# Network Pharmacology-Based Study on the Mechanism ofPinellia ternata in Asthma Treatment

Authors: Yanmin Lyu; Xiangjing Chen; Qing Xia; Shanshan Zhang; Chengfang Yao
Journal: Evidence-Based Complementary and Alternative Medicine
 (2020)

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## Abstract

Background. Pinellia ternata (PT), a medicinal plant, has had an extensive application in the treatment of asthma in China, whereas its underlying pharmacological mechanisms remain unclear. Methods. Firstly, a network pharmacology method was adopted to collect activated components of PT from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Targets of PT were assessed by exploiting the PharmMapper website; asthma-related targets were collected from the OMIM website, and target-target interaction networks were built. Secondly, critical nodes exhibiting high possibility were identified as the hub nodes in the network, which were employed to conduct Gene Ontology (GO) comment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis. Finally, the tissue expression profiles of key candidate genes were identified by the Gene Expression Omnibus (GEO) database, and the therapeutic effect of PT was verified by an animal experiment. Results. 57 achievable targets of PT on asthma were confirmed as hub nodes through using the network pharmacology method. As revealed from the KEGG enrichment analysis, the signaling pathways were notably enriched in pathways of the T-cell receptor signaling pathway, JAK-STAT signaling pathway, and cytokine-cytokine receptor interaction. The expression profiles of candidate genes including Mmp2, Nr3c1, il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vefgα were identified. Moreover, according to transcriptome RNA sequencing data from lung tissues of allergic mice compared to normal mice, the mRNA level of Mmp2 and il-4 was upregulated (P<0.001). In animal experiments, PT could alleviate the allergic response of mice by inhibiting the activation of T-helper type 2 (TH2) cells and the expression of Mmp2 and il-4. Conclusions. Our study provides candidate genes that may be either used for future studies related to diagnosis/prognosis or as targets for asthma management. Besides, animal experiments showed that PT could treat asthma by regulating the expression of Mmp2 and il-4.


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## Body

## 1. Background

Asthma refers to an inflammatory disorder of the airways which is induced by common aeroallergens (e.g., house dust mites, fungi, and air pollutants) and characterized by airway hyperresponsiveness, variable airflow limitation, and mucous secretion, as well as chronic inflammation [1]. In asthma, there exists selective expansion of T lymphocytes (particularly of TH2 cells) that secrete a cluster of cytokines such as interleukins (IL)-2, IL-4, IL-5, IL-9, and IL-13 and granulocyte-macrophage colony-stimulating factor (GM-CSF), leading to mast cell differentiation and maturation, eosinophil maturation and survival, basophil recruitment, B cell differentiation, and production of IgE, which jointly orchestrate the allergic inflammatory cascade [2, 3]. On the whole, inhaled corticosteroid (ICS) or ICS combined with long-acting β2-adrenergic agonists has been the common clinical treatment to mitigate asthma. In some cases, however, corticosteroid overload raises a high risk of glucocorticoid-related adverse events and huge difficulty in controlling the onset of asthma [4]. For the asthma incidence, a single-ingredient medicine cannot satisfy the existing treatment of asthma, and there is an urgent need for practical, multitarget drugs to treat asthma that can remedy the deficiencies exhibited by those that are currently available.Traditional Chinese medicine (TCM) has been indispensable to the health of China.Pinellia ternata, also known as BanXia in Chinese, has been a common and potent medicinal herb in TCM practice. It has been clinically applied for suppression of the cough center to exert antitussive effects, facilitate cell division, decrease the viscosity of whole blood, and reduce inflammation, in conjunction with other herbs. However, the detailed mechanisms of their operation at molecular levels are still unknown, making it difficult in the design of more efficient and less toxic drugs for treatment. Thus, comprehensive and appropriate strategies are urgently required to gain deeper insights into how herbal formulae impact diverse biological processes in the treatment of diseases. Network pharmacology refers to an efficient and robust method having been employed extensively for several decades to treat a range of diseases, as coupled with high-throughput group analysis and virtual computing, as well as network database retrieval [5, 6]. Using network pharmacology in search of TCM is conducive to identifying the relationships between drug and disease, discovering active ingredients, elucidating the mechanism of action, and assessing drug safety [7]. In this study, a network pharmacological research was conducted to delve into practical components and action objectives of PT as an attempt to explore the underlying mechanisms of action of asthma treatment; furthermore, the therapeutic effect of PT was verified by OVA-induced allergic mice, and the regulation of key proteins was also examined (Figure 1).Figure 1
The schematic illustration of network pharmacology analysis and animal experimental verification for exhibiting pharmacological pathways of PT against asthma.


## 2. Materials and Methods

### 2.1. Animal Experiments

#### 2.1.1. Mice Model

All animal experiments were approved by the Institutional Animal Care and Use Committee of the Shandong Academy of Medical Sciences. Female C57BL/6 mice, aged 7-8 weeks, were provided by Shandong Laboratory Animal Facility (Shandong Academy of Medical Sciences, Jinan, China). All animals were housed and bred in a temperature- and light-controlled environment under a 12 h light-dark cycle, and they were maintained under specific pathogen-free (SPF) conditions. Subsequently, mice were randomly split into three groups (the control group, the model group, and the herb group) covering four mice, respectively. The model group and herb group were injected intraperitoneally (i.p.) with 50μg OVA (Sigma-Aldrich, MO, USA) and 2.25 mg adjuvant aluminum hydroxide (Thermo Scientific, Pittsburgh, PA, USA) in a total volume of 100 μL on day 0, 7, and 14. On day 21, the mice were intranasally (i.n.) administrated with 100 μg OVA in 50 μL [8, 9]. The herb group was administrated with PT (1.95 g/kg/d) (provided by Affiliated Hospital of Shandong University of Traditional Chinese Medicine, China) water decoction for 21 days, whereas the control mice were given the identical volume of phosphate-buffered saline (PBS). All mice were prepared for tissue harvesting.


