Systems pharmacology-based study of Tanreqing injection in airway mucus hypersecretion

Journal Pre-proof Systems pharmacology-based study of Tanreqing injection in airway mucus hypersecretion Wei Liu, Xiawei Zhang, Bing Mao, Hongli Jiang PII:

S0378-8741(19)33159-9

DOI:

https://doi.org/10.1016/j.jep.2019.112425

Reference:

JEP 112425

To appear in:

Journal of Ethnopharmacology

Received Date: 7 August 2019 Revised Date:

9 November 2019

Accepted Date: 22 November 2019

Please cite this article as: Liu, W., Zhang, X., Mao, B., Jiang, H., Systems pharmacology-based study of Tanreqing injection in airway mucus hypersecretion, Journal of Ethnopharmacology (2019), doi: https:// doi.org/10.1016/j.jep.2019.112425. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

Systems pharmacology analyses SBG

UFP

STC

LJT

Rat experiment

FSV

Tanreqing injection

Compound screening Cholic acid

Baicalin

Rats model establishment

Wogonin

Control group

Model group

TRQ group

Pathology Target analyses

Sample collections

Sample analyses Immunohistochemistry

** 30 20

**

C-T, T-D networks

TNF-α expression (pg/mL)

40

40

0

ELISA **

**

30 20 10 0

qPCR

10

TNF-α mRNA expression (fold change)

TNF-α expression (pg/mL)

Functional and pathway analyses

5 4 3 2 1 0

**

*

1

Systems Pharmacology-Based Study of Tanreqing Injection in Airway Mucus

2

Hypersecretion

3

Wei Liua,b, MD; Xiawei Zhangc, MS; Bing Maoa, MD and Hongli Jianga, MD

4 5 6

a

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Medicine, West China Hospital of Sichuan University, 37 Guoxuexiang Lane, Chengdu,

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Sichuan 610041, P. R. China

Division of Respiratory Medicine, Department of Integrated Traditional and Western

b

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Department of Pulmonary Diseases, State Key Laboratory of Biotherapy of China,

10

West China Hospital of Sichuan University, 1 Keyuansilu Road, Chengdu, Sichuan

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610041, P. R. China

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c

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Medicine, West China School of Medicine, Sichuan University, 37 Guoxuexiang Lane,

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Chengdu, Sichuan 610041, P. R. China

Division of Respiratory Medicine, Department of Integrated Traditional and Western

15 16

Correspondence to: Dr. Hongli Jiang, Division of Respiratory Medicine, Department of

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Integrated Traditional and Western Medicine, West China Hospital, Sichuan University,

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37 Guoxue Lane, Chengdu, Sichuan 610041, P. R. China; Telephone number: 0086-

19

18980606651; Fax number: 0086-028-854202210.

20 21

E-mail

22

[email protected] (Xiawei Zhang), [email protected] (Bing Mao),

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[email protected] (Hongli Jiang).

24

address

for

authors:

[email protected]

(Wei

Liu),

25

Abstract

26

Ethnopharmacological relevance

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Mucus hypersecretion (MH) is recognized as a key pathophysiological and clinical

28

feature of many airway inflammatory diseases. MUC5AC is a major component of

29

airway mucus. Tanreqing injection (TRQ) is a widely used herbal formula for the

30

treatment of respiratory inflammations for years in China. However, a holistic network

31

pharmacology approach to understanding its therapeutic mechanisms against MH has

32

not been pursued.

33 34

Aim of the study

35

This study aimed to explore the systems-level potential active compounds and

36

therapeutic mechanisms of TRQ in the treatment of MH.

37 38

Materials and methods

39

We established systems pharmacology-based strategies comprising compound

40

screenings, target predictions, and pathway identifications to speculate the potential

41

active compounds and therapeutic targets of TRQ. We also applied compound-target

42

and target-disease network analyses to evaluate the possible action mechanisms of

43

TRQ. Then, lipopolysaccharide (LPS)-induced Sprague-Dawley (SD) rat model was

44

constructed to assess the effect of TRQ in the treatment of MH and to validate the

45

possible molecular mechanisms as predicted in systems pharmacology approach.

46 47

Results

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The comprehensive compound collection successfully generated 55 compound

49

candidates from TRQ. Among them, 11 compounds with high relevance to the potential

50

targets were defined as representative and potential active ingredients in TRQ formula.

51

Target identification revealed 172 potential targets, including pro-inflammatory

52

cytokines of tumor necrosis factor α (TNF-α), interleukin (IL)-6, and IL-8. Pathway

53

analyses uncovered the possible action of TRQ in the regulation of IL-17 signaling

54

pathway and its downstream protein MUC5AC. Then in vivo experiment indicated that

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TRQ could significantly inhibit LPS stimulated MUC5AC over-production as well as

56

the expression of TNF-α, IL-6, IL-8, and IL-17A, in both protein and mRNA levels.

57 58

Conclusions

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Based on the systems pharmacology method and in vivo experiment, our work provided

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a general knowledge on the potential active compounds and possible therapeutic targets

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of TRQ formula in its anti-MH process. This work might suggest directions for further

62

research on TRQ and provide more insight into better understanding the chemical and

63

pharmacological mechanisms of complex herbal prescriptions in a network perspective.

64

Keywords

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Tanreqing injection; Systems pharmacology; Mucus hypersecretion; Signaling pathway;

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Traditional Chinese Medicine

67 68

Abbreviations

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MH: mucus hypersecretion; LPS, lipopolysaccharide; TNF-α, tumor necrosis factor α;

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IL, interleukin; COPD, chronic obstructive pulmonary disease; TRQ, Tanreqing

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injection; SBG, Scutellaria baicalensis Georgi; PFU, Pulvis Fellis Ursi; CST, Cornu

72

Saigae Tataricae; LJT, Lonicera japonica Thunb; FSV, Forsythia suspensa (Thunb.)

