Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 2630357 | 14 pages | https://doi.org/10.1155/2019/2630357

Network Pharmacology-Based Prediction of the Active Compounds, Potential Targets, and Signaling Pathways Involved in Danshiliuhao Granule for Treatment of Liver Fibrosis

Academic Editor: Ferdinando Nicoletti
Received03 Apr 2019
Revised13 May 2019
Accepted22 May 2019
Published03 Jul 2019

Abstract

This study aims to predict the active ingredients, potential targets, signaling pathways and investigate the “ingredient-target-pathway” mechanisms involved in the pharmacological action of Danshiliuhao Granule (DSLHG) on liver fibrosis. Pharmacodynamics studies on rats with liver fibrosis showed that DSLHG generated an obvious anti-liver fibrosis action. On this basis, we explored the possible mechanisms underlying its antifibrosis effect using network pharmacology approach. Information about compounds of herbs in DSLHG was collected from TCMSP public database and literature. Furthermore, the oral bioavailability (OB) and drug-likeness (DL) were screened according to ADME features. Compounds with OB≥30% and DL≥0.18 were selected as active ingredients. Then, the potential targets of the active compounds were predicted by pharmacophore mapping approach and mapped with the target genes of the specific disease. The compound-target network and Protein-Protein Interaction (PPI) network were built by Cytoscape software. The core targets were selected by degree values. Furthermore, GO biological process analysis and KEGG pathway enrichment analysis were carried out to investigate the possible mechanisms involved in the anti-hepatic fibrosis effect of DSLHG. The predicted results showed that there were 108 main active components in the DSLHG formula. Moreover, there were 192 potential targets regulated by DSLHG, of which 86 were related to liver fibrosis, including AKT1, EGFR, and IGF1R. Mechanistically, the anti-liver fibrosis effect of DSLHG was exerted by interfering with 47 signaling pathways, such as PI3K-Akt, FoxO signaling pathway, and Ras signaling pathway. Network analysis showed that DSLHG could generate the antifibrosis action by affecting multiple targets and multiple pathways, which reflects the multicomponent, multitarget, and multichannel characteristics of traditional Chinese medicine and provides novel basis to clarify the mechanisms of anti-liver fibrosis of DSLHG.

1. Introduction

Liver fibrosis is a common pathological feature of chronic liver disease and a main contributor in the development of cirrhosis caused by chronic liver injury [1, 2]. It is characterized by excessive deposition of collagen-based extracellular matrix (ECM) in the liver and abnormal liver function. Various chemical/microbial factors could contribute to the occurrence of hepatic fibrosis, including alcohol, virus, drugs, toxicants, and autoimmune process [36]. Previous studies [7, 8] indicated that liver fibrosis, especially in early stage, could be reversed, while once the liver fibrosis develops into cirrhosis stage, it could not. Thereby, more and more scholars are working to find novel drugs that can effectively reverse or delay the development of hepatic fibrosis.

Since the 1970s, the traditional Chinese medicine(TCM) have been used for the treatment of liver fibrosis. Cumulating data have shown that the TCM compound possesses obvious advantages in treatment of liver fibrosis. However, due to the multicomponent, multitarget, and multichannel characteristics of TCM and their complex formulations, the detailed medicinal ingredients of Chinese medicines and their complex models of action are unclear, which impeded the modernization and clinical application of TCM [911]. In recent years, network pharmacology based on systems biology and polypharmacology is emerging. Network pharmacology explores the relevance of drug-disease from a holistic and systematic perspective, emphasizes the transition of research mode from “single target” to “multiple targets,” and systematically expounds the principles and rules of interaction between organism and drugs. As a powerful tool for exploring the potential mechanisms of various actions of TCM formulas and novel active ingredients via constructing a network of interactions between drugs and genes, targets and diseases, it has been widely used in TCM research and became a new approach for the development of TCM [1012].

Danshiliuhao Granule (DSLHG, composed of Lysimachiae Herba (LH), Polygoni Cuspidati Rhizoma Et Radix (PC), Radix et Rhizoma Rhei (RR), Gardeniae Fructus (GF), Aurantii Fructus (AF), Aucklandiae Radix (AR), Corydalis Rhizoma (CR), and Sodium Sulfate(SS)) is a Chinese herb preparation based on TCM prescription with wonderful anti-liver fibrosis effect and preservation of liver function. Our work provided theoretical basis for the registration of pharmaceutical preparations and laid the foundation for the subsequent acquisition of new medicine certificates. Among these herbal medicines, RR is a commonly used TCM. Many studies have proven that emodin has good clinical value in the treatment of liver fibrosis [1315]. The rhein possesses anti-inflammatory and free radical scavenging effects and antifibrosis effect, protects liver cells, and reduces liver damage [16]. Additionally, PC exerts antibacterial, antiviral, and liver protection effect that can promote liver cell regeneration and recovery. Thereby, the beneficial effects of PC make it have therapeutic effect on acute hepatic ischemic injury, as evidenced by abrogated acute liver injury caused by carbon tetrachloride (CCl4) [17, 18]. In our study, we found that DSLHG could alleviate liver fibrosis induced by CCl4 in rats and raised one new hopeful drug for the treatment of hepatic fibrosis. However, due to its complex components and molecular mechanisms against liver fibrosis, which have not been fully elucidated, it is of great significance to explore the anti-liver fibrosis mechanisms of DSLHG through network pharmacology.

In this work, we investigated the pharmacological mechanisms by which DSLHG regulates liver fibrosis. For this purpose, we established and analyzed active compound-target(C-T)network. First of all, we selected the active ingredients according to pharmacokinetic parameters (oral bioavailability and drug-likeness) from public database and searched the potential targets of these compounds by PharmMapper server. Furthermore, we built a network of interactions among active components regarding their related-targets and target genes of liver fibrosis. Moreover, we conducted GO function analysis and relevant pathway enrichment analysis for the potential core targets of DSLHG acting on targeted disease. The schematic illustration of this study is shown in Figure 1.

