Research Article Mechanism of Action of Bu-Fei-Yi-Shen Formula In

Hindawi BioMed Research International Volume 2020, Article ID 9105972, 12 pages https://doi.org/10.1155/2020/9105972

Research Article Mechanism of Action of Bu-Fei-Yi-Shen Formula in Treating Chronic Obstructive Pulmonary Disease Based on Network Pharmacology Analysis and Molecular Docking Validation

Longchuan Wu, Yu Chen, Jiao Yi, Yi Zhuang, Lei Cui, and Chunhui Ye

Department of Respiratory Medicine, Huai’an Hospital of Traditional Chinese Medicine, Huai’an, 223001, China

Correspondence should be addressed to Chunhui Ye; [email protected]

Received 15 August 2020; Revised 13 November 2020; Accepted 19 November 2020; Published 27 November 2020

Academic Editor: Kun Li

Copyright © 2020 Longchuan Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. To explore the mechanism of action of Bu-Fei-Yi-Shen formula (BFYSF) in treating chronic obstructive pulmonary disease (COPD) based on network pharmacology analysis and molecular docking validation. Methods. First of all, the pharmacologically active ingredients and corresponding targets in BFYSF were mined by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the analysis platform, and literature review. Subsequently, the COPD-related targets (including the pathogenic targets and known therapeutic targets) were identiﬁed through the TTD, CTD, DisGeNet, and GeneCards databases. Thereafter, Cytoscape was employed to construct the candidate component-target network of BFYSF in the treatment of COPD. Moreover, the cytoHubba plug-in was utilized to calculate the topological parameters of nodes in the network; then, the core components and core targets of BFYSF in the treatment of COPD were extracted according to the degree value (greater than or equal to the median degree values for all nodes in the network) to construct the core network. Further, the Autodock vina software was adopted for molecular docking study on the core active ingredients and core targets, so as to verify the above-mentioned network pharmacology analysis results. Finally, the Omicshare database was applied in enrichment analysis of the biological functions of core targets and the involved signaling pathways. Results. In the core component-target network of BFYSF in treating COPD, there were 30 active ingredients and 37 core targets. Enrichment analysis suggested that these 37 core targets were mainly involved in the regulation of biological functions, such as response to biological and chemical stimuli, multiple cellular life processes, immunity, and metabolism. Besides, multiple pathways, including IL-17, Toll-like receptor (TLR), TNF, and HIF-1, played certain roles in the eﬀect of BFYSF on treating COPD. Conclusion. BFYSF can treat COPD through the multicomponent, multitarget, and multipathway synergistic network, which provides basic data for intensively exploring the mechanism of action of BFYSF in treating COPD.

