and 30-50 min, 40-50% B. e flow rate was 1.0 mL/min, and the detection wavelength was 203 nm. Chromatograms are shown in Supplementary Figure 1. e retention time of diosgenin was 7.439 min.

Hematoxylin and Eosin (H&E) Staining.
e lung tissues were collected and infused with 4% paraformaldehyde and were then dehydrated, paraffin-embedded, and cut into 5µm-thick sections. e sections were then stained with hematoxylin for 3 min and eosin for 3 min. Finally, the pathological morphology of the mouse lung tissue was observed using a digital camera (Leica Microsystems Inc., IL, USA).

Quantitative Real-Time PCR (qRT-PCR) Analysis.
Total RNA was extracted from lung tissues using TRIzol ® Reagent (Invitrogen, CA, USA) and reverse transcribed into cDNA using the PrimeScript ™ RT Reagent Kit (Takara, Japan). qRT-PCR was performed on a 7500 Real-Time PCR System ( ermoFisher Scientific, MD, USA) at 95°C for 3 min, 40 cycles of 95°C for 12 s, and 62°C for 40 s. e primers used are listed in Supplementary Table 1. e relative expression of genes was calculated using the 2 −ΔΔCT method, and GAPDH was used as an internal control.

Western
Blotting. Total proteins were extracted using RIPA lysis buffer (Beyotime, China), separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and transferred to polyvinylidene fluoride membranes. e membranes were blocked with 5% skim milk for 1 h and incubated with the primary antibodies (1 : 1000, Abcam Co., MA, USA) at 4°C overnight and then incubated with the secondary antibodies at room temperature for 1 h. Finally, enhanced chemiluminescent reagents (FDbio, China) were used to visualize the protein bands.
2.11. Statistical Analysis. Data are presented as the means with standard deviation and were analyzed using SPSS 27.0. e differences between the groups were analyzed by oneway ANOVA, followed by Tukey's test. Differences were considered statistically significant at p < 0.05.

e Compound-Target Network of RDN.
Two components of RDN were obtained from the TCMSP database. Studies have shown that 7-epitaxol and deoxyvasicinone are the major bioactive and principal toxic compounds of RDN. erefore, 7-epitaxol and deoxyvasicinone were considered as the candidate components in this study. Collectively, we identified a total of 4 bioactive compounds and 55 targets for RDN, the details of which are shown in Tables 1 and 2. We subsequently constructed a compound-target network of RDN using Cytoscape ( Figure 1).

GO and KEGG Pathway Enrichment Analyses of RDN.
To further investigate the 55 targets of RDN, GO enrichment analysis was performed using GO terms from the BP, CC, and MF categories. e resultant targets of RDN were classified into 130 BP terms, 27 CC terms, and 31 MF terms, according to p < 0.01. e GO analysis results with the top 10 markedly enriched BP, CC, and MF terms are shown in Figures 2(a)-2(c). KEGG pathway enrichment analysis showed that the targets of the components in RDN were enriched in 12 associated pathways (p < 0.01). e top 10 KEGG pathways in which the targets of RDN were enriched are presented in Figures 2(d) and Table 3.

e Network of Asthma-Related Targets.
e occurrence and development of asthma involve the co-regulation of multiple genes. Investigation of gene and gene-environment interactions is beneficial for elucidating the pathogenesis of asthma. In this study, 55 asthma-related targets were obtained from human genomic databases. e number of these targets in the GeneCards and DisGeNET databases was 19 and 38, respectively. ese targets are detailed in Table 4.

e PPI Network of RDN and Asthma.
e PPI networks of RDN and asthma were constructed using BisoGenet. e asthma-related target network was constructed with 1,583 nodes and 29,712 edges ( Figure 3(A)). In addition, the PPI network of RDN-related genes was constructed with 2,950 Evidence-Based Complementary and Alternative Medicine   Figure 3(B)). ese two networks were subsequently merged into an intersection with 681 nodes and 15,546 edges ( Figure 3(C)). CytoNCA was used to assess the intersection of the PPI network by topological analysis. An RDN on asthma PPI network, with 151 nodes and 3,975 edges, was first screened based on the criteria of "BC ≥ 180333, CC ≥ 0.4842, and DC ≥ 52" (Figure 3(D)). A core-target PPI network was further screened using the criteria "BC ≥ 179866, CC ≥ 0.5113, and DC ≥ 7," and consisted of 76 nodes and 1,587 edges ( Figure 3(E)).