73

Vahl; CBM, China BioMedical Literature; CNKI, Chinese National Knowledge

74

Infrastructure; TCM, Traditional Chinese Medicine; TCMSP, Traditional Chinese

75

Medicines for Systems Pharmacology Database and Analysis Platform; BATMAN-

76

TCM, Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese

77

Medicine; SysDT, Systems Drug Targeting; RF, Random Forest; SVM, Support Vector

78

Machine; PPI, protein-protein interactions; HINT, High-quality INTeractomes; OMIM,

79

Online Mendelian Inheritance in Man; CTD, Comparative Toxicogenomics Database;

80

TTD, Therapeutic Target Database; MeSHs, Medical Subject Headings; C-T,

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compound-target; pC-pT: potential compound-potential target; pC-dT, potential

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compound-respiratory diseases related target; T - D, target-disease; GO, gene ontology;

83

KEGG, Kyoto Encyclopedia of Genes and Genomes; SD, Sprague-Dawley; SPF,

84

specific pathogen free; AB, Alcian blue; PAS, periodic acid-Schiff; IOD, integrated

85

optical density; PTGS2, prostaglandin G/H synthase 2; DPPIV, dipeptidyl peptidase IV

86 87

1. Introduction Airway mucus hypersecretion (MH) is recognized as a prominent

88

pathophysiological feature in the progress of many airway inflammatory diseases such

89

as chronic obstructive pulmonary disease (COPD) and asthma. MH might directly link

90

to declined lung functions, elevated frequency and duration of respiratory infections,

91

and increased morbidity and mortality in susceptible patients by obstructing airways and

92

impairing gas exchange (Jeffery, 2001). Currently, the conventional mucolytic strategies

93

have variable and limited efficacy in inhibiting airway mucus oversecretion (Rogers and

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Barnes, 2006).

95

The major components of airway mucus secretion are mucins, a family of high

96

molecular weight glycoproteins that are predominantly produced by goblet cells in the

97

epithelium. In 19 mucins (namely MUC1, 2, 3A, 3B, 4, 5AC, 5B, 6 - 9, 11-13 and 16-

98

20) that have been currently recognized in human respiratory secretions, MUC5AC

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appears to be most abundantly expressed, in bronchial epithelium and also submucosal

100

glands (Rogers and Barnes, 2006). Previous studies showed that MUC5AC-deficient

101

mice were protected from severity and abundance of mucus plugging compared with

102

wild-type mice following allergen challenge (Evans et al., 2015).

103

In the search for new therapeutics for MH, a lot of herbal remedies have been proved

104

to possess expectorant and mucolytic properties through inhibiting MUC5AC secretion,

105

and are commonly adopted for expectorant management (Kwon et al., 2009; Oliviero et

106

al., 2016). Among them, traditional Chinese medicine (TCM) as a promising candidate

107

has a confirmed therapeutic effect for MH via the down-regulation of MUC5AC in

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many studies (Jiang et al., 2011; Li et al., 2013; Wei et al., 2013). Tanreqing injection

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(TRQ) is a well-known TCM prescription and consists of a complex mixture of

110

chemical ingredients extracted from five TCM drugs: Scutellaria baicalensis Georgi

111

(SBG, Huangqin), Pulvis Fellis Ursi (PFU, Xiongdanfen), Cornu Saigae Tataricae

112

(CST, Shanyangjiao), Lonicera japonica Thunb (LJT, Jinyinhua), and Forsythia

113

suspensa (Thunb.) Vahl (FSV, Lianqiao). TRQ was prepared with the following steps:

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obtain the extracting solution of SBG through water extraction, alcohol precipitation,

115

acid precipitation, ultra-filtration, and purification; extract total chololicacid from PFU

116

through saponification and purification; extract total amino acid from CST through

117

hydration, purification and ultra-filtration; obtain dry extract paste from JLT through

118

boiling, purification and drying; obtain dry extract paste from FSV via ultrafiltration

119

and low temperature vacuum drying; mix the above extractions and dissolve them in

120

water and then propylene glycol to make injection preparations (Patent No.

121

CN1947746B). With its positive activities against various infections being largely

122

confirmed in pharmacological studies, TRQ has been predominately used in patients

123

with inflammatory airway diseases in China (Li et al., 2010; Liu et al., 2016a; Zhong et

124

al., 2010). However, its effects on MUC5AC expression and MH are less investigated.

125

In the present study, firstly, we would explore the active compounds, potential

126

therapeutic targets as well as the compound-target-disease interactions of TRQ by using

127

a previously developed systems pharmacology strategy that integrated sufficient high-

128

dimensional biological data through compounds, targets, pathways and networks

129

analyses. Secondly, we would observe the effect of TRQ on the expression of

130

MUC5AC and some of the potential molecular targets as indicated in systems

131

pharmacology in lipopolysaccharide (LPS)-induced rat models. Our study may throw

132

light on the possible therapeutic mechanisms of TRQ in the treatment of airway MH

133

and provide the basis for further studies.

134

2. Methods and materials

135

2.1. Candidate compound screening

136

The compounds of five components in TRQ were collected by means of data

137

mining. We did systematical literature searching in electronic databases including China

138

BioMedical Literature (CBM), Chinese National Knowledge Infrastructure (CNKI),

139

Cqvip Database, Wanfang Database, MEDLINE (PubMed), EMBASE (Ovid) and

140

Google Scholar. We also did a wide-scale data retrieval from professional

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pharmaceutical databases including Shanghai Institute of Organic Chemistry, Chinese

142

Academy of Sciences (http://www.organchem.csdb.cn/scdb/default.asp), Traditional

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Chinese Medicines for Systems Pharmacology Database and Analysis Platform

144

(TCMSP) (http://tcmspw.com/tcmsp.php) (Ru et al., 2014), Bioinformatics Analysis

145

Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM)

146

(http://bionet.ncpsb.org/batman-tcm/index.php/Home/Index/index) (Liu et al., 2016b),

147

NCBI PubChem database (https://pubchem.ncbi.nlm.nih.gov), and DrugBank database

148

(https://www.drugbank.ca).

149 150

2.2. Potential therapeutic targets analysis

151

Firstly, Systems Drug Targeting (SysDT) algorithm, a pharmacophore modeling

152

approach, was employed to predict the possible treatment targets based on the collected

153

candidate compounds (Yu et al., 2012). This model involves protein and ligand

154

encoding vectors. It takes Random Forest (RF) and Support Vector Machine (SVM) as

155

the major ensemble-based methods and incorporates the chemical, genomic and

156

pharmacological information into an integrated framework using DrugBank database.

157

RF score ≥ 0.8 and SVM score ≥ 0.7 were set as the thresholds to screen potential

158

targets (Li et al., 2015). Next, we used the Retrieve/ID mapping tool in UniProt

159

database (https://www.uniprot.org/uploadlists/) to standardize the target-related genes

160

and screened genes for Homo sapiens. Secondly, we did protein-protein interactions

161

(PPI) analysis for potential treatment targets using High-quality INTeractomes (HINT)

162

database (http://hint.yulab.org/) to explore proteins that interact with these targets.

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Thirdly, we matched these targets with respiratory disease-related targets that provided

164

by Online Mendelian Inheritance in Man (OMIM) (https://omim.org/), a comprehensive

165

and authoritative compendium of human genes and genetic phenotypes, to further

166

narrow the scope of therapeutic targets.