1. Introduction bidity and mortality rates and causes tremendous socioeco- nomic burdens, which has become an important public Chronic obstructive pulmonary disease (COPD) is a common health issue [6]. In China, COPD is a major chronic respira- clinical disease characterized by respiratory system symptoms tory system disease that severely impairs human health [7, 8]. and flow limitation [1]. It is generally induced by airway and Modern medicine can not attain satisfactory therapeutic (or) alveolar abnormalities due to exposure to harmful parti- effect or safety on stable COPD patients, and the limited cles and gases (like cigarette smoke), and its pathogenesis is efficacy must be maintained by long-term inhalation of mainly related to chronic airway and pulmonary inflamma- bronchodilators and glucocorticoids [9–11]. By contrast, tra- tion, oxidative stress (OS), protease/antiprotease imbalance, ditional Chinese medicine (TCM) exhibits obvious advan- and cell apoptosis [2–5]. COPD is associated with high mor- tages in the treatment of COPD, which obviously improves 2 BioMed Research International the clinical symptoms of patients according to syndrome dif- cornuside, rehmaglutin D, and atractylenolide I, which did ferentiation. In the TCM theory, COPD patients mostly suf- not satisfy the criteria of bioavailability and DL values but fer from syndrome of deficiency of both the lung and kidney. were reported to possess extensive pharmacological activities Therefore, tonifying the lung, strengthening the spleen, and or had high contents in single herbal medicine or were used boosting the kidney are the basic TCM therapeutic methods as the identification compounds of single medicine in the for COPD [12, 13]. Bu-Fei-Yi-Shen formula (BFYSF) is a Pharmacopeia [24–27], were also enrolled into the potential cipher prescription for the treatment of COPD at Huai’an compounds of BFYSF. The potential targets of active Hospital of Traditional Chinese Medicine, which is consti- ingredients were mined and integrated using the TCSMP tuted by 6 Chinese herbal medicines, including Ginseng and BATMAN-TCM databases (http://bionet.ncpsb.org/ Radix et Rhizoma Rubra (Hongshen (HS)), Astragali Radix batman-tcm/) [28] according to the drug structural similarity (Huangqi (HQ)), Epimedii Folium (Yinyanghuo (YYH)), evaluation and reverse molecular docking, for the construc- Corni Fructus (Shanzhuyu (SZY)), Rehmanniae Radix tion of a potential target set of BFYSF. (Dihuang (DH)), and Atractylodis Macrocephalae Rhizoma (Baizhu (BZ)). It has the effects of tonifying lung and spleen 2.2. Mining of COPD-Related Targets. The targets related to and warming and invigorating kidney yang. In clinic, BFYSF the COPD pathogenesis or therapeutics were obtained can effectively relieve the symptoms in COPD patients, through retrieving the TTD database (http://db.idrblab.net/ improve the lung function, and enhance the patient exercise ttd/, Last update by June 1, 2020) [29], DisGeNet database tolerance and quality of life. In addition, it can alleviate the (https://www.disgenet.org/, v7.0) [30], GeneCards database cough severity, frequency, and expectoration amount and (https://www.genecards.org/, version: 5.0) [31], and CTD improve the wheezing symptom [14, 15], but its precise database (http://ctdbase.org/, Last update by June, 2020) mechanism of action remains unclear so far. [32] using the keyword “chronic obstructive pulmonary dis- Chinese medicine formula is the organic whole consti- ease.” Specifically, drugs with abnormal status and the corre- tuted by multiple traditional Chinese herbal medicines, sponding targets were eliminated in mining against the TTD which has a complex chemical composition [16]. Nonethe- database. In the DisGeNet database, the targets were ranked less, only partial chemical components possessing favorable based on the disease specificity index (DSI) value from the pharmacokinetic properties can play therapeutic roles, and highest to the lowest, and those greater than the median were the therapeutic efficacy of a Chinese medicine formula may selected. In the CTD database, the top 200 genes in terms of be derived from the joint action of multiple components the Inference score were selected. In the GeneCards, targets [17]. Due to the complexity in chemical components of with the score ≥ 10 were screened. Later, targets obtained Chinese medicine and human body interaction, it is relatively from the above four databases were integrated to construct difficult to illustrate the molecular mechanism of action of the COPD-related target set. Chinese medicine formula in treating disease [18]. Network pharmacology is one of the key technical means to investigate 2.3. Construction of the Candidate and Core Component- the Chinese medicine formula in systems biology, which Target Network of BFYSF in Treating COPD. First of all, allows to reveal the pharmacological actions of herbal medi- the UniProt database (https://www.uniprot.org/) [33] was cines and formulas, along with the molecular mechanisms, selected, the species was selected as “Homo sapiens,” and based on multidisciplinary integration, such as high- the names of targets obtained in the above two steps were throughput omics, computer technology, pharmacology, standardized to gain the unique UniProt IDs and gene and network database retrieval [19, 20]. This study applied names. Subsequently, the two target sets were input, respec- the network pharmacology technology in predicting the tively, to the Venny tool (https://bioinfogp.cnb.csic.es/tools/ pharmacodynamic material basis and molecular mechanism venny/, version: 2.1) to acquire the common targets, which of BFYSF in treating COPD, and molecular docking technol- were the candidate targets of BFYSF in treating COPD. ogy was used for verification, so as to provide theoretical Thereafter, the Cytoscape software (version 3.7.2) [34] was foundation for carrying out fundamental experiment study employed to construct the candidate active component- and reasonable clinical application of BFYSF. target network of BFYSF in treating COPD, with “active component” being set as square, while “target” being set as 2. Materials and Methods circle. Then, the cytoHubba plug-in (version 0.1) [35] was employed to calculate and rank the degree of all nodes. After- 2.1. Screening of Potential Pharmacodynamic Compounds wards, nodes with degree greater than or equal to the median and Related Targets in BFYSF. Using the Traditional Chinese degree values for all nodes in the network (namely, the core Medicine Systems Pharmacology (TCMSP, Version: 2.3, active ingredients and core targets of BFYSF in treating https://tcmspw.com/index.php) database and the analysis COPD) were selected to establish the core component- platform [21], the names of six Chinese herbal medicines target network. The node size in the core network was related were input in succession to obtain the corresponding chemi- to the degree value. cal compounds and related information. Then, the potential compounds in BFYSF were screened at the thresholds of 2.4. Verification of the Compound-Target Interactions. The bioavailability ðOBÞ ≥ 30% and drug likeness ðDLÞ ≥ 0:18 interactions between compounds and targets were validated according to the literature reported method [22, 23]. More- using the Autodock vina software (version: 1.1.2) [36]. The over, some compounds, such as astragaloside I, icariresinol, mol2 structure of compound was downloaded from the BioMed Research International 3