#### 2.1.2. Flow Cytometric Staining

Lungs were cut into small pieces and then incubated with 1 mg/ml collagenase D (Sigma-Aldrich, MO, USA) for 30 min and were digested into single-cell suspension. Rat anti-mouse antibodies cover APC-CD45, PE-Cy7-CD3, PerCP-CD4, and PE-IL-4 (BD Biosciences, CA, USA). Intracellular cytokine staining was performed following the previous description [10]. Flow cytometric (BD FACSVerse™, BD Biosciences, USA) data were analyzed with FlowJo software.


#### 2.1.3. Measurement of IL-4 in Bronchoalveolar Lavage Fluid (BALF)

24 h after the final OVA challenge, all mice were sacrificed to prepare BALF. Lungs were lavaged in situ with 1 mL of ice-cold phosphate buffer solution (PBS) via the tracheal cannula. After three times lavage, approximately 2 mL of BALF was recovered and centrifuged at 4°C (1500 r/min) for 10 min [11]. The supernatants were rapidly collected and frozen for further cytokine measurement by ELISA. The concentration of IL-4 in BALF was measured using a specific mouse ELISA kit (R&D Systems, Minn., USA). ELISA experiments were performed according to the manufacturer’s instructions.


#### 2.1.4. Reverse Transcription- (RT-) qPCR

Total RNA was extracted with TRIzol (CW.BIO, Beijing, China) from the lung tissue of mice. The concentrations of RNA were determined before reverse transcription. Total RNA (200 ng) was further reserve-transcribed to cDNA, and detail operation steps were done as previously described [9]. The primers used in the present study were as follows: for Mmp2, sense CAAGTTCCCCGGCGATGTC and antisense TTCTGGTCAAGGTCACCTGTC; for il-4, sense GGTCTCAACCCCCAGCTAGT and antisense GCCGATGATCTCTCTCAAGTGAT. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an endogenous control, and the comparative threshold cycle (2−ΔΔCT) equation was used to calculate the relative expression levels.


### 2.2. Screening Bioactive Components of PT

The components’ data of PT originated from the TCMSP systems pharmacology database (http://lsp.nwu.edu.cn/tcmsp.php) [12]. To achieve the bioactive components’ screening of PT in pharmacodynamics studies, two critical indicators have been usually taken as the screening criteria in ADME processes (e.g., absorption, distribution, metabolism, and excretion) and drug design, which include oral bioavailability (OB) and drug similarity (DL), and components were retained only if OB was over 30% and DL was more than 0.18 to satisfy criteria [13]. In this study, based on the relevant literature and the PubChem network database (https://pubchem.ncbi.nlm.nih.gov/), 11 bioactive components of PT were retained, and their corresponding information (PubChem ID, OB, and DL) was also acquired for subsequent analysis [14].


### 2.3. Component-Related Target Proteins’ Prediction

To ascertain the relevant targets of the bioactive components in PT, the PharmMapper database (http://lilab.ecust.edu.cn/pharmmapper/get.php) was adopted [15]. In brief, for a given small molecule, potential candidate targets were identified using the reverse pharmacodynamic profiling method. Through the retrieving process, 115 assessed targets were screened out after duplicates were deleted, and they were made to conform to the correct UniProt ID. Component-target network maps were plotted by Cytoscape 3.7.2 for these data [16]. Subsequently, GO enrichment and KEGG network pathway analyses were conducted on the targets acquired by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.nicifcrf.gov/, updated in Mar. 2017) online [17]. Values of P<0.05 were considered exhibiting statistical significance.


### 2.4. Asthma-Related Targets’ Prediction

Data on the asthma-related targets were acquired from the Online Mendelian Inheritance in Man database (OMIM, updated December 21, 2019) [18, 19], a comprehensive, authoritative compendium of human genes and genetic phenotypes, which is freely available and daily updated. The keyword was “asthma,” and 259 asthma-related targets were harvested jointly.


### 2.5. Protein-Protein Interaction (PPI) Network Construction

To delve into the interaction between target proteins from a systematic and holistic perspective, PPI network mapping was conducted with STRING (http://string-db.org) [20]. Accordingly, the “component-target-disease” network was established to express the relationships between the targets corresponding to PT and interacting protein; furthermore, such network was visually analyzed with Cytoscape 3.7.2 software. As critical parameters of network analysis, the degree of centricity (DC), betweenness centrality (BC), close to the central (CC), the network centricity (NC), and local edge connectivity topology selection (LAC) were adopted to obtain the significance of the respective target. The node exhibiting a DC value higher than twice the median degree of all nodes and achieving the values of the other four indexes over the corresponding median values was defined as a hub [21]. To standardly describe these genes, GO enrichment and KEGG pathway analyses were conducted by using DAVID. Values of P<0.05 were considered exhibiting statistical significance.


### 2.6. Gene Expression Analysis

The pattern of gene expression in lung tissues may indicate that the targets involved in the pathway of asthma development. GSE6858 was used to analyze expression patterns for genes acquired from the GEO database (http://www.ncbi.nlm.nih.gov/geo). In GSE6858, we compared the expression of Mmp2, Nr3c1, il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vefgα in OVA-stimulated mouse lung tissues compared to the normal group.


## 2.1. Animal Experiments

### 2.1.1. Mice Model

All animal experiments were approved by the Institutional Animal Care and Use Committee of the Shandong Academy of Medical Sciences. Female C57BL/6 mice, aged 7-8 weeks, were provided by Shandong Laboratory Animal Facility (Shandong Academy of Medical Sciences, Jinan, China). All animals were housed and bred in a temperature- and light-controlled environment under a 12 h light-dark cycle, and they were maintained under specific pathogen-free (SPF) conditions. Subsequently, mice were randomly split into three groups (the control group, the model group, and the herb group) covering four mice, respectively. The model group and herb group were injected intraperitoneally (i.p.) with 50μg OVA (Sigma-Aldrich, MO, USA) and 2.25 mg adjuvant aluminum hydroxide (Thermo Scientific, Pittsburgh, PA, USA) in a total volume of 100 μL on day 0, 7, and 14. On day 21, the mice were intranasally (i.n.) administrated with 100 μg OVA in 50 μL [8, 9]. The herb group was administrated with PT (1.95 g/kg/d) (provided by Affiliated Hospital of Shandong University of Traditional Chinese Medicine, China) water decoction for 21 days, whereas the control mice were given the identical volume of phosphate-buffered saline (PBS). All mice were prepared for tissue harvesting.


