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Article

Evaluating the Potential of Glycyrrhiza uralensis (Licorice) in Treating Alcoholic Liver Injury: A Network Pharmacology and Molecular Docking Analysis Approach

by
Lichun Zhu
1,†,
Shuangquan Xie
2,†,
Zhihua Geng
1,
Xuhai Yang
1,3,* and
Qian Zhang
1,4,*
1
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
2
College of Life Sciences, Shihezi University, Shihezi 832003, China
3
Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi University, Shihezi 832003, China
4
Engineering Research Center for Production Mechanization of Oasis Special Economic Crop, Ministry of Education, Shihezi University, Shihezi 832003, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Processes 2022, 10(9), 1808; https://doi.org/10.3390/pr10091808
Submission received: 15 August 2022 / Revised: 30 August 2022 / Accepted: 5 September 2022 / Published: 7 September 2022
(This article belongs to the Special Issue Network Pharmacology Modelling for Drug Discovery)
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Abstract

:
Glycyrrhiza uralensis is used to treat alcoholic liver injury in China; however, its pharmacological mechanism remains to be clarified. Here, the potential of G. uralensis for the treatment of alcoholic liver injury was explored using a network pharmacology and molecular docking approach. The effective components and targets of G. uralensis were searched in a traditional Chinese medicine systems pharmacology database and analysis platform. Disease targets were obtained using the GeneCards and OMIM databases. The target genes of G. uralensis and alcoholic liver injury were compared to obtain common target genes. Symbol conversion was carried out using the Uniport database, and the composition–target network of G. uralensis and alcoholic liver injury was prepared. A protein–protein interaction network was constructed. Gene Ontology functional enrichment and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. AutoDock was used for the molecular docking of core compounds and key targets. One hundred and ninety-three common target genes of G. uralensis and alcoholic liver injury were screened. We identified targets of five active components of G. uralensis, namely, formononetin, 3′-methoxyglabridin, glypallichalcone, 1-methoxyphaseollidin, and glabridin. The main targets identified via the protein–protein interaction network analysis were JUN, MAPK3, STAT3, AKT1, and MAPK1. The biological processes associated with xenobiotic stimulus and lipopolysaccharide metabolism were involved. These biological processes were common between Glycyrrhiza treatment and liver injury. They mainly involved lipid and atherosclerosis, chemical carcinogenic gene-receptor activation, and Kaposi sarcoma-associated herpesvirus. Shinpterocarpin and 7-methoxy-2-methyl-isoflavone had good docking effects with MAPK3, and their binding energies were less than −5 kcal/mol. Based on the network pharmacology and molecular docking analyses, the chemical compositions, potential targets, and pathways involved in G. uralensis treatment of alcoholic liver injury were successfully predicted. This study lays a foundation for the selection of drugs to treat alcoholic liver injury.

