Int J Clin Exp Med 2019;12(12):13446-13454www.ijcem.com /ISSN:1940-5901/IJCEM0101387

Original ArticleA systematic elucidation of the sanguinarine mechanism in human disease using network pharmacology

Yiyang Zhang*, Jingtong Shuai, Shenhe Su, Qiang Li*

Institute of Tobacco Control & Health Sciences, Agronomy College, Hunan Agricultural University, Changsha 410128, Hunan Province, P. R. China. *Equal contributors.

Received August 23, 2019; Accepted October 10, 2019; Epub December 15, 2019; Published December 30, 2019

Abstract: Sanguinarine (SNG), [C20H14NO4]+, is a quaternary benzo phenanthridine alkaloid, which can be discovered

in many Papaveraceae plants. It is chiefly collected from the roots of the hematogenous plant, the seeds of the Mexican prickly poppy, and the leaves and fruits of the plume poppy. There is no systematic analysis of its pharma-cological mechanism yet. The TCMSP was used to evaluate the medicinal properties of SNG. The potential target of SNG was determined using the Comparative Toxicogenomics Database (CTD). Utilizing the WebGestalt website, the pathways of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed. For visual observation, a drug-target-pathway network was established using Cytoscape. Our results indicated that SNG has excellent drug potency with 50 hypothetical target genes. Through an analysis of GO, KEGG, and the drug network, these targets were associated with apoptosis, platinum drug resistance, the p53 signaling pathways, pathways in cancer, and so on. SNG is predicted to play a systematic pharmacological role in a network of multiple targeted proteins and pathways.

Keywords: Sanguinarine, druggability, enrichment analysis, target prediction, network pharmacology

Introduction

A large number of natural products have shown suitable pharmacological activities in many fields [1]. Many new drugs derived from natural products, which were used to treat diseases, for instance, both bacterial and fungal infec-tions [2], Alzheimer’s disease [3], cancer [4], diabetes, atopic dermatitis [5], and dyslipid-emia [6]. A website named Natural products and traditional Chinese medicine (TCM), which contains plentiful active substances, is among the most complete medication resources for drug development [7]. Benzylisoquinoline alka-loids (BIAs) mainly exist in Papaveraceae, Ranunculaceae, Lauraceae, Annonaceae, Me- nispermaceae, and Berberidaceae medicinal plants. BIAs are synthesized by tyrosine and phenylalanine in natural plants, and they are a kind of alkaloid more abundant and with a more complex structure in nature [8]. The chemical structure expression of SNG (13-methyl-[1,3] benzodioxole [5,6-c]-1,3-dioxolo[4,5-i] phenan-

thridinium) (C20H14NO4) is shown in Figure 1A. SNG has been demonstrated to exercise a lar- ge range of pharmacological functions, which include anti-inflammatory, anti-bacterial, regu-lating cardiovascular disease, improving liver function, and so on [9-12]. In the past few de- cades, lots of experts interested in SNG have noted its anti-cancer properties [13-15]. The above studies indicate that SNG can be used as a valuable chemical probe to identify the influ-encial relationship between compounds, genes, and metabolic pathways in biological metabo-lism, and it is also likely to be developed as drugs for corresponding target genes. Getting suitable pharmacological activity compounds from natural products is an essential way to get new drugs [16].

Network pharmacology is a method for design-ing drugs, which includes network construction & analysis, systems biology, pleiotropy, redun-dancy, and connectivity [17]. Therefore, we sys-tematically elucidated the pharmacological

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action of SNG with the method of calculation. First, we evaluated the druggability of SHG with the website “Traditional Chinese Medicine Sys- tems Pharmacology Database” (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php) [18]. Second, a chemical-gene interaction analysis was used to predict the possible target genes [19]. Also, identified target genes were used to study gene ontology and pathway analysis. Last but not least, a drug target network was constructed to systematically review the potential target genes of SNG and its functional mechanism. Figure 1B shows the workflow chart of the prediction and analysis of the SNG target gene.