### 2.1.2. Flow Cytometric Staining

Lungs were cut into small pieces and then incubated with 1 mg/ml collagenase D (Sigma-Aldrich, MO, USA) for 30 min and were digested into single-cell suspension. Rat anti-mouse antibodies cover APC-CD45, PE-Cy7-CD3, PerCP-CD4, and PE-IL-4 (BD Biosciences, CA, USA). Intracellular cytokine staining was performed following the previous description [10]. Flow cytometric (BD FACSVerse™, BD Biosciences, USA) data were analyzed with FlowJo software.


### 2.1.3. Measurement of IL-4 in Bronchoalveolar Lavage Fluid (BALF)

24 h after the final OVA challenge, all mice were sacrificed to prepare BALF. Lungs were lavaged in situ with 1 mL of ice-cold phosphate buffer solution (PBS) via the tracheal cannula. After three times lavage, approximately 2 mL of BALF was recovered and centrifuged at 4°C (1500 r/min) for 10 min [11]. The supernatants were rapidly collected and frozen for further cytokine measurement by ELISA. The concentration of IL-4 in BALF was measured using a specific mouse ELISA kit (R&D Systems, Minn., USA). ELISA experiments were performed according to the manufacturer’s instructions.


### 2.1.4. Reverse Transcription- (RT-) qPCR

Total RNA was extracted with TRIzol (CW.BIO, Beijing, China) from the lung tissue of mice. The concentrations of RNA were determined before reverse transcription. Total RNA (200 ng) was further reserve-transcribed to cDNA, and detail operation steps were done as previously described [9]. The primers used in the present study were as follows: for Mmp2, sense CAAGTTCCCCGGCGATGTC and antisense TTCTGGTCAAGGTCACCTGTC; for il-4, sense GGTCTCAACCCCCAGCTAGT and antisense GCCGATGATCTCTCTCAAGTGAT. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an endogenous control, and the comparative threshold cycle (2−ΔΔCT) equation was used to calculate the relative expression levels.


## 2.1.1. Mice Model

All animal experiments were approved by the Institutional Animal Care and Use Committee of the Shandong Academy of Medical Sciences. Female C57BL/6 mice, aged 7-8 weeks, were provided by Shandong Laboratory Animal Facility (Shandong Academy of Medical Sciences, Jinan, China). All animals were housed and bred in a temperature- and light-controlled environment under a 12 h light-dark cycle, and they were maintained under specific pathogen-free (SPF) conditions. Subsequently, mice were randomly split into three groups (the control group, the model group, and the herb group) covering four mice, respectively. The model group and herb group were injected intraperitoneally (i.p.) with 50μg OVA (Sigma-Aldrich, MO, USA) and 2.25 mg adjuvant aluminum hydroxide (Thermo Scientific, Pittsburgh, PA, USA) in a total volume of 100 μL on day 0, 7, and 14. On day 21, the mice were intranasally (i.n.) administrated with 100 μg OVA in 50 μL [8, 9]. The herb group was administrated with PT (1.95 g/kg/d) (provided by Affiliated Hospital of Shandong University of Traditional Chinese Medicine, China) water decoction for 21 days, whereas the control mice were given the identical volume of phosphate-buffered saline (PBS). All mice were prepared for tissue harvesting.


## 2.1.2. Flow Cytometric Staining

Lungs were cut into small pieces and then incubated with 1 mg/ml collagenase D (Sigma-Aldrich, MO, USA) for 30 min and were digested into single-cell suspension. Rat anti-mouse antibodies cover APC-CD45, PE-Cy7-CD3, PerCP-CD4, and PE-IL-4 (BD Biosciences, CA, USA). Intracellular cytokine staining was performed following the previous description [10]. Flow cytometric (BD FACSVerse™, BD Biosciences, USA) data were analyzed with FlowJo software.


## 2.1.3. Measurement of IL-4 in Bronchoalveolar Lavage Fluid (BALF)

24 h after the final OVA challenge, all mice were sacrificed to prepare BALF. Lungs were lavaged in situ with 1 mL of ice-cold phosphate buffer solution (PBS) via the tracheal cannula. After three times lavage, approximately 2 mL of BALF was recovered and centrifuged at 4°C (1500 r/min) for 10 min [11]. The supernatants were rapidly collected and frozen for further cytokine measurement by ELISA. The concentration of IL-4 in BALF was measured using a specific mouse ELISA kit (R&D Systems, Minn., USA). ELISA experiments were performed according to the manufacturer’s instructions.


## 2.1.4. Reverse Transcription- (RT-) qPCR

Total RNA was extracted with TRIzol (CW.BIO, Beijing, China) from the lung tissue of mice. The concentrations of RNA were determined before reverse transcription. Total RNA (200 ng) was further reserve-transcribed to cDNA, and detail operation steps were done as previously described [9]. The primers used in the present study were as follows: for Mmp2, sense CAAGTTCCCCGGCGATGTC and antisense TTCTGGTCAAGGTCACCTGTC; for il-4, sense GGTCTCAACCCCCAGCTAGT and antisense GCCGATGATCTCTCTCAAGTGAT. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an endogenous control, and the comparative threshold cycle (2−ΔΔCT) equation was used to calculate the relative expression levels.