GO Functional Enrichment and Pathway Analysis.
GO annotation showed that the targets of the PPI network were classified into 191 BP terms, 49 CC terms, and 68MF terms according to p < 0.01. KEGG pathway analysis revealed that the targets of the PPI network were assigned to 43 KEGG pathways based on p < 0.01. In the BP ontology, the targets of the PPI network were primarily associated with telomere organization, DNA replication-dependent nucleosome assembly, chromatin silencing at rDNA, protein heterotetramerization, negative regulation of gene expression, epigenetics, beta-catenin-TCF complex assembly, positive regulation of gene expression, cellular protein metabolic processes, nucleosome assembly, and gene silencing by RNA (Figure 4(a)).
For the CC ontology, the targets were located mainly in the protein complex, nuclear chromosome, telomeric region, nucleoplasm, nucleosome, cell-cell adherens junction, nucleus, membrane, extracellular matrix, and extracellular exosome (Figure 4(b)).
Based on GO annotation of MF, it was observed that the targets were mainly involved in nucleosomal DNA binding, histone binding, protein domain-specific binding, cadherin binding involved in cell-cell adhesion, protein heterodimerization activity, transcription factor binding, enzyme binding, ubiquitin-protein ligase binding, poly(A) RNA binding, and protein binding (Figure 4(c)).
In addition to functional GO annotation, KEGG pathway analysis was also conducted on the targets of the PPI network. As shown in Figure 4(d), KEGG pathway enrichment analysis suggested that the targets were mainly related to signaling pathways, such as systemic lupus erythematosus, alcoholism, viral carcinogenesis, cell cycle, prostate cancer, transcriptional misregulation in cancer, the thyroid hormone signaling pathway, hepatitis B, the PI3K-Akt signaling pathway, and pathways in cancer. e distribution of the key targets in systemic lupus erythematosus,  Figure 5. e results indicated that the action targets of the main bioactive components of RDN were distributed in different metabolic pathways. e "multicomponents, multitargets, and multi-pathways" mutual regulation is a possible mechanism in the treatment of asthma.

Enrichment Analysis of the Hub Genes.
Based on the coretarget PPI network, hub genes were selected using Cyto-Hubba. e results showed that EZH2, SIRT1, CDK1, CUL7, NTRK1, MAPK1, RACK1, PARP1, CDK2, and BRCA1 were the top 10 hub nodes in the core-target PPI network. e PPI network of the hub genes is shown in Figure 6.

Molecular Docking Verification.
e molecular docking assay was used to verify the binding ability of the bioactive components of RDN (detailed information regarding the bioactive components is listed in Table 5) and the top nine hub genes (CDK2, PDB: 1b39; SIRT1, PDB: 4jt8; RACK1, PDB: 4u6r; EZH2, PDB: 5ij8; NTRK1, PDB: 5wr7; MAPK1, PDB: 6g54; CDK1, PDB: 6gu7; CUL7, PDB: 6o60; and PARP1, PDB: 7kkq). As shown in Table 6, we obtained 32   Evidence-Based Complementary and Alternative Medicine bronchial lumen, and an intact airway mucosal epithelium; the damage to the lung tissue in the model + AKT agonist + RDN group was significantly reduced compared with that observed in the model group, and the inflammatory cell infiltration around the bronchi was reduced, but still heavier than that observed in the model + PA and model + RDN groups (Figure 8(a)). We also detected the expression levels of the predicted targets and the PI3K-AKT pathway using qRT-PCR and Western blot analysis. As shown in Figure 8(b), compared with the control group, the expression of SIRT1 and MAPK1 was significantly decreased, while CDK2 expression significantly increased in the model and model + AKT agonist groups. is phenomenon was reversed by RDN administration. In addition, the expression of RACK1 and NTRK1 was significantly upregulated in the other groups compared with that in the control group, with no significant differences between the other groups. Furthermore, PI3K and AKT expression was not significantly different between the groups.
Western blotting showed that the expression of PARP1, EZH2, p-PI3K, and p-AKT was significantly increased in the model and model + AKT agonist groups compared with that in the control group, in contrast to the significantly decreased expression of MAPK1 and SIRT1. However, this inhibition or enhancement was reversed by the administration of RDN (Figure 8(c)).

