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

Authors: Weiyi Wang; Liying Xu; Lingming Zhou; Shanhong Wan; Libin Jiang
Journal: Evidence-Based Complementary and Alternative Medicine
 (2022)

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## Abstract

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.


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## Body

## 1. 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]. The 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. Therefore, 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 [6–8]. As a supplementary medicine, TCM is widely used in the treatment of asthma with good results and few side effects [9].Rhizoma Dioscoreae Nipponicae (RDN) (ChuanShanlong) is indicated in many medical books and has various physiological effects, such as clearing heat, decreasing phlegm, relieving cough and asthma, dispersing blood stasis, and relieving pain [10]. Several findings have shown that RDN alleviates rheumatoid arthritis, treats hyperuricemia, improves thyroiditis, and relieves pulmonary fibrosis [11–14]. Moreover, our previous studies showed that RDN exhibited therapeutic effects in mice with chronic asthma [15–18]. However, the mechanisms of RDN in asthma treatment remain unclear.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.


## 2. Materials and Methods

### 2.1. 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 [22–24]. 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. The targets were collected with a score cutoff of ≥40. The obtained targets, including the name, gene ID, and organism, were confirmed using the UniProt protein database (https://www.uniprot.org) [27]. The duplicate targets were removed and then uploaded to the Cytoscape platform to construct the herb-component-target network.


### 2.2. Identification of Asthma-Related Targets

The 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.The 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.


### 2.3. Construction of the Protein-Protein Interaction (PPI) Network

A PPI network was constructed and visually analyzed using the Cytoscape plugin BisoGenet, which contains six PPI databases: IntAct Molecular Interaction Database (IntAct), Biomolecular Interaction Network Database (BIND), Biological General Repository for Interaction Datasets (BioGRID), Database of Interacting Proteins (DIP), Molecular INTeraction Database (MINT), and Human Protein Reference Database (HPRD) [30]. PPI networks of RDN targets and asthma-related targets were constructed using Cytoscape.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].


### 2.4. 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).


### 2.5. Chemical Analysis of RDN

The RDN test sample was prepared according to a previous study [33]. Standard diosgenin was purchased from the Tauto Biological Technology Company (China). The 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). The following gradient conditions were used: 0–5 min, 15% B; 5–30 min, 15–40% B; and 30–50 min, 40–50% B. The flow rate was 1.0 mL/min, and the detection wavelength was 203 nm. Chromatograms are shown in Supplementary Figure 1. The retention time of diosgenin was 7.439 min.


### 2.6. Molecular Docking Analysis

The chemical structures of the RDN ingredients were downloaded from the ZINC website (https://zinc15.docking.org) [34], and three-dimensional structures of the hub genes were downloaded from the RCSB Protein Data Bank (PDB, https://www.pdb.org/) [35]. AutoDockTools (v1.5.6) and PyMol (v2.3.0) were used to remove water molecules, add polar hydrogen, and redistribute charge. Molecular docking was performed using AutoDock Vina (v1.1.2) and visualized using PyMol (v2.3.0).


### 2.7. Asthma Model Establishment and Treatment

Female BALB/c mice (6–8 weeks old, Shanghai SIPPR-BK Laboratory Animal Co. Ltd., China) were housed in a specific pathogen-free environment with a 12-h light-dark cycle, temperature of 23 ± 2°C, and had free access to water and food. All animal experiments were approved by the Ethics Committee of Animal Experiments of the First Affiliated Hospital of Zhejiang Chinese Medical University.After one week, mice were randomly divided into six groups (n = 6): control group, model group, model + RDN group, model + AKT agonist group, model + AKT agonist + RDN group, and model + prednisone acetate (PA) group. Mice were challenged by intraperitoneal injection of 0.2 ml of saline solution containing 50 μg of ovalbumin (grade V, Sigma-Aldrich, MO, USA) and 0.8 mg of aluminum hydroxide (Sigma-Aldrich) on days 0–3 and day 14. On day 14, day 17, day 20, day 23, and day 27, mice in the control group received inhaled phosphate-buffered saline using an ultrasonic nebulizer (402AI, Yuyue Medical Equipment Co. Ltd, China) for 20 min each time, and the rest of the mice received inhaled 2% ovalbumin solution. On days 14–27, mice in the model, model + RDN, model + AKT agonist (SC79, Sigma-Aldrich), model + AKT agonist + RDN, and model + PA (Sigma-Aldrich) groups were challenged with ovalbumin and were administered RDN (5 mg/kg administered by gavage), AKT agonist (10 mg/kg intraperitoneally), PA (5 mg/kg administered by gavage), or AKT agonist + RDN. Mice in the control group were challenged with phosphate-buffered saline.


### 2.8. Hematoxylin and Eosin (H&E) Staining

The lung tissues were collected and infused with 4% paraformaldehyde and were then dehydrated, paraffin-embedded, and cut into 5-µm-thick sections. The 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).


### 2.9. 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 (ThermoFisher Scientific, MD, USA) at 95°C for 3 min, 40 cycles of 95°C for 12 s, and 62°C for 40 s. The primers used are listed in Supplementary Table1. The relative expression of genes was calculated using the 2−ΔΔCT method, and GAPDH was used as an internal control.


