Hindawi Evidence-Based Complementary and Alternative Medicine Volume 2019, Article ID 3053458, 11 pages https://doi.org/10.1155/2019/3053458

Research Article A Network Pharmacology Approach to Uncover the Molecular Mechanisms of Herbal Formula Kang-Bai-Ling for Treatment of Vitiligo

Manyuan Xu,1 Jianxin Shi,2 Zhongsheng Min,2 Hongliu Zhu,3 and Weiguo Sun 1

1Department of Dermatology, e Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian 223300, China 2Department of Dermatology, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China 3Department of Dermatology, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin 214400, China

Correspondence should be addressed to Weiguo Sun; [email protected]

Received 8 July 2019; Revised 15 September 2019; Accepted 4 October 2019; Published 3 November 2019

Academic Editor: Darren R. Williams

Copyright © 2019 Manyuan Xu et al. -is 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. Background. Kang-bai-ling (KBL), a Chinese patent medicine, has been demonstrated as an effective therapy for vitiligo in China. However, the pharmacological mechanisms of KBL have not been completely elucidated. Methods. In this study, the potential multicomponent, multitarget, and multipathway mechanism of KBL against vitiligo was clarified by using network pharma- cology-based strategy. In brief, potential targets of KBL were collected based on TCMSP databases, followed by network es- tablishment concerning the interactions of potential targets of KBL with well-known therapeutic targets of vitiligo by using protein-protein interaction (PPI) data. As a result, key nodes with higher level of seven topological parameters, including “degree centrality (DC),” “betweenness centrality (BC),” “closeness centrality (CC),” “eigenvector centrality (EC),” “network centrality (NC),” and “local average connectivity (LAC)” were identified as the main targets in the network, followed by subsequent incorporation into the ClueGO for GO and KEGG signaling pathway enrichment analysis. Results. In accordance with the topological importance, a total of 23 potential targets of KBL on vitiligo were identified as main hubs. Additionally, enrichment analysis suggested that targets of KBL on vitiligo were mainly clustered into multiple biological processes (associated with DNA translation, lymphocyte differentiation and activation, steroid biosynthesis, autoimmune and systemic inflammatory reaction, neuron apoptosis, and vitamin deficiency) and related pathways (TNF, JAK-STAT, ILs, TLRs, prolactin, and NF-κB), indicating the underlying mechanisms of KBL on vitiligo. Conclusion. In this work, we successfully illuminated the “multicompounds, multitargets” therapeutic action of KBL on vitiligo by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of KBL on vitiligo treatment.