2. Materials and Methods

2.1. Reagents and Chemicals

Silibinin capsules were provided by Tianjin TASLY SANTS Pharmaceutical CO., Ltd. (Tianjin, China). CCl4 was obtained from Shanghai Aladdin Bio-Chem Technology Co., Ltd. (Shanghai, China). Olive oil was purchased from Shanghai Yien Chemical Technology Co., Ltd. (Shanghai, China). CCl4 was diluted to 40% (v/v) with olive oil before being used. The detection kits of alanine aminotransferase(ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and albumin (ALB) were purchased from the Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Enzyme-linked immunosorbent assay(ELISA) kits for the measurement of laminin(LN), hyaluronic acid(HA), procollagen-III-peptide (PIIINP), and collagen type IV (ColIV) were offered by Shanghai Jiang Lai Biological Technology Co., Ltd. (Shanghai, China).

2.2. Animals and Experimental Design

Sixty healthy male Sprague Dawley (SD) rats weighting 180-220g were purchased from the Changsha Tianqin Biotechnology Co., Ltd. (Changsha, China). All the animals were housed under the same controlled conditions with a 12 h light-dark cycle at 23±2°C temperature in the Laboratory Animal Center of Zunyi Medical University (Guizhou, China). Then, the rats were randomly divided into 6 groups with 10 rats in each group: named (I): normal control group,(II): model group, (III): positive control group (Silibinin 36mg/kg·d−1), (IV): DSLHG high-dose group (27g/kg·d−1), (V): DSLHG medium-dose group (13.5g/kg·d−1), (VI): DSLHG low-dose group (6.75g/kg·d−1). All groups were given normal water and standard rat feed. From the first day of the experiment, the abdominal cavities of other rats were injected 40% CCl4 with olive oil (0.2mL per 100gof bodyweight) twice a week (Tuesday and Friday) except for the normal control group, and the normal rats were handled by intraperitoneal injection with olive oil solution(2mL/kg) for seven weeks. From the 2th week, the positive control group was intragastrically administered with a dose of 10mL/kg body weight of silibinin, and the DSLHG groups were gavaged with 10mL/kg body weight of the corresponding drug concentration. In addition, the normal control group and the model group were given a gavage of distilled water of the same volume with the drug. Administration of all animals was continued for six weeks.

At the end of the eighth week, all rats were anesthetized by ether after the last gavage. Then, blood samples were collected from the rat eyelid by capillary. The whole blood was centrifuged at 3500rpm for 10min, and the upper serum was taken in a cryogenic vial and then immediately stored in a refrigerator at -20°C to test related biochemical indicators using corresponding kits. The liver tissues of the rats were removed rapidly and rinsed in pre-cold physiological saline. The right lobes of the liver (approximately 1×1×1cm3) were quickly fixed in 10% neutral formaldehyde solution for histopathological examination.

This protocol was approved by the Animal Care and Use Committee of the Zunyi Medical University (2019A-002) and all experimental procedures were in compliance with the National Institutes of Health Guide for Care and Use of Laboratory Animals.

2.3. Histopathological Examination

The fixed liver tissues with 10% neutral buffered formaldehyde solution were dehydrated by automatic dehydrator, embedded in paraffin, and then sliced to a thickness of 4-6 μm sections. Hematoxylin-eosin (H&E) staining and Masson’s trichrome staining were performed to observe the pathological and fibrosis changes, respectively. The degree of inflammation was graded based on the METAVIR scoring system: A0: no activity; A1: mild activity; A2: moderate activity; A3: severe activity. The degree of liver fibrosis was graded on a 5-point scale as either F0, F1, F2, F3, or F4, i.e., no fibrosis, portal fibrosis without septa, portal fibrosis with few septa, numerous septa without cirrhosis, and cirrhosis.

2.4. Chemical Ingredients Database Construction

Information of chemical compounds of each herbal medicine in DSLHG was obtained from free public database and literature. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform [19](TCMSP, http://lsp.nwu.edu.cn/tcmsp.php), a unique system pharmacology platform designed for herbal medicines, provides a chemical screening process based on ADME features. According to the pharmacokinetic characteristics given by the TCMSP data platform, the chemical components that cannot satisfy both oral bioavailability (OB)≥30% and drug-likeness (DL) ≥0.18 are deleted [20]. The 2D structure of the candidate compound was searched by the PubChem database and drew with ChemBioDraw 14.0 software. Finally, the 2D structure was converted to 3D structure and saved as a MOL2 file.

2.5. Target Genes Related to the Identified Compounds

The target prediction for the main active compounds was performed using the PharmMapper server[21] (http://www.lilab-ecust.cn/pharmmapper/) with the “Homo sapiens” species setting, which is a free public web-server that uses active small molecules as a probe to search potential drug targets. The 10 targets with the highest Fit Score were selected, and gene information including gene ID, name, and organism was identified using UniProt database(https://www.uniprot.org/).

2.6. Potential Target Genes for Liver Fibrosis

The target genes associated with hepatic fibrosis were collected from the following resources: (1)GeneCards[22](https://www.genecards.org/) is a human gene database that automatically integrates data from approximately 150 web sources. We searched GeneCards database with the keywords “hepatic fibrosis” or “liver fibrosis” and found 1,222 genes related to hepatic fibrosis from the database. (2)The Online Mendelian Inheritance in Man(OMIM) database [23] is a continuously updated database of human genes and genetic disorders. We retrieved 50 hepatic fibrosis-related genes from the OMIM database. (3) Literature, we gathered liver fibrosis-related literature that contains 81 target genes. We finally collected a total of 1,280 genes associated with liver fibrosis through the above sources.