### 2.10. 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. The 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. The differences between the groups were analyzed by one-way ANOVA, followed by Tukey’s test. Differences were considered statistically significant atp<0.05.


## 2.1. 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 [22–24]. 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. The targets were collected with a score cutoff of ≥40. The obtained targets, including the name, gene ID, and organism, were confirmed using the UniProt protein database (https://www.uniprot.org) [27]. The duplicate targets were removed and then uploaded to the Cytoscape platform to construct the herb-component-target network.


## 2.2. Identification of Asthma-Related Targets

The 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.The 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.


## 2.3. Construction of the Protein-Protein Interaction (PPI) Network

A PPI network was constructed and visually analyzed using the Cytoscape plugin BisoGenet, which contains six PPI databases: IntAct Molecular Interaction Database (IntAct), Biomolecular Interaction Network Database (BIND), Biological General Repository for Interaction Datasets (BioGRID), Database of Interacting Proteins (DIP), Molecular INTeraction Database (MINT), and Human Protein Reference Database (HPRD) [30]. PPI networks of RDN targets and asthma-related targets were constructed using Cytoscape.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].


## 2.4. 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).


## 2.5. Chemical Analysis of RDN

The RDN test sample was prepared according to a previous study [33]. Standard diosgenin was purchased from the Tauto Biological Technology Company (China). The 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). The following gradient conditions were used: 0–5 min, 15% B; 5–30 min, 15–40% B; and 30–50 min, 40–50% B. The flow rate was 1.0 mL/min, and the detection wavelength was 203 nm. Chromatograms are shown in Supplementary Figure 1. The retention time of diosgenin was 7.439 min.


## 2.6. Molecular Docking Analysis

The chemical structures of the RDN ingredients were downloaded from the ZINC website (https://zinc15.docking.org) [34], and three-dimensional structures of the hub genes were downloaded from the RCSB Protein Data Bank (PDB, https://www.pdb.org/) [35]. AutoDockTools (v1.5.6) and PyMol (v2.3.0) were used to remove water molecules, add polar hydrogen, and redistribute charge. Molecular docking was performed using AutoDock Vina (v1.1.2) and visualized using PyMol (v2.3.0).


## 2.7. Asthma Model Establishment and Treatment

Female BALB/c mice (6–8 weeks old, Shanghai SIPPR-BK Laboratory Animal Co. Ltd., China) were housed in a specific pathogen-free environment with a 12-h light-dark cycle, temperature of 23 ± 2°C, and had free access to water and food. All animal experiments were approved by the Ethics Committee of Animal Experiments of the First Affiliated Hospital of Zhejiang Chinese Medical University.After one week, mice were randomly divided into six groups (n = 6): control group, model group, model + RDN group, model + AKT agonist group, model + AKT agonist + RDN group, and model + prednisone acetate (PA) group. Mice were challenged by intraperitoneal injection of 0.2 ml of saline solution containing 50 μg of ovalbumin (grade V, Sigma-Aldrich, MO, USA) and 0.8 mg of aluminum hydroxide (Sigma-Aldrich) on days 0–3 and day 14. On day 14, day 17, day 20, day 23, and day 27, mice in the control group received inhaled phosphate-buffered saline using an ultrasonic nebulizer (402AI, Yuyue Medical Equipment Co. Ltd, China) for 20 min each time, and the rest of the mice received inhaled 2% ovalbumin solution. On days 14–27, mice in the model, model + RDN, model + AKT agonist (SC79, Sigma-Aldrich), model + AKT agonist + RDN, and model + PA (Sigma-Aldrich) groups were challenged with ovalbumin and were administered RDN (5 mg/kg administered by gavage), AKT agonist (10 mg/kg intraperitoneally), PA (5 mg/kg administered by gavage), or AKT agonist + RDN. Mice in the control group were challenged with phosphate-buffered saline.


## 2.8. Hematoxylin and Eosin (H&E) Staining

The lung tissues were collected and infused with 4% paraformaldehyde and were then dehydrated, paraffin-embedded, and cut into 5-µm-thick sections. The 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).


## 2.9. 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 (ThermoFisher Scientific, MD, USA) at 95°C for 3 min, 40 cycles of 95°C for 12 s, and 62°C for 40 s. The primers used are listed in Supplementary Table1. The relative expression of genes was calculated using the 2−ΔΔCT method, and GAPDH was used as an internal control.


## 2.10. 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. The 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. The differences between the groups were analyzed by one-way ANOVA, followed by Tukey’s test. Differences were considered statistically significant atp<0.05.