1. Background -e present clinical therapeutic strategies mainly focus on the enhanced melanogenesis of melanocytes in skin le- Vitiligo, a progressive and acquired depigmentation disease, sions and the restoration of skin color to the normal level [8]. is characterized by the presence of circumscribed white In addition, vitamin D analogs, excimer laser, and steroid macules on the skin [1]. An estimated 1–4% of the global therapy aim at the restoration of pigmentation [9–11], the populations are influenced by vitiligo [2], with certain successful rates of which, however, are restricted by the subgroups at higher risk (up to 2-3%) [3]. In terms of the failure to follow the primary pathogenesis of vitiligo. Of mechanism of pigmentation loss, a series of speculations note, the administration of psoralens combined with sub- have been proposed, including oxidative stress, autoim- sequent exposure to ultra-violet light (sun light) is con- munity, injured melanocyte migration, and/or proliferation sidered as one of the most common therapies of vitiligo, as well as genetic and neural theories [4–7]. nevertheless only 61% of patients could obtain over 25% 2 Evidence-Based Complementary and Alternative Medicine repigmentation [12]. Moreover, the above therapy could concept for assessment of the structural similarity of induce serious adverse events, including phototoxic re- compounds with clinical therapeutics in the DrugBank sponse, perioral dermatitis, cutaneous atrophy, and long- database, is determined early after drug discovery [25]. -e term carcinogenic risk [13]. To this end, accumulative efforts detailed calculations of these two parameters have been have been made to identify novel therapeutic medicine and reported by Wang et al. Briefly, OB � 30% along with drug- to evaluate multitargets, in order to examine the complicated likeness index � 0.18 was defined as the cutoff value for the network of the therapeutic mechanism of vitiligo. selection of candidate compounds [26]. In our study, several Traditional Chinese medicine (TCM), a comprehensive compounds, including psoralen and angelicin, were initially medicinal system, plays a key role in maintaining health for omitted in line with these screening criteria. Nevertheless, Asian population [14]. Accumulative prevalence has been they were still included into candidate compounds for gained on TCM in western countries due to the robust further analysis, due to their widely acknowledged phar- therapeutic effects and relatively few side effects. According macological activities [27]. to the theory of traditional Chinese herbal medical science, TCM is considered as promising preventive and therapeutic sources for complicated disorders, including vitiligo [15–18]. 2.3. Potential Drug Targets Prediction for KBL. -e identi- Kang-bai-ling (KBL), a Chinese patent medicine, con- fication of targets is considered as a pivotal stage during drug sists of five medicinal herbs, including Psoraleae Fructus exploitation. Herein, systematic drug-targeting approach, (Buguzhi, BGZ), Angelicae Dahuricae Radix (Baizhi, BZ), which was proposed by Li et al. [28], was utilized for accurate Stephaniae Tetrandrae Radix (Fangji, FJ), Mume Fructus determination of potential targets for medical compositions. (Wumei, WM), and Glycyrrhizae Radix et Rhizoma (Gan- cao, GC). KBL, first proposed by Professor Min Zhongsheng 2.4. Collection of Known Vitiligo-Related Targets. Known in 1990s, has been utilized to treat a great number of patients vitiligo-associated targets were acquired from five existing with vitiligo ever since. Previous studies have indicated the resources: (1) MalaCards human disease database [29] good curative effect of KBL in clinics [19]. -e mechanism of (https://www.malacards.org); (2) OMIM database [30] KBL against vitiligo is related to the up-regulated expression (https://omim.org); (3) -erapeutic Target Database [31] of tyrosinase in melanin cells, enhanced activity and induced (http://bidd.nus.edu.sg/group/cjttd/); (4) DrugBank data- synthesis of melanin [20]. However, the scientific basis as base [32] (https://www.drugbank.ca); (5) Genetic Associa- well as potential pharmacological mechanisms of KBL is still tion Database [33] (https://geneticassociationdb.nih.gov). unclear and needs further investigations. -e detailed information of these known therapeutic targets -e therapeutic effects of the majority of TCM drugs are is summarized in Table S3. Finally, 98 known vitiligo-related generally regulated by diverse targets and pathways in hu- targets were selected after redundancy deletion. man body due to the complicated active components of TCM, which could not easily be accurately determined through traditional approaches. -us, it is urgent to develop 2.5. PPI Data. Afterwards, PPI data were retrieved from a novel and appropriate strategies [21]. Herein, in this study, a prevalently used Cytoscape plugin BisoGenet [34] through comprehensive approach (a combination of multiple net- six present PPI datasets, including Biological General Re- work-based computational and algorithm-based ap- pository for Interaction Datasets (BioGRID), Database of proaches) was utilized, by combining prediction of active Interacting Proteins (DIP), Biomolecular Interaction Net- compounds based on multiple pharmacokinetic parameters work Database (BIND), Molecular INTeraction Database and excavation of diverse drug targets and network analysis, (MID), Human Protein Reference Database (HPRD), and aiming at illumination of the underlying mechanisms of InAct. In addition, the detailed information of these PPI KBL on vitiligo. datasets is summarized in Table S6.