2.7. Construction of Network

In order to understand the mechanisms of DSLHG for the treatment of liver fibrosis, network analysis was performed. The corresponding network was established and visualized by Cytoscape 3.7 software. Nodes in the network represent active components and target genes. The edges indicate that the ingredients interact with the targets. The Protein-Protein Interaction (PPI) network of DSLHG on liver fibrosis was constructed using the STRING database (https://string-db.org/) combined with the Network Analyzer plugin of Cytoscape to filter out hub genes.

2.8. Pathway and Functional Enrichment Analysis

We carried out Gene Ontology (GO) function analysis and signaling pathway enrichment analysis for predicting targets of DSLHG on liver fibrosis by using DAVID bioinformatics resources 6.8 (https://david.ncifcrf.gov/).

2.9. Statistical Analysis

All experimental data were taken on the mean ± standard deviation (SD). Statistical analyses were performed with GraphPad Prism 6.0 statistical software. Rank data were analyzed with the rank sum test. A one-way analysis of variance (ANOVA) test was used to compare the mean of different groups. The data were compared by Student’s t test between two groups, and P<0.05 was considered as statistically significant.

3. Results

3.1. Animal Experiments

The results of serum biochemical indicators in rats are shown in Figure 2. Compared with the model group, ALT, AST, HA, LN, PIIINP, and Col IV levels in rat serum were significantly reduced in DSLHG groups, as well as in the positive control group (P<0.05 or P<0.01).

HE and Masson staining of liver sections were shown in Supplementary Figure 1. As shown in Figure 3, in normal control group, the hepatic lobule structure was clear, and hepatic cords were arranged radially around the central veins, and only a small amount of blue collagen fiber deposition was observed, while the liver tissue of model group indicated thickened capsule, destroyed or disappearing partial normal hepatic lobular structure, and structurally appearing “pseudolobule.” Compared with model group, the histopathological examination showed that collagen deposition, liver inflammation activity, and liver fibrosis significantly reduced after treatment with DSLHG and silibinin. Additionally, no obvious pseudolobules were observed.

As shown in Table 1, rank sum test showed statistical difference in the pathological and fibrosis changes between all treatment groups and the model group(P<0.05). Unfortunately, none of the DSLHG groups were significantly different from the silibinin group in either of these parameters (P>0.05). These results showed that DSLHG had an obvious anti-liver fibrosis effect.


GroupsNAF
A0A1A2A3F0F1F2F3F4

Normal6240033000
Model60015##00024##
Silibinin6033001410
6023100420
6042002310
6024002400

A: activity grade, A0-A3; F: fibrosis stage, F0-F4. ##P<0.01, compared with the normal control group; P<0.01, compared with the model group.
3.2. Active Ingredients Filtering

Total of 513 chemical constituents of seven individual herbs(Sodium Sulfate was not found in the database) in DSLHG were retrieved from TCMSP, including 61 ingredients in LH, 92 in RR, 62 in PC, 98 in GF, 17 in AF, 106 in AR, and 77 in CR. All compounds were subjected to ADME screening, and a total of 111 active compounds had OB≥30% and DL≥0.18. There are 10 compounds in LH, 16 compounds in RR, 10 compounds in PC, 15 compounds in GF, 5 compounds in AF, 6 compounds in AR, and 49 compounds in CR, respectively. Due to duplication, fifteen of these compounds were deleted, resulting in 96 effective active ingredients. In addition, among the compounds below the OB and DL screening criteria, 12(emodin, chrysophanol, physcion[2426], resveratrol[27], polydatin[28], geniposide, genipin, geniposidic acid, genipin 1-gentiobioside[2932], dehydrocostus lactone[33], neohesperidin[34], and tetrahydropalmatine[35]) were considered to be biologically active compounds, and their protective effect on liver has been reported. Therefore, the selected 108 compounds (Supplementary Table 1) from DSLHG formula were subjected to further analysis.

3.3. Potential Targets in DSLHG

Top 10 targets with the highest Fit Score were selected. Total of 192 potential targets were obtained from 108 active ingredients after removing the redundancy (Supplementary Table 2), including MDM2, TNFBR1, MAPK14, and SRC, involved in inflammation, hepatic lipid metabolism, and the development of hepatic fibrosis. In order to make the targets more concentrated, we deleted 106 target genes which could not correspond to the disease. As shown in Table 2, 86 of 192 potential targets were associated with liver fibrosis.