## 3. Results

### 3.1. The 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. Therefore, 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 Tables1 and 2. We subsequently constructed a compound-target network of RDN using Cytoscape (Figure 1).Table 1
Bioactive compounds ofRhizoma Dioscoreae Nipponicae.
ChemicalMolecular formulaMolecular weight (g/mol)OB (%)DLCompound 2D structure7-EpitaxolC47H51NO14853.9945.180.24DeoxyvasicinoneC11H10N20186.2156.290.1Diosgenin acetateC29H44O4456.716.720.72Diosgenin, dehydroC27H40O2396.612.670.82Table 2
Targets ofRhizoma Dioscoreae Nipponicae.
Gene symbolProtein nameHTR45-hydroxytryptamine receptor 4GABRA2Gamma-aminobutyric acid receptor subunit alpha-2PROS1Vitamin K-dependent protein SPROZVitamin K-dependent protein ZAVPR1AVasopressin V1a receptorNQO2Ribosyldihydronicotinamide dehydrogenaseHTR1B5-hydroxytryptamine receptor 1BGABRA3Gamma-aminobutyric acid receptor subunit alpha-3F7Coagulation factor VIIF9Coagulation factor IXGGCXVitamin K-dependent gamma-carboxylaseF10Coagulation factor XVKORC1Vitamin K epoxide reductase complex subunit 1JUNTranscription factor AP-1PROCVitamin K-dependent protein COPRM1Mu-type opioid receptorAGTR2Type 2 angiotensin II receptorBGLAPOsteocalcinPTGS1Prostaglandin G/H synthase 1AVPR2Vasopressin V2 receptorHTR1A5-hydroxytryptamine receptor 1APTGS2Prostaglandin G/H synthase 2HTR3A5-hydroxytryptamine receptor 3AGABRA1Gamma-aminobutyric acid receptor subunit alpha-1NQO1NAD(P)H dehydrogenase [quinone] 1AGTR1Type 1 angiotensin II receptorVKORC1L1Vitamin K epoxide reductase complex subunit 1-like protein 1F2ProthrombinAGTAlanine-glyoxylate aminotransferaseCBR4Carbonyl reductase family member 4AVPPyrophosphate-energized vacuolar membrane proton pump 1PAX2Paired box protein Pax-2DAB2Disabled homolog 2-interacting proteinMGPMatrix Gla proteinF3Coagulation factor IIIWLSProtein wntless homologGPR27Probable G protein-coupled receptor 27SERPINB7Serpin B7ACTN2Alpha-actinin-2MAP2Microtubule-associated protein 2BCL2Apoptosis regulator Bcl-2NR1I2Nuclear receptor subfamily 1 group I member 2MAPTMicrotubule-associated protein tauTUBB1Tubulin beta-1 chainMAP4Microtubule-associated protein 4TUBA4ATubulin alpha-4A chainSPAG16Sperm-associated antigen 16 proteinSLC4A10Sodium-driven chloride bicarbonate exchangerSLC6A2Sodium-dependent noradrenaline transporterSLC22A1Solute carrier family 22 member 1HMGCR3-hydroxy-3-methylglutaryl-coenzyme A reductaseITGB2Integrin beta-2ITGALIntegrin alpha-LHDAC2Histone deacetylase 2NR3C2Mineralocorticoid receptorFigure 1
Component-target network of Rhizoma Dioscoreae Nipponicae. The red node represents the herbs, the pink represents the components, and the green represents the targets.


### 3.2. 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. The resultant targets of RDN were classified into 130 BP terms, 27 CC terms, and 31 MF terms, according top<0.01. The 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). The top 10 KEGG pathways in which the targets of RDN were enriched are presented in Figures 2(d) and Table 3.Figure 2
Gene ontology and KEGG pathway enrichment analysis of the bioactive compounds of Rhizoma Dioscoreae Nipponicae. (a) Representative bubble plots of the biological processes (BP) of the identified targets. (b) Representative bubble plots of the cellular components (CC) of the identified targets. (c) Representative bubble plots of the molecular function (MF) of the identified targets. (d) Representative bubble plots of the KEGG pathway of the identified targets. Gene ratio = count/set size.
(a)(b)(c)(d)Table 3
Top 10 KEGG pathways ofRhizoma Dioscoreae Nipponicae.
IDDescriptionp.adjustCounthsa04080Neuroactive ligand-receptor interaction2.27E − 0712hsa04610Complement and coagulation cascades2.05E − 067hsa00130Ubiquinone and other terpenoid-quinone biosynthesis2.69E − 054hsa04726Serotonergic synapse3.72E − 046hsa04614Renin-angiotensin system0.0073506023hsa05032Morphine addiction0.0145160674hsa04723Retrograde endocannabinoid signaling0.0191559264hsa05033Nicotine addiction0.0213322453hsa05169Epstein–Barr virus infection0.0312921664hsa05140Leishmaniasis0.0609261943