2. Methods 2.6. Network Construction. Based on the interaction data obtained from the Cytoscape plugin BisoGenet, we first 2.1. Data Preparation. Data for all ingredients of each herb constructed an interaction network of the known vitiligo- in KBL were collected from the Chinese Academy of Sci- associated targets and potential drug targets of KBL, fol- ences Chemistry Database [22] (http://www.organchem. lowed by further visualization using Cytoscape (Version csdb.cn/scdb/default.asp), Traditional Chinese Medicine 3.6.0). System Pharmacology Database [23] (http://lsp.nwu.edu.cn/ tcmsp.php), and relevant literatures, followed by subsequent addition into the ingredient database. 2.7. Defining of Network Topological Feature Set. We then assessed the topological property of every node in the in- teraction network by calculating six parameters with a 2.2. OB and Drug-Likeness Screening. OB prescreening, Cytoscape plugin CytoNCA [35]: “degree centrality (DC),” defined as the distribution degree of an oral dose of drug into “betweenness centrality (BC),” “closeness centrality (CC),” bloodstream, is one of the most requisite premises in terms “eigenvector centrality (EC),” “network centrality (NC),” of oral drug discovery as well as clinical application [24]. and “local average connectivity (LAC).” -e definitions and Additionally, drug-likeness, which is defined as a qualitative computational formulas of these parameters have been Evidence-Based Complementary and Alternative Medicine 3 previously defined and represent the topological importance 3.2. Potential Target Prediction for KBL. TCM formulas were of a node in the network. -ese six parameters were rep- generally effective in terms of preventive and curative ef- resentatives of the topological significance of each individual fects on complicated disorders, entirely dependent on the node of the entire network. To be specific, a larger quan- combined effects of various compounds and targets. -us, titative value indicated the greater significance of the node in apart from the clarification of probable active compounds this network. of KBL, we should explore the therapeutic targets as well. Herein, potential targets of candidate compounds were predicted based on the integration of chemical, genomic, 2.8. Gene Ontology and Pathway Enrichment Analysis. and pharmacological data. For 168 candidate compounds, GO analysis: targets were performed in Omicshare [36] for 299 potential targets were predicted (Table S2). -e more intensive understanding of their role in three cate- numbers of potential targets linked by BGZ, BZ, FJ, WM, gories: biological processes (BP), molecular function (MF), and GC were 86, 96, 71, 232, and 275, respectively. Despite and cell component (CC). A P value of ≤0.05 indicated the different numbers of targets related to each herb, statistical significance, followed by applied hypergeometric significant overlaps were observed in the five herbs, in- examination to identify enriched GO terms. dicating that diverse components in KBL may exert con- ClueGO [37], a widely used Cytoscape plugin to visualize generic effects possibly by modulating the similar targets. the nonredundant biological terms for large clusters of genes For instance, both BGZ and BZ have been validated to in a functionally grouped network, was used to reflect the affect the pathogenesis and progression of vitiligo through enrichment analysis of 23 candidate targets of KBL. -e regulating immune- and inflammation-related pathways present findings were categorized into two large clusters in and promoting both adhesion and migration of melano- this study: biological processes and the signaling pathway. cytes [43]. ClueGO network, established by kappa statistics, was used to For further understanding of the roles of the potential reflect the relationships of terms in accordance with the targets involved in diverse biological processes, cellular similarity of their related genes. component as well as molecular functions, preliminary GO (Gene Ontology) analysis was conducted with Omicshare, a 3. Results free database based on DAVID, revealing that the potential targets were enriched in response to various types of stimuli 3.1. Candidate Compound Screening for KBL. (ROS, chemical, drugs, inflammatory factors and hormone), Accumulative efforts have been made to clarify the thera- immune systems, cellular metabolism, cell-cell communi- peutic mechanisms of TCM, however, with sluggish progress cation, cell growth, and death and development, all of which on the molecular level. Due to the unavailable effective participated in the pathologic process of vitiligo (Figure 1 methods specifically developed for identification of the and Table S7) [44–47]. active compounds in medicinal herbs, OB screening com- For more comprehensive understanding of KBL bined with drug-likeness assessment may be a feasible component-targets network from a systematic and holistic strategy. In our study, a total of 123 possible compounds perspective, Cytoscape was used to construct a network with proper values of these two parameters were collected map, which altogether consisted of 490 nodes as well as from the herbal constituents of KBL. In addition, 45 4303 edges (Figure 2). To be specific, the degree of node compounds (such as psoralen and angelicin) with lower indicated the number of edges or targets correlating with values of OB or drug-likeness parameter, exhibiting node based on the topological analysis. In our network, 93 massive pharmacological activities, were typical compo- candidate components were identified with a median ≥20 nents of herbal drugs (reported in literature) and were also degrees. To be specific, quercetin, kaempferol, and ursolic collected as the candidate active compounds. -us, these acid acted on 162, 72, and 61 targets, respectively, which compounds were chosen as candidate-active compounds as were therefore considered as the critical bioactive in- well. Finally, 168 compounds of five herbs were recognized gredients in KBL. Quercetin has been demonstrated to “candidate compounds” (Table S1). To be specific, the decrease hydrogen peroxide-induced intracellular redox numbers of candidate compounds in BGZ, BZ, FJ, WM, imbalance in melanocyte and thereby achieving repig- and GC was 13, 38, 9, 13, and 117, respectively. Among all mentation by regulating certain crucial pathways [48, 49]. the candidate compounds, 17 of them were distributed in In addition, kaempferol and ursolic acid have also been more than one herb of KBL, with validated diverse bi- validated to increase melanin content via positively regu- ological activities. For instance, linoleic acid, existing in lating the activity of tyrosinase directly [50]. -ese findings almost all the three herbs except for FJ and GC, exerts might shed novel light on the pleiotropic effects of TCM potent antioxidant, anti-inflammatory, metabolic regula- herbs. tion, anticancer, and immune-regulatory activities [38]. Similarly, quercetin, ursolic acid, stigmasterol, and angelicin, which were distributed in at least two herbs of 3.3. Collection of Known Vitiligo-Related Targets. Vitiligo is a KBL, also exert various pharmacological properties, par- polygenic predisposing disorder. Genetic-environmental ticipating in the regulation of multiple physiological and interaction has been revealed to play a role in the patho- pathological processes, such as inflammation, immune genesis of vitiligo. Herein, 98 vitiligo-related targets were response, and stress process [39–42]. retrieved from five present resources. Notably, 15 of the 4 Evidence-Based Complementary and Alternative Medicine