TCMSP database. In addition, the 3D structures of all target For the comprehensive understanding of the component- proteins were acquired based on the RCSB PDB database target network within BFYSF in a systemic and holistic man- (http://www.rcsb.org/) [37]. The MOE approach and Auto- ner, a network map was established using Cytoscape, which dockTools (version: 1.5.6) [38] were used to prepare ligands included 1147 edges along with 343 nodes (Figure 1). In the and proteins before molecular docking. For target protein, map, node degree was the number of targets or edges associ- its crystal structure was subjected to pretreatments, which ated with the node according to topological analysis. Alto- included water molecule removal, protonation and 3D gether 145 potential components were identified with the hydrogenation, protein structural correction, energy optimi- median ≥ 6 degrees from the constructed network. Among zation, and retention of target active region. Ligand structure them, quercetin, kaempferol, beta-sitosterol, 7-O-methyliso- should satisfy the low-energy conformation. The coordinates mucronulatol, stigmasterol, and formononetin exerted func- and box size for molecular docking were finalized according tions on 158, 72, 55, 47, 42, and 39 targets, respectively, which to the ligand location. To achieve a higher computational might be the significant active ingredients in BFYSF. accuracy, we set the exhaustiveness parameter to 20. All other parameters were set as default values unless otherwise 3.2. Mining of the Core Targets of BFYSF in Treating COPD. specified. Then, active ingredients were coupled into target COPD is a polygenic genetic disease, and its pathogenesis protein in a semiflexible way, and 9 conformations were gen- can be potentially illustrated by studying the gene-gene or erated in total. The conformation with the highest affinity gene-environment associations [39]. Through retrieving the was selected as the final docking one. TTD, DisGeNet, CTD, and GeneCards databases, altogether, 276 COPD-related targets (Table S3) were obtained. In 2.5. GO-BP and KEGG Enrichment Analyses of Core Targets. addition, 65 out of the identified targets for the potential To further explore the biological processes (BPs) and the reg- components of BFYSF were identified as the COPD- or ulatory signaling pathways involved in the core targets of therapeutic-related targets, suggesting that BFYSF showed BFYSF in treating COPD, the screened core target informa- certain therapeutic efficacy on COPD (Figure 2(a) and tion was imported, and the Omicshare online software Table S4). These 65 targets were all candidate targets of (https://www.omicshare.com/) was used for GO-BP and BFYSF in treating COPD, while the corresponding active KEGG enrichment analyses of the targets at the threshold ingredients in BFYSF (n =48) were the candidate active of p <0:05. compounds of BFYSF. Later, the Cytoscape software was employed to construct 3. Results the candidate component-target network of BFYSF in treating COPD (Figure 2(b)). To further screen the core compo- 3.1. Screening of Potential Pharmacodynamic Components nents and core targets of BFYSF in treating COPD, the and Targets in BFYSF. Through TCMSP database retrieval, cytoHubba plug-in was employed to calculate and sort the a total of 486 chemical components in the six herbal medi- topological parameters (degree) of the nodes in the above- cines constituting BFYSF were collected. Thereafter, the 486 mentioned network (Table S5). Then, the median degree of components were screened at the active ingredient thresh- all nodes was calculated, and nodes with values greater than olds of OB ≥ 30% and DL ≥ 0:18, and 58 potential compo- or equal to the median (degree = 2) of all nodes were nents were obtained. In addition, among those eliminated selected as the core components and core targets of BFYSF components, 13 compounds, which did not satisfy the in treating COPD, respectively. Subsequently, the core inclusion criteria of bioavailability and DL values but were component-target network (thirty-seven core targets and reported to possess extensive pharmacological activities or thirty core components) was constructed (Figure 2(c), had high contents in single herbal medicine or were used as Tables 1 and 2). Among all the core components, quercetin, the identification compounds of single medicine in the phar- kaempferol, beta-sitosterol, stigmasterol, and formononetin macopeia, were also enrolled as the potential components of ranked the top five in terms of degree, which were derived BFYSF. Finally, HS, HQ, YYH, SZY, DH, and BZ had 10, 23, from the HQ, HS, SZY, and DH in BFYSF, respectively. 2, 23, 4, and 13 active ingredients, respectively. Among them, Some of them have been verified in previous studies to delay beta-sitosterol and stigmasterol extensively existed in multi- the course of COPD and improve symptoms [40, 41]. In ple herbal medicines. The basic information of the potential addition, among all targets, PTGS2, PPARG, and NOS2 were pharmacodynamic components of BFYSF is presented in the top three sorted by degree. Table S1. Subsequently, the targets of these 71 potential components 3.3. Verification of the Core Compound-Target Network. The were retrieved against the TCMSP and BATMAN-TCM data- associations of components with targets were assessed bases, and a total of 269 were identified (Table S2). As through molecular docking study, which reduced the observed, HS, HQ, YYH, SZY, DH, and BZ had 77, 218, 21, network complexity and improved the accuracy. Virtual 129, 42, and 34 potential targets, separately. There were screening performed to determine the binding affinity obvious overlaps among those six herbal medicines, even between protein models and 9 core active compounds (the though the target number of every herbal medicine was top three active ingredients in the order of degree value in different. This suggested that the different components in the network and the outside core compounds with the top BFYSF might show heterogeneous effects through regulating 1 degree value in each single Chinese herbal medicine similar targets. constituting BFYSF), namely, atractylenolide I, quercetin, 4 BioMed Research International