No. Protein nameGene nameUniProt

P1Angiotensin-converting enzymeACEP12821
P2Disintegrin and metalloproteinase domain-containing protein 17ADAM17P78536
P3RAC-alpha serine/threonine-protein kinaseAKT1P31749
P4Serum albuminALBP02768
P5Aldehyde dehydrogenase, mitochondrialALDH2P05091
P6AngiogeninANGP03950
P7Androgen receptorARP10275
P8Aurora kinase AAURKAO14965
P9Bone morphogenetic protein 2BMP2P12643
P10Bone morphogenetic protein 7BMP7P18075
P11Carbonyl reductase [NADPH] 1CBR1P16152
P12Cystathionine beta-synthaseCBSP35520
P13Cyclin-A2CCNA2P20248
P14Cyclin-dependent kinase 2CDK2P24941
P15Cyclin-dependent kinase 6CDK6Q00534
P16Cytochrome P450 2C9CYP2C9P11712
P17Dipeptidyl peptidase 4DPP4P27487
P18Epidermal growth factor receptorEGFRP00533
P19Receptor tyrosine-protein kinase erbB-4ERBB4Q15303
P20Estrogen receptorESR1P03372
P21ProthrombinF2P00734
P22Coagulation factor VIIF7P08709
P23Fatty acid-binding protein, adipocyteFABP4P15090
P24GastrotropinFABP6P51161
P25Fibroblast growth factor receptor 1FGFR1P11362
P26Fibroblast growth factor receptor 2FGFR2P21802
P27GalactokinaseGALK1P51570
P28Vitamin D-binding proteinGCP02774
P29Glycogen synthase kinase-3 betaGSK3BP49841
P30Glutathione S-transferase A1GSTA1P08263
P31Glutathione S-transferase PGSTP1P09211
P32Tyrosine-protein kinase HCKHCKP08631
P33Histone deacetylase 8HDAC8Q9BY41
P34Hexokinase-1HK1P19367
P35Corticosteroid 11-beta-dehydrogenase isozyme 1HSD11B1P28845
P36Estradiol 17-beta-dehydrogenase 1HSD17B1P14061
P37Insulin-like growth factor IIGF1P05019
P38Insulin-like growth factor 1 receptorIGF1RP08069
P39Tyrosine-protein kinase JAK2JAK2O60674
P40Vascular endothelial growth factor receptor 2KDRP35968
P41Tyrosine-protein kinase LckLCKP06239
P42Galectin-3LGALS3P17931
P43Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidaseMAN1B1Q9UKM7
P44Mitogen-activated protein kinase 10MAPK10P53779
P45Mitogen-activated protein kinase 14MAPK14Q16539
P46E3 ubiquitin-protein ligase Mdm2MDM2Q00987
P47Hepatocyte growth factor receptorMETP08581
P48NeprilysinMMEP08473
P49Macrophage metalloelastaseMMP12P39900
P50Collagenase 3MMP13P45452
P51Stromelysin-1MMP3P08254
P52Neutrophil collagenaseMMP8P22894
P53Nitric oxide synthase, endothelialNOS3P29474
P54NAD(P)H dehydrogenase [quinone] 1NQO1P15559
P55Oxysterols receptor LXR-betaNR1H2P55055
P56Bile acid receptorNR1H4Q96RI1
P57Nuclear receptor subfamily 1 group I member 2NR1I2O75469
P58Nuclear receptor subfamily 1 group I member 3NR1I3Q14994
P59Glucocorticoid receptorNR3C1P04150
P60Mineralocorticoid receptorNR3C2P08235
P61Ornithine carbamoyltransferase, mitochondrialOTCP00480
P62Phenylalanine-4-hydroxylasePAHP00439
P63Poly [ADP-ribose] polymerase 1PARP1P09874
P64Phosphoenolpyruvate carboxykinase, cytosolic [GTP]PCK1P35558
P65cAMP-specific 3′,5′-cyclic phosphodiesterase 4BPDE4BQ07343
P66Peroxisome proliferator-activated receptor alphaPPARAQ07869
P67Peroxisome proliferator-activated receptor deltaPPARDQ03181
P68Peroxisome proliferator-activated receptor gammaPPARGP37231
P69Glycogen phosphorylase, liver formPYGLP06737
P70Retinoic acid receptor betaRARBP10826
P71Retinol-binding protein 4RBP4P02753
P72ReninRENP00797
P73GTP-binding protein RhebRHEBQ15382
P74Transforming protein RhoARHOAP61586
P75Nuclear receptor ROR-alphaRORAP35398
P76Retinoic acid receptor RXR-alphaRXRAP19793
P77Alpha-1-antitrypsinSERPINA1P01009
P78Sex hormone-binding globulinSHBGP04278
P79Superoxide dismutase [Mn], mitochondrialSOD2P04179
P80Proto-oncogene tyrosine-protein kinase SrcSRCP12931
P81Signal transducer and activator of transcription 1-alpha/betaSTAT1P42224
P82Bile salt sulfotransferaseSULT2A1Q06520
P83Tyrosine-protein kinase SYKSYKP43405
P84TGF-beta receptor type-1TGFBR1P36897
P85Trafficking protein particle complex subunit 3TRAPPC3O43617
P86TransthyretinTTRP02766

3.4. Compounds-Targets(C-T) Network Construction and Analysis

As shown in Figure 4, the 108 active compounds and related 192 target genes constructed the network schematic diagram. Totally, this C-T network is composed of 300 nodes (108 active compounds and 192 potential targets) and 1080 edges. In the picture, the edges indicate an association between the active ingredients and the targets. The degree values indicate the intensity of the interaction between the components and the targets. Green triangle nodes represent the active compounds of DSLHG. Circular nodes represent the potential targets of DSLHG, and the overlaps between the potential targets of the DSLHG and the liver fibrosis-related targets are indicated with pink circular nodes.

According to the network, one target gene may be complicatedly linked to multiple active ingredients of different herbs, which suggest that these compounds might synergistically contribute to the pharmacological effect of the DSLHG in the treatment of hepatic fibrosis. In addition, different active ingredients can also target the same gene, reflecting its multicomponent, multitarget, and antifibrosis effect, that was consistent with the multicomponent and multitarget characteristics of TCM.

3.5. PPI Network Construction and Analysis

86 target genes associated with active ingredients and liver fibrosis were imported into the STRING database for PPI network construction and analysis. There are 83 interacting targets in the network (HDAC8, RORA, and PDE4B are not involved in protein interaction), resulting in 594 edges representing the interaction between proteins (Figure 5(a)). Then, the PPI network was screened according to the degree values. In this work, the target genes with a degree value greater than 10 were selected as the core genes for further analysis and a total of 47 targets were screened out.

3.6. GO Functional Annotation Analysis

A total of 47 key potential genes that may be involved in liver fibrosis were uploaded to the DAVID panel for analyzing their biological process. The functional distribution of 47 targets was explored by GO functional analysis. The results of GO analysis of the predicted key targets of DSLHG acting on liver fibrosis are shown in Figure 5(b), which listed the 20 GO terms with low P values and more targets enrichment. The results showed that these targets have a strong association with physiological mechanisms, such as signal transduction, protein autophosphorylation, and positive regulation of cell proliferation. These results indicated that DSLHG may play a key role in antihepatic fibrosis by manipulating these biological processes which lead to the pathogenesis of liver fibrosis.