Level2 GO terms of out 300

250

200

150 Number of genes 100

50

0 cell Growth Binding Synapse Cell part Behavior Nucleoid Signaling Organelle Cell killing Membrane Localization Locomotion Synapse part Cell junction Pigmentation Reproduction Detoxifcation Organelle part Membrane part Other organism Cellular process Cell aggregation Catalytic activity Cell proliferation Rhythmic process Metabolic process Biological adhesion Extracellular region Nitrogen utilization Transporter activity Antioxidant activity Other organism part Biological regulation Response to stimulus Reproductive process Cargo receptor activity Developmental process Multi-organism process Immune system process Extracellular region part Supramolecular complex Structural molecule activity Membrane-enclosed lumen Protein-containing complex Hijacked molecular function Molecular function regulator Molecular transducer activity Transcription regulator activity Regulation of biological process Multicellular organismal process Positive regulation of biological process Negative regulation of biological process Cellular component organization or biogenesis

Biological process Cellular component Molecular function

Biological process Cellular component Molecular function Figure 1: Gene ontology analysis of the potential targets of KBL. Potential targets of KBL was performed in Omicshare to gain more insights into their involvement in various Biological Processes (red section), Molecular Function (blue section) and Cell Component (green section). We considered a P value cuto• of 0.05 as signicant and applied hypergeometric test to identify enriched GO terms. Terms of same category are ordered by P values, left terms are more signicant. Information of the number of involved genes in a term are shown in y-axis. ≤ identied potential targets of the KBL were also the well- clarication of the mechanisms of KBL on vitiligo. No- recognized vitiligo disease (or therapeutic drugs)-related tably, vitiligo has been accumulatively reported to su•er targets, indicating the potential therapeutics of this formula from uncontrolled neurocrine regulation, all of which (Figure 3(a) and Table S4). a•ect every situation and bring greater challenges to To understand the possible pharmacological mecha- vitiligo treatment [51]. In the present study, our analysis nisms of KBL on vitiligo, GO and KEGG enrichment suggested that KBL might be of therapeutic e•ects on analyses were performed on the above-described 15 tar- vitiligo by a•ecting catecholamine metabolism, neuro- gets (Figures 3(b) and 3(c) and Tables S8 and S9). Con- transmitter degradation (MAOA and MAOB), and in- sistent with the current pathological mechanism of tracellular steroid hormone receptor signaling pathway vitiligo, they were mainly enriched in homeostatic pro- (ESR1). Consistent with previous outcomes, we conrmed cess, cell growth and death, cellular metabolism, immune that KBL exerted a curative role on not only melanocytes and in†ammatory responses, and neuroendocrine signal but also neuroendocrine system from the network conduction. Moreover, KBL was found to modulate vit- pharmacologic level. Vitiligo-related drugs causing sys- iligo via the regulation of activity of multiple series en- temic alterations would not only control vitiligo but might zymes such as tyrosinase (TYR), oxidoreductase (GSTP1, play also a therapeutic or side role on other disorders. By CAT, MPO, and GSTM1), and controlling signal trans- contrast, drugs regulating systemic alterations could a•ect ducer (NFE2L2). Furthermore, in†ammatory factors and vitiligo as well. Taken together, we hypothesized that interleukin (IL) signaling pathway (IL4 and IL10), vitiligo might respond towards systemic neuro-immuno- adaptive immune response (IFNG), and cell apoptosis in†ammatory disorder in the skin tissue, which could be (FASLG and TNF) also played critical roles in the systematically controlled by KBL. Evidence-Based Complementary and Alternative Medicine 5

SOAT2

LDLR ABAT ABCC1 GOT1 ADIPOQ

SREBF1 CCND2 CASP1 SOAT1 UGT1A8 CTSB FASLG CDK6

MAPK3 CES1 GSR INPPL1 CREB1 MMP10 PRKCG NQO1 MAPK8IP2 FASN FGF2 PLB1 CAT CSF2

PTPN6 DCAF5 LITAF CYP19A1 ENPP7 ATF2 GSTP1 MMP3 CLDN4 VEGFA MMP9 MAPK8 MMP2 CRP HIF1A CDKN1A APOB GAP43 PTGER3 SPP1 ERBB3 HK2 MCL1 OLR1 GSTM2 ICAM1 IGFBP3 PPP3CA AKR1C1 CHUK MGAM SULT1E1 HSF1 HSPA5 MOL005009-1 NFKBIA MOL001506 PSMD3 MTTP GSTM1 CDKN2A BAD ACACA STAT3 MPO HERC5 CYP3A4 MOL005009-5 FOS DUOX2 IL1B

MOL000511-4 PRKCB MYC GJA1 MOL004328 MOL004860 MOL000511-5 PARP1 GABBR1 F11R RB1 BIRC5 EIF6 ODC1 BCL2L1 SOD1 CDK4 NR1I2 RASA1 CYP1B1 PTEN PCOLCE AKR1C3 TP53 JAK2 RUNX2 SLC2A1 HMGCR PECAM1 MOL004905 HSP90AA1 TOP2A CCND1 CAV1 ALOX5