MOL002032 MOL000379 MOL000359-5 MOL001680 MOL000359-4 MOL001771 MOL005503 MOL002883 MOL001495 MOL003137 MOL000178 MOL000211 MOL002879 MOL000022 MOL001494

MOL000026 MOL000044

MOL002820 MOL000043

MOL005531 MOL000403

MOL000433 MOL000401

MOL000033-6 MOL000072

MOL000033-2 MOL005344

MOL000062 MOL000387 CDK1 PHYH EGF HSD3B2 CAV1 OPRM1 ABCB1 PLOD3 CXCR4 CDKN1A NFE2L2 P3H3 CD40LG P3H1 PIM1 IFNG

ATP5B EGLN2 TNF MGAM CHEK1 IL1B ATP1A1 DPP4 KCNH2 ACACA PLOD2 HIF1A CCNA2 PPARA GABRA3 DRD1 MOL000063 MOL000554 HSPB1 DBH IL2 MAPK8 ACHE PIK3CG PTEN GABRA6 NOS3 PON1 RUNX2 CHRM3 PTGS1 SLC2A4 SLC6A3 NR1I2

MOL003732 IGF2 CASP1 CCNB1 RASA1 AKR1C4 MPO ELK1 TOP2 TOP2B ADH1B E2F1 NKX3-1 MAPK14 PGF RUNX1T1 CHRM2 MOL000442

ADRB1 SLPI CRP CTSD GJA1 PLOD1 SLC23A1 CASP3 STAT1 RHOA ADH1C GSTM2 LCT P4HA1 ADRA1D LYZ MOL005557 MOL004426 NCOA2 GLRA3 AHR CTRB1 NCF1 VCAM1 SCN5A FOS IL10 NPEPPS HSPA5 ESR2 SLCO1B1 BCL2L1 SLC6A4 CHRM4

CCL2 OGFOD1 MMP9 SIRT1 OLR1 CA2 RELA P3H2 PTGIR RASSF1 CYP3A4 PRSS1 GRIA2 BCL2 EIF6 AKR1B1 MOL012335 MOL000049 OPRD1 TOP1 POR THBD SLC6A2 AKR1C3 CYP1B1 PLA2G2E IGFBP3 CXCL2 TMLHE PCOLCE TP53 MAPK1 PPP3CA ALOX5

MOL005280 AR CYP1A1 HAS2 ATP1A3 AKT1 CXCL8 GSK3B TPMT CHUK ICAM1 MLNRHSP90AA1 PSMG1 CXCL10 CHEK2 RXRB MOL000239 MMP3 RXRA NR1I3 MAOB DUOX2 ALB PARP1 EGFR CDK2 NCOA1 ESR1 SOD1 ATP1A2 ADRA2A TGFB1 IL1A MOL005282 MOL005530 PTGER3 F7 CXCL11 DCAF5 MAP2 CASP8 CHRNA7 DDC SERPINE1 P2RY12 NR3C1 AHSA1 RB1 VEGFA PLAU JUN

INSR CDKN2A PYGM PPARG SULT1E1CYP51A1 HSD3B1 PCP4 CHRM5 ODC1 ACPP ADCYAP1 CHRM1 AKR1C1 PSMD3 OGFOD2 MOL005481 MOL000417 MYC HTR2A PRKCA ADRB2 NR3C2 IL4 XDH MAP2K4 AKR1C2 CLDN4 HMOX1 LTA4H PTGIS COL20A1 F3 MET

MOL012329 SLCO1B3 IKBKB PAM MT-ND6GABRA1 ABCG2 PDE3A RAF1 CASP9 GABRA2 MMP2 NFKBIA PRKCB GABRA5 PPARD PGR MOL000296 CYP1A2 P4HTM MMP1 PADI4 CCND1 ADRA2C HK2 NEU2 ALKBH2SLC47A1 MAOA CHRNA2 BAX PRKACA HSF1 TOP2A MOL012333 MOL000908 NOS2 GSTM1 PKIA ERBB2 ERBB3 SELE BIRC5 KDM5D E2F2 F10 SPP1 EGLN1 F2 EGLN3 PTGS2 IRF1

MOL000439 IL6 PTPN1 ADRA1B PLAT HTR3A KCNMA1 GSTP1 GABRB3 DIO1 TEP1 BBOX1 ALKBH3 KDR MOL000380

MOL004425 MOL001683

MOL005360 MOL000371

MOL000405 MOL008457

MOL000438 MOL000354

MOL000021 MOL005546

MOL000028 MOL000392 MOL000374 MOL000449-5 MOL002372 MOL000449-4 MOL005486 MOL000378 MOL005489 MOL000358-4 MOL000398 MOL005552 MOL000098 MOL000358-1 MOL000422

Targets

Astragali Radix(Huangqi, HQ) Corni Fructus(Shanzhuyu, SZY)

Epimedii Folium(Yinyanghuo, YYH) Rehmanniae Radix(Dihuang, DH)