Materials and methods

Pharmacokinetic characteristics investigated by TCMSP

The TCMSP website (http://lsp.nwu.edu.cn/tcmsp.php) is an unparalleled forum for Chin- ese herbal medicine pharmacology, which finds the connections between targets, drugs, and diseases [18]. The database collects data from chemical, target, and drug target networks, and related drug target-disease network relation-ships. It also includes the pharmacokinetic properties of natural compounds such as drug similarity, oral bioavailability, the blood-brain barrier, intestinal epithelial permeability, and water solubility [20]. In this paper, the keyword SNG was input into the UISearchBar for study-ing its pharmacokinetic characteristics.

site, we obtained the predicted candidate tar-gets of SNG.

Analysis through the GeneMANIA website

GeneMANIA (http://www.genemania.org) is an excellent and reliable network analysis website. It can be used to find relevant genes which are bound up with a group of import genes [21]. The linked data involves protein-hereditary interactions, pathways, co-expression, co-ori-entation, and protein domain similarity.

Depending on the specified search list, the GeneMANIA can list relevant genes with shared attributes, either function similar to the initial query. It makes use of functional relationship networks to illustrate the relationships between lists, as well as proteomics and genomics data. Selecting the Homo sapiens organism option, we put the list of candidate potential target genes into the query box, which results would be further sided.

Analysis of functional gene pathway

The gestalt (http://www.webgestalt.org/option.php) is a widely used tool of gene set enrich-ment analysis that helps users extract vital information from genes of interest [22]. Web- Gestalt can be used to genuinely comprehend the functions and pathways that enrich the information of responsive genes [23]. The potential candidate targets were entered into the Gestalt tool utilizing the gene ontology (GO)

Figure 1. A. The chemical constitution of SNG which acquired from the Pub-Chem data library (CID: 5154, MF: C20H14NO4

+, MW: 332.335 g/mol); B. The workflow of SNG ADME evaluation, the interaction of chemical-gene (CTD), analyses of GO and the KEGG pathway, and the drug network. Abbrevia-tions: ADME evaluation (Absorption, Distribution, Metabolism, and Excre-tion evaluation); CTD (Comparative Toxicogenomics Database); GO (gene ontology); KEGG (Kyoto Encyclopedia of Genes and Genomes).

Appraisal of the target genes

The Comparative Toxicogeno- mics Database (CTD, http://ctdbase.org/) is an open and searchable database of toxi-cology genome information. It has critical data on protein in- teractions, chemical-gene, ge- ne-disease, and chemical-dis-ease relationships based on peer-reviewed research pap- ers. To date, the CTD website involves 42,060,266 toxico- genomic contacts associated with chemicals, genes, pro-teins, and so on [19]. For spe-cific compounds, CTD can be arranged in descending order according to their interactions, providing the corresponding target genes. On the CTD web-

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Table 1. Pharmacological and molecular properties of SNGname MW AlogP Hdon Hacc OB (%) Caco-2 BBB DL FASA- TPSA RBN HLSNG 332.35 3.47 0 4 37.81 1.26 0.15 0.86 0.3 40.8 0 7.84Abbreviations: OB, oral bioavailability; Caco-2, Caco-2 permeability; BBB, the blood-brain barrier; DL, drug-likeness.

and the overrepresentation enrichment analy-sis method of the Kyoto Genome Encyclopedia (KEGG) database. GO analysis, as a standard method, can be used for annotating genes or gene products with molecular functions, cellu-lar components, and biological pathways. The information of gene functions and connected high-caliber genomic function can be examined by KEGG [24, 25].

Network construction

For a further understanding of the complicated associations among compounds, targets, and diseases, we constructed and analyzed a three-layer network using the Cytoscape website (https://www.nigms.nih.gov/, v. 3.6.1).

Results

Pharmacokinetics properties of SNG

The dispensing of pharmaceutical mixtures has been described using ADME characteristics. TCMCP offers information on 12 fundamental aspects of ADME-related features, such as human OB, BBB, Caco-2, and Lipinski’s five principles for drug screening and evaluation [18]. TCMSP was used to study the MADE-related characteristics of SNG deeply. It is note-worthy that the OB calculation of SNG is 37.81% (Table 1).

Targets identification of SNG

Following the methods in the “Materials and Methods” section, the potential targets of SNG were predicted through the CTD website [26]. After adopting the threshold of chemical-gene interaction, 50 specific target genes for SNG identified by CTD that were more significant than or equal to 1 and non-human genes were removed (Table 2). The 50 interacting genes selected would be used for further research.