167 168

2.3. Network construction and functional analysis

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The treatable diseases related to the potential targets were collected through

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DrugBank, Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/),

171

Therapeutic Target Database (TTD) (http://bidd.nus.edu.sg/group/cjttd/) and PharmGkb

172

database (https://www.pharmgkb.org/). The information of obtained disease was

173

classified into different groups using Medical Subject Headings (MeSHs)

174

(https://www.ncbi.nlm.nih.gov/mesh/) in PubMed database.

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All results obtained above were integrated to constitute the compound-target (C-T)

176

network and target-disease (T-D) network using Cytoscape 3.6.1 software (Shannon et

177

al., 2003). The topological properties of networks were evaluated by the plugin of

178

Cytoscape (Bindea et al., 2009). In graphical networks, the degree of a node was

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defined as the number of edges connected with it, representing the importance of the

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node in a network. We performed gene ontology (GO) and Kyoto Encyclopedia of

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Genes and Genomes (KEGG) pathway enrichment to facilitate the biological

182

interpretations. We conducted the functional and pathway analysis for targets related to

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TRQ using ClueGo assay (Bindea et al., 2009).

184 185 186

2.4. Animals and experimental groups Male Sprague-Dawley (SD) rats of 10-12 weeks old were obtained from Jianyang

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Animal and Science Co., Ltd. (Jianyang, Sichuan, China). The animal experiment was

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conducted following guidelines set forth by the Animal Care and Use Committee of

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West China Hospital of Sichuan University. After 7 days of adaptive feeding in

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polycarbonate boxes under specific pathogen-free (SPF) conditions, 30 SD rats were

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randomly assigned to one of three experimental groups (n=10 per group): control group,

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model group, and TRQ group. In the first day, 120 µL LPS (Escherichia coli 055: B5;

193

Sigma, USA, 2 mg/mL) (model group, and TRQ group) or saline (control group) was

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intratracheally instilled. TRQ (Shanghai Kai Bao Pharmaceutical Co., LTD, China, 2.8

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ml/kg) (TRQ group) or saline (control group and model group) was intraperitoneally

196

injected 1 h before intratracheal instillation. Rats were anesthetized with intraperitoneal

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4 % sodium pentobarbital (40 mg/kg), and the right lungs were collected at 24 h as

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prescribed before (Liu et al., 2016a).

199 200

2.5. Histopathological staining

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Paraffin-embedded sections (4 µm) were stained with haematoxylin and eosin (HE)

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for airway morphology evaluation and Alcian blue (AB)/periodic acid-Schiff (PAS) for

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mucus secretion assessment. The evaluation of inflammation lesions was performed

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using a previously reported numeric scale, which scored the

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peribronchial/peribronchiolar, perivascular and alveolar inflammations from 0 to 10 in

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total (Liu et al., 2016a). Percentage of AB/PAS positively stained area to the total area

207

of the bronchial epithelium was measured for MH evaluation (Tesfaigzi et al., 2004).

208 209 210

2.6. Ιmmunohistochemical staining Ιmmunohistochemical staining for MUC5AC was done using standard histological

211

methods as previously established (Liu et al., 2016a). Frozen lung specimens embedded

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in paraffin were sectioned, deparaffinized, rehydrated and washed. Nonspecific binding

213

was blocked for 1 h with 1% BSA in PBS containing 0.05 % Tween 20. Specimens

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were then incubated with antibodies against MUC5AC (Cat. Number: M00612;

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dilution: 1:500, BosterBio, Pleasanton, CA, USA). A rabbit isotype control (Cat.

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Number: ab199376, Abcam, Burlingame, CA, USA) was used to replace the primary

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antibody as a negative control. The integrated optical density (IOD) was calculated by

218

measuring 10 consecutive visual fields for each sample at a magnification of 400 ×,

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using an optical microscope equipped with an Image-Pro Plus software (version 6.0,

220

Media Cybernetics, Silver Spring, MD, USA). The quantification of histological

221

staining was carried out by a pathologist unaware of group identity.

222 223 224

2.7. ELISA analysis Quantitation of TNF-α, IL-6, CXCL-1/CINC-1 (rat analogue of human IL-8), IL-

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17A and MUC5AC in lung homogenate was determined by ELISA technique,

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according to the manufacturer’s instructions. Three replicates were carried out for each

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of the different treatments. Rat TNF-α ELISA kit (Cat. Number: RTA00; Sensitivity: 5

228

pg/mL; Assay Range: 12.5-800 pg/mL), IL-6 ELISA kit (Cat. Number: R6000B;

229

Sensitivity: 0.7 pg/mL; Assay Range: 3.1-700 pg/mL) and CXCL-1/CINC-1 ELISA kit

230

(Cat. Number: RCN100; Sensitivity: 1.3 pg/mL; Assay Range: 7.8-500 pg/mL) were

231

purchased from R&D Systems (Minneapolis, MN, USA). Rat MUC5AC ELISA kit

232

(Cat. Number: MBS005394, Sensitivity: 1.0 ng/mL; Assay Range: 6.25-200 ng/mL)

233

was purchased from MyBioSource (San Diego, CA, USA). Rat IL-17A ELISA kit (Cat.

234

Number: ab214028, Sensitivity: 1.1 pg/ml; Assay Range: 6.25-400 pg/m) was

235

purchased from Abcam (Burlingame, CA, USA). The optical density of each well was

236

determined at 450 nm using a microplate reader (Bio-Rad, Richmond, CA, USA) within

237

30 min.

238 239 240

2.8. Real-time q-PCR analysis Quantitation of mRNA expression of TNF-α, IL-6, CXCL-1/CINC-1, IL-17A, and

241

MUC5AC in lung homogenate was determined by real-time q-PCR method. Total

242

RNAs from rat lung tissues were extracted using E.Z.N.ATM HP total RNA kit (Omega

243

Biotech, Norcross, GA, USA) and were reversely transcribed to prepare the first-strand

244

cDNA using iScript cDNA synthesis kit (Bio-Rad Laboratories, Hercules, CA, USA).