## 2.2. Screening Bioactive Components of PT

The components’ data of PT originated from the TCMSP systems pharmacology database (http://lsp.nwu.edu.cn/tcmsp.php) [12]. To achieve the bioactive components’ screening of PT in pharmacodynamics studies, two critical indicators have been usually taken as the screening criteria in ADME processes (e.g., absorption, distribution, metabolism, and excretion) and drug design, which include oral bioavailability (OB) and drug similarity (DL), and components were retained only if OB was over 30% and DL was more than 0.18 to satisfy criteria [13]. In this study, based on the relevant literature and the PubChem network database (https://pubchem.ncbi.nlm.nih.gov/), 11 bioactive components of PT were retained, and their corresponding information (PubChem ID, OB, and DL) was also acquired for subsequent analysis [14].


## 2.3. Component-Related Target Proteins’ Prediction

To ascertain the relevant targets of the bioactive components in PT, the PharmMapper database (http://lilab.ecust.edu.cn/pharmmapper/get.php) was adopted [15]. In brief, for a given small molecule, potential candidate targets were identified using the reverse pharmacodynamic profiling method. Through the retrieving process, 115 assessed targets were screened out after duplicates were deleted, and they were made to conform to the correct UniProt ID. Component-target network maps were plotted by Cytoscape 3.7.2 for these data [16]. Subsequently, GO enrichment and KEGG network pathway analyses were conducted on the targets acquired by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID, https://david.nicifcrf.gov/, updated in Mar. 2017) online [17]. Values of P<0.05 were considered exhibiting statistical significance.


## 2.4. Asthma-Related Targets’ Prediction

Data on the asthma-related targets were acquired from the Online Mendelian Inheritance in Man database (OMIM, updated December 21, 2019) [18, 19], a comprehensive, authoritative compendium of human genes and genetic phenotypes, which is freely available and daily updated. The keyword was “asthma,” and 259 asthma-related targets were harvested jointly.


## 2.5. Protein-Protein Interaction (PPI) Network Construction

To delve into the interaction between target proteins from a systematic and holistic perspective, PPI network mapping was conducted with STRING (http://string-db.org) [20]. Accordingly, the “component-target-disease” network was established to express the relationships between the targets corresponding to PT and interacting protein; furthermore, such network was visually analyzed with Cytoscape 3.7.2 software. As critical parameters of network analysis, the degree of centricity (DC), betweenness centrality (BC), close to the central (CC), the network centricity (NC), and local edge connectivity topology selection (LAC) were adopted to obtain the significance of the respective target. The node exhibiting a DC value higher than twice the median degree of all nodes and achieving the values of the other four indexes over the corresponding median values was defined as a hub [21]. To standardly describe these genes, GO enrichment and KEGG pathway analyses were conducted by using DAVID. Values of P<0.05 were considered exhibiting statistical significance.


## 2.6. Gene Expression Analysis

The pattern of gene expression in lung tissues may indicate that the targets involved in the pathway of asthma development. GSE6858 was used to analyze expression patterns for genes acquired from the GEO database (http://www.ncbi.nlm.nih.gov/geo). In GSE6858, we compared the expression of Mmp2, Nr3c1, il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vefgα in OVA-stimulated mouse lung tissues compared to the normal group.


## 3. Results

### 3.1. Screening for the Active Component of PT

The 115 reported active ingredients of PT were retrieved from the TCMSP database. Then, the values of OB and DL were employed to screen potential active components, and 13 of the mentioned bioactive ingredients were preliminarily screened out. Besides, only 11 active ingredients with corresponding targets were screened out after the final screening from the PubChem database (Table1), most of which were sterols (β-sitosterol, stigmasterol, and cycloartenol), flavonoids (baicalein and baicalin), unsaturated fatty acid, and lipid, exhibiting high oral bioavailability (ranging from 30.7% to 44.72%) and high drug similarity (0.2∼0.81). Moreover, these ingredients generally displayed the pharmacological activities of antibacterial, anti-inflammatory, and antioxidant.Table 1
Active components and ADME parameters ofPinellia ternata.
PubChem IDMolecule nameOBDL548420224-Ethylcholest-4-en-3-one36.080.76193148Cavidine35.640.815281605Baicalein33.520.2164982Baicalin40.120.75222284β-Sitosterol36.910.755280794Stigmasterol43.830.765282768Gondoic acid30.70.2389888Coniferin31.110.32536568710,13-Eicosadienoic acid39.990.292110Cycloartenol38.690.7864959β-D-Ribofuranoside, xanthine-944.720.21ADME: absorption, distribution, metabolism, and excretion; OB: oral bioavailability; DL: drug similarity.


### 3.2. Component-Target Network Construction and Enrichment Analysis

Component-related targets were obtained from the results of the BATMAN-TCM database. Based on the data acquired above, the component-target network was established with Cytoscape 3.7.2 software. After clustering analysis was conducted on the obtained component-target network, six clusters were obtained (Figure2). Studies have shown that inflammatory responses (immune cell differentiation, cytokine secretion, and receptor signaling) and metabolic responses (drug metabolism and energy metabolism) play an important role in the development of asthma [22]. For example, in the network, cluster 1 includes components baicalin and β-D-ribofuranoside, xanthine-9, and their targets which were enriched in signaling pathways of drug metabolism, glutathione metabolism, platinum drug resistance, etc. Cluster 2 includes components cavidine, coniferin, 10,13-eicosadienoic acid, and gondoic acid and their targets, which were enriched in pathways of PPAR signaling pathway and Th17 cell differentiation. Cluster 3 includes component cycloartenol and targets, which exhibit anti-inﬂammatory functions and are associated with pathways of MAPK and PI3K.Figure 2
Component-target network (C-T network). The network of 11 active components (the purple nodes) ofPinellia ternata and 115 putative targets (the blue nodes) was clustered with Cytoscape version 3.7.2. All protein targets were represented by their gene symbol.