Ginseng Radix et Rhizoma Rubra(Hongshen, HS) Atractylodis Macrocephalae Rhizoma(Baizhu, BZ)

Figure 1: Construction of the BFYSF pharmacodynamic ingredient-target network. The active compounds (compounds ID) collected from different herbal medicines were linked with corresponding targets to construct the pharmacodynamic ingredient-target network, where a node indicates an active compound (the different colors of squares stand for different herbal medicines) and the target (green circles).

formononetin, kaempferol, beta-sitosterol, stigmasterol, 3D structures of 5kir, 3wmh, 4nos, and 4p6w proteins. As gallic acid-3-O-(6′-O-galloyl)-glucoside, icariresinol, and seen from the binding free energy results shown in Table 1, ginsenoside rh2. Upon molecular docking, 4 targets (PTGS2, all the 9 core compounds had relatively tight bond to the 4 PPARG, NOS2, and NR3C1) together with 9 compounds core targets. Among them, the compounds that showed most were identiﬁed (Table 3 and Figure 3). closely bound to PTGS2, PPARG, NOS2, and NRC31 were PTGS2, PPARG, NOS2, and NR3C1 were searched quercetin, gallic acid-3-O-(6′-O-galloyl)-glucoside, querce- against the PDB protein database, separately, to obtain the tin, and stigmasterol, respectively (Figure 3). Quercetin has BioMed Research International 5

COPD-related targets

204 65 211

Potential pharmacodynamic targets in BFYSF (a)

MOL005557 MOL001771 MOL005280 MOL000211 MOL000359 MOL005282 MOL000401 MOL000063 MOL005546 MOL012335 MOL000062

ALB MOL000403 NR3C1 MOL005531

MOL001683 MOL000044 SPP1 CDKN2A TGFB1 MOL000178 NFE2L2IGFBP3 TP53 SIRT1 CRP MOL000358 AHR MOL000449 HTR2A PON1 PLAU MAPK1 MOL000387 IL6 VEGFA MAPK8 MOL000296 IL4 CXCL2 SOD1 BCL2 MAPK14 AKT1 MOL000239 MOL000392 CXCL10 GSTM2 CASP3 MOL000908 MOL000380 JUN MMP9 SERPINE1 MOL008457 MOL000043 SELE CHRM5 TNF CYP1A1 MOL005530 CCL2 MOL000098 MOL005344 PTGS2 HSPA5 MOL000378 NOS3 RELA MOL000049 MMP2 PARP1 NOS2 MOL004426 PLAT CYP1A2 IL1B MOL000422 PPARG MOL001495 STAT1 MOL000072 MOL005481 F3 IL10 HMOX1 IFNG MOL000371 MOL000417 MOL000379 CXCL8 GSTM1 ALOX5 MOL001494 MOL000022 FOS MMP1 MOL000354 ICAM1 NFKBIA MOL000554 MOL005503 IL2 IL1A GSTP1 VCAM1 MOL000442 MOL002820 MOL002883 MPO EGFR MOL002879 SLPI

Candidate targets

Candidate compounds

(b)

MOL000359

NR3C1

MOL000044

TP53 TGFB1 MOL000358 MOL000449 HTR2A PON1 PLAU MOL000387 VEGFA IL6 ALB MOL000296 BCL2 MAPK14 MOL000239 AKT1 MOL000392 GSTM2 CASP3 MOL000908 MOL000380 JUN MOL008457 MOL000043 SELE CHRM5 CYP1A1 MOL005530 MOL000098 MOL005344 PTGS2 MOL000378 RELA NOS3 TNF MOL000049 NOS2 MOL004426 CYP1A2 IL1B MOL000422 PPARG MOL001495 STAT1 MOL000072 IFNG HMOX1 MOL000371 MOL000417 GSTM1 ALOX5 MOL001494 MOL000022 MMP1 MOL000354 ICAM1 NFKBIA GSTP1 MOL000554 MOL005503

VCAM1 MOL000442 MOL002883 MPO

Core targets

Core compounds (c)

Figure 2: Excavation of core components and targets of BFYSF in treating COPD. (a) The Venn diagram showed that 65 potential targets in BFYSF were the same as the known pathological course-related targets of COPD. (b) The candidate component-target network of BFYSF in treating COPD. (c) The core component-target network of BFYSF in treating COPD. 6 BioMed Research International