MMP1 MT2A IL10 MOL000663 ELK1 EGF SELE ESR2 MAPK1 IL2 CCL2 PYGM VCAM1 MOL004917 CDK1 TYR SERPINE1 CCNA2 TGFB1 CASP8

HSPB1 MOL000098-5 CYP1A2 CXCL10 PTPN1 BAX MOL007514 MOL005013 NR1I3 RASSF1 AHSA1 TNF MOL005802 POR E2F1 ACHE NOS2 RELA PLAU CASP9 MOL000391-4 THBD RAF1

MOL001494 BCL2 CCNB1 MOL005020 MOL000098-4 CHRM4 ADRA1A CASP3 F3 PPARA MOL002356 XDH IL6 MOL005017 MOL001789 MOL004885 MOL004988 MOL004949 STAT1 EGFR MOL004959 AKT1 MOL001953-1 MOL000211 MOL004993 MOL005800 MOL002644 MOL000422-4 MOL003592 SLC2A4 IRF1 COL3A1 MOL004836 MOL004848 MOL004866 CHEK2 MOL008601 MOL004827 MOL004853 MOL001939 MOL000170 MOL004908 MOL001956 MOL004910 MOL004808 MOL005043 MOL004829 DIO1 MOL005806 PTGS2 MOL000354 MOL004978 MOL004945 HMOX1 RUNX1T1 MOL013430 MOL004913 MOL000251 CXCL11 MAOB MOL004815 MOL000422-5 PRKCA E2F2 MOL001942 MOL000019 MOL000239 HAS2 MOL000500 MOL004989 MOL002333 MOL004904 MOL000358-4 MOL005786 MOL000497 MOL004805 IKBKB MOL004857 MOL000467 SERPIND1 MOL004841 PTGS1 ADRB2 INSR IFNG MOL004964 NCF1 NFE2L2 MOL000953-2 PCDH12 MOL004912 MOL004903 MOL005018 MOL003656 PIK3CG MOL004907 MOL004880 SLC6A4 MOL000486 PRKACA MOL004838 GSK3B MOL004811 MOL004914 MOL000358-2 MOL000448 NPEPPS MOL001953-2 NCOA1 MOL000116 GABRA3 MOL004793 MOL000358-1 PON1 AHR MOL003588 ERBB2 JUN MOL004898 MOL000474 MOL000358-3 AKR1B1 CHEK1 MOL000057 TOP2B OPRM1 MOL004835 NR3C1 CHRM1 DPP4 CXCL2 MOL002565 CTSD IGF2 MOL002850 MOL001792 F7 GRIN3A MOL002341 SLC6A2 MOL004935 PPARD IL1A CHRM3 MOL004883 MOL004891 MOL004879 TOP1 ABCG2 MOL004849 MAPK14 NOS3 RXRA MOL001941 PLAT MOL004911 MOL004864 OPRD1 MOL002331 MOL005012 MOL000671-5 NCOA2 PRSS1 MOL005016 COL1A1 MOL004980 SLPI MOL004856 MOL001484 CYP1A1 SFTPB MOL000953-4 PCP4 MOL000118-3 MOL004974 PPARG CHRNA7 MOL005003 SCN5A CD40LG MOL004863 KCNMA1 MOL005008 MOL002844 KDR ITM2C MOL000359 MOL004941 CA2 MOL001040-1 MOL004723-5 MOL000417 MOL000449-4 ESR1 MAPK10 MOL000103 HTR2A GABRA1 MOL000561-5 CDK2 AR MOL000449-1 F10 ADRA1B LACTBL1 MOL004957 MOL000118-5 PDE3A MOL005000 BCHE ACPP MOL005007 PKIA MOL004882 KCNH2 MOL004961 MOL003896 IGHG1 MOL001599 MOL000392 BMP4 MOL000131-2 MOL004881 MOL004936 MOL004996 DRD1 CTRB1 MOL004385 CXCL8 MOL000676-2 MOL000391-5 MOL000252 MOL004855 SLC6A3 MOL004814 HTR3A PGR GABRA5 MOL004828 MOL004824 MOL003218 MAOA NR3C2 CHRM2 MOL000449-2 MOL001099 MOL000671-3 MOL001040-4 MOL000131-4 MOL003590-1 MOL004991 MAP2 CYP2E1 MOL000118-2 MOL000561-1 MOL004985 PLA2G2E MOL000202 MOL004820 MOL000676-5 LTA4H MOL004924 MOL002311 NKX3-1 MOL001950 MOL004966 LPL MOL000035 MOL000131-1 MOL003791 MOL001201 MOL005001 CRH MOL005792 MOL004990 MOL002883 MOL004915 MOL004884 MOL004806 MOL004810 MOL004948 MOL001415 CYP2D6 MOL001749 MOL000023 MOL004833 GABRA2 ADRB1 IGHD CHRM5 MOL005789 CHRNA2 FOSL2 MOL003590-2 GABRA6 PIM1 SERTAD3 MOL004723-2 ADRA2A MOL004832 MOL001456