### 3.3. The Network of Asthma-Related Targets

The 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. The number of these targets in the GeneCards and DisGeNET databases was 19 and 38, respectively. These targets are detailed in Table4.Table 4
Targets of asthma.
Gene symbolGene descriptionSYKTyrosine-protein kinase SYKMAPK14Mitogen-activated protein kinase 14AIMP2Aminoacyl tRNA synthase complex-interacting multifunctional protein 2SERPINE1Plasminogen activator inhibitor 1IL13Interleukin-13 receptor subunit alpha-2POLDIP2Polymerase delta-interacting protein 2RAG1V(D)J recombination-activating protein 1COL9A3Collagen alpha-3(IX) chainCOL9A1Collagen alpha-1(IX) chainCTNNB1Catenin beta-1SCN8ASodium channel protein type 8 subunit alphaMAPK1Mitogen-activated protein kinase 1TGFB2Transforming growth factor beta-2 proproteinCRKAdapter molecule crkACTBActin, cytoplasmic 1ANXA2Annexin A2LGALS9Galectin-9COMPCartilage oligomeric matrix proteinCYSLTR1Cysteinyl leukotriene receptor 1MMP9Matrix metalloproteinase-9COL9A2Collagen alpha-2(IX) chainEGFREpidermal growth factor receptorGRAP2GRB2-related adapter protein 2SEMA7ASemaphorin-7AMPPE1Metallophosphoesterase 1AHSA1Activator of 90 kDa heat shock protein ATPase homolog 1TLR6Toll-like receptor 6RNF19AE3 ubiquitin-protein ligase RNF19ATRPV4Transient receptor potential cation channel subfamily V member 4PLFPulmonary functionSSRP1FACT complex subunit SSRP1LCN2Neutrophil gelatinase-associated lipocalinTRPV1Transient receptor potential cation channel subfamily V member 1PDCD5Programmed cell death protein 5HMGB1High mobility group protein B1TACR1Substance-P receptorALOX5Polyunsaturated fatty acid 5-lipoxygenaseCCL11EotaxinTNFTumor necrosis factorADRB2Beta-2 adrenergic receptorMUC7Mucin-7HNMTHistamine N-methyltransferaseSCGB3A2Secretoglobin family 3A member 2PLA2G7Platelet-activating factor acetylhydrolaseBRCA2Breast cancer type 2 susceptibility proteinMIR126miRNA-126MIR148AmiRNA-148AMIR148BmiRNA-148BMIR152miRNA-152HLA-GHLA class I histocompatibility antigenASRT3Asthma-related traits, susceptibility to, 3ASRT4Asthma-related traits, susceptibility to, 4ASRT8Asthma-related traits, susceptibility to, 8ASRT6Asthma-related traits, susceptibility to, 6


### 3.4. The PPI Network of RDN and Asthma

The PPI networks of RDN and asthma were constructed using BisoGenet. The asthma-related target network was constructed with 1,583 nodes and 29,712 edges (Figure3(A)). In addition, the PPI network of RDN-related genes was constructed with 2,950 nodes and 65,942 edges (Figure 3(B)). These 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)).Figure 3
Identification of the core targets for Rhizoma Dioscoreae Nipponicae against asthma. (A) The PPI network of Rhizoma Dioscoreae Nipponicae-related targets (2,950 nodes and 65,942 edges). (B) The PPI network of asthma-related targets (1,583 nodes and 29,712 edges). (C) Intersection of PPI networks (681 nodes and 15,546 edges). (D) PPI network obtained with the screening criteria of BC ≥ 180333, CC ≥ 0.4842, and DC ≥ 52 (151 nodes and 3,975 edges). (E) Core-target PPI network obtained with the screening criteria of BC ≥ 179866, CC ≥ 0.5113, and DC ≥ 73 (76 nodes and 1,587 edges). Abbreviations: BC, betweenness centrality; CC, closeness centrality; DC, degree centrality.


### 3.5. 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 top<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 (Figure4(a)).Figure 4
Gene ontology and KEGG pathway enrichment analysis of the core targets for Rhizoma Dioscoreae Nipponicae against asthma. (a) Representative bubble plots of the biological processes (BP) of the core targets. (b) Representative bubble plots of the cellular components (CC) of the core targets. (c) Representative bubble plots of the molecular function (MF) of the core targets. (d) Representative bubble plots of the KEGG pathway of the core targets. Gene ratio = count/set size.
(a)(b)(c)(d)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 (Figure4(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 (Figure4(c)).In addition to functional GO annotation, KEGG pathway analysis was also conducted on the targets of the PPI network. As shown in Figure4(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. The distribution of the key targets in systemic lupus erythematosus, alcoholism, and viral carcinogenesis pathways is shown in Figure 5. The results indicated that the action targets of the main bioactive components of RDN were distributed in different metabolic pathways. The “multicomponents, multitargets, and multi-pathways” mutual regulation is a possible mechanism in the treatment of asthma.Figure 5
Distribution of key targets in the most enriched pathways. (a) Distribution of key targets in viral carcinogenesis. (b) Distribution of key targets in alcoholism. (c) Distribution of key targets in systemic lupus erythematosus. The green rectangle represents the key targets.
(a)(b)(c)


### 3.6. Enrichment Analysis of the Hub Genes

Based on the core-target PPI network, hub genes were selected using CytoHubba. The 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. The PPI network of the hub genes is shown in Figure6.Figure 6
The top 10 hub genes network of Rhizoma Dioscoreae Nipponicae against asthma.