### 3.3. Asthma-Related Targets

By searching the OMIM databases, the target data for the treatment of asthma were collected. A total of 259 targets were screened after false-positive information was checked and removed, including cytokines (e.g., TNF, IL-4, IL-5, and IL-33), receptors (e.g., CCR4, IL-6R, and IL-9R), chemokines (e.g., CCL2, CCL11, and CCL18), and transcription factors (e.g., STAT4, STAT6, and GATA3) (Table2).Table 2
Related targets of asthma.
PDE4DHLA-GIL6RTNFRSF4TPSB2CD274ADORA2BPTGIRHNMTADAM33TYRO3SERPINB3VEGFAIL25TACR2CFBVDRPLA2G7MMP12SERPINB4SUV39H1PDCD1LG2CYP26A1TACR3MMP2IRAK3GABRA2FKBP5HRH1TPSD1TRPV1CTSKBTKC5GAD2CPN1FABP4IL31TOP2ACTSLNR1I2CCL11HAVCR1ADCY9STAT4NKAIN2ROS1CTSBADH5LTC4SMUC7CCL18MUC5BJAMLSELECTSSPDE4BC5AR1MARCKSCLCA1IL11RAORMDL1CXCR2AOC3NR3C1FLGGABRB2C3AR1CX3CR1ORMDL2PDE3ACCR4NPSR1IL12BTPT1RASGRP4IL12RB1TAS2R31SELENOPNOCTIL13ORMDL3CCR5SETDB2CCR6ACTG2SELLCNOT6TNFSCGB1A1PTENPOSTNJAG1CLCADRB2TLR9IL4RTBX21DAP3DENND1BSATB1MIFMCL1UTS2RIL9HLA-DRB1TLR4CRHR1JAG2IL17FF2TSLPALOX5HLA-DRB1GAD1CD74CXADRMYL3ADRB3CSF2RBPTGDRHLA-DRB1TNIP1FCARH3C1ABCA1PDE4APTGER4TBXA2RSCGB3A2C3ITGB6CST7CAV3HRTLR7PTGDR2MMP9FCER1AIL11ALOX15BHPS1ESRRANOS2CYSLTR1IL13RA1PRKCAALOX15FFAR2ALG9ITGA5NANOS2IL4CHI3L1IRF4LTAKCNMB1CDHR3CCL2SERPINE1IL5CMA1TNFSF10TRAF5SIGLEC5IL5RAPRSS1CBR1IL13RA2MS4A2SOCS3CFTRCBX5ANGPT2PLA2G1BITGA4ALOX5APIL9RTNFSF14MUC5ACICOSCHRNA7HALKCNMA1CYSLTR2CHIASART1SNCAFCGR2BANGPT1SYKMAP3K9SELPGATA3BPIFA1NFKB1CD209MC4RTACR1ADRA2CPTAFRRUNX3SPRED1NFKB2TAS2R14IL17AMICU1BDKRB2PTGER3CCL24GLCCI1PTGER2TAS2R10IL17RASCN9AOPN4CCR3SPINK5USP38PTPN6PDE11ACHRM4SCN10APDE5ALTB4R2NOD1IL10SPRR2AHDAC3CHRM2TBXAS1CSF2IL33PHF11LGALS3SPRR2BKCNMB2CHRM3LTB4RADRB1PLA2G4ADPP10NFKBIATNCLY96CAMPACKR3CHRM5STAT6HSD11B2SERPINB2TPSAB1CXCL16CHAMP1ADORA1MMEATP2A2ACETYK2


### 3.4. Hub Nodes’ Screening and PPI Network Construction

To elucidate the molecular mechanism underlying the effects of PT against asthma, the component-related targets and asthma-related targets were uploaded to STRING to acquire the information on PPI. 365 nodes and 3726 correlations were covered in the network. Next, according to the topology of DC, BC, CC, NC, and LAC, 57 hub nodes and 808 relationships were ascertained. We constructed a PPI network among these 57 hub nodes and presumed those targets as the putative targets of PT for the treatment of asthma (Figures3(a) and 3(b)).Figure 3
Network of drug and disease targets. (a) The number of asthma-related targets and component targets showed by Venn. (b) Network of hub nodes. The property of the purple nodes is asthma-related targets, the property of the pink nodes is component-related targets, and the property of the green nodes is common targets of asthma and components.
(a)(b)


### 3.5. Hub Nodes’ Enrichment Analysis

To identify relevant pathways and functions, GO functional analysis and KEGG pathway enrichment analysis were performed for these 57 putative targets using DAVID. A total of 276 enrichment results were obtained, covering 226 biological processes (BP), 32 molecular functions (MF), and 18 cellular components (CC); the enriched molecular functions of the target proteins are mainly associated with response to stimulus, biological regulation, and cellular process (Figure4(a)). As revealed from the KEGG enrichment analysis, the signaling pathways were notably enriched in pathways of T-cell receptor signaling pathway, JAK-STAT signaling pathway, and cytokine-cytokine receptor interaction. After extensive pathways were excluded, the top 15 relevant signaling pathways are presented in Figure 4(b). Values of P<0.05 were considered exhibiting statistical significance; the lower the P value is, the more prominent the relevance will be.Figure 4
Enrichment analysis of the hub genes forPinellia ternata in the treatment of asthma. (a) The GO enrichment analysis of hub nodes. (b) The top 15 KEGG pathway enrichment analyses of hub nodes.
(a)(b)


### 3.6. Expression Profiles of Key Targets in Asthma

Based on the above data, genes matrix metalloproteinase-2 (Mmp2) and nuclear receptor subfamily 3, group C, member 1 (Nr3c1) are both component target and highly relevant targets for asthma. il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vegfα are top 10 hub nodes in the PPI network (Figure 5(a)), so we inquired the expression profiles of these genes in asthma from the GEO database. In GSE6858, the expression level of Mmp2 and il-4 in OVA-stimulated mice was upregulated than these of the control group (P<0.001); il-10 and il-2 were downregulated in allergic mice, and there was no significant difference in the expression levels of other genes (Figure 5(b)).Figure 5
Expression levels of key nodes in lung tissues of allergic mice. (a) The top 10 hub nodes showed by Cytoscape. (b) Gene expression levels of lung tissues in GSE6858 from the GEO database.
(a)(b)