ADRA2C DEFB4A

ADH1C MOL005807 HIRA ADRA1D LYZ

ADH1B TLR4

IL4

MT-ND6

RASGRF2 BAK1

TLR2 GRIA2 GRIK2 ATP5B SIRT1 HSD3B2 GLS2

TPI1

TRPV1 HSD3B1 SRC NR1H4 ADH1A GRIN1

IVL

NOS1 HDC CPB1

BGZ WM

BZ GC

FJ Targets Figure 2: Construction of the KBL compound-potential target network. -e compound-potential target network was constructed by linking the candidate compounds and their potential targets of the 5 herbs, which are constituents of KBL. -e nodes representing candidate compounds are shown as polychrome triangle and the targets are indicated by blue circular.

3.4. Identification of Candidate Targets for KBL against potential target network (394 nodes and 24238 edges) and a Vitiligo. As indicated by recent evidences in network bi- defined vitiligo-associated target network (295 nodes and ology, disease-related genes or proteins do not work alone; 14043 edges) were established by utilizing PPI data of these instead, there are diverse interactive pathways and molecular genes, respectively. We then merged these two networks to networks on various levels. To clarify the pharmacological obtain a core protein-protein interaction (CPPI) network mechanism by which KBL ameliorates vitiligo, we con- that consisted of 87 nodes and 1946 edges. Subsequently, structed protein-protein networks that may reflect the be- candidate antivitiligo targets of KBL were screened by using havior and properties of biological molecules. To this end, a the topological features of CPPI. A node with the topological 6 Evidence-Based Complementary and Alternative Medicine

TYR IL10

NFE2L2 MOL002356 MOL001456

MOL004935 IFNG MOL000474 MOL004891 MOL005007 MOL001789 GSTM1 MOL001956 MOL000391 MPO MOL004911 MOL003791 MOL004907 MOL001942 MOL004385 MOL004949MOL004883 MOL004863 MOL000448 MOL004793 MOL000057 MOL004848 MOL000098 MOL004808 MOL004908MOL004827 MOL004820 MOL005789 MOL000422 MOL005800 MOL003592 MOL005043 MOL004898MOL004811 MOL005000 MOL004912 MOL004815MOL004991 MOL000676 MOL003656 MOL004853 MOL001941 MOL004328 MOL005792 MOL004866 MOL004824 MOL005001 MOL000035 MOL002311 MOL004806 MOL000511 MOL000170 MOL003896 MOL000131MOL004881 MOL000486 TNF MOL004961 GSTP1 MOL003218 MOL004856 MOL005018 MOL004988 MOL004814 MOL005786 MOL004836 MOL000354 MOL013430 MOL004964 MOL000251 MOL004978 FASLG PTGS2 MOL004990 MOL001939 MOL004833 MOL000239 MOL000252 MOL004885 MOL004959 MOL004974 MOL005806 MOL005020 MOL001201 MOL004913 CAT MOL005008 MOL000358 MAOB MOL001484 MOL005017 MOL004723 ESR1 MOL004966 MOL004864 MOL000467 MOL000118 MOL004980 MOL004936 MOL004904 MOL000671 MOL004993 MOL004829 MOL001415 MOL004957 MOL001792 MOL000500 MOL004841 MOL004838 MOL000392 MOL005012 MOL000359 MOL004835 MOL004915 MOL002844 MOL004857 MOL004805 MOL004882 MOL004880 MOL004910MOL004941 MOL004879 MOL004855 MOL004849 MOL000417MOL004884 MOL000103 MOL002331 MOL004810 MOL002565MOL003588 MOL004828 MOL000449 MOL004948 MOL001040 MOL000953 MOL004924 MOL002644 MOL007514 MOL008601 MOL000561 MOL005807 MOL005016 MOL004945 MOL004903 MOL004989 MOL002341MOL000497 MOL004914 MAOA MOL000019 MOL001950 MOL001099MOL000023 MOL005003 MOL000211 MOL000202 MOL001494 IL4 MOL000116 MOL004832

Targets

Targets (a) P value

Reactive oxygen species metabolic process 0.000125 Cellular detoxification Response to external stimulus Neurotransmitter metabolic process Regulation of programmed cell death 0.000100 Oxidoreductase activity Cytokine-mediated signaling pathway Response to corticosteroid Response to cytokine 0.000075 Response to steroid hormone Response to vitamin Positive regulation of JAK-STAT cascade Top GO terms 0.000050 Cytokine receptor binding Regulation of biological quality Inflammatory response Drug catabolic process 0.000025 Biosynthetic process Response to antibiotic Aging Response to tumor necrosis factor

0.00 0.02 0.04 0.06 Rich factor

Gene number 4 8 12 6 10 14 (b) Figure 3: Continued. Evidence-Based Complementary and Alternative Medicine 7