245

Primers used are: TNF-α forward primer (5’-TGCTATCTCATACCAGGAGA-3’) and

246

reverse primer (5’-GACTCCGCAAAGTCTAAGTA-3’); IL-6 forward primer (5’-

247

TCTTGGGACTGATGTTGTTG-3’) and reverse primer (5’-

248

TAAGCCTCCGACTTGTGAA-3’); CXCL-1/CINC-1 forward primer (5’-

249

CTCCAGCCACACTCCAACAGA-3’) and reverse primer (5’-

250

CACCCTAACACAAAACACGAT-3’); MUC5AC forward primer (5’-

251

CAATAACCACCCGGTCCAG-3’) and reverse primer (5’-

252

CAACTCCAGCAGAAGACTGT-3’); IL-17A forward primer (5’-

253

ATTCTGTTCTCATCCAGCAAG-3’) and reverse primer (5’-

254

AGGTCTCTGTTTAGGACGCA -3’); β-actin forward primer (5’-

255

CCTCATGAAGATCCTGACCG-3’) and reverse primer (5’-

256

ACCGCTCATTGCCGATAGTG-3’).

257 258 259

2.9. Statistical analysis Numerical data were shown as mean value ± standard deviation. The Kruskal-Wallis

260

H test was used for multiple comparisons between groups. A probability value of P <

261

0.05 were considered significant in all analyses. Statistical analyses were processed

262

using software package SPSS 23.0 (IBM SPSS Inc., Chicago, IL, USA).

263

264

3. Results

265

3.1. Compound determination

266

Comprehensive compound screening yielded a total of 55 compound candidates

267

from TRQ (Supplementary Table A). The number of compound candidates in LJT, SBG,

268

FSV, PFU and CST was 28, 18, 10, 13 and 9, respectively, with some compounds shared

269

by more than one herbal constitutes. By observing the candidate pool, the compounds

270

could be classified into several categories: phenylpropanoids, flavonoids, alkaloids, and

271

iridoids, according to their structures.

272

3.2. Target prediction

273

Based on the target fishing approach, 172 potential targets were read out and

274

validated from 41 potential compounds, with 14 compounds hitting no targets (Figure 1

275

and Supplementary Table B). A distinct target overlap between the 5 TCM drug

276

constituents was found. These potential targets were mainly receptors and enzymes, and

277

were widely distributed in multiple systems involving the respiratory tract, circulation

278

system, brain, liver, and kidney. The potential compounds in TRQ targeted many

279

proteins that played pivotal roles in physiological and pathological processes of

280

respiratory inflammatory diseases, including prostaglandin G/H synthase 2 (PTGS2),

281

dipeptidyl peptidase IV (DPPIV), and some pro-inflammatory cytokines such as TNF-α,

282

IL-6, and IL-8.

283

3.3. C-T network

284

In order to identify the interaction between the filtered 41 potential compounds (pC)

285

and 172 potential targets (pT), a pC-pT network was established (Figure 1). According

286

to Figure 1, the degree value of the compounds that reflected centrality of the network

287

varied significantly from 1 to 45, with an average value of 9.2 (Supplementary Table B).

288

Eleven compounds possessed a degree value higher than 10, which indicated that they

289

might be crucial for the therapeutic effect of TRQ. Among them, 4 highly degreed

290

molecules, wogonin (degree=45), baicalein (degree=37), chrysin 7-O-glucuronide

291

(degree=21), and phenylalanine (degree=21), were identified as the major chemical

292

components from Scutellaria baicalensis Georgi (Liau et al., 2019; Liu et al., 2014).

293

Two other representative compounds, chenodeoxycholic acid (degree=33) and

294

ursodeoxycholic acid (degree=21), were critical endogenous bile acids extracted from

295

Pulvis Fellis Ursi. Amino acids including glutamic acid (degree=27), phenylalanine

296

(degree=21), tyrosine (degree=20), valine (degree=20), and isoleucine (degree=11) were

297

identified as the main chemical compositions in Cornu Saigae Tataricae.

298 299 300

Figure 1. pC-pT network (where green nodes represent compounds and blue nodes represent targets, while the lines represent the interactions between them).

301 302 303

After the PPI analysis, we matched potential targets to specific diseases, and finally chose 75 respiratory diseases-related targets (dT) to do further in-depth research. A

304

potential compound - respiratory disease-related target (pC-dT) network composed of

305

116 nodes along with 236 edges was constructed (Figure 2). In this net, it was visually

306

notable that most of the potential compounds hit multiple targets, verifying the

307

“multiple compounds, network targets” feature of TRQ.

308

309 310 311

Figure 2. pC-dT network (where green nodes represent compounds and blue nodes

312

represent targets, while the lines represent the interactions between them).

313 314

3.4. GO and KEGG pathway analyses

315

The GO and KEGG pathway analyses were performed for understanding the

316

concerned biological processes, molecular functions and target-related pathways. As

317

shown in Figure 3, the biological processes of potential targets were mostly related to

318

the response to oxygen-containing compound (21.43 %), intracellular signal

319

transduction (21.43 %) and adrenergic receptor signaling pathway (14.29 %) (Figure 3

320

A). The molecular functions of potential targets mainly involved actions such as

321

carboxylic acid binding (28.12 %), zinc ion binding (21.88 %), catecholamine binding

322

(12.5 %) and transition metal ion binding (9.38 %) (Figure 3 B). The reactome of

323

potential targets were mainly related to the activation of IL-17 signaling pathway (56.04

324

%), arginine biosynthesis (16.48 %) and neuroactive ligand-receptor interaction (12.09

325

%) (Figure 3 C).

326 327

Figure 3. GO and KEGG pathway analyses concerned biological processes (A),

328

molecular functions (B) and target-related pathways (C).

329 330

3.5. T-D network

331

A T-D network was constructed based on potential targets and their related diseases

332

(Supplementary Table C). Two hundred diseases were classified into 21 groups

333

according to the MeSH Browser. As was shown in Figure 4, collected diseases were

334

mainly neoplasms (50/200), cardiovascular diseases (35/200), nervous system diseases

335

(26/200), mental disorders (13/200) and respiratory tract diseases (9/200).

336 337

Figure 4. T-D network (where blue nodes represent targets, yellow nodes represent

338

disease classification and purple nodes represent diseases, while the lines represent the

339

interactions between them).

340 341 342

3.6. Effects of TRQ on airway morphological changes Compared with control group (Figure 5 A), lung tissues of rats in model group

343

showed acute bronchopneumonia involving prominent thickening of the airway

344

epitheliums and conspicuous peribronchovascular inflammatory cell infiltration after

345

LPS instillation (Figure 5 B). TRQ treatment decreased the inflammatory lesion scores

346

by 29.8% (P < 0.05) (Figure 5 C, D).

347

A

C

B

348

Lung inflammation score

D 15

**

*

10

5

0

349 350

Figure 5: H&E staining of rat lung tissues in A) control group, B) model group and C)

351

TRQ group. Scale bars = 50 µm. D) Lung inflammation scores for histopathological

352

damage.

control group;

model group;

TRQ group. * P < 0.05, ** P < 0.01.