### 3.7. PT Ameliorated OVA-Induced Asthma via Downregulating the Mmp2 and Il-4 Expressions

Asthma is a chronic allergic respiratory disease. Here, we validated the anti-inflammatory and antiasthmatic therapeutic properties of PT, using a mouse model of OVA-induced allergic asthma; the timeline of administration with OVA or PT in mice is shown in Figure6(a). Unquestionably, OVA-sensitized mice model exhibited an enhanced type 2 immune response as the percentage of TH2 cells in the lung was elevated compared with that of PBS-sensitized control mice (∗P<0.05); in the case of drug treatment, the percentage of TH2 cells was noticeably reversed with PT decoction therapy (#P<0.05) (Figure 6(b)). As suggested by ELISA analysis, the concentration of il-4 was significantly upregulated by OVA treatment as compared with the control group, whereas PT treatment led to the downregulation of il-4 in the BALF of allergic mice (Figure 6(c)). The mRNA levels of Mmp2 and il-4 were decreased in drug treatment mice compared to OVA-sensitized mice (Figure 6(d)).Figure 6
PT ameliorated OVA-induced asthma. (a) The timeline of administration in animal experiments. (b) The percentages of TH2 cells (CD45+, CD3+, CD4+, and IL4+) in lymphocytes by FACS. (c) The concentration of IL-4 in BALF. (d) The mRNA levels of il-4 and Mmp2 in the lung by RT-qPCR. Data shown were normalized to the reference gene GAPDH. Values were expressed as mean ± SEM. ∗P value <0.05 compared to the control group; #P value <0.05 compared to the model group.
(a)(b)(c)(d)


## 3.1. Screening for the Active Component of PT

The 115 reported active ingredients of PT were retrieved from the TCMSP database. Then, the values of OB and DL were employed to screen potential active components, and 13 of the mentioned bioactive ingredients were preliminarily screened out. Besides, only 11 active ingredients with corresponding targets were screened out after the final screening from the PubChem database (Table1), most of which were sterols (β-sitosterol, stigmasterol, and cycloartenol), flavonoids (baicalein and baicalin), unsaturated fatty acid, and lipid, exhibiting high oral bioavailability (ranging from 30.7% to 44.72%) and high drug similarity (0.2∼0.81). Moreover, these ingredients generally displayed the pharmacological activities of antibacterial, anti-inflammatory, and antioxidant.Table 1
Active components and ADME parameters ofPinellia ternata.
PubChem IDMolecule nameOBDL548420224-Ethylcholest-4-en-3-one36.080.76193148Cavidine35.640.815281605Baicalein33.520.2164982Baicalin40.120.75222284β-Sitosterol36.910.755280794Stigmasterol43.830.765282768Gondoic acid30.70.2389888Coniferin31.110.32536568710,13-Eicosadienoic acid39.990.292110Cycloartenol38.690.7864959β-D-Ribofuranoside, xanthine-944.720.21ADME: absorption, distribution, metabolism, and excretion; OB: oral bioavailability; DL: drug similarity.


## 3.2. Component-Target Network Construction and Enrichment Analysis

Component-related targets were obtained from the results of the BATMAN-TCM database. Based on the data acquired above, the component-target network was established with Cytoscape 3.7.2 software. After clustering analysis was conducted on the obtained component-target network, six clusters were obtained (Figure2). Studies have shown that inflammatory responses (immune cell differentiation, cytokine secretion, and receptor signaling) and metabolic responses (drug metabolism and energy metabolism) play an important role in the development of asthma [22]. For example, in the network, cluster 1 includes components baicalin and β-D-ribofuranoside, xanthine-9, and their targets which were enriched in signaling pathways of drug metabolism, glutathione metabolism, platinum drug resistance, etc. Cluster 2 includes components cavidine, coniferin, 10,13-eicosadienoic acid, and gondoic acid and their targets, which were enriched in pathways of PPAR signaling pathway and Th17 cell differentiation. Cluster 3 includes component cycloartenol and targets, which exhibit anti-inﬂammatory functions and are associated with pathways of MAPK and PI3K.Figure 2
Component-target network (C-T network). The network of 11 active components (the purple nodes) ofPinellia ternata and 115 putative targets (the blue nodes) was clustered with Cytoscape version 3.7.2. All protein targets were represented by their gene symbol.