### 3.7. Molecular Docking Verification

The 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 Table5) 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 pairs of docking results when setting the threshold affinity to < −5 kcal/mol. Among them, diosgenin, dehydro had a strong binding ability with CDK1 (affinity = −10.2 kcal/mol), RACK1 (affinity = −9.3 kcal/mol), and SIRT1 (affinity = −8.3 kcal/mol). Additionally, 7-epitaxol had a strong binding ability with RACK1 (affinity = −9.3 kcal/mol) and CUL7 (affinity = −9.1 kcal/mol). Diosgenin acetate had a strong binding ability with SIRT1 (affinity = −9.1 kcal/mol), CDK2 (affinity = −9 kcal/mol), RACK1 (affinity = −8.5 kcal/mol), and CUL7 (affinity = −8.4 kcal/mol). Deoxyvasicinone had a strong binding ability with NTRK1 (affinity = −8.4 kcal/mol) (Figure 7).Table 5
Chemical structure of active compounds.
SynonymsCASMolecular formula2D structure7-Epitaxol105454-04-4C47H51NO14Deoxyvasicinone530-53-0C11H10N20Diosgenin acetate1061-54-7C29H44O4Diosgenin, dehydro1672-65-7C27H40O2Table 6
Results of molecular docking.
ChemicalPDBGeneBest affinityDiosgenin, dehydro6gu7CDK1−10.27-Epitaxol4u6rRACK1−9.3Diosgenin, dehydro4u6rRACK1−9.37-Epitaxol6o60CUL7−9.1Diosgenin acetate4jt8SIRT1−9.1Diosgenin acetate1b39CDK2−9Diosgenin acetate4u6rRACK1−8.5Deoxyvasicinone5wr7NTRK1−8.4Diosgenin acetate6o60CUL7−8.4Diosgenin, dehydro4jt8SIRT1−8.3Diosgenin, dehydro1b39CDK2−8.3Diosgenin, dehydro6o60CUL7−8.3Deoxyvasicinone4jt8SIRT1−8Diosgenin acetate6gu7CDK1−7.9Deoxyvasicinone7kkqPARP1−7.7Diosgenin acetate7kkqPARP1−7.7Diosgenin, dehydro5ij8EZH2−7.6Diosgenin acetate5ij8EZH2−7.5Diosgenin, dehydro7kkqPARP1−7.4Diosgenin, dehydro6g54MAPK1−7.3Deoxyvasicinone6gu7CDK1−7.2Deoxyvasicinone1b39CDK2−7.2Deoxyvasicinone5ij8EZH2−7.2Deoxyvasicinone4u6rRACK1−7Diosgenin, dehydro5wr7NTRK1−6.9Diosgenin acetate5wr7NTRK1−6.77-Epitaxol6g54MAPK1−6.4Deoxyvasicinone6g54MAPK1−6.4Deoxyvasicinone6o60CUL7−6.3Diosgenin acetate6g54MAPK1−6.37-Epitaxol4jt8SIRT1−5.77-Epitaxol5ij8EZH2−5Figure 7
Molecular docking of bioactive compound hub genes. (a) Diosgenin, dehydro to CDK1, affinity = −10.2 kcal/mol. (b) 7-Epitaxol to RACK1, affinity = −9.3 kcal/mol. (c) Diosgenin, dehydro to RACK1, affinity = −9.3 kcal/mol. (d) 7-Epitaxol to CUL7, affinity = −9.1 kcal/mol. (e) Diosgenin acetate to SIRT1, affinity = −9.1 kcal/mol. (f) Diosgenin acetate to CDK2, affinity = −9 kcal/mol. (g) Diosgenin acetate to RACK1, affinity = −8.5 kcal/mol. (h) Deoxyvasicinone to NTRK1, affinity = −8.4 kcal/mol. (i) Diosgenin acetate to CUL7, affinity = −8.4 kcal/mol. (j) Diosgenin, dehydro to SIRT1, affinity = −8.3 kcal/mol.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)


### 3.8. Verification with Animal Experimentation

To further verify the therapeutic effect of RDN on asthma, we created a mouse model of ovalbumin-induced asthma. H&E staining results showed that the alveoli of mice in the control group had clear texture, no obvious inflammatory cell infiltration, regular bronchial lumen, no obvious thickening, intact airway mucosal epithelial cells, and an intact alveolar wall structure. The lung tissues of mice in the model and model + AKT agonist groups showed clear alveolar texture, a small amount of inflammatory cell infiltration, regular 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 (Figure8(a)).Figure 8
Action of Rhizoma Dioscoreae Nipponicae against asthma via the PI3K-AKT pathway. (a) Hematoxylin and eosin staining of the lung slices in each group. (b) The expression of SIRT1, MAPK1, CDK2, RACK1, NTRK1, PI3K, and AKT in lung tissues from asthma mice was determined by qRT-PCR. (c) Western blot was used to detect the expression of PARP1, EZH2, MAPK1, SIRT1, p-PI3K, and p-AKT.∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001 vs. control group; p#<0.05, p##<0.01, and p###<0.001 vs. model group; pΔΔΔ<0.001 vs. model + AKT agonist group.
(a)(b)(c)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 Figure8(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. This 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 (Figure8(c)).