353 354 355

3.7. Effect of TRQ on airway goblet cell hyperplasia and mucus production Compared with control group (Figure 6 A), noticeable mucus overproduction and

356

goblet cell hyperplasia were observed along the airway surface epithelium in LPS-

357

treated mice, with a significant positive AB/PAS staining being detected (Figure 6 B,

358

D). TRQ treatment for 24 hours contributed to a significant relief in both mucous

359

hypersecretion and mucus cell hyperplasia by 30.5% (P < 0.01) (Figure 6 C, D).

360

A

C

B

361

AB/PAS positive staining area (%)

D 20

**

**

15 10 5 0

362 363

Figure 6: AB/PAS staining of rat lung tissues in A) control group, B) model group and

364

C) TRQ group. Scale bars = 50 µm; upper right insert scale bars = 25 µm. D) The

365

percentage of AB/PAS positively staining area to total epithelial area in rat airways.

366

control group;

model group;

TRQ group. * P < 0.05, ** P < 0.01.

367 368 369

3.8. Effect of TRQ on MUC5AC expression in lung tissues Compared with control group (Figure 7 A), MUC5AC positively stained granules in

370

rat lungs were significantly increased 24 hours after LPS challenge in model group

371

(Figure 7 B, D). TRQ injection significantly attenuated LPS-induced MUC5AC

372

expression in lung tissues by 68.5% (P < 0.01) (Figure 7 C, D).

A

C

B

373 D

374 375

Figure 7: MUC5AC immunohistochemical staining of rat lung tissues in A) control

376

group, B) model group and C) TRQ group. Scale bars 50 µm. D) IOD value of positive

377 378

MUC5AC staining in rat lung tissues.

control group;

model group;

TRQ

group. ** P < 0.01.

379 380 381

3.9. Effect of TRQ on MUC5AC expression in lung homogenate Compared with control group, a noticeable increase in the protein and mRNA

382

expression of MUC5AC was observed 24 hours after LPS administration. Pre-treatment

383

with TRQ helped to decrease the level of MUC5AC in both protein and gene levels by

384

25.1% and 17.4%, respectively (P < 0.05) (Figure 8).

385

0.6 0.4 0.2 0.0

**

*

MUC5AC mRNA expression (fold change)

MUC5AC expression (ng/m L)

0.8

40

**

*

30 20 10 0

386 387

Figure 8: Protein and mRNA levels of MUC5AC.

control group;

model group;

TRQ group. * P < 0.05, ** P < 0.01.

388 389 390

3.10. Effect of TRQ on TNF-α, IL-6, and CXCL-1/CINC-1 expressions in lung

391

homogenate

392

Compared with control group, LPS treatment induced significant elevation in the

393

protein and gene levels of TNF-α, IL-6, and CXCL-1/CINC-1 at 24 h. Intraperitoneal

394

injection of TRQ effectively inhibited the protein expression of TNF-α, IL-6, and

395

CXCL-1/CINC-1 by 26.9%, 35.6%, and 35.1%, respectively (P < 0.01), and the mRNA

396

expression by15.4%, 18.9%, and 15.4%, respectively ((P < 0.05). (Figure 9).

397 398

**

20 10 0

**

**

100

50

400 401

**

*

**

*

**

*

4 3 2 1 0

3 2 1 0

**

**

100

50

0

5 4

(fold change)

150

CXCL-1/CINC-1 mRNA expression

CXCL-1/CINC-1 expression (pg/mL)

0

399

5

4

IL-6 mRNA expression (fold change)

150

IL-6 expression (pg/mL)

**

30

TNF-α mRNA expression (fold change)

TNF-α expression (pg/mL)

40

3 2 1 0

Figure 9: Protein and mRNA levels of TNF-α, IL-6, and CXCL-1/CINC-1. group;

model group;

control

TRQ group. * P < 0.05, ** P < 0.01

402 403 404

3.11. Effect of TRQ on IL-17A expressions in lung homogenate Compared with control group, protein and mRNA levels of IL-17A distinctively

405

increased in model group. TRQ treatment effectively reduced LPS-stimulated IL-17A

406

expression in both protein and mRNA levels by 72.0% and 69.2%, respectively (P <

407

0.05) (Figure 10).

408

410 411 412

150 100 50

**

*

IL-17A mRNA expression (fold change)

IL-17A expression (pg/mL)

409

200

0

Figure 10: Protein and mRNA Levels of IL-17A.

*

4

*

3 2 1 0

control group;

TRQ group. (* P < 0.05, ** P < 0.01)

model group;

413 414

4. Discussion As TCM pipeline has grown in recent years, systems pharmacology as an effective

415

way to expedite the pharmacological discovery of TCM has gained considerable ground

416

over the past decades. Systems pharmacology is considered to be a well-characterized

417

approach to highlight the manner in which the active compounds and

418

polypharmacological therapeutic mechanisms of multi-compound TCM drugs could be

419

reliably identified. In this study, we conducted systems pharmacology and rat

420

experiment to accumulate the “systems-level” information of TRQ. The results of this

421

work were expected to facilitate the pharmacological discovery of TRQ and laid a

422

foundation for further studies.

423

According to the degree of nodes in C-T network system, we identified 11 chemical

424

constituents as the potential active compounds which might take part in the

425

administrative processes of TRQ in the treatment of MH. Specifically, the top two

426

degreed compounds, baicalein and wogonin, have well established roles in airway

427

inflammation (Dinda et al., 2017; Kim et al., 2018; Ku and Bae, 2015; Luo et al., 2016;

428

Qi et al., 2013; Qinghe et al., 2019) and mucous hypersecretion (Lee et al., 2010; Lucas

429

et al., 2015).