## 3.3. Asthma-Related Targets

By searching the OMIM databases, the target data for the treatment of asthma were collected. A total of 259 targets were screened after false-positive information was checked and removed, including cytokines (e.g., TNF, IL-4, IL-5, and IL-33), receptors (e.g., CCR4, IL-6R, and IL-9R), chemokines (e.g., CCL2, CCL11, and CCL18), and transcription factors (e.g., STAT4, STAT6, and GATA3) (Table2).Table 2
Related targets of asthma.
PDE4DHLA-GIL6RTNFRSF4TPSB2CD274ADORA2BPTGIRHNMTADAM33TYRO3SERPINB3VEGFAIL25TACR2CFBVDRPLA2G7MMP12SERPINB4SUV39H1PDCD1LG2CYP26A1TACR3MMP2IRAK3GABRA2FKBP5HRH1TPSD1TRPV1CTSKBTKC5GAD2CPN1FABP4IL31TOP2ACTSLNR1I2CCL11HAVCR1ADCY9STAT4NKAIN2ROS1CTSBADH5LTC4SMUC7CCL18MUC5BJAMLSELECTSSPDE4BC5AR1MARCKSCLCA1IL11RAORMDL1CXCR2AOC3NR3C1FLGGABRB2C3AR1CX3CR1ORMDL2PDE3ACCR4NPSR1IL12BTPT1RASGRP4IL12RB1TAS2R31SELENOPNOCTIL13ORMDL3CCR5SETDB2CCR6ACTG2SELLCNOT6TNFSCGB1A1PTENPOSTNJAG1CLCADRB2TLR9IL4RTBX21DAP3DENND1BSATB1MIFMCL1UTS2RIL9HLA-DRB1TLR4CRHR1JAG2IL17FF2TSLPALOX5HLA-DRB1GAD1CD74CXADRMYL3ADRB3CSF2RBPTGDRHLA-DRB1TNIP1FCARH3C1ABCA1PDE4APTGER4TBXA2RSCGB3A2C3ITGB6CST7CAV3HRTLR7PTGDR2MMP9FCER1AIL11ALOX15BHPS1ESRRANOS2CYSLTR1IL13RA1PRKCAALOX15FFAR2ALG9ITGA5NANOS2IL4CHI3L1IRF4LTAKCNMB1CDHR3CCL2SERPINE1IL5CMA1TNFSF10TRAF5SIGLEC5IL5RAPRSS1CBR1IL13RA2MS4A2SOCS3CFTRCBX5ANGPT2PLA2G1BITGA4ALOX5APIL9RTNFSF14MUC5ACICOSCHRNA7HALKCNMA1CYSLTR2CHIASART1SNCAFCGR2BANGPT1SYKMAP3K9SELPGATA3BPIFA1NFKB1CD209MC4RTACR1ADRA2CPTAFRRUNX3SPRED1NFKB2TAS2R14IL17AMICU1BDKRB2PTGER3CCL24GLCCI1PTGER2TAS2R10IL17RASCN9AOPN4CCR3SPINK5USP38PTPN6PDE11ACHRM4SCN10APDE5ALTB4R2NOD1IL10SPRR2AHDAC3CHRM2TBXAS1CSF2IL33PHF11LGALS3SPRR2BKCNMB2CHRM3LTB4RADRB1PLA2G4ADPP10NFKBIATNCLY96CAMPACKR3CHRM5STAT6HSD11B2SERPINB2TPSAB1CXCL16CHAMP1ADORA1MMEATP2A2ACETYK2


## 3.4. Hub Nodes’ Screening and PPI Network Construction

To elucidate the molecular mechanism underlying the effects of PT against asthma, the component-related targets and asthma-related targets were uploaded to STRING to acquire the information on PPI. 365 nodes and 3726 correlations were covered in the network. Next, according to the topology of DC, BC, CC, NC, and LAC, 57 hub nodes and 808 relationships were ascertained. We constructed a PPI network among these 57 hub nodes and presumed those targets as the putative targets of PT for the treatment of asthma (Figures3(a) and 3(b)).Figure 3
Network of drug and disease targets. (a) The number of asthma-related targets and component targets showed by Venn. (b) Network of hub nodes. The property of the purple nodes is asthma-related targets, the property of the pink nodes is component-related targets, and the property of the green nodes is common targets of asthma and components.
(a)(b)


## 3.5. Hub Nodes’ Enrichment Analysis

To identify relevant pathways and functions, GO functional analysis and KEGG pathway enrichment analysis were performed for these 57 putative targets using DAVID. A total of 276 enrichment results were obtained, covering 226 biological processes (BP), 32 molecular functions (MF), and 18 cellular components (CC); the enriched molecular functions of the target proteins are mainly associated with response to stimulus, biological regulation, and cellular process (Figure4(a)). As revealed from the KEGG enrichment analysis, the signaling pathways were notably enriched in pathways of T-cell receptor signaling pathway, JAK-STAT signaling pathway, and cytokine-cytokine receptor interaction. After extensive pathways were excluded, the top 15 relevant signaling pathways are presented in Figure 4(b). Values of P<0.05 were considered exhibiting statistical significance; the lower the P value is, the more prominent the relevance will be.Figure 4
Enrichment analysis of the hub genes forPinellia ternata in the treatment of asthma. (a) The GO enrichment analysis of hub nodes. (b) The top 15 KEGG pathway enrichment analyses of hub nodes.
(a)(b)


## 3.6. Expression Profiles of Key Targets in Asthma

Based on the above data, genes matrix metalloproteinase-2 (Mmp2) and nuclear receptor subfamily 3, group C, member 1 (Nr3c1) are both component target and highly relevant targets for asthma. il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vegfα are top 10 hub nodes in the PPI network (Figure 5(a)), so we inquired the expression profiles of these genes in asthma from the GEO database. In GSE6858, the expression level of Mmp2 and il-4 in OVA-stimulated mice was upregulated than these of the control group (P<0.001); il-10 and il-2 were downregulated in allergic mice, and there was no significant difference in the expression levels of other genes (Figure 5(b)).Figure 5
Expression levels of key nodes in lung tissues of allergic mice. (a) The top 10 hub nodes showed by Cytoscape. (b) Gene expression levels of lung tissues in GSE6858 from the GEO database.
(a)(b)


## 3.7. PT Ameliorated OVA-Induced Asthma via Downregulating the Mmp2 and Il-4 Expressions

Asthma is a chronic allergic respiratory disease. Here, we validated the anti-inflammatory and antiasthmatic therapeutic properties of PT, using a mouse model of OVA-induced allergic asthma; the timeline of administration with OVA or PT in mice is shown in Figure6(a). Unquestionably, OVA-sensitized mice model exhibited an enhanced type 2 immune response as the percentage of TH2 cells in the lung was elevated compared with that of PBS-sensitized control mice (∗P<0.05); in the case of drug treatment, the percentage of TH2 cells was noticeably reversed with PT decoction therapy (#P<0.05) (Figure 6(b)). As suggested by ELISA analysis, the concentration of il-4 was significantly upregulated by OVA treatment as compared with the control group, whereas PT treatment led to the downregulation of il-4 in the BALF of allergic mice (Figure 6(c)). The mRNA levels of Mmp2 and il-4 were decreased in drug treatment mice compared to OVA-sensitized mice (Figure 6(d)).Figure 6
PT ameliorated OVA-induced asthma. (a) The timeline of administration in animal experiments. (b) The percentages of TH2 cells (CD45+, CD3+, CD4+, and IL4+) in lymphocytes by FACS. (c) The concentration of IL-4 in BALF. (d) The mRNA levels of il-4 and Mmp2 in the lung by RT-qPCR. Data shown were normalized to the reference gene GAPDH. Values were expressed as mean ± SEM. ∗P value <0.05 compared to the control group; #P value <0.05 compared to the model group.
(a)(b)(c)(d)