## 3.1. The 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. Therefore, 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 Tables1 and 2. We subsequently constructed a compound-target network of RDN using Cytoscape (Figure 1).Table 1
Bioactive compounds ofRhizoma Dioscoreae Nipponicae.
ChemicalMolecular formulaMolecular weight (g/mol)OB (%)DLCompound 2D structure7-EpitaxolC47H51NO14853.9945.180.24DeoxyvasicinoneC11H10N20186.2156.290.1Diosgenin acetateC29H44O4456.716.720.72Diosgenin, dehydroC27H40O2396.612.670.82Table 2
Targets ofRhizoma Dioscoreae Nipponicae.
Gene symbolProtein nameHTR45-hydroxytryptamine receptor 4GABRA2Gamma-aminobutyric acid receptor subunit alpha-2PROS1Vitamin K-dependent protein SPROZVitamin K-dependent protein ZAVPR1AVasopressin V1a receptorNQO2Ribosyldihydronicotinamide dehydrogenaseHTR1B5-hydroxytryptamine receptor 1BGABRA3Gamma-aminobutyric acid receptor subunit alpha-3F7Coagulation factor VIIF9Coagulation factor IXGGCXVitamin K-dependent gamma-carboxylaseF10Coagulation factor XVKORC1Vitamin K epoxide reductase complex subunit 1JUNTranscription factor AP-1PROCVitamin K-dependent protein COPRM1Mu-type opioid receptorAGTR2Type 2 angiotensin II receptorBGLAPOsteocalcinPTGS1Prostaglandin G/H synthase 1AVPR2Vasopressin V2 receptorHTR1A5-hydroxytryptamine receptor 1APTGS2Prostaglandin G/H synthase 2HTR3A5-hydroxytryptamine receptor 3AGABRA1Gamma-aminobutyric acid receptor subunit alpha-1NQO1NAD(P)H dehydrogenase [quinone] 1AGTR1Type 1 angiotensin II receptorVKORC1L1Vitamin K epoxide reductase complex subunit 1-like protein 1F2ProthrombinAGTAlanine-glyoxylate aminotransferaseCBR4Carbonyl reductase family member 4AVPPyrophosphate-energized vacuolar membrane proton pump 1PAX2Paired box protein Pax-2DAB2Disabled homolog 2-interacting proteinMGPMatrix Gla proteinF3Coagulation factor IIIWLSProtein wntless homologGPR27Probable G protein-coupled receptor 27SERPINB7Serpin B7ACTN2Alpha-actinin-2MAP2Microtubule-associated protein 2BCL2Apoptosis regulator Bcl-2NR1I2Nuclear receptor subfamily 1 group I member 2MAPTMicrotubule-associated protein tauTUBB1Tubulin beta-1 chainMAP4Microtubule-associated protein 4TUBA4ATubulin alpha-4A chainSPAG16Sperm-associated antigen 16 proteinSLC4A10Sodium-driven chloride bicarbonate exchangerSLC6A2Sodium-dependent noradrenaline transporterSLC22A1Solute carrier family 22 member 1HMGCR3-hydroxy-3-methylglutaryl-coenzyme A reductaseITGB2Integrin beta-2ITGALIntegrin alpha-LHDAC2Histone deacetylase 2NR3C2Mineralocorticoid receptorFigure 1
Component-target network of Rhizoma Dioscoreae Nipponicae. The red node represents the herbs, the pink represents the components, and the green represents the targets.


## 3.2. 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. The resultant targets of RDN were classified into 130 BP terms, 27 CC terms, and 31 MF terms, according top<0.01. The 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). The top 10 KEGG pathways in which the targets of RDN were enriched are presented in Figures 2(d) and Table 3.Figure 2
Gene ontology and KEGG pathway enrichment analysis of the bioactive compounds of Rhizoma Dioscoreae Nipponicae. (a) Representative bubble plots of the biological processes (BP) of the identified targets. (b) Representative bubble plots of the cellular components (CC) of the identified targets. (c) Representative bubble plots of the molecular function (MF) of the identified targets. (d) Representative bubble plots of the KEGG pathway of the identified targets. Gene ratio = count/set size.
(a)(b)(c)(d)Table 3
Top 10 KEGG pathways ofRhizoma Dioscoreae Nipponicae.
IDDescriptionp.adjustCounthsa04080Neuroactive ligand-receptor interaction2.27E − 0712hsa04610Complement and coagulation cascades2.05E − 067hsa00130Ubiquinone and other terpenoid-quinone biosynthesis2.69E − 054hsa04726Serotonergic synapse3.72E − 046hsa04614Renin-angiotensin system0.0073506023hsa05032Morphine addiction0.0145160674hsa04723Retrograde endocannabinoid signaling0.0191559264hsa05033Nicotine addiction0.0213322453hsa05169Epstein–Barr virus infection0.0312921664hsa05140Leishmaniasis0.0609261943