3.7. Kyoto Encyclopedia of Genes and Genomes (KEGG) Signaling Pathway Analysis

To determine the relevant signaling pathways involved in the anti-liver fibrosis effect of DSLHG, we conducted pathway enrichment analysis using KEGG pathways. A total of 47 targets obtained 60 KEGG signaling pathways, and 47 channels were significantly enriched (P<0.05). The senior bubble map visually showed these significantly enriched pathways (Figure 6). The color and size of the nodes in the bubble graph were decided by the number of associated genes and the P values. The colors from green to red reflected the P values from high to low, and the size of the nodes indicated how many target genes are associated. The results of KEGG pathways enrichment analysis indicated the multiple channels and mechanisms of action of DSLHG against liver fibrosis.

The top 10 pathways with lower P values and more genes enrichment are listed in Table 3, including PI3K-Akt signaling pathway, AMPK signaling pathway, Ras signaling pathway, T cell receptor signaling pathway, PPAR signaling pathway, VEGF signaling pathway, and FoxO signaling pathway being enriched. These signaling pathways involve inflammation, metabolism, and ECM deposition. Taken together, these signaling pathways seem to be closely related to the beneficial effects of DSLHG against liver fibrosis. The illustrated network that contains main chemicals-targets-signaling pathway of DSLHG was established to understand their interaction (Figure 7).


Pathway IDTermTarget genesP-Value

hsa04151PI3K-Akt signaling pathwayAKT1, EGFR, FGFR1, FGFR2, RXRA, MET, IGF1, CDK2, KDR, PCK1, IGF1R, GSK3B, MDM2, NOS3, JAK27.21E-0.9
hsa04068FoxO signaling pathwayAKT1, EGFR, IGF1R, MAPK14, MDM2, IGF1, CDK2, PCK1, SOD21.14E-06
hsa04014Ras signaling pathwayAKT1, EGFR, IGF1R, FGFR1, FGFR2, MET, RHOA, IGF1, KDR5.39E-05
hsa04919Thyroid hormone signaling pathwayAKT1, RXRA, GSK3B, ESR1, MDM2, STAT1, SRC6.15E-05
hsa04015Rap1 signaling pathwayAKT1, EGFR, FGFR2, IGF1R, FGFR1, MAPK14, MET, RHOA, IGF1, SRC, KDR3.55E-07
hsa04510Focal adhesionAKT1, EGFR, IGF1R, GSK3B, MET, RHOA, IGF1, SRC, KDR2.77E-05
hsa05205Proteoglycans in cancerAKT1, EGFR, IGF1R, FGFR1, ERBB4, MAPK14, MET, RHOA, ESR1, MDM2, IGF1, SRC, KDR1.35E-09
hsa05200Pathways in cancerAKT1, EGFR, FGFR2, FGFR1, AR, BMP2, RXRA, MET, PPARG, IGF1, STAT1, CDK2, IGF1R, GSK3B, RHOA, MDM2, GSTP14.03E-10
hsa05215Prostate cancerAKT1, FGFR2, EGFR, IGF1R, FGFR1, AR, GSK3B, MDM2, IGF1, CDK21.91E-09
hsa04066HIF-1 signaling pathwayAKT1, EGFR, IGF1R, IGF1, HK1, NOS32.70E-04

4. Discussion

Hepatic fibrosis is considered as the wound healing in response to the chronic liver damage caused by various factors. Excessive production and abnormal deposition of ECM are caused by the complicated interaction between various types of cells and cytokines in the liver, characterized by imbalance between the degradation and synthesis of ECM, resulting in a series of changes during the initiation and progression of liver fibrosis [3, 36, 37]. The occurrence of liver fibrosis is often accompanied by a certain degree of inflammatory response. Inflammation is one of the most typical features of viral, alcoholic, fatty, and autoimmune chronic liver disease. For a long time, many scholars have been working to explore the certain role of inflammation in the pathogenesis of liver fibrosis, such as the role of inflammatory mediators in the hepatic stellate cells (HSCs) activation and development of liver fibrosis [38]. HSCs are the main cells responsible for synthesis of ECM in normal and fibrotic liver and play central role in the development of liver fibrosis [3941]. Once HSCs are activated through paracrine and autocrine routes, the imbalance between ECM synthesis and degradation was interfered, which results in aberrant accumulation of ECM in the liver [42, 43].

FoxO, a key downstream regulator in the PI3K-Akt signaling pathway belonging to subclass of the forkhead proteins family, regulates metabolic homeostasis in response to oxidative stress. Moreover, oxidative stress resistance, apoptosis, and glucose metabolism were also modulated by FoxO [44]. Importantly, FoxO transcription factors benefit liver fibrosis through inhibiting proliferation and transdifferentiation of HSCs [45].

As the downstream factor of the PI3K-Akt signaling pathway, FoxOs are controlled by Akt (protein kinase B, PKB). The PI3K/Akt signaling pathway is one of the important signaling pathways, which has increasingly raised concern under various diseases context. The PI3K-Akt signaling pathway can be activated by many types of cellular stimuli or toxic insults and regulates fundamental cellular functions, such as transcription, translation, proliferation, growth, and survival [46]. Some researchers [47, 48] have reported that PI3K and Akt are important in the process of the activation of hepatic stellate cells (HSCs), cell proliferation, and collagen synthesis. In contrast, PI3K and Akt are also involved in the regulation of HSCs apoptosis, which may be one of the important pathways delaying the development of liver fibrosis.

