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

Authors: Yueying Tao; Kunming Tian; Ji Chen; Danfeng Tan; Yan Liu; Yongai Xiong; Zehui Chen; Yingbiao Tian
Journal: Evidence-Based Complementary and Alternative Medicine
 (2019)

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## 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.


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## Body

## 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 [3–6]. 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 [9–11]. 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 [10–12].Danshiliuhao Granule (DSLHG, composed ofLysimachiae 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 [13–15]. 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.Figure 1
The schematic illustration of network pharmacology analysis.


## 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 4-6μm 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’sttest between two groups, andP<0.05 was considered as statistically significant.


## 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 4-6μm 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’sttest between two groups, andP<0.05 was considered as statistically significant.


## 3. Results

### 3.1. Animal Experiments

The results of serum biochemical indicators in rats are shown in Figure2. 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 orP<0.01).Figure 2
Effect of DSLHG on serum biochemical levels in CCl4-induced liver fibrosis rats (mean ± SD). ##P<0.01, compared with the normal control group; ∗P<0.05, ∗∗P<0.01, compared with the model group; aP<0.05, bP<0.01, compared with the positive control group. I: the normal control group; II: the model group; III: the positive control group; IV: DSLHG high-dose group; V: DSLHG medium-dose group; VI: DSLHG low-dose group.HE and Masson staining of liver sections were shown in Supplementary Figure1. 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.Figure 3
Histological results of liver tissues for each group. (a) HEstaining (×200); (b) Masson’s trichrome staining(×200). NC: the normal control group; Model: the model group; Silibinin: the positive control group; DSLHGH: DSLHG high-dose group; DSLHGM: DSLHG medium-dose group; DSLHGL: DSLHG low-dose group.
(a) (b)As shown in Table1, 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.Table 1
Comparison of histological activity and fibrosis stages.
Groups N A F A0 A1 A2 A3 F0 F1 F2 F3 F4 Normal 6 2 4 0 0 3 3 0 0 0 Model 6 0 0 1 5## 0 0 0 2 4## Silibinin 6 0 3 3 0∗∗ 0 1 4 1 0∗∗ DSLHGH 6 0 2 3 1∗∗ 0 0 4 2 0∗∗ DSLHGM 6 0 4 2 0∗∗ 0 2 3 1 0∗∗ DSLHGL 6 0 2 4 0∗∗ 0 2 4 0 0∗∗ 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[24–26], resveratrol[27], polydatin[28], geniposide, genipin, geniposidic acid, genipin 1-gentiobioside[29–32], 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 Table2), 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.Table 2
The overlapping targets for potential targets of the active compounds in DSLHG formula and the liver fibrosis-related targets.