GeneMANIA analysis

Through comparing the relationship of 50 tar-gets and their interacting proteins, 64.59% dis-

played similar co-expressions, 10.84% Path- way, and 10.75% shared protein domains char-acteristics were found. Figure 2 shows some additional results, including Physical Interacti- ons, Genetic Interactions, and Co-Localization.

GO and pathway enrichment analysis

To thoroughly investigate the 50 identified tar-get genes, we used WebGestalt to conduct GO and KEGG enrichment analyses. As shown in Figure 3, biological processes such as GO:00- 65007 (biological regulation), GO:0050896 (response to stimulus), GO:0008152 (metabol-ic process), GO:0007154 (cell communication), GO:0032501 (multicellular organismal pro-cess), GO:0032502 (developmental process), and GO:0016043 (cellular component organi-zation) were included (Figure 3A). The cellular components included GO:0016020 (mem-brane), GO:0005829 (cytosol), GO:0005634 (nucleus), GO:0032991 (protein-containing complex), GO:0031974 (membrane-enclosed lumen), and GO:0005739 (mitochondrion) (Figure 3B). In addition, the 50 identified target genes also prominently affected the molecular functions, such as GO:0005515 (protein bind-ing), GO:0043167 (ion binding), GO:0016787 (hydrolase activity), GO:0030234 (enzyme reg-ulator activity), GO:0003676 (nucleic acid bind-ing), and GO:0016740 (transferase activity) (Figure 3C). These practical terms have a high correlation with liver function improvement, T lymphocytes and B lymphocytes stimulation, and a significantly reduction in serum lactate dehydrogenase levels.

Through pathway analysis, 50 targets were examined that participate in 10 KEGG path-ways, including apoptosis, platinum drug resis-tance, p53 signaling pathways, pathways in cancer, and so on. There was a significant false discovery rate (FDR)-regulated P-value (Figure 4).

Network analysis

According to pathway analyses and the targets of the indicated genes, a complete network of

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Table 2. Potential targets of SNGNum Gene ID Gene symbol Gene name1 196 AHR aryl hydrocarbon receptor2 405 ARNT aryl hydrocarbon receptor nuclear translocator3 581 BAX BCL2 associated X, apoptosis regulator4 27113 BBC3 BCL2 binding component 35 596 BCL2 BCL2 apoptosis regulator6 79370 BCL2L14 BCL2 like 147 637 BID BH3 interacting domain death agonist8 638 BIK BCL2 interacting killer9 329 BIRC2 baculoviral IAP repeat-containing 210 665 BNIP3L BCL2 interacting protein 3 like11 59082 CARD18 caspase recruitment domain family member 1812 84674 CARD6 caspase recruitment domain family member 613 834 CASP1 caspase 114 843 CASP10 caspase 1015 836 CASP3 caspase 316 837 CASP4 caspase 417 838 CASP5 caspase 518 841 CASP8 caspase 819 842 CASP9 caspase 920 9075 CLDN2 claudin 221 1365 CLDN3 claudin 322 1364 CLDN4 claudin 423 7122 CLDN5 claudin 524 1543 CYP1A1 cytochrome P450 family 1 subfamily A member 125 1545 CYP1B1 cytochrome P450 family 1 subfamily B member 126 162989 DEDD2 death effector domain containing 227 1958 EGR1 early growth response 128 355 FAS Fas cell surface death receptor29 356 FASLG Fas ligand30 2940 GSTA3 glutathione S-transferase alpha 331 2950 GSTP1 glutathione S-transferase pi 132 27306 HPGDS hematopoietic prostaglandin D synthase33 4050 LTB lymphotoxin beta34 5594 MAPK1 mitogen-activated protein kinase 135 5595 MAPK3 mitogen-activated protein kinase 336 5599 MAPK8 mitogen-activated protein kinase 837 5601 MAPK9 mitogen-activated protein kinase 938 4313 MMP2 matrix metallopeptidase 239 4318 MMP9 matrix metallopeptidase 940 64332 NFKBIZ NFKB inhibitor zeta41 22861 NLRP1 NLR family pyrin domain containing 142 1728 NQO1 NAD(P)H quinone dehydrogenase 143 5366 PMAIP1 phorbol-12-myristate-13-acetate-induced protein 144 29108 PYCARD PYD and CARD domain-containing45 6615 SNAI1 snail family transcriptional repressor 146 7124 TNF tumor necrosis factor47 8795 TNFRSF10B TNF receptor superfamily member 10b48 7133 TNFRSF1B TNF receptor superfamily member 1B49 8743 TNFSF10 TNF superfamily member 1050 331 XIAP X-linked inhibitor of apoptosis