Table 1: Degree values of core targets for BFYSF against COPD. (6′-O-galloyl)-glucoside-PPARγ complex might be stabilized through H-bond as well as π-cation bonds with CYS285, Targets name Degree TYR327,SER289, and LYS367 (Figure 3(b)). Similarly, the PTGS2 29 stigmasterol-NR3C1 complex was stabilized by one H-bonds PPARG 24 with residue CYS643 and hydrophobic interaction with resi- 560 736 566 NOS2 24 dues MET ,CYS , and LEU (Figure 3(d)). NOS3 19 3.4. Core Target Enrichment Analysis of BFYSF in Treating NR3C1 17 COPD. To further understand the multitarget and multipath- MAPK14 15 way mechanism of BFYSF in treating COPD, this study uti- JUN 5 lized the Omicshare online tool for the GO-PB and KEGG CASP3 5 enrichment analyses of core targets, so as to mine the biolog- BCL2 5 ical processes and signaling pathways involved in BFYSF in HTR2A 4 treating COPD (p <0:05, FDR < 0:05). The most signifi- TGFB1 3 cantly enriched GO-BP items were response to biological and chemical stimuli, multiple cellular biological process, cell RELA 3 proliferation, immunity, metabolism, exercise, and biological PON1 3 regulation (Figure 4(a)). The above-mentioned core targets PLAU 3 were mainly enriched into multiple KEGG pathways IL1B 3 (Figure 4(b)), which revealed that these pathways exerted IFNG 3 vital roles in BFYSF in treating COPD: (1) eleven immune- ALOX5 3 related pathways, including interleukin- (IL-) 17, FCζRI, ff ALB 3 RIG-I-like receptor, C-type lectin receptor, Th17 cell di er- entiation, NOD-like receptor, Toll-like receptor, T and B- VEGFA 2 cell receptor, Th1 and Th2 cell differentiation, and TNF; (2) VCAM1 2 four oxidative stress (OS) and inflammation related path- TP53 2 ways, including the hypoxia-inducible factor 1 (HIF1), Fork TNF 2 head transcription factor O (FoxO), TNF, and JAK-STAT; STAT1 2 (3) one protease/antiprotease imbalance related pathway, SELE 2 which was the transforming growth factor β (TGF-β) signal- NFKBIA 2 ing pathway; (4) two pathways which were related to airway MPO 2 remodeling and airway mucus hypersecretion, including the MAPK and VEGF signaling pathway; and (5) one pathway MMP1 2 which was related to cell growth and death, which was cell IL6 2 apoptosis. ICAM1 2 HMOX1 2 4. Discussion GSTP1 2 GSTM2 2 BFYSF is a cipher prescription used in the Department of GSTM1 2 Respiration at our hospital to treat COPD, which is most suitable for patients with lung and kidney deficiency. In the CYP1A2 2 clinical treatment for COPD, BFYSF has significant thera- CYP1A1 2 peutic efficacy, but its active ingredients and mechanism CHRM5 2 remain unclear, which has hindered the further development AKT1 2 and utilization of this prescription. Network pharmacology is a new strategy of drug design and development proposed based on the rapid development of systems biology and polypharmacology. Hopkins first been reported to decrease the expression and activity of COX- put forward this concept in 2007 [47]; in 2008, he proposed 2 induced by ischemia reperfusion or arsenite [42, 43]. In to transform the original new drug discovery pattern addition, quercetin inhibited inflammatory response induced of “disease-single target-single drug” to the pattern of by LPS through iNOS/FAK/paxillin or p38/iNOS/NF-kappaB “disease-multiple targets-multiple drugs.” This thinking signaling pathway [44–46]. In the virtual docking (Figures 3(a) coincides with the “holistic view” in TCM [20, 48]. Therefore, and 3(c)), quercetin was demonstrated to form hydrogen applying the network pharmacology approach contributes to bonds with amino acid TYR355,PHE518,SER353, and GLN192 providing some research thinking for illustrating the BFYSF of COX-2 and VAL352,TYR347, and GLN263 of iNOS. At the mechanism in the treatment of COPD. same time, quercetin also formed a strong hydrophobic It was discovered in this study that all the six single herbal interaction with residues LEU352 and VAL523 of COX-2 and medicines in this formula contained large amounts of com- π-anion bonds with residues GLU377. The gallic acid-3-O- ponents, and the components acted on multiple targets, or BioMed Research International 7

Table 2: 30 core pharmacologically active ingredients of BFYSF in the treatment of COPD.

Compound no. Degree in core network Compound name MOL000098 57 Quercetin MOL000422 24 Kaempferol MOL000358 21 Beta-sitosterol MOL000449 13 Stigmasterol MOL000392 8 Formononetin MOL000378 7 7-O-Methylisomucronulatol MOL000354 6 Isorhamnetin MOL005344 6 Ginsenoside rh2 MOL008457 6 Tetrahydroalstonine MOL000043 5 Atractylenolide I MOL000371 5 3,9-Di-O-methylnissolin MOL000554 5 Gallic acid-3-O-(6′-O-galloyl)-glucoside MOL000239 4 Jaranol MOL000296 4 Hederagenin MOL000380 4 (6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol MOL000417 4 Calycosin MOL000442 4 1,7-Dihydroxy-3,9-dimethoxy pterocarpene MOL000908 4 Beta-elemene MOL004426 4 Icariresinol MOL005530 4 Hydroxygenkwanin MOL000049 3 3β-Acetoxyatractylone MOL000022 3 14-Acetyl-12-senecioyl-2E,8Z,10E-atractylentriol MOL000044 3 Atractylenolide II MOL000072 3 8β-Ethoxy atractylenolide III MOL000359 2 Sitosterol MOL000387 2 Bifendate MOL001494 2 Mandenol MOL001495 2 Ethyl linolenate MOL002883 2 Ethyl oleate (NF) MOL005503 2 Cornudentanone