430

We linked the potential compounds with their corresponding targets through C-T

431

network construction. Based on the approaches combined with chemometric method,

432

information integration, and data-mining, we demonstrated that PTGS2, DPPIV, as well

433

as pro-inflammatory cytokines including TNF-α, IL-6, and IL-8 might be the potential

434

targets hit by TRQ compound candidates. As the combinatorial art of TCM formula

435

may shift “one drug, one target” paradigm to “multiple component, network target”

436

strategy (Fitzgerald et al., 2006), we consider that the “network target” system can work

437

in two different ways in the condition of MH. On one hand, these targets individually

438

act as inhibitors in mucus overproduction. For example, DPPIV is a serine exopeptidase

439

that could inactivate various pro-inflammatory molecules (such as neuropeptides,

440

chemokines, and cytokines) by selectively removing the N-terminal dipeptides from

441

their biological structures (Landis et al., 2008). It could be helpful in MH by

442

inactivating the functional ability of Substance P, a peptide that could stimulate

443

submucosal gland secretion and exacerbate inflammatory conditions (Grouzmann et al.,

444

2002). On the other hand, protein-protein interactions might happen in the network and

445

contribute to the therapeutic process. In the last decades, TNF-α, IL-6, and IL-8 were

446

primarily proved to be potent inducers of airway mucus overproduction by upregulating

447

MUC5AC expression in airway epithelial cells (Chakir et al., 2003; Chen et al., 2003;

448

Hashimoto et al., 2004; Wang et al., 2007), which indicated the association between

449

airway inflammation and mucus production. PTGS2 is a pivotal synthase involved in

450

the conversion of various prostaglandins. It is not typically present under normal

451

conditions in most cells but rapidly induced by inflammatory cytokines associated with

452

inflammations. Studies have shown that PTGS2 is implicated in TNF-α stimulated

453

MUC5AC expression (Ricciotti and FitzGerald, 2011)(Li and Zhou, 2008).

454

We constructed multilevel networks (molecular-target-disease) to explore the

455

correlations between the potential compounds of TRQ and diseases from a systematic

456

perspective. It is currently understood that airway inflammation is a complex disorder

457

associated with multiple alterations in molecular pathways and complex interactions at

458

the cellular and organ levels (Loscalzo et al., 2007). Compared with the treatment with a

459

single, “magic bullet” therapy, TRQ regimen with a nature of “multiple component,

460

network target” may exert a therapeutic benefit on MH through comprehensive effects

461

on tumorigenesis, cardiovascular system, immune system, nutritional and metabolic

462

conditions and nervous system.

463

The pathway analysis showed that most targets of potential compounds in TRQ

464

were associated with IL-17 signaling pathway, which indicated that IL-17 might be

465

involved in the molecular mechanisms of TRQ. IL-17 (IL-17A) is a signature cytokine

466

secreted by activated CD4+ Th17 cells, γδ T cells, natural killer T cells, and innate

467

lymphoid cells. Studies proved that IL-17 stimulation resulted in a consistent and strong

468

upregulation on MUC5AC expression in both human and animal models (Chakir et al.,

469

2003; Chen et al., 2003). The results of KEGG analyses in our study also demonstrated

470

that MUC5AC was a pivotal downstream protein in IL-17 signaling pathway cascade.

471

Besides, previous studies showed that IL-17 could induce a dramatic increase in the

472

expression of IL-6, IL-8 and TNF-α from bronchial epithelial cells (Kawaguchi et al.,

473

2001) and macrophages (Jovanovic et al., 1998). Moreover, IL-17 played a synergistic

474

augmenting effect on TNF-α stimulated IL-8 and IL-6 secretion in human airways

475

(Henness et al., 2004; Honda et al., 2016). Therefore, IL-17 suppression might play a

476

role in the treatment of MH through a direct way by inhibiting the downstream

477

MUC5AC production and an indirect way by downregulating the proinflammatory

478

cytokines.

479

5. Conclusion

480

To conclude, TRQ contains various ingredients with different pharmacologic

481

properties that act on multiple targets; TRQ plays a therapeutic effect against mucus

482

hypersecretion and MUC5AC production, likely at least partially, through the inhibition

483

of IL-6, IL-8 and TNF-α and IL-17. Further studies on the molecular basis are needed to

484

verify the results of the current work and to discover more about therapeutic

485

mechanisms of TRQ. Systems pharmacology as a novel analytical method can be

486

rational and reliable in processing the compound discovery and understanding the

487

scientific connotation of TCM from a biological perspective.

488 489

Conflicts of interest The authors declare that they have no conflicts of interest.

490

491

Acknowledgements

492

This work was supported by the National Natural Science Foundation of China

493

(grant number 81700024), the China Postdoctoral Science Foundation Grant (grant

494

number 2018M643505), and the Post-Doctor Research Project, West China Hospital,

495

Sichuan University (grant number 2018HXBH039).

496 497 498

Author contributions Wei Liu and Xiawei Zhang contributed equally to this work; Wei Liu, Bing Mao

499

and Hongli Jiang conceived and designed the experiments; Wei Liu and Xiawei Zhang

500

conducted the experimental work and analysis; Wei Liu drafted the manuscript; Hongli

501

Jiang provided major revisions and comments to the manuscript. All authors reviewed

502

and approved the final manuscript.

503 504 505 506

507

References

508

Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A.,

509

Fridman, W.H., Pagès, F., Trajanoski, Z., Galon, J., 2009. ClueGO: a Cytoscape plug-in

510

to decipher functionally grouped gene ontology and pathway annotation networks.

511

Bioinformatics 25, 1091-1093. https://doi.org/10.1093/bioinformatics/btp101

512

Chakir, J., Shannon, J., Molet, S., Fukakusa, M., Elias, J., Laviolette, M., Boulet, L.P.,

513

Hamid, Q., 2003. Airway remodeling-associated mediators in moderate to severe

514

asthma: effect of steroids on TGF-beta, IL-11, IL-17, and type I and type III collagen

515

expression. J. Allergy. Clin. Immunol. 111, 1293-1298.

516

Chen, Y., Thai, P., Zhao, Y.H., Ho, Y.S., DeSouza, M.M., Wu, R., 2003. Stimulation of

517

airway mucin gene expression by interleukin (IL)-17 through IL-6 paracrine/autocrine

518

loop. J. Biol. Chem. 278, 17036-17043. https://doi.org/10.1074/jbc.M210429200

519

Dinda, B., Dinda, S., DasSharma, S., Banik, R., Chakraborty, A., Dinda, M., 2017.

520

Therapeutic potentials of baicalin and its aglycone, baicalein against inflammatory

521

disorders. Eur. J. Med. Chem. 131, 68-80. https://doi.org/10.1016/j.ejmech.2017.03.004

522

Evans, C.M., Raclawska, D.S., Ttofali, F., Liptzin, D.R., Fletcher, A.A., Harper, D.N.,

523

McGing, M.A., McElwee, M.M., Williams, O.W., Sanchez, E., Roy, M.G., Kindrachuk,

524

K.N., Wynn, T.A., Eltzschig, H.K., Blackburn, M.R., Tuvim, M.J., Janssen, W.J.,

525

Schwartz, D.A., Dickey, B.F., 2015. The polymeric mucin Muc5ac is required for

526

allergic airway hyperreactivity. Nat. Commun. 6, 6281.