No. Protein name Gene name UniProt P1 Angiotensin-converting enzyme ACE P12821 P2 Disintegrin and metalloproteinase domain-containing protein 17 ADAM17 P78536 P3 RAC-alpha serine/threonine-protein kinase AKT1 P31749 P4 Serum albumin ALB P02768 P5 Aldehyde dehydrogenase, mitochondrial ALDH2 P05091 P6 Angiogenin ANG P03950 P7 Androgen receptor AR P10275 P8 Aurora kinase A AURKA O14965 P9 Bone morphogenetic protein 2 BMP2 P12643 P10 Bone morphogenetic protein 7 BMP7 P18075 P11 Carbonyl reductase [NADPH] 1 CBR1 P16152 P12 Cystathionine beta-synthase CBS P35520 P13 Cyclin-A2 CCNA2 P20248 P14 Cyclin-dependent kinase 2 CDK2 P24941 P15 Cyclin-dependent kinase 6 CDK6 Q00534 P16 Cytochrome P450 2C9 CYP2C9 P11712 P17 Dipeptidyl peptidase 4 DPP4 P27487 P18 Epidermal growth factor receptor EGFR P00533 P19 Receptor tyrosine-protein kinase erbB-4 ERBB4 Q15303 P20 Estrogen receptor ESR1 P03372 P21 Prothrombin F2 P00734 P22 Coagulation factor VII F7 P08709 P23 Fatty acid-binding protein, adipocyte FABP4 P15090 P24 Gastrotropin FABP6 P51161 P25 Fibroblast growth factor receptor 1 FGFR1 P11362 P26 Fibroblast growth factor receptor 2 FGFR2 P21802 P27 Galactokinase GALK1 P51570 P28 Vitamin D-binding protein GC P02774 P29 Glycogen synthase kinase-3 beta GSK3B P49841 P30 Glutathione S-transferase A1 GSTA1 P08263 P31 Glutathione S-transferase P GSTP1 P09211 P32 Tyrosine-protein kinase HCK HCK P08631 P33 Histone deacetylase 8 HDAC8 Q9BY41 P34 Hexokinase-1 HK1 P19367 P35 Corticosteroid 11-beta-dehydrogenase isozyme 1 HSD11B1 P28845 P36 Estradiol 17-beta-dehydrogenase 1 HSD17B1 P14061 P37 Insulin-like growth factor I IGF1 P05019 P38 Insulin-like growth factor 1 receptor IGF1R P08069 P39 Tyrosine-protein kinase JAK2 JAK2 O60674 P40 Vascular endothelial growth factor receptor 2 KDR P35968 P41 Tyrosine-protein kinase Lck LCK P06239 P42 Galectin-3 LGALS3 P17931 P43 Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase MAN1B1 Q9UKM7 P44 Mitogen-activated protein kinase 10 MAPK10 P53779 P45 Mitogen-activated protein kinase 14 MAPK14 Q16539 P46 E3 ubiquitin-protein ligase Mdm2 MDM2 Q00987 P47 Hepatocyte growth factor receptor MET P08581 P48 Neprilysin MME P08473 P49 Macrophage metalloelastase MMP12 P39900 P50 Collagenase 3 MMP13 P45452 P51 Stromelysin-1 MMP3 P08254 P52 Neutrophil collagenase MMP8 P22894 P53 Nitric oxide synthase, endothelial NOS3 P29474 P54 NAD(P)H dehydrogenase [quinone] 1 NQO1 P15559 P55 Oxysterols receptor LXR-beta NR1H2 P55055 P56 Bile acid receptor NR1H4 Q96RI1 P57 Nuclear receptor subfamily 1 group I member 2 NR1I2 O75469 P58 Nuclear receptor subfamily 1 group I member 3 NR1I3 Q14994 P59 Glucocorticoid receptor NR3C1 P04150 P60 Mineralocorticoid receptor NR3C2 P08235 P61 Ornithine carbamoyltransferase, mitochondrial OTC P00480 P62 Phenylalanine-4-hydroxylase PAH P00439 P63 Poly [ADP-ribose] polymerase 1 PARP1 P09874 P64 Phosphoenolpyruvate carboxykinase, cytosolic [GTP] PCK1 P35558 P65 cAMP-specific 3′,5′-cyclic phosphodiesterase 4B PDE4B Q07343 P66 Peroxisome proliferator-activated receptor alpha PPARA Q07869 P67 Peroxisome proliferator-activated receptor delta PPARD Q03181 P68 Peroxisome proliferator-activated receptor gamma PPARG P37231 P69 Glycogen phosphorylase, liver form PYGL P06737 P70 Retinoic acid receptor beta RARB P10826 P71 Retinol-binding protein 4 RBP4 P02753 P72 Renin REN P00797 P73 GTP-binding protein Rheb RHEB Q15382 P74 Transforming protein RhoA RHOA P61586 P75 Nuclear receptor ROR-alpha RORA P35398 P76 Retinoic acid receptor RXR-alpha RXRA P19793 P77 Alpha-1-antitrypsin SERPINA1 P01009 P78 Sex hormone-binding globulin SHBG P04278 P79 Superoxide dismutase [Mn], mitochondrial SOD2 P04179 P80 Proto-oncogene tyrosine-protein kinase Src SRC P12931 P81 Signal transducer and activator of transcription 1-alpha/beta STAT1 P42224 P82 Bile salt sulfotransferase SULT2A1 Q06520 P83 Tyrosine-protein kinase SYK SYK P43405 P84 TGF-beta receptor type-1 TGFBR1 P36897 P85 Trafficking protein particle complex subunit 3 TRAPPC3 O43617 P86 Transthyretin TTR P02766