KEGG pathway annotation Human diseases Infectious diseases 6 Cardiovascular diseases 5 Immune diseases 5 Drug resistance 5 Endocrine and metabolic diseases 3 Substance dependence 2 Neurodegenerative diseases 2 Metabolism Xenobiotics biodegradation and metabolism 5 Amino acid metabolism 4 Metabolism of other amino acids 2 Organismal systems Immune system 6 Nervous system 4 Endocrine system 4 Development 2 Environmental information processing Signal transduction 7 Signaling molecules and interaction 5 Cellular processes Cell growth and death 3 Transport and catabolism 2 Genetic information processing Folding, sorting and degradation 2

0246810 Number of gene (c)

Figure 3: KBL shared 15 potential targets with known pathological course related targets of vitiligo. (a) -e compound potential target network was constructed by linking the overlapped targets (between KBL potential and known vitiligo-related) and the homologous candidate compounds of KBL. -e nodes representing candidate compounds are shown as pink triangle and the targets are presented as purple circular. (b) 15 overlapped targets (between KBL potential and known vitiligo-related) was performed in Omicshare to gain more insights into their involvement in various GO terms. We considered a P value cutoff of ≤0.05 as significant and applied hypergeometric test to identify enriched GO terms. Following chart shows an overview of the gene ontology analysis with up to 20 significantly enriched terms. (c) 15 overlapped targets (between KBL potential and known vitiligo-related) was performed in Omicshare to gain more insights into their involvement in KEGG pathways. features exceeding that of median of all nodes was considered as accumulation, neuron apoptotic process, and vitamin de- a hub in the network (candidate targets in our study). As a ficiency, could indirectly affect the progression of vitiligo, result, the candidate targets were identified according to a providing potent evidence for our speculation that KBL widely used plugin CytoNCA, six topological features, including exerted a curative role on vitiligo via systemic neuro-immuno- “DC,” “BC,” “CC,” “EC,” “NC,” and “LAC.” -e median values inflammatory modulation from a genetic perspective. of “DC,” “BC,” “CC,” “EC,” “NC,” and “LAC” were 43, -e involved pathway was mainly composed of TNF, 23.3953542332676, 0.666666666666666, 0.103930160403251, JAK-STAT, ILs, TLRs, NF-κB, prolactin, lymphocyte dif- 37.2721127301037, and 35.15, respectively. -erefore, 23 can- ferentiation-related, and autoimmune and systemic in- didate targets, with “DC” >43, “BC” >23.3953542332676, “CC” flammatory reaction-related pathways (Figure 4(b)). -e >0.666666666666666, “EC” >0.103930160403251, “NC” >37.27 above pathways could further be categorized into three 21127301037, and “LAC” >35.15, were identified. -e detailed representative modules, namely, immunoregulation, mela- topological characteristics and the PPI network are shown in nogenesis, and neuroendocrine regulation. Inflammation Table S5. and immune mechanisms are prone to play a role in the melanocyte destruction of vitiligo, while ILs, TLRs, and JAK- STAT signaling pathways participate in regulating proin- 3.5. Enrichment Analysis of Candidate Targetsfor KBL against flammatory and immune reactions, as well as lymphocyte Vitiligo. To further reveal the probable role of 23 candidate differentiation. In addition, destructive melanocytes are targets, ClueGO, an extensively utilized Cytoscape plugin, well-known characteristics of vitiligo. NF-κB, which is ca- was employed to find out interrelations of functional groups pable of decreasing apoptosis of vitiliginous keratinocytes, with their potential scientific connotation in biological thereby makes contributions to the proliferation as well as networks, followed by division into GO biological processes differentiation of melanocytes, further promoting melanin as well as signaling pathway (Figure 4). To be specific, bi- biosynthesis. Moreover, there is intimate interaction be- ological processes were mainly associated with DNA tween melanocytes and nerve fibers as well as extracellular translation, lymphocyte differentiation and activation, STAT matrix, while mediators might directly modulate the func- signal transduction, steriod biosynthesis, and peripheral tions of melanocytes. Hyperactive prolactin pathway is likely nervous system myelin maintenance (Figure 4(a)). to cause hyperproduction of H2O2, which is capable of not And multiple pathological processes and diseasees, including only changing calcium homeostasis to influencing vitiligo inflammatory cytokine stimulation, intracellular ROS progression via the modulation of synaptic transmission, 8 Evidence-Based Complementary and Alternative Medicine