527

https://doi.org/10.1038/ncomms7281

528

Fitzgerald, J.B., Schoeberl, B., Nielsen, U.B., Sorger, P.K., 2006. Systems biology and

529

combination therapy in the quest for clinical efficacy. Nat. Chem. Biol. 2, 458-466.

530

https://doi.org/10.1038/nchembio817

531

Grouzmann, E., Monod, M., Landis, B., Wilk, S., Brakch, N., Nicoucar, K., Giger, R.,

532

Malis, D., Szalay-Quinodoz, I., Cavadas, C., Morel, D.R., Lacroix, J.S., 2002. Loss of

533

dipeptidylpeptidase IV activity in chronic rhinosinusitis contributes to the neurogenic

534

inflammation induced by substance P in the nasal mucosa. FASEB. J. 16, 1132-1134.

535

https://doi.org/10.1096/fj.01-0939fje

536

Hashimoto, K., Graham, B.S., Ho, S.B., Adler, K.B., Collins, R.D., Olson, S.J., Zhou,

537

W., Suzutani, T., Jones, P.W., Goleniewska, K., O'Neal, J.F., Peebles, R.S., Jr., 2004.

538

Respiratory syncytial virus in allergic lung inflammation increases Muc5ac and gob-5.

539

Am. J. Respir. Crit. Care. Med. 170, 306-312. https://doi.org/10.1164/rccm.200301-

540

030OC

541

Henness, S., Johnson, C.K., Ge, Q., Armour, C.L., Hughes, J.M., Ammit, A.J., 2004. IL-

542

17A augments TNF-alpha-induced IL-6 expression in airway smooth muscle by

543

enhancing mRNA stability. J. Allergy. Clin. Immunol. 114, 958-964.

544

https://doi.org/10.1016/j.jaci.2004.06.023

545

Honda, K., Wada, H., Nakamura, M., Nakamoto, K., Inui, T., Sada, M., Koide, T.,

546

Takata, S., Yokoyama, T., Saraya, T., Kurai, D., Ishii, H., Goto, H., Takizawa, H., 2016.

547

IL-17A synergistically stimulates TNF-alpha-induced IL-8 production in human airway

548

epithelial cells: A potential role in amplifying airway inflammation. Exp. Lung. Res. 42,

549

205-216. https://doi.org/10.1080/01902148.2016.1190796

550

Jeffery, P.K., 2001. Remodeling in asthma and chronic obstructive lung disease. Am. J.

551

Respir. Crit. Care. Med. 164, S28-38.

552

https://doi.org/10.1164/ajrccm.164.supplement_2.2106061

553

Jiang, D.P., Perelman, J.M., Kolosov, V.P., Zhou, X.D., 2011. Effects of scutellarin on

554

MUC5AC mucin production induced by human neutrophil elastase or interleukin 13 on

555

airway epithelial cells. Journal of Korean medical science 26, 778-784.

556

https://doi.org/10.3346/jkms.2011.26.6.778

557

Jovanovic, D.V., Di Battista, J.A., Martel-Pelletier, J., Jolicoeur, F.C., He, Y., Zhang, M.,

558

Mineau, F., Pelletier, J.P., 1998. IL-17 stimulates the production and expression of

559

proinflammatory cytokines, IL-beta and TNF-alpha, by human macrophages. J.

560

Immunol. 160, 3513-3521.

561

Kawaguchi, M., Kokubu, F., Kuga, H., Matsukura, S., Hoshino, H., Ieki, K., Imai, T.,

562

Adachi, M., Huang, S.K., 2001. Modulation of bronchial epithelial cells by IL-17. J.

563

Allergy. Clin. Immunol. 108, 804-809. https://doi.org/10.1067/mai.2001.119027

564

Kim, Y.J., Kim, H.J., Lee, J.Y., Kim, D.H., Kang, M.S., Park, W.S., 2018. Anti-

565

inflammatory effect of baicalein on polyinosinic–polycytidylic acid-Induced RAW

566

264.7 mouse macrophages. Viruses 10, 224.

567

Ku, S.K., Bae, J.S., 2015. Baicalin, baicalein and wogonin inhibits high glucose-

568

induced vascular inflammation in vitro and in vivo. BMB. Rep. 48, 519-524.

569

Kwon, S.H., Nam, J.I., Kim, S.H., Kim, J.H., Yoon, J.H., Kim, K.S., 2009. Kaempferol

570

and quercetin, essential ingredients in Ginkgo biloba extract, inhibit interleukin-1beta-

571

induced MUC5AC gene expression in human airway epithelial cells. Phytother. Res. 23,

572

1708-1712. https://doi.org/10.1002/ptr.2817

573

Landis, B.N., Grouzmann, E., Monod, M., Busso, N., Petak, F., Spiliopoulos, A.,

574

Robert, J.H., Szalay-Quinodoz, I., Morel, D.R., Lacroix, J.S., 2008. Implication of

575

dipeptidylpeptidase IV activity in human bronchial inflammation and in

576

bronchoconstriction evaluated in anesthetized rabbits. Respiration 75, 89-97.

577

https://doi.org/10.1159/000106267

578

Lee, H.J., Lee, S.Y., Kim, Y.S., Jeon, B.K., Lee, J.W., Bae, H.S., Jae, C., 2010. Effects

579

of baicalin, baicalein and schizandrin on airway Mucin production induced by

580

epidermal growth factor and phorbol ester. Biomol. Ther. (Seoul) 18, 396-401.

581

Li, J., Zhao, P., Li, Y., Tian, Y., Wang, Y., 2015. Systems pharmacology-based dissection

582

of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for

583

chronic obstructive pulmonary disease. Sci. Rep. 15, 15290.

584

https://doi.org/10.1038/srep15290

585

Li, Q., Zhou, X.D., 2008. TNF-alpha upregulate MUC5AC mucin secretion through

586

COX2/PGE2 mechanism. Chin. J. Appl. Phys. 24, 120-121.

587

Li, Q., Zhou, X.D., Kolosov, V.P., Perelman, J.M., 2013. Salidroside reduces cold-

588

induced mucin production by inhibiting TRPM8 activation. International journal of

589

molecular medicine 32, 637-646. https://doi.org/10.3892/ijmm.2013.1434

590

Li, W., Mao, B., Wang, G., Wang, L., Chang, J., Zhang, Y., Wan, M.H., Guo, J., Zheng,

591

Y.Q., 2010. Effect of Tanreqing Injection on treatment of acute exacerbation of chronic

592

obstructive pulmonary disease with Chinese medicine syndrome of retention of phlegm

593

and heat in Fei. Chin. J. Integr. Med. 16, 131-137. https://doi.org/10.1007/s11655-010-

594

0131-y

595

Liau, P.R., Wu, M.S., Lee, C.K., 2019. Inhibitory effects of Scutellaria baicalensis root

596

extract on linoleic acid hydroperoxide-induced lung mitochondrial lipid peroxidation

597

and antioxidant activities. Molecules (Basel, Switzerland) 24, E2143.