## 4. Discussion

The existing number of patients with bronchial asthma worldwide reaches over 300 million, and considerable patients still face difficulty in mitigating their symptoms after undergoing conventional atomization therapy, thereby developing refractory bronchial asthma [23]. The mechanism of asthma pathogenesis remains unclear. Over the past years, numerous researchers have considered that airway inflammation cytokines exhibit abnormally high level on the pathogenesis of asthma and play a critical role in the process. To be specific, IL-4 is capable of stimulating the B lymphocyte to secret large specific IgE, which induces gathered eosinophils and increased airway mucosa, even airway hyperresponsiveness. IL-5 could control the differentiation, maturation, migration, and survival of eosinophils. IL-10 is capable of effectively reducing the secretion activity of mast cells and TH2 cells and exerts a definite effect on the reduction of airway inflammation. Both IL-13 and IL-17 are critical proinflammatory cytokines in the body, thereby facilitating the synthesis of IgE, elevating the level of vascular adhesion molecules, and increasing the degree of eosinophil infiltration in the airway [2, 24]. All of these genes are included in the asthma-related targets (Table 2). Besides, obesity, metabolic disease, and age can also act as risk factors exacerbating asthma [25]. As impacted by heterogeneous in terms of severity, individual difference, and treatment responsiveness, the diverseness of this disease results in multiple therapeutic strategies. Several studies have reported that PT possesses anti-inflammatory and antiallergic effects both in asthma and in COPD [26, 27]. However, the physiological and pharmacological actions of PT on asthma and its biological function have not been effectively studied. The incorporation of traditional Chinese medicine into clinical therapy via network pharmacology can provide insights into the possible mechanism and enhance the specificity and effectiveness of the treatment scheme [5].In our study, active ingredients were screened in PT, primarily ﬂavonoids, sterols, glycosides, and unsaturated fatty acid, and proteins which were considered as both potential targets of ingredients and targets of asthma treatment were predicted. In the PPI network of component targets and asthma-related targets, hub nodes were screened. Based on the GO terms, the activity of hub nodes was associated with numerous biologic processes (e.g., inflammatory response, chemotaxis, and immune response), a variety of molecular functions (e.g., cytokine activity, growth factor activity, and protein tyrosine kinase activity), and diversified cellular components (e.g., extracellular space, extracellular region, and external side of the plasma membrane). Thus, the results demonstrated that PT primarily exerted various therapeutic effects on asthma by participating in these processes and functions. As further revealed from the KEGG enrichment analysis, the JAK-STAT signaling pathway is one of a handful of pleiotropic cascades and plays essential roles during development and cellular processes (e.g., segmentation, proliferation, and organogenesis) [28]. JAK kinases could selectively phosphorylate STATs, leading to their activation. To be specific, STAT6 acted as one of the primary members in the JAK-STAT pathway, capable of specifically inducing TH2 cell differentiation and promoting the secretion of IL-4 [29]. Our flow cytometry analysis revealed that, in the allergic mice model, PT could attenuate allergic immune response by inhibiting the activation of TH2 cells and the mRNA levels of IL-4. However, the regulation of IL-4 is depended on the way of direct binding to an upstream distal promoter element or on the way of direct activation of the transcription factors such as STAT6 which needed further study.Notably,Mmp2 and Nr3c1 are two hub nodes, which are members of both component targets and asthma targets, making them crucial, accounting for PT in asthma treatment. Mmps are a family of enzymes characterized by a common zinc ion at their active site. In general, Mmps are thought to be involved in the normal maintenance of the extracellular matrix and inflammation and cell-cell signaling [30]. Studies reported that the main function of Mmp2 protease is to degrade extracellular matrix proteins and participate in airway remodeling in asthma [31]. Using Mmp2-cleavable activatable cell-penetrating peptides (ACPPs) with the cleavage sequence PLGC (Me) AG, it is shown that Mmp2 is upregulated in the lungs of allergen OVA-challenged mice [32]; this result is consistent with findings in Figure 5(b). Besides, Nr3c1 is capable of impacting the number of the glucocorticoid receptor (GR) and the affinity with glucocorticoid [33]. The stress hormone cortisol binds to glucocorticoid receptors produced by Nr3c1, resulting in reduced inflammation [34]. Reduced expression of Nr3c1 may lead to glucocorticoid resistance, which gives rise to the inflammatory response [35]. Actually, many individuals with asthma rely on corticosteroid medications to control asthma symptoms [36]. However, in animal experiments, Nr3c1 apparently revealed a large gap in lung tissues of normal mice and ubiquitously expressed lower in most allergic mice lung tissues (Figure 5(b)); this is the reason why we did not test it in subsequent animal studies.


## 5. Conclusions

TCM has been commonly used to treat various diseases and exhibits favorable efficacy, whereas the molecular mechanism of TCM has not been elucidated. In the present study, the network pharmacology method was used to study the scientific basis of PT in the treatment of asthma. 57 hub nodes were screened, and signaling pathways associated with asthma were enriched; these data lay a scientific basis for the clinical treatment of asthma and also provide insights into the development and utilization of drugs. In animal experiments, PT could alleviate the allergic response of mice by inhibiting the activation of TH2 cells and the expression of IL-4 andMmp2; furthermore, systematic and rigorous experiments are required to verify our findings.



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*Source: 9732626-2020-10-20.xml*