## 3.3. The Network of Asthma-Related Targets

The 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. The number of these targets in the GeneCards and DisGeNET databases was 19 and 38, respectively. These targets are detailed in Table4.Table 4
Targets of asthma.
Gene symbolGene descriptionSYKTyrosine-protein kinase SYKMAPK14Mitogen-activated protein kinase 14AIMP2Aminoacyl tRNA synthase complex-interacting multifunctional protein 2SERPINE1Plasminogen activator inhibitor 1IL13Interleukin-13 receptor subunit alpha-2POLDIP2Polymerase delta-interacting protein 2RAG1V(D)J recombination-activating protein 1COL9A3Collagen alpha-3(IX) chainCOL9A1Collagen alpha-1(IX) chainCTNNB1Catenin beta-1SCN8ASodium channel protein type 8 subunit alphaMAPK1Mitogen-activated protein kinase 1TGFB2Transforming growth factor beta-2 proproteinCRKAdapter molecule crkACTBActin, cytoplasmic 1ANXA2Annexin A2LGALS9Galectin-9COMPCartilage oligomeric matrix proteinCYSLTR1Cysteinyl leukotriene receptor 1MMP9Matrix metalloproteinase-9COL9A2Collagen alpha-2(IX) chainEGFREpidermal growth factor receptorGRAP2GRB2-related adapter protein 2SEMA7ASemaphorin-7AMPPE1Metallophosphoesterase 1AHSA1Activator of 90 kDa heat shock protein ATPase homolog 1TLR6Toll-like receptor 6RNF19AE3 ubiquitin-protein ligase RNF19ATRPV4Transient receptor potential cation channel subfamily V member 4PLFPulmonary functionSSRP1FACT complex subunit SSRP1LCN2Neutrophil gelatinase-associated lipocalinTRPV1Transient receptor potential cation channel subfamily V member 1PDCD5Programmed cell death protein 5HMGB1High mobility group protein B1TACR1Substance-P receptorALOX5Polyunsaturated fatty acid 5-lipoxygenaseCCL11EotaxinTNFTumor necrosis factorADRB2Beta-2 adrenergic receptorMUC7Mucin-7HNMTHistamine N-methyltransferaseSCGB3A2Secretoglobin family 3A member 2PLA2G7Platelet-activating factor acetylhydrolaseBRCA2Breast cancer type 2 susceptibility proteinMIR126miRNA-126MIR148AmiRNA-148AMIR148BmiRNA-148BMIR152miRNA-152HLA-GHLA class I histocompatibility antigenASRT3Asthma-related traits, susceptibility to, 3ASRT4Asthma-related traits, susceptibility to, 4ASRT8Asthma-related traits, susceptibility to, 8ASRT6Asthma-related traits, susceptibility to, 6


## 3.4. The PPI Network of RDN and Asthma

The PPI networks of RDN and asthma were constructed using BisoGenet. The asthma-related target network was constructed with 1,583 nodes and 29,712 edges (Figure3(A)). In addition, the PPI network of RDN-related genes was constructed with 2,950 nodes and 65,942 edges (Figure 3(B)). These 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)).Figure 3
Identification of the core targets for Rhizoma Dioscoreae Nipponicae against asthma. (A) The PPI network of Rhizoma Dioscoreae Nipponicae-related targets (2,950 nodes and 65,942 edges). (B) The PPI network of asthma-related targets (1,583 nodes and 29,712 edges). (C) Intersection of PPI networks (681 nodes and 15,546 edges). (D) PPI network obtained with the screening criteria of BC ≥ 180333, CC ≥ 0.4842, and DC ≥ 52 (151 nodes and 3,975 edges). (E) Core-target PPI network obtained with the screening criteria of BC ≥ 179866, CC ≥ 0.5113, and DC ≥ 73 (76 nodes and 1,587 edges). Abbreviations: BC, betweenness centrality; CC, closeness centrality; DC, degree centrality.


## 3.5. 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 top<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 (Figure4(a)).Figure 4
Gene ontology and KEGG pathway enrichment analysis of the core targets for Rhizoma Dioscoreae Nipponicae against asthma. (a) Representative bubble plots of the biological processes (BP) of the core targets. (b) Representative bubble plots of the cellular components (CC) of the core targets. (c) Representative bubble plots of the molecular function (MF) of the core targets. (d) Representative bubble plots of the KEGG pathway of the core targets. Gene ratio = count/set size.
(a)(b)(c)(d)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 (Figure4(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 (Figure4(c)).In addition to functional GO annotation, KEGG pathway analysis was also conducted on the targets of the PPI network. As shown in Figure4(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. The distribution of the key targets in systemic lupus erythematosus, alcoholism, and viral carcinogenesis pathways is shown in Figure 5. The results indicated that the action targets of the main bioactive components of RDN were distributed in different metabolic pathways. The “multicomponents, multitargets, and multi-pathways” mutual regulation is a possible mechanism in the treatment of asthma.Figure 5
Distribution of key targets in the most enriched pathways. (a) Distribution of key targets in viral carcinogenesis. (b) Distribution of key targets in alcoholism. (c) Distribution of key targets in systemic lupus erythematosus. The green rectangle represents the key targets.
(a)(b)(c)


## 3.6. Enrichment Analysis of the Hub Genes

Based on the core-target PPI network, hub genes were selected using CytoHubba. The 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. The PPI network of the hub genes is shown in Figure6.Figure 6
The top 10 hub genes network of Rhizoma Dioscoreae Nipponicae against asthma.