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Figure 2. SNG protein network. Notes: The target protein is represented by black nodes, and different correlations are indicated by the differently colored connections.

the compounds, targets, and diseases was es- tablished using Cytoscape (v. 3.7.1). The com-pounds, diseases, and target interaction net-work have 61 nodes with 201 edges. The green triangle, red circular rectangle, and blue circu-lar graph are used to represent the target genes, SNG and pathway respectively (Figure 5).

Discussion

The lack of pharmacokinetic and toxicity re- search foundations is one of the most signifi-cant reasons for the high cost and long cycle of drug research and development. So, more and more people agree that the specific character-istics of the drug development process should be given priority [27]. Computer analysis can moderate prediction and pharmacokinetic mo- deling, together with metabolic and toxicity endpoints. All this simplifies and speeds up the process of drug research [16, 28].

In 1997, Christopher Lipinksi and his colleagues developed the “Five Rules” (Ro5) because Pfizer used high throughput screening (HTS),

which led to the pursuit of more vibrant, more lipophilic compounds, which often had a lower permeability and solubil-ity [29]. Today, the five rules are commonly regarded as the guidelines for drug optimiza-tion [30]. In Table 1, the phar-macokinetic characteristics of SNG reached these require-ments, indicating that SNG is the right candidate for drug research.

Target gene identification and screening is the essential work of drug research. There are more active compounds or drugs that have been proved to interact with a variety of genes or proteins [31-35]. For this purpose, many kinds of target gene recognition methods ha- ve made significant progress and are widely used. Table 1 shows that 50 potential tar-gets for SNG have been identi-

fied with the method of calculation. The analy-sis results of GeneMANIA revealed that the target proteins and their interacting proteins may have the same or similar functions, and showed the information about the co-location, co-expression, and sharing of protein domains with interaction effects. We identified that SNG played an anti-inflammatory role to improve liver function. SNG has been acknowledged as an anticancer drug candidate [36]. Qi, Xiao-Yi et al. reported SNG could destroy the liver func-tions of Oncomelania hupensis by reducing glu-cogen content and changing the activity of some essential enzymes in snail livers [36]. The administration of SNG can regulate the immune-related genes of goldfish, which can affect their ability to resist bacterial pathogens to a certain extent [37]. The above results are consistent with the results of our GO and KEGG analysis.

The drug-target network uncovered that SNG has a variety of targets, which further indicates its numerous pharmacological activities (Fig- ure 5). Adhami et al. have also shown that the mitochondrial pathway and Bcl-2 family pro-

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Figure 4. KEGG enrichment pathway analysis of potential target genes.

teins were also involved in the SNG-mediated apoptosis of immortalized keratinocytes [38]. SNG leads apoptosis of human osteosarcoma cells via the extrinsic and intrinsic pathways [39], and it also activates the pathways of poly-cyclic aromatic hydrocarbon-related metabo-lism in human oral keratinocytes and tissues [40]. Multi-target drugs are more effective in treating complex diseases, such as cancer, and are less susceptible to adaptive resistance. Thus, SNG may be a valuable plant resource for

future drug research as a chemical component, lead compound, or active ingredient.

In conclusion, we need to point out that SNG is an active ingredient or potential compound derived from plants, which can be used to develop safe and efficient multi-target antican-cer drugs. We provide a new perspective and challenge for the study and are looking forward to the future drug development and clinical application of SNG.

Figure 3. GO maps of the assumed target genes. Notes: Biological process categories (A). Cellular component cat-egories (B). Molecular function categories (C).

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Disclosure of conflict of interest

None.

Address correspondence to: Qiang Li, Institute of Tobacco Control & Health Sciences, Agronomy College, Hunan Agricultural University, Changsha 410128, Hunan Province, P. R. China. Tel: +86-0731-84635356; E-mail: zqiangli@126.com

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