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

As shown in Figure4, 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.Figure 4
Potential active compound-target network of DSLHG acting on liver fibrosis. A compound node and a target node are linked if the protein is targeted by the related compounds. Green triangle nodes represent the active compounds of DSLHG; ellipse and circular nodes are the potential targets of DSLHG; the overlap of the potential targets of the active compounds in DSLHG formula and the liver fibrosis-related targets is 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 (Figure5(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.Figure 5
The protein interaction of overlap of the potential targets of the chemical components in DSLHG with the liver fibrosis-related target genes and annotation analysis of core genes. (a) The PPI network of 86 overlap genes. (b) GO function analysis for hub genes.Notes. The nodes were overlap protein genes; the size of nodes from small to large represent the degree value from low to high; and these nodes color changes from green to red to indicate that the degree values change from low to high.
(a) (b)


### 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 Figure5(b), which listed the 20 GO terms with lowP 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 theP values. The colors from green to red reflected theP 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.Figure 6
KEGG pathway enrichment analysis for core targets of DSLHG acting on liver fibrosis.The top 10 pathways with lowerPvalues 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).Table 3
The important signaling pathways of core target genes.
Pathway ID Term Target genes P-Value hsa04151 PI3K-Akt signaling pathway AKT1, EGFR, FGFR1, FGFR2, RXRA, MET, IGF1, CDK2, KDR, PCK1, IGF1R, GSK3B, MDM2, NOS3, JAK2 7.21E-0.9 hsa04068 FoxO signaling pathway AKT1, EGFR, IGF1R, MAPK14, MDM2, IGF1, CDK2, PCK1, SOD2 1.14E-06 hsa04014 Ras signaling pathway AKT1, EGFR, IGF1R, FGFR1, FGFR2, MET, RHOA, IGF1, KDR 5.39E-05 hsa04919 Thyroid hormone signaling pathway AKT1, RXRA, GSK3B, ESR1, MDM2, STAT1, SRC 6.15E-05 hsa04015 Rap1 signaling pathway AKT1, EGFR, FGFR2, IGF1R, FGFR1, MAPK14, MET, RHOA, IGF1, SRC, KDR 3.55E-07 hsa04510 Focal adhesion AKT1, EGFR, IGF1R, GSK3B, MET, RHOA, IGF1, SRC, KDR 2.77E-05 hsa05205 Proteoglycans in cancer AKT1, EGFR, IGF1R, FGFR1, ERBB4, MAPK14, MET, RHOA, ESR1, MDM2, IGF1, SRC, KDR 1.35E-09 hsa05200 Pathways in cancer AKT1, EGFR, FGFR2, FGFR1, AR, BMP2, RXRA, MET, PPARG, IGF1, STAT1, CDK2, IGF1R, GSK3B, RHOA, MDM2, GSTP1 4.03E-10 hsa05215 Prostate cancer AKT1, FGFR2, EGFR, IGF1R, FGFR1, AR, GSK3B, MDM2, IGF1, CDK2 1.91E-09 hsa04066 HIF-1 signaling pathway AKT1, EGFR, IGF1R, IGF1, HK1, NOS3 2.70E-04Figure 7
Main active compound(degree>3) core target-pathway network of DSLHG on treating liver fibrosis. Core target genes are shown with blue circular nodes; green diamond nodes represent the active compounds in DSLHG formula and purple V nodes are signaling pathways.