Table 3: Virtual docking of core bioactive ingredients and core multiple components acted on the same target, which targets for BFYSF in treating COPD. reflected the multicomponent and multitarget synergistic features of BFYSF. At the same time, the targets might be reg- Binding energy/(kcal mol-1) Core ingredients ulated by single herbal medicine or multiple herbal medicines PTGS2 PPARG NOS2 NR3C1 of BFYSF, which showed that in a formula, both the unique MOL000043 -7.2 -6.9 -7.8 -7.8 contribution of single herbal medicine and the synergy of MOL000098 -9.6 -8.4 -9.1 -8.4 multiple herbal medicines existed. As discovered from the MOL000358 -4.8 -5.4 -6.9 -9.3 component-target network of BFYSF in treating COPD, 48 MOL000392 -7.3 -8.3 -8.3 -7.2 components in this prescription might act on 65 targets to ff MOL000422 -9.5 -8.3 -8.6 -8.2 exert the therapeutic e ect. Furthermore, based on the topological parameters of nodes in the component-target net- MOL000449 -5.2 -6.2 -7.4 -9.5 work, it was speculated that PTGS2, PPARG, NOS2, and MOL000554 -6 -8.7 -9 -7.8 NR3C1 were the core targets for BFYSF in treating COPD. MOL004426 -6 -6.8 -7.5 -7.6 Besides, the interactions between core components and core MOL005344 -8.9 -6.2 -7.5 -4.3 targets were verified through molecular docking study. The subsequent GO-BP and KEGG enrichment analyses revealed that these targets mainly participated in regulating inflammatory immune response, OS, suppressing protease/antiprotease imbalance, improving airway remodeling and airway 8 BioMed Research International

LEU PHE A:384 ILE CYS A:264 PHE A:285 ILE PHE A:517 A:363 A:341 HIS SER A:518 GLN CYS A:266 PHE GLY A:342 PHE TRP A:192 A:285 A:284 A:381 A:282 ARG A:387 VAL LYS GLY A:367 A:288 PHE A:526 A:523 O O A:287 O–H MET O SER ALA LEU A:364 O A:530 A:516 O A:453 O O HIS O O ARG A:449 TYR A:513 O TYR O A:385 A:473 TYR OO O O–H A:327 GLU HIS GLN A:343 O O A:90 A:286 TYR O O LEU SER A:333 A:348 A:289 LEU LEU A:330 VAL A:352 ALA SER A:349 A:353 ILE A:527 TYR A:326 ALA A:355 A:292

Interactions Interactions

Van der weals Pi–sigma Van der weals Pi–donor hyderogen bond

Conventional hydrogen bond Pi–alkyl Conventional hydrogen bond Pi–sulfur

Carbon hydrogen bond Pi–pi stacked

Pi–cation Pi–alkyl (a) (b)

TRP PHE A:463 A:749 CYS TYR A:638 MET ASN A:735 CYS ALA A:601 A:564 MET A:736 A:560 A:351 MET MET ARG OO HEM A:604 A:388 A:510 LEU A:639 A:753 ASP O VAL TRP A:382 A:352 A:600 O O PHE H TRP O O A:369 GLY A:346 H A:567 ASN ALA GLN GLN O A:370 A:605 LEU A:642 CYS ARG A:263 H A:563 A:643 ILE A:266 A:629 TYR GLU A:347 TYR A:377 GLY ILE A:373 A:371 LEU A:559 PRO LEU A:566 A:350 A:608 GLN PHE A:570 A:623 TRP ARG A:372 A:611

Interactions Interactions Van der weals Alkyl Van der weals Pi–donor hydrogen bond Conventional hydrogen bond Pi–Alkyl Conventional hydrogen bond Pi–sigma

Pi–anion Pi–alkyl (c) (d)