At present, most of the western medicines used for the treatment of liver fibrosis are effective only under a certain condition, with difficulty in achieving multitarget and multiechelon coverage. However, most diseases are associated with multiple targets; thus, it is difficult to achieve appropriate therapeutic results against a single target. The pharmacodynamic mechanisms of TCM against liver fibrosis have multiple levels and multiple targets and pay attention to the characteristics of overall regulation [10]. A new approach that analyzes TCM with network pharmacology may be a reliable way to overcome disease [49]. Cumulating data have shown that network pharmacology can reveal the interactions between multiple targets of compounds present in Chinese herbal medicines [50].

In this study, through the analysis of DSLHG compound-target-pathway network, we found that the main active components of DSLHG could act on multiple targets, indicating the multicomponent, multitarget, and overall regulation of formula. In addition, DSLHG generates the anti-liver fibrosis effect by acting on multiple pathways, multiple targets, and multiple biological processes. The predicted targets and signaling pathways in this study provide ideas for experiments to verify the key target proteins and mechanisms of antifibrosis of DSLHG in the future.

5. Conclusion

In summary, this study used a network pharmacology approach to construct a biological network to display the interactions between compounds and protein/gene targets at a molecular or systemic level. The results indicate that DSLHG can achieve antifibrosis by acting on multiple targets and multiple pathways. Through our network analysis and prediction, the mechanisms of DSLHG formula at the molecular level are clarified, which might provide new ideas for developing new drug treatment or novel therapeutical strategies for liver fibrosis. Next, we will verify the results of network analysis prediction through manipulating the targeted molecular via pharmacological or genetical methods.

Data Availability

The data used to support the findings of this study are included within the article and the supplementary information files.

Disclosure

Yueying Tao and Kunming Tian are co-first authors, and Zehui Chen and Yingbiao Tian are joint corresponding authors.

Conflicts of Interest

There are no conflicts of interest to declare.

Authors’ Contributions

Yongai Xiong, Zehui Chen, and Yingbiao Tian proposed the idea and designed the study. Yueying Tao and Kunming Tian performed the study. Ji Chen, Danfeng Tan, and Yan Liu participated in ‘in silico’ analysis and data analysis. Yueying Tao and Kunming Tian wrote and improved the manuscript. All authors read and approved the final manuscript. Yueying Tao and Kunming Tian contributed equally to this work.

Acknowledgments

This study was supported by the project which named 8 kinds of hospital preparations and clinical research such as Fuke Qingre Zhidai granules[No. 20153028]. And the project was approved by the Science and Technology Department of Guizhou Province, China.

Supplementary Materials

. Supplementary Table 1: the information of active compounds in DSLHG formula. Supplementary 2. Supplementary Table 2: the 192 potential targets of active ingredients in DSLHG formula. Supplementary 3. Supplementary Figure 1: HE and Masson staining of liver sections for each group. (Supplementary Materials)