## 3.7. Molecular Docking Verification

The 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 Table5) 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 pairs of docking results when setting the threshold affinity to < −5 kcal/mol. Among them, diosgenin, dehydro had a strong binding ability with CDK1 (affinity = −10.2 kcal/mol), RACK1 (affinity = −9.3 kcal/mol), and SIRT1 (affinity = −8.3 kcal/mol). Additionally, 7-epitaxol had a strong binding ability with RACK1 (affinity = −9.3 kcal/mol) and CUL7 (affinity = −9.1 kcal/mol). Diosgenin acetate had a strong binding ability with SIRT1 (affinity = −9.1 kcal/mol), CDK2 (affinity = −9 kcal/mol), RACK1 (affinity = −8.5 kcal/mol), and CUL7 (affinity = −8.4 kcal/mol). Deoxyvasicinone had a strong binding ability with NTRK1 (affinity = −8.4 kcal/mol) (Figure 7).Table 5
Chemical structure of active compounds.
SynonymsCASMolecular formula2D structure7-Epitaxol105454-04-4C47H51NO14Deoxyvasicinone530-53-0C11H10N20Diosgenin acetate1061-54-7C29H44O4Diosgenin, dehydro1672-65-7C27H40O2Table 6
Results of molecular docking.
ChemicalPDBGeneBest affinityDiosgenin, dehydro6gu7CDK1−10.27-Epitaxol4u6rRACK1−9.3Diosgenin, dehydro4u6rRACK1−9.37-Epitaxol6o60CUL7−9.1Diosgenin acetate4jt8SIRT1−9.1Diosgenin acetate1b39CDK2−9Diosgenin acetate4u6rRACK1−8.5Deoxyvasicinone5wr7NTRK1−8.4Diosgenin acetate6o60CUL7−8.4Diosgenin, dehydro4jt8SIRT1−8.3Diosgenin, dehydro1b39CDK2−8.3Diosgenin, dehydro6o60CUL7−8.3Deoxyvasicinone4jt8SIRT1−8Diosgenin acetate6gu7CDK1−7.9Deoxyvasicinone7kkqPARP1−7.7Diosgenin acetate7kkqPARP1−7.7Diosgenin, dehydro5ij8EZH2−7.6Diosgenin acetate5ij8EZH2−7.5Diosgenin, dehydro7kkqPARP1−7.4Diosgenin, dehydro6g54MAPK1−7.3Deoxyvasicinone6gu7CDK1−7.2Deoxyvasicinone1b39CDK2−7.2Deoxyvasicinone5ij8EZH2−7.2Deoxyvasicinone4u6rRACK1−7Diosgenin, dehydro5wr7NTRK1−6.9Diosgenin acetate5wr7NTRK1−6.77-Epitaxol6g54MAPK1−6.4Deoxyvasicinone6g54MAPK1−6.4Deoxyvasicinone6o60CUL7−6.3Diosgenin acetate6g54MAPK1−6.37-Epitaxol4jt8SIRT1−5.77-Epitaxol5ij8EZH2−5Figure 7
Molecular docking of bioactive compound hub genes. (a) Diosgenin, dehydro to CDK1, affinity = −10.2 kcal/mol. (b) 7-Epitaxol to RACK1, affinity = −9.3 kcal/mol. (c) Diosgenin, dehydro to RACK1, affinity = −9.3 kcal/mol. (d) 7-Epitaxol to CUL7, affinity = −9.1 kcal/mol. (e) Diosgenin acetate to SIRT1, affinity = −9.1 kcal/mol. (f) Diosgenin acetate to CDK2, affinity = −9 kcal/mol. (g) Diosgenin acetate to RACK1, affinity = −8.5 kcal/mol. (h) Deoxyvasicinone to NTRK1, affinity = −8.4 kcal/mol. (i) Diosgenin acetate to CUL7, affinity = −8.4 kcal/mol. (j) Diosgenin, dehydro to SIRT1, affinity = −8.3 kcal/mol.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)


## 3.8. Verification with Animal Experimentation

To further verify the therapeutic effect of RDN on asthma, we created a mouse model of ovalbumin-induced asthma. H&E staining results showed that the alveoli of mice in the control group had clear texture, no obvious inflammatory cell infiltration, regular bronchial lumen, no obvious thickening, intact airway mucosal epithelial cells, and an intact alveolar wall structure. The lung tissues of mice in the model and model + AKT agonist groups showed clear alveolar texture, a small amount of inflammatory cell infiltration, regular 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 (Figure8(a)).Figure 8
Action of Rhizoma Dioscoreae Nipponicae against asthma via the PI3K-AKT pathway. (a) Hematoxylin and eosin staining of the lung slices in each group. (b) The expression of SIRT1, MAPK1, CDK2, RACK1, NTRK1, PI3K, and AKT in lung tissues from asthma mice was determined by qRT-PCR. (c) Western blot was used to detect the expression of PARP1, EZH2, MAPK1, SIRT1, p-PI3K, and p-AKT.∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001 vs. control group; p#<0.05, p##<0.01, and p###<0.001 vs. model group; pΔΔΔ<0.001 vs. model + AKT agonist group.
(a)(b)(c)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 Figure8(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. This 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 (Figure8(c)).


## 4. 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. Three 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 considered as the key sections of RDN in asthma treatment at the gene level. Third, 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. There were a total of four bioactive components in the RDN with the standard. The main bioactive components of RDN are 7-epitaxol and deoxyvasicinone. The 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.The 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 beta-catenin-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]. The PI3K-Akt pathway is thought to be closely associated with airway 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. The 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. The 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.


## 5. 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. The 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. This study provides a basis for the treatment of asthma by RDN from the perspective of network pharmacology. These 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 morein vitro and in vivo experiments to verify the proposed molecular targets.



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*Source: 4749613-2022-03-30.xml*