Positive regulation Schwann cell of immunoglobulin differentiation secretion CD4-positive, Peripheral nervous alpha-beta T cell Intrinsic apoptotic system myelin Positive regulation Regulation of Positive regulation differentiation Positive regulation signaling pathway maintenance of DNA-templated chemokine of tyrosine involved in immune of interleukin-12 in response to transcription, biosynthetic phosphorylation of response biosynthetic process STAT protein oxidative stress Positive regulation of initiation cyclin-dependent protein process serine/threonine kinase activity involved in G1/S transition of mitotic cell cycle Extrinsic apoptotic Positive regulation signaling pathway Positive regulation of tyrosine Regulation of phosphorylation of Stat3 protein of acute in absence of ligand activated T cell Positive regulation Regulation of steriod inflammatory proliferation of protein import biosynthetic process Positive regulation response Positive regulation of interleukin-6 into nucleus, translocation of pri-miRNA biosynthetic T-helper 17 cell transcription from process differentiation RNA polymerase II Regulation of NF-kappaB import into nucleus promoter Positive regulation Protein import intoT-helper cell lineage commitment of regulatory T cell nucleus, differentiation translocation (a)

Age-rage Inflammatory bowel signaling pathway Th17 cell TNF signaling Adipocytokine T cell receptor disease (IBD) in diabetic differentiation pathway signaling pathway signaling pathway complications Osteoclast differentiation NF-kappa B Th1 and Th2 cell Hepatitis B Graft-versus-host Toll-like receptor signaling pathway differentiation disease signaling pathway Amyotrophic lateral sclerosis (ALS) B cell receptor Prolactin signaling Jak-STAT signaling signaling pathway pathway pathway Apoptosis Allograft rejection Pancreatic cancer Legionellosis

Intestinal immune Type I diabetes network for IgA mellitus Cytosolic HIF-1 signaling production IL-17 signaling Chronic myeloid DNA-sensing pathway Rheumatoid arthritis pathway leukemia pathway (b)

Figure 4: Enrichment analysis of candidate targets for KBL against vitiligo. -e enrichment analysis is generated by ClueGo and the most vital term in the group is labeled. Functionally related groups partially overlap. Representative enriched biological process or pathway (P < 0.05) interactions among main KBL targets. (a) Candidate KBL targets enriched in the representative biological process are shown. (b) Candidate KBL targets enriched in the representative signaling pathway are shown. neuronal excitability, and neurosecretion but also inducing [54, 55]. Large-scale GWAS, mainly performed in European- the direct death of melanocytes. To sum up, the present derived white and Chinese populations, have reported about outcomes demonstrate the vital pharmacological mecha- 56 different genetic loci contributed to vitiligo risk, part of nism of KBL against vitiligo and also shed novel light on the which also made contribution to oxidative stress, melano- drug exploitation strategy of vitiligo. genesis of melanocytes, neuroendocrine modulation, and autoimmunity [56, 57]. -us, better understandings of the 4. Discussion possible mechanisms might contribute to more specificity and effectiveness of the therapeutic regimens. Vitiligo, the most prevalent depigmentation skin disease, is -e 30-year application KBL has proved its practical ef- caused by dysfunction or destruction of melanocytes, the fectiveness on vitiligo, however, without systematical un- major pigment-producing cells [52]. Vitiligo is psycholog- derstanding of the pharmacological mechanisms. In our study, ically destructive in general, though it is not physically network pharmacology was employed to further investigate harmful [53]. -e present accessible therapeutic regimens, the mechanisms of KBL on vitiligo. To this end, we mainly such as steroid therapy, vitamin D analogs, and excimer focused on three aspects. Firstly, common targets of KBL of laser, are not proper for the majority of patients due to the vitiligo were enriched in pathological changes of intracellular complicated, ineffective, and time-consuming effects redox equilibrium and ion homeostasis. Secondly, enrichment Evidence-Based Complementary and Alternative Medicine 9 analysis showed the regulatory effects of KBL on the differ- Acknowledgments entiation of T-lymphocytes and that the cytokines produced may be the most important mechanism of vitiligo treatment. -is work was supported by the Natural Science Foundation -irdly, neuroendocrine regulation was also considered as the of China (no. 8180150888). -e authors thank the members critical sections of KBL on vitiligo therapy. To sum up, the of their laboratory and their collaborators for their research therapeutic effect of KBL on vitiligo was mediated by con- work. trolling the levels of its targets, which have been shown to be critically involved in vitiligo progression in a multicomponent, Supplementary Materials multitarget, and multilink pattern. As is known to all, lymphocytes, especially T-lympho- Table S1: all the candidate compounds of KBL. Table S2: all cytes along with T cells-generated cytokines, play crucial the potential targets of KBL. Table S3: known vitiligo-related roles in vitiligo progression, as indicated by multiple re- targets. Table S4: KBL shared 15 potential targets with search studies. Due to the advanced inflammation specu- known vitiligo-related targets. Table S5: topological feature lations, the imbalance of -1/-2 has become a paradigm in values of all/significant/candidate targets for KBL against vitiligo pathogenesis, which can affect on a variety of in- vitiligo Table S6: detailed information on six existing pro- flammatory cytokines, such as ILs [58, 59]. -e latest studies tein-protein interaction databases. Table S7: gene ontology have shown that various immune and inflammatory cells can analysis of the potential targets of KBL. 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