598

https://doi.org/10.3390/molecules24112143

599

Liu, J., Wang, S., Sun, J., Shi, J., Li, Y., Gou, J., Li, A., He, L., 2014. Screening of

600

osteoanagenesis-active compounds from Scutellaria baicalensis Georgi by

601

hPDLC/CMC-online-HPLC/MS. Fitoterapia 93, 105-114.

602

https://doi.org/10.1016/j.fitote.2013.12.020

603

Liu, W., Jiang, H.L., Cai, L.L., Yan, M., Dong, S.J., Mao, B., 2016a. Tanreqing injection

604

attenuates lipopolysaccharide-Induced airway inflammation through MAPK/NF-kappaB

605

signaling pathways in rats model. Evid. Based. Complement. Alternat. Med. 2016,

606

5292346. https://doi.org/10.1155/2016/5292346

607

Liu, Z., Guo, F., Wang, Y., Li, C., Zhang, X., Li, H., Diao, L., Gu, J., Wang, W., Li, D.,

608

He, F., 2016b. BATMAN-TCM: a bioinformatics analysis tool for molecular

609

mechANism of Traditional Chinese Medicine. Sci. Rep. 6, 21146.

610

https://doi.org/10.1038/srep21146

611

Loscalzo, J., Kohane, I., Barabasi, A.L., 2007. Human disease classification in the

612

postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol.

613

3, 124. https://doi.org/10.1038/msb4100163

614

Lucas, C.D., Dorward, D.A., Sharma, S., Rennie, J., Felton, J.M., Alessandri, A.L.,

615

Duffin, R., Schwarze, J., Haslett, C., Rossi, A.G., 2015. Wogonin induces eosinophil

616

apoptosis and attenuates allergic airway inflammation. Am. J. Respir. Crit. Care. Med.

617

191, 626-636. https://doi.org/10.1164/rccm.201408-1565OC

618

Luo, J., Kong, J., Dong, B., Huang, H., Wang, K., Wu, L., Hou, C., Liang, Y., Li, B.,

619

Chen, Y., 2016. Baicalein attenuates the quorum sensing-controlled virulence factors of

620

Pseudomonas aeruginosa and relieves the inflammatory response in P. aeruginosa-

621

infected macrophages by downregulating the MAPK and NFκB signal-transduction

622

pathways. Drug Des. Devel. Ther. 10, 183-203. https://doi.org/10.2147/dddt.s97221

623

Oliviero, M., Romilde, I., Beatrice, M.M., Matteo, V., Giovanna, N., Consuelo, A.,

624

Claudio, C., Giorgio, S., Filippo, M., Massimo, N., 2016. Evaluations of thyme extract

625

effects in human normal bronchial and tracheal epithelial cell lines and in human lung

626

cancer cell line. Chem. Biol. Interact. 256, 125-133.

627

https://doi.org/10.1016/j.cbi.2016.06.024

628

Qi, Z., Yin, F., Lu, L., Shen, L., Qi, S., Lan, L., Luo, L., Yin, Z., 2013. Baicalein reduces

629

lipopolysaccharide-induced inflammation via suppressing JAK/STATs activation and

630

ROS production. Inflamm. Res. 62, 845-855. https://doi.org/10.1007/s00011-013-0639-

631

7

632

Qinghe, G., Guangyu, L., Xiaobin, L., Qingsong, H., 2019. Baicalein exerts a protective

633

role in pneumonia caused by Streptococcus pneumoniae. Front. Biosci. 24, 849-858.

634

Ricciotti, E., FitzGerald, G.A., 2011. Prostaglandins and Inflammation. Arterioscler.

635

Thromb. Vasc. Biol. 31, 986-1000. https://doi.org/10.1161/atvbaha.110.207449

636

Rogers, D.F., Barnes, P.J., 2006. Treatment of airway mucus hypersecretion. Ann. Med.

637

38, 116-125.

638

Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y.,

639

Xu, X., Li, Y., Wang, Y., Yang, L., 2014. TCMSP: a database of systems pharmacology

640

for drug discovery from herbal medicines. J. Cheminform. 6, 13.

641

https://doi.org/10.1186/1758-2946-6-13

642

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N.,

643

Schwikowski, B., Ideker, T., 2003. Cytoscape: A Software Environment for Integrated

644

Models of Biomolecular Interaction Networks. Genome. Res. 13, 2498-2504.

645

https://doi.org/10.1101/gr.1239303

646

Tesfaigzi, Y., Harris, J.F., Hotchkiss, J.A., Harkema, J.R., 2004. DNA synthesis and Bcl-

647

2 expression during development of mucous cell metaplasia in airway epithelium of rats

648

exposed to LPS. Am. J. Physiol. Lung. Cell. Mol. Physiol. 286, L268-274.

649

https://doi.org/10.1152/ajplung.00172.2003

650

Wang, I.J., Wu, C.Y., Hu, F.R., 2007. Effect of proinflammatory cytokines on the human

651

MUC5AC promoter activity in vitro and in vivo. Clin. Ophthalmol. 1, 71-77.

652

Wei, M., Xie, X., Chu, X., Yang, X., Guan, M., Wang, D., 2013. Dihydroartemisinin

653

suppresses ovalbumin-induced airway inflammation in a mouse allergic asthma model.

654

Immunopharmacol. Immunotoxicol. 35, 382-389.

655

https://doi.org/10.3109/08923973.2013.785559

656

Yu, H., Chen, J., Xu, X., Li, Y., Zhao, H., Fang, Y., Li, X., Zhou, W., Wang, W., Wang,

657

Y., 2012. A systematic prediction of multiple drug-target interactions from chemical,

658

genomic, and pharmacological data. PloS One 7, e37608.

659

https://doi.org/10.1371/journal.pone.0037608

660

Zhong, Y., Mao, B., Wang, G., Fan, T., Liu, X., Diao, X., Fu, J., 2010. Tanreqing

661

injection combined with conventional Western medicine for acute exacerbations of

662

chronic obstructive pulmonary disease: a systematic review. J. Altern. Complement.

663

Med. 16, 1309-1319. https://doi.org/10.1089/acm.2009.0686

664