References

  1. U. E. Lee and S. L. Friedman, “Mechanisms of hepatic fibrogenesis,” Best Practice & Research: Clinical Gastroenterology, vol. 25, no. 2, pp. 195–206, 2011. View at: Publisher Site | Google Scholar
  2. E. Novo, S. Cannito, C. Paternostro, C. Bocca, A. Miglietta, and M. Parola, “Cellular and molecular mechanisms in liver fibrogenesis,” Archives of Biochemistry and Biophysics, vol. 548, pp. 20–37, 2014. View at: Publisher Site | Google Scholar
  3. D. Li, W. Li, Y. Chen et al., “Anti-fibrotic role and mechanism of Periplaneta americana extracts in CCl4-induced hepatic fibrosis in rats,” Acta Biochimica et Biophysica Sinica, vol. 50, no. 5, pp. 491–498, 2018. View at: Publisher Site | Google Scholar
  4. Y. S. Chiu, C. Wei, Y. Lin, Y. Hsu, and M. Chang, “IL-20 and IL-20R1 antibodies protect against liver fibrosis,” Hepatology, vol. 60, no. 3, pp. 1003–1014, 2014. View at: Publisher Site | Google Scholar
  5. T. R. Harris, S. Kodani, J. Yang et al., “An ω-3-enriched diet alone does not attenuate CCl4-induced hepatic fibrosis,” The Journal of Nutritional Biochemistry, vol. 38, pp. 93–101, 2016. View at: Publisher Site | Google Scholar
  6. Y. Guo, X. Liang, M. Meng et al., “Hepatoprotective effects of Yulangsan flavone against carbon tetrachloride (CCl4)-induced hepatic fibrosis in rats,” Phytomedicine, vol. 33, pp. 28–35, 2017. View at: Publisher Site | Google Scholar
  7. E. Seki and D. A. Brenner, “Recent advancement of molecular mechanisms of liver fibrosis,” Journal of Hepato-Biliary-Pancreatic Sciences, vol. 22, no. 7, pp. 512–518, 2015. View at: Publisher Site | Google Scholar
  8. H. M. Atta, “Reversibility and heritability of liver fibrosis: Implications for research and therapy,” World Journal of Gastroenterology, vol. 21, no. 17, pp. 5138–5148, 2015. View at: Publisher Site | Google Scholar
  9. D. Liu and X. B. Jia, “Analysis and discussion about current development of relevant studies on “traditional Chinese medicine components”,” Zhongguo Zhong Yao Za Zhi, vol. 39, no. 2, pp. 171–174, 2014. View at: Google Scholar
  10. S. Li and B. Zhang, “Traditional Chinese medicine network pharmacology: theory, methodology and application,” Chinese Journal of Natural Medicines, vol. 11, no. 2, pp. 110–120, 2013. View at: Publisher Site | Google Scholar
  11. F. Liu, X. Du, P. Liu, Y. Sun, and Y. Zhang, “Screening and analysis of key active constituents in Guanxinshutong capsule using mass spectrum and integrative network pharmacology,” Chinese Journal of Natural Medicines, vol. 16, no. 4, pp. 302–312, 2018. View at: Publisher Site | Google Scholar
  12. J. Fang, L. Wang, T. Wu et al., “Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment,” Journal of Ethnopharmacology, vol. 196, pp. 281–292, 2017. View at: Publisher Site | Google Scholar
  13. M. X. Dong, Y. Jia, Y. B. Zhang et al., “Emodin protects rat liver from CCl4-induced fibrogenesis via inhibition of hepatic stellate cells activation,” World Journal of Gastroenterology, vol. 15, no. 38, pp. 4753–4762, 2009. View at: Publisher Site | Google Scholar
  14. X. Wang, C. Niu, X. Zhang et al., “Emodin suppresses activation of hepatic stellate cells through p38 mitogen-activated protein kinase and Smad signaling pathways in vitro,” Phytotherapy Research, vol. 32, no. 12, pp. 1–11, 2018. View at: Publisher Site | Google Scholar
  15. Y. Zhan, D. Li, H. Wei et al., “Emodin on hepatic fibrosis in rats,” Chinese Medical Journal, vol. 113, no. 7, pp. 599–601, 2000. View at: Google Scholar
  16. M. Z. Guo, X. S. Li, H. R. Xu et al., “Rhein inhibits liver fibrosis induced by carbon tetrachloride in rats,” Acta Pharmacologica Sinica, vol. 23, no. 8, pp. 739–744, 2002. View at: Google Scholar
  17. Y. Jiao, Y. Wu, and D. Du, “Polydatin inhibits cell proliferation, invasion and migration, and induces cell apoptosis in hepatocellular carcinoma,” Brazilian Journal of Medical and Biological Research, vol. 51, no. 4, Article ID e6867, 2018. View at: Publisher Site | Google Scholar
  18. X. Zhao, R. Li, Y. Liu et al., “Polydatin protects against carbon tetrachloride-induced liver fibrosis in mice,” Archives of Biochemistry and Biophysics, vol. 629, pp. 1–7, 2017. View at: Google Scholar
  19. J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, no. 1, p. 13, 2014. View at: Google Scholar
  20. A. Y. Lee, W. Park, T. Kang, M. H. Cha, and J. M. Chun, “Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis,” Journal of Ethnopharmacology, vol. 221, pp. 151–159, 2018. View at: Publisher Site | Google Scholar
  21. X. Wang, Y. Shen, S. Wang et al., “PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database,” Nucleic Acids Research, vol. 45, no. 1, pp. W356–W360, 2017. View at: Publisher Site | Google Scholar
  22. G. Stelzer, R. Rosen, I. Plaschkes et al., “The GeneCards suite: from gene data mining to disease genome sequence analysis,” Current Protocols in Bioinformatics, vol. 54, pp. 1.30.1–1.30.33, 2016. View at: Google Scholar
  23. A. Hamosh, A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. McKusick, “Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders,” Nucleic Acids Research, vol. 33, pp. D514–D517, 2005. View at: Publisher Site | Google Scholar
  24. L. Feng, J. Zhang, J. Qian, W. Gang, and M. Zhenyu, “Emodin alleviates CCl4-induced liver fibrosis by suppressing epithelial-mesenchymal transition and transforming growth factor-ß1 in rats,” Molecular Medicine Reports, vol. 18, no. 3, pp. 3262–3270, 2018. View at: Google Scholar
  25. Y. Lian, X. Xia, H. Zhao, and Y. Zhu, “The potential of chrysophanol in protecting against high fat-induced cardiac injury through Nrf2-regulated anti-inflammation, anti-oxidant and anti-fibrosis in Nrf2 knockout mice,” Biomedicine & Pharmacotherapy, vol. 93, pp. 1175–1189, 2017. View at: Publisher Site | Google Scholar
  26. Y. L. Lin, C. F. Wu, and Y. T. Huang, “Phenols from the roots of Rheum palmatum attenuate chemotaxis in rat hepatic stellate cells,” Planta Medica, vol. 74, no. 10, pp. 1246–1252, 2008. View at: Publisher Site | Google Scholar