Discussion
Asthma is a common, complex respiratory disease with high morbidity and mortality rates. RDN has been demonstrated to have an effect on asthma, but its pharmacological mechanisms have not been elucidated systematically and comprehensively. In this study, through network pharmacology, we further explored the mechanisms of RDN in asthma. ree aspects of our observations gained our attention. First, four bioactive compounds of RDN were identified. Second, systemic lupus erythematosus, alcoholism, viral carcinogenesis, cell cycle, and prostate cancer were  Evidence-Based Complementary and Alternative Medicine considered as the key sections of RDN in asthma treatment at the gene level. ird, ten hub targets for asthma were enriched in pathological changes in airway inflammation and remodeling as well as other diseases.
In this study, we analyzed the targets of RDN and identified potential signaling pathways. ere were a total of four bioactive components in the RDN with the standard. e main bioactive components of RDN are 7-epitaxol and deoxyvasicinone. e bioactive component-target network showed that these compounds had multiple targets. 7-Epitaxol (OB � 45.18, DL � 0.24) is a metabolite of taxol and has the same activity as taxol [36,37]. Taxol has been reported to have a good clinical effect on asthma [38,39]. Deoxyvasicinone (OB � 56.29, DL � 0.1) consists of a quinazolinone moiety conjugated with pyrrolidine, a naturally occurring alkaloid with antibacterial, anti-inflammatory, and antiproliferative activities [40,41]. Moreover, HLPC analysis showed that diosgenin is an important component of RDN.
e results of GO enrichment analysis showed that all targets in the PPI network were mainly involved in beta-catenin-TCF complex assembly and other biological processes. An increasing body of evidence suggests that betacatenin-TCF plays a key role in asthma. Beta-catenin-TCF is a pleiotropic cytokine involved in airway inflammatory responses and fibrotic tissue remodeling in the pathogenesis of asthma [42,43].
KEGG pathway enrichment analysis showed enrichment in systemic lupus erythematosus, alcoholism, the PI3K-AKT signaling pathway, and other signaling pathways. A link between asthma and systemic lupus erythematosus (SLE) has been reported [44]. Charoenngam et al. found a significant association between asthma and an increased risk of SLE using a meta-analysis technique [45]. Collectively, alcoholism induces airway mast cells to release histamine, which exacerbates asthma in susceptible populations [46]. e PI3K-Akt pathway is thought to be closely associated with airway         Evidence-Based Complementary and Alternative Medicine 13 inflammation and is regulated by various upstream proteins. Azithromycin has been reported to affect airway remodeling in asthma via the PI3K/Akt pathway [47]. Brg1 exacerbates asthma airway inflammation by inhibiting the PI3K/Akt/ mTOR pathway [48]. In addition, lncRNA-CASC7 enhances corticosteroid sensitivity by inhibiting the PI3K/AKT signaling pathway by targeting miR-21 in asthma [49]. In this study, 10 hub targets of RDN were identified for asthma treatment, and RACK1 and SIRT1 were shown to play important roles in asthma. e receptor for activated protein kinase C1 (RACK1), a component of the ribosome, located in the basal airway epithelium [50], plays an important role in a variety of biological responses, including cell growth, differentiation, and migration. Apoptosis and epithelial-mesenchymal transition (EMT) are two key components in the pathogenesis of asthma, both of which are mediated by TGF-β1. Knockdown of RACK1 significantly inhibited apoptosis and reduced TGF-β1 upregulation of EMT-related protein levels (N-calmodulin and Snail) in vitro [51]. Sirtuin 1 (SIRT1) is a class III histone deacetylase that plays an important role in the regulation of many pathophysiological processes, such as inflammation, autoimmunity, and apoptosis [52]. Lai et al. found that myeloid-specific SIRT1 deficiency exacerbated the airway inflammatory response in a mouse model of allergic asthma [53], which also suggests an important role for SIRT1 gene activation in asthma treatment. Finally, we validated the results of the network pharmacological approach using molecular docking and in vivo experiments in asthmatic mice. e molecular docking assay showed that the binding energy of the active compounds in RDN to the predicted targets was less than −5 kcal/mol, suggesting that RDN binds well to the therapeutic targets in asthma. In addition, Western blot results showed that RDN reduced the expression of p-PI3K and p-AKT, suggesting that RDN resistance to asthma may be achieved through the PI3K-AKT pathway.

Conclusions
In summary, network pharmacology showed that the bioactive components of RDN, particularly 7-epitaxol and deoxyvasicinone, could act on multiple targets. RDN was effective in treating asthma mainly through the pathways related to systemic lupus erythematosus and alcoholism, the PI3K-AKT signaling pathway, and other signaling pathways. e results of molecular docking, qRT-PCR, and Western blot analysis suggest that PARP1, CDK2, and MAPK1 might play important roles in the treatment of asthma. is study provides a basis for the treatment of asthma by RDN from the perspective of network pharmacology. ese findings have guiding significance for the clinical application of RDN and provide new clues for the future study and development of new drugs. Of course, our study also needs more in vitro Data Availability e raw data used to support the findings of this study are included within the article.

Conflicts of Interest
e authors declare that they have no conflicts of interest.