## 3.1. Animal Experiments

The results of serum biochemical indicators in rats are shown in Figure2. 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 orP<0.01).Figure 2
Effect of DSLHG on serum biochemical levels in CCl4-induced liver fibrosis rats (mean ± SD). ##P<0.01, compared with the normal control group; ∗P<0.05, ∗∗P<0.01, compared with the model group; aP<0.05, bP<0.01, compared with the positive control group. I: the normal control group; II: the model group; III: the positive control group; IV: DSLHG high-dose group; V: DSLHG medium-dose group; VI: DSLHG low-dose group.HE and Masson staining of liver sections were shown in Supplementary Figure1. 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.Figure 3
Histological results of liver tissues for each group. (a) HEstaining (×200); (b) Masson’s trichrome staining(×200). NC: the normal control group; Model: the model group; Silibinin: the positive control group; DSLHGH: DSLHG high-dose group; DSLHGM: DSLHG medium-dose group; DSLHGL: DSLHG low-dose group.
(a) (b)As shown in Table1, 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.Table 1
Comparison of histological activity and fibrosis stages.
Groups N A F A0 A1 A2 A3 F0 F1 F2 F3 F4 Normal 6 2 4 0 0 3 3 0 0 0 Model 6 0 0 1 5## 0 0 0 2 4## Silibinin 6 0 3 3 0∗∗ 0 1 4 1 0∗∗ DSLHGH 6 0 2 3 1∗∗ 0 0 4 2 0∗∗ DSLHGM 6 0 4 2 0∗∗ 0 2 3 1 0∗∗ DSLHGL 6 0 2 4 0∗∗ 0 2 4 0 0∗∗ 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[24–26], resveratrol[27], polydatin[28], geniposide, genipin, geniposidic acid, genipin 1-gentiobioside[29–32], 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 Table2), 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.Table 2
The overlapping targets for potential targets of the active compounds in DSLHG formula and the liver fibrosis-related targets.
No. Protein name Gene name UniProt P1 Angiotensin-converting enzyme ACE P12821 P2 Disintegrin and metalloproteinase domain-containing protein 17 ADAM17 P78536 P3 RAC-alpha serine/threonine-protein kinase AKT1 P31749 P4 Serum albumin ALB P02768 P5 Aldehyde dehydrogenase, mitochondrial ALDH2 P05091 P6 Angiogenin ANG P03950 P7 Androgen receptor AR P10275 P8 Aurora kinase A AURKA O14965 P9 Bone morphogenetic protein 2 BMP2 P12643 P10 Bone morphogenetic protein 7 BMP7 P18075 P11 Carbonyl reductase [NADPH] 1 CBR1 P16152 P12 Cystathionine beta-synthase CBS P35520 P13 Cyclin-A2 CCNA2 P20248 P14 Cyclin-dependent kinase 2 CDK2 P24941 P15 Cyclin-dependent kinase 6 CDK6 Q00534 P16 Cytochrome P450 2C9 CYP2C9 P11712 P17 Dipeptidyl peptidase 4 DPP4 P27487 P18 Epidermal growth factor receptor EGFR P00533 P19 Receptor tyrosine-protein kinase erbB-4 ERBB4 Q15303 P20 Estrogen receptor ESR1 P03372 P21 Prothrombin F2 P00734 P22 Coagulation factor VII F7 P08709 P23 Fatty acid-binding protein, adipocyte FABP4 P15090 P24 Gastrotropin FABP6 P51161 P25 Fibroblast growth factor receptor 1 FGFR1 P11362 P26 Fibroblast growth factor receptor 2 FGFR2 P21802 P27 Galactokinase GALK1 P51570 P28 Vitamin D-binding protein GC P02774 P29 Glycogen synthase kinase-3 beta GSK3B P49841 P30 Glutathione S-transferase A1 GSTA1 P08263 P31 Glutathione S-transferase P GSTP1 P09211 P32 Tyrosine-protein kinase HCK HCK P08631 P33 Histone deacetylase 8 HDAC8 Q9BY41 P34 Hexokinase-1 HK1 P19367 P35 Corticosteroid 11-beta-dehydrogenase isozyme 1 HSD11B1 P28845 P36 Estradiol 17-beta-dehydrogenase 1 HSD17B1 P14061 P37 Insulin-like growth factor I IGF1 P05019 P38 Insulin-like growth factor 1 receptor IGF1R P08069 P39 Tyrosine-protein kinase JAK2 JAK2 O60674 P40 Vascular endothelial growth factor receptor 2 KDR P35968 P41 Tyrosine-protein kinase Lck LCK P06239 P42 Galectin-3 LGALS3 P17931 P43 Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase MAN1B1 Q9UKM7 P44 Mitogen-activated protein kinase 10 MAPK10 P53779 P45 Mitogen-activated protein kinase 14 MAPK14 Q16539 P46 E3 ubiquitin-protein ligase Mdm2 MDM2 Q00987 P47 Hepatocyte growth factor receptor MET P08581 P48 Neprilysin MME P08473 P49 Macrophage metalloelastase MMP12 P39900 P50 Collagenase 3 MMP13 P45452 P51 Stromelysin-1 MMP3 P08254 P52 Neutrophil collagenase MMP8 P22894 P53 Nitric oxide synthase, endothelial NOS3 P29474 P54 NAD(P)H dehydrogenase [quinone] 1 NQO1 P15559 P55 Oxysterols receptor LXR-beta NR1H2 P55055 P56 Bile acid receptor NR1H4 Q96RI1 P57 Nuclear receptor subfamily 1 group I member 2 NR1I2 O75469 P58 Nuclear receptor subfamily 1 group I member 3 NR1I3 Q14994 P59 Glucocorticoid receptor NR3C1 P04150 P60 Mineralocorticoid receptor NR3C2 P08235 P61 Ornithine carbamoyltransferase, mitochondrial OTC P00480 P62 Phenylalanine-4-hydroxylase PAH P00439 P63 Poly [ADP-ribose] polymerase 1 PARP1 P09874 P64 Phosphoenolpyruvate carboxykinase, cytosolic [GTP] PCK1 P35558 P65 cAMP-specific 3′,5′-cyclic phosphodiesterase 4B PDE4B Q07343 P66 Peroxisome proliferator-activated receptor alpha PPARA Q07869 P67 Peroxisome proliferator-activated receptor delta PPARD Q03181 P68 Peroxisome proliferator-activated receptor gamma PPARG P37231 P69 Glycogen phosphorylase, liver form PYGL P06737 P70 Retinoic acid receptor beta RARB P10826 P71 Retinol-binding protein 4 RBP4 P02753 P72 Renin REN P00797 P73 GTP-binding protein Rheb RHEB Q15382 P74 Transforming protein RhoA RHOA P61586 P75 Nuclear receptor ROR-alpha RORA P35398 P76 Retinoic acid receptor RXR-alpha RXRA P19793 P77 Alpha-1-antitrypsin SERPINA1 P01009 P78 Sex hormone-binding globulin SHBG P04278 P79 Superoxide dismutase [Mn], mitochondrial SOD2 P04179 P80 Proto-oncogene tyrosine-protein kinase Src SRC P12931 P81 Signal transducer and activator of transcription 1-alpha/beta STAT1 P42224 P82 Bile salt sulfotransferase SULT2A1 Q06520 P83 Tyrosine-protein kinase SYK SYK P43405 P84 TGF-beta receptor type-1 TGFBR1 P36897 P85 Trafficking protein particle complex subunit 3 TRAPPC3 O43617 P86 Transthyretin TTR P02766