  27. S. B. Abdu and F. M. Al-Bogami, “Influence of resveratrol on liver fibrosis induced by dimethylnitrosamine in male rats,” Saudi Journal of Biological Sciences, vol. 26, no. 1, pp. 201–209, 2019. View at: Publisher Site | Google Scholar
  28. R. Li, J. Li, Y. Huang et al., “Polydatin attenuates diet-induced nonalcoholic steatohepatitis and fibrosis in mice,” International Journal of Biological Sciences, vol. 14, no. 11, pp. 1411–1425, 2018. View at: Publisher Site | Google Scholar
  29. M. Inao, S. Mochida, A. Matsui et al., “Japanese herbal medicine Inchin-ko-to as a therapeutic drug for liver fibrosis,” Journal of Hepatology, vol. 41, no. 4, pp. 584–591, 2004. View at: Publisher Site | Google Scholar
  30. A. Kitano, S. Saika, O. Yamanaka et al., “Genipin suppresses subconjunctival fibroblast migration, proliferation and myofibroblast transdifferentiation,” Ophthalmic Research, vol. 38, no. 6, pp. 355–360, 2006. View at: Publisher Site | Google Scholar
  31. S. J. Kim, K. M. Kim, J. Park, J. H. Kwak, Y. S. Kim, and S. M. Lee, “Geniposidic acid protects against D-galactosamine and lipopolysaccharide-induced hepatic failure in mice,” Journal of Ethnopharmacology, vol. 146, no. 1, pp. 271–277, 2013. View at: Publisher Site | Google Scholar
  32. T. Zhou, W. Zhao, G. Fan, Y. Chai, and Y. Wu, “Isolation and purification of iridoid glycosides from Gardenia jasminoides Ellis by isocratic reversed-phase two-dimensional preparative high-performance liquid chromatography with column switch technology,” Journal of Chromatography B, vol. 858, no. 1-2, pp. 296–301, 2007. View at: Publisher Site | Google Scholar
  33. J. Mao, M. Yi, R. Wang et al., “Protective effects of costunolide against d-galactosamine and lipopolysaccharide-induced acute liver injury in mice,” Frontiers in Pharmacology, vol. 9, p. 1469, 2018. View at: Google Scholar
  34. N. Miceli, M. R. Mondello, M. T. Monforte et al., “Hypolipidemic effects of citrus bergamia risso et poiteau juice in rats fed a hypercholesterolemic diet,” Journal of Agricultural and Food Chemistry, vol. 55, no. 26, pp. 10671–10677, 2007. View at: Publisher Site | Google Scholar
  35. C. Sun, Z. Chen, H. Wang, and K. Ding, “Tetrahydropalmatine prevents high-fat diet-induced hyperlipidemia in golden hamsters (mesocricetus auratus),” Medical Science Monitor, vol. 24, pp. 6564–6572, 2018. View at: Publisher Site | Google Scholar
  36. J. Ding, J. Yu, C. Wang et al., “Ginkgo biloba extract alleviates liver fibrosis induced by CCl4 in rats,” Liver International, vol. 25, no. 6, pp. 1224–1232, 2005. View at: Publisher Site | Google Scholar
  37. B. Gao and R. Bataller, “Alcoholic liver disease: pathogenesis and new therapeutic targets,” Gastroenterology, vol. 141, no. 5, pp. 1572–1585, 2011. View at: Publisher Site | Google Scholar
  38. E. Seki and R. F. Schwabe, “Hepatic inflammation and fibrosis: functional links and key pathways,” Hepatology, vol. 61, no. 3, pp. 1066–1079, 2014. View at: Publisher Site | Google Scholar
  39. X. Jiang, E. Tsitsiou, S. E. Herrick, and M. A. Lindsay, “MicroRNAs and the regulation of fibrosis,” FEBS Journal, vol. 277, no. 9, pp. 2015–2021, 2010. View at: Publisher Site | Google Scholar
  40. X. P. Jiang, W. B. Ai, L. Y. Wan et al., “The roles of microRNA families in hepatic fibrosis,” Cell & Bioscience, vol. 7, no. 1, pp. 34–42, 2017. View at: Google Scholar
  41. T. Higashi, S. L. Friedman, and Y. Hoshida, “Hepatic stellate cells as key target in liver fibrosis,” Advanced Drug Delivery Reviews, vol. 121, pp. 27–42, 2017. View at: Publisher Site | Google Scholar
  42. J. P. Li, Y. Gao, S. F. Chu et al., “Nrf2 pathway activation contributes to anti-fibrosis effects of ginsenoside Rg1 in a rat model of alcohol and CCl4-induced hepatic fibrosis,” Acta Pharmacologica Sinica, vol. 35, no. 8, pp. 1031–1044, 2014. View at: Publisher Site | Google Scholar
  43. F. Tacke and R. Weiskirchen, “Update on hepatic stellate cells: pathogenic role in liver fibrosis and novel isolation techniques,” Expert Review of Gastroenterology & Hepatology, vol. 6, no. 1, pp. 67–80, 2012. View at: Publisher Site | Google Scholar
  44. X. C. Dong, “FOXO transcription factors in non-alcoholic fatty liver disease,” Liver Research, vol. 1, no. 3, pp. 168–173, 2017. View at: Google Scholar
  45. M. Adachi, Y. Osawa, H. Uchinami, T. Kitamura, D. Accili, and D. A. Brenner, “The forkhead transcription factor FoxO1 regulates proliferation and transdifferentiation of hepatic stellate cells,” Gastroenterology, vol. 132, no. 4, pp. 1434–1446, 2007. View at: Publisher Site | Google Scholar
  46. A. M. Martelli, M. Nyåkern, G. Tabellini et al., “Phosphoinositide 3-kinase/Akt signaling pathway and its therapeutical implications for human acute myeloid leukemia,” Leukemia, vol. 20, no. 6, pp. 911–928, 2006. View at: Publisher Site | Google Scholar
  47. Y. Xiao, J. Wang, Y. Chen et al., “Up-regulation of miR-200b in biliary atresia patients accelerates proliferation and migration of hepatic stallate cells by activating PI3K/Akt signaling,” Cellular Signalling, vol. 26, no. 5, pp. 925–932, 2014. View at: Publisher Site | Google Scholar
  48. A. Li, J. Wang, M. Wu, X. Zhang, and H. Zhang, “The inhibition of activated hepatic stellate cells proliferation by arctigenin through G0/G1 phase cell cycle arrest: persistent p27Kip1 induction by interfering with PI3K/Akt/FOXO3a signaling pathway,” European Journal of Pharmacology, vol. 747, pp. 71–87, 2015. View at: Publisher Site | Google Scholar
  49. W. Dai, H. Chen, and C. Y. Chen, “A network pharmacology-based approach to investigate the novel TCM formula against huntington’s disease and validated by support vector machine model,” Evidence-Based Complementary and Alternative Medicine, vol. 2018, Article ID 6020197, 29 pages, 2018. View at: Publisher Site | Google Scholar
  50. S. Yue, J. Liu, W. Feng et al., “System pharmacology-based dissection of the synergistic mechanism of huangqi and huanglian for diabetes mellitus,” Frontiers in Pharmacology, vol. 8, article no. 694, 2017. View at: Publisher Site | Google Scholar

Copyright © 2019 Yueying Tao 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.


More related articles

934 Views | 498 Downloads | 3 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.