Figure 3: Virtual docking of core components and core targets of BFYSF in treating COPD. The virtual docking of quercetin with PTGS2 (a) and NOS2 (c), respectively. (b) The docking of PPARG with gallic acid-3-O-(6′-O-galloyl)-glucosid. (d) The virtual docking of stigmasterol with NRC31. mucus hypersecretion, energy metabolism, and modulating improving airway and pulmonary inflammation or the the related signaling pathways. antioxidant activity. For instance, isorhamnetin can sup- Airway and pulmonary inflammation, protease/anti- press the TNF-induced airway inflammatory response; protease imbalance, and oxidation/antioxidation imbalance astragaloside and gallic acid can alleviate inflammation to are the major pathogenic mechanisms of COPD. As suppress the cigarette-induced COPD; and quercetin and suggested in research, the Th17 cell proportion and kaempferol can treat COPD through its antioxidation COX-2 expression in the lung tissue and peripheral blood and anti-inflammatory activities [41, 57]. of COPD patients significantly increase, the inflammatory Airway remodeling and airway mucus hypersecretion are cytokine TNF-α levels in blood and airway elevate, the the major pathological features of COPD, which are mainly neutrophil elastase level in serum elevates, α1-AT level manifested as airway wall structural changes, luminal steno- decreases, and the protease/antiprotease balance is sis, and flow limitation [58, 59]. Research suggests that destroyed [49–53]. In the COPD model mice induced by MAPK activation is related to the airway remodeling in cigarette smoke stimulation, the body produces OS COPD [60]. Reducing the VEGF and COX-2 expression response, which then activates the Nrf2 nuclear transcrip- levels in COPD rats can suppress airway remodeling, thus tion and suppresses its target gene HOMX1 expression improving COPD [61, 62]. PPAR activation can inhibit the [54]. Regulating the SIRT3/FoxO pathway and suppressing inflammatory and OS reactions and participate in regulating HIF-1 expression and release can improve the inflamma- the mucus hypersecretion in COPD patients [63]. Quercetin, tory and OS statuses in COPD [55, 56]. It is reported in the core ingredient in BFYSF, has been verified to improve plenty of articles that multiple core active ingredients of pulmonary function and treat COPD through protecting BFYSF in treating COPD can treat COPD through from airway remodeling. BioMed Research International 9

Top 20 of GO Enrichment p value Top 20 of KEGG Enrichmentq value Response to cytokine TNF signaling pathway Response to bacterium IL−17 signaling pathway Cell proliferation C−type lectin receptor signaling pathway Apoptotic process HIF−1 signaling pathway 7.5e−06 Regulation of programmed cell death Toll−like receptor signaling pathway Response to oxidative stress NF−kappa B signaling pathway Infammatory response 0.010 T17 cell diferentiation Cellular response to drug NOD−like receptor signaling pathway Aging 5.0e−06 Apoptosis Regulation of cell motility T cell receptor signaling pathway

GO term Nitric oxide biosynthetic process

Pathway MAPK signaling pathway Response to tumor necrosis factor Regulation of dna−binding transcription VEGF signaling pathway Factor activity T1 and T2 cell diferentiation 0.005 Regulation of cell adhesion 2.5e−06 B cell receptor signaling pathway Leukocyte activation Immune system development RIG−I−like receptor signaling pathway Leukocyte diferentiation Fc epsilon RI signaling pathway Angiogenesis Jak−STAT signaling pathway Secretion FoxO signaling pathway TGF−beta signaling pathway −0.05 0.00 0.05 0.10 0.15 0.00 0.05 0.10 0.15 Rich factor Rich factor Gene number 10 20 Gene number 15 25 5.0 10.0 7.5 12.5 (a) (b)

Figure 4: Omicshare-based enrichment analysis of core targets of BFYSF in treating COPD. The top twenty enriched GO-biological process (a) terms along with KEGG pathways (b) are shown. The abscissa corresponds to the GO terms or KEGG pathways, and the ordinate shows the enrichment factor. The color of the dot represents the adjusted p value/q value, and the size of the dot represents the number of core targets mapped to the reference GO terms or pathways.

5. Conclusion Acknowledgments

To sum up, the mechanism of action by which BFYSF treats This research was funded by the Program for High-Level COPD may be related to the regulation of immune Medical Talents in Six Industries of Jiangsu Province (No. inﬂammatory response, antioxidation, suppression of pro- LGY2017049) and the Innovation Service Capacity Building ’ tease/antiprotease imbalance, and improvement of airway Program of Huai an (No. HAP202003). The authors thank remodeling and airway mucus hypersecretion. all members of their laboratory and their collaborators for their research work.

Data Availability Supplementary Materials

The datasets used and/or analyzed during the current study Supplementary 1. Table S1: all the pharmacodynamic ingre- are available from the corresponding author on reasonable dients of BFYSF. request. Supplementary 2. Table S2: all the potential pharmacodynamic targets of BFYSF. Conflicts of Interest Supplementary 3. Table S3: known COPD-related targets. Supplementary 4. Table S4: BFYSF shared 65 potential phar- The authors declare no conﬂicts of interest. macodynamic targets with known COPD-related targets. Supplementary 5. Table S5: degree values of nodes in the can- Authors’ Contributions didate active ingredient-target network of BFYSF in treating COPD. YCH conceived and designed the experiments; WLC and CY References performed the experiments and wrote the paper; WLC, YJ, ZY, and CL analyzed the data. All authors have read and [1] J. L. Lopez-Campos, J. J. Soler-Cataluna, and M. Miravitlles, approved the ﬁnal manuscript. Longchuan Wu and Yu Chen “Global strategy for the diagnosis, management, and preven- made the same contribution to the study. tion of chronic obstructive lung disease 2020 report: future 10 BioMed Research International

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