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

As shown in Figure4, 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.Figure 4
Potential active compound-target network of DSLHG acting on liver fibrosis. A compound node and a target node are linked if the protein is targeted by the related compounds. Green triangle nodes represent the active compounds of DSLHG; ellipse and circular nodes are the potential targets of DSLHG; the overlap of the potential targets of the active compounds in DSLHG formula and the liver fibrosis-related targets is 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 (Figure5(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.Figure 5
The protein interaction of overlap of the potential targets of the chemical components in DSLHG with the liver fibrosis-related target genes and annotation analysis of core genes. (a) The PPI network of 86 overlap genes. (b) GO function analysis for hub genes.Notes. The nodes were overlap protein genes; the size of nodes from small to large represent the degree value from low to high; and these nodes color changes from green to red to indicate that the degree values change from low to high.
(a) (b)


## 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 Figure5(b), which listed the 20 GO terms with lowP 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 theP values. The colors from green to red reflected theP 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.Figure 6
KEGG pathway enrichment analysis for core targets of DSLHG acting on liver fibrosis.The top 10 pathways with lowerPvalues 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).Table 3
The important signaling pathways of core target genes.
Pathway ID Term Target genes P-Value hsa04151 PI3K-Akt signaling pathway AKT1, EGFR, FGFR1, FGFR2, RXRA, MET, IGF1, CDK2, KDR, PCK1, IGF1R, GSK3B, MDM2, NOS3, JAK2 7.21E-0.9 hsa04068 FoxO signaling pathway AKT1, EGFR, IGF1R, MAPK14, MDM2, IGF1, CDK2, PCK1, SOD2 1.14E-06 hsa04014 Ras signaling pathway AKT1, EGFR, IGF1R, FGFR1, FGFR2, MET, RHOA, IGF1, KDR 5.39E-05 hsa04919 Thyroid hormone signaling pathway AKT1, RXRA, GSK3B, ESR1, MDM2, STAT1, SRC 6.15E-05 hsa04015 Rap1 signaling pathway AKT1, EGFR, FGFR2, IGF1R, FGFR1, MAPK14, MET, RHOA, IGF1, SRC, KDR 3.55E-07 hsa04510 Focal adhesion AKT1, EGFR, IGF1R, GSK3B, MET, RHOA, IGF1, SRC, KDR 2.77E-05 hsa05205 Proteoglycans in cancer AKT1, EGFR, IGF1R, FGFR1, ERBB4, MAPK14, MET, RHOA, ESR1, MDM2, IGF1, SRC, KDR 1.35E-09 hsa05200 Pathways in cancer AKT1, EGFR, FGFR2, FGFR1, AR, BMP2, RXRA, MET, PPARG, IGF1, STAT1, CDK2, IGF1R, GSK3B, RHOA, MDM2, GSTP1 4.03E-10 hsa05215 Prostate cancer AKT1, FGFR2, EGFR, IGF1R, FGFR1, AR, GSK3B, MDM2, IGF1, CDK2 1.91E-09 hsa04066 HIF-1 signaling pathway AKT1, EGFR, IGF1R, IGF1, HK1, NOS3 2.70E-04Figure 7
Main active compound(degree>3) core target-pathway network of DSLHG on treating liver fibrosis. Core target genes are shown with blue circular nodes; green diamond nodes represent the active compounds in DSLHG formula and purple V nodes are signaling pathways.


## 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 [39–41]. 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.



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*Source: 2630357-2019-07-03.xml*