1. Introduction

The term “network pharmacology” was coined by Hopkins, a British pharmacologist, in 2007. Network pharmacology is based on systems biology and bioinformatics, and the concept is used to understand the relationship between drugs and the body from the perspective of network balance [1]. This multi-level research strategy is consistent with the holistic principle of compound Chinese medicine [2]. Thus, this approach provides a new means for studying the pharmacodynamics of substances and the mechanisms of traditional Chinese medicine (TCM) or compound Chinese medicine [3]. The therapeutic principles of TCM involve determining how and which ingredients target a disease process, as well as the synergistic characteristics of interactions among constituents [4]. Systematic elucidation of a component’s mechanism of action and the molecular basis of such an action through traditional pharmacology is difficult. Thus, new research methods and network pharmacology-based analyses have gained attention, as they can better reveal the molecular mechanisms of Chinese medicine or Chinese medicinal compounds in treating diseases. Consequently, using such approaches to understand the pharmacological mechanisms of TCM components is currently a major goal of traditional Chinese medicine research [5].
Alcoholic liver injury is a liver lesion caused by excessive alcohol intake into the human body [6]. Cirrhosis and its complications are the main contributors to the high mortality associated with alcoholic liver disease. Hepatic fibrosis is an early reversible stage of cirrhosis, during which prevention and treatment measures can be taken to inhibit the occurrence and development of cirrhosis [7]. At present, western medicine mainly focuses on liver protection, nutritional support, and liver transplantation as part of the therapeutic strategies for alcoholic liver disease; however, there is no ideal and effective treatment [8]. Although acute alcoholic liver injury is not known in TCM, it is classified as “alcoholic injury,” “alcoholic jaundice,” “alcoholic addiction,” “hypochondriac,” and other diseases according to its etiology, pathology, and clinical characteristics.
G. uralensis contains many active ingredients. Yang et al. [9] have reported that glycyrrhetinic acid can reduce the hepatotoxicity induced by reverse transcription factors, titanium dioxide nanoparticles, and cyclophosphamide and can protect against liver injury induced by carbon tetrachloride. The reported hepatoprotective mechanisms of glycyrrhetinic acid are mainly attributed to the induction of antioxidant defense and inhibition of the inflammatory response and cytochrome P450 expression. Zhang et al. [10] have reported that in mice gavaged glycyrrhetinic acid oral emulsion (GTS-LE), the activities of alanine aminotransferase and aspartate aminotransferase were significantly inhibited, the content of malondialdehyde was reduced, and the hepatocyte structure tended to be normal. Ma et al. [11] have reported that the reduction in liver toxicity by processing Tripterygium wilfordii with licorice may be related to the inhibition of activation of the HMGB1 inflammatory pathway. Zhang [12] reported that administering silibinin meglumine tablets combined with compound glycyrrhizin tablets can effectively relieve the clinical symptoms of patients with fatty liver and enhance the therapeutic effect. Mou et al. [13] have reported that in an idiosyncratic drug-induced liver injury model mediated by lipopolysaccharide, exposure of mice to lipopolysaccharide and an antidepressant paroxetine triggered aberrant inflammasome activation and induced specific hepatotoxicity, which can be ameliorated by licorice chalcone pretreatment.
The above findings indicate that Glycyrrhiza uralensis has a role in the treatment of liver disease to some extent, and we assumed that G. uralensis also has a good therapeutic effect against alcoholic liver injury. In this study, network pharmacology and molecular docking approaches were applied to elucidate the molecular mechanism of action of the licorice species G. uralensis in the treatment of chronic alcoholic liver injury. Consequently, we screened the active ingredients and targets of licorice and explored the multiple components of the plant, targets, links, and approaches for the treatment of alcoholic liver damage. The findings can provide a useful reference for follow-up studies to expand the treatment options and understand the pathological mechanisms of alcoholic liver disease.

2. Materials and Methods

2.1. Screening and Application of the Main Active Ingredients and Targets of G. uralensis

Screening of active ingredients was initiated by searching the TCM systems pharmacology database and analysis platform (TCMSP) (TCMSP. Available online: https://tcmspw.com/index.php (accessed on 23 May 2022)), using “licorice” as the search term to determine licorice ingredients and targets [10], according to oral bioavailability (30% or higher) and drug class (0.18 or higher) screening criteria for the effective components. The obtained protein targets were standardized after library retrieval via the Drug Bank (Drug Bank. Available online: https://go.drugbank.com/ (accessed on 23 May 2022)) and Uniprot (Uniprot. Available online: https://www.uniprot.org/ (accessed on 23 May 2022)) databases. Repeated or invalid data were deleted [14].

2.2. Screening of Alcoholic Liver Injury Targets

Screening of gene targets was initiated by searching the GeneCards (GeneCards. Available online: https://www.genecards.org/ (accessed on 25 May 2022)) and OMIM (OMIM. Available online: https://omim.org/ (accessed on 25 May 2022)) databases using the keyword “alcoholic liver injury” for retrieval [15]. These data were analyzed to determine the disease targets of alcoholic liver injury for the subsequent analyses.

2.3. Selection of Intersecting Targets and Venn Diagram Creation for G. uralensis Treatment and Alcoholic Liver Injury

The intersecting components of G. uralensis and the alcoholic liver injury disease targets were selected. Next, the Venn Diagram installation package of R software4.0.5 (http://www.r-project.org/ (accessed on 26 May 2022)) [16] was used to draw the Venn diagram for the intersecting targets.

2.4. Construction of a Component–Target Network of G. uralensis and Alcoholic Liver Injury

The component–target network of G. uralensis and alcoholic liver injury was constructed on the Cytoscape3.8.0 platform [17] after the intersecting components–targets of G. uralensis and alcoholic liver injury were identified.

2.5. Protein–Protein Interaction (PPI) Network Construction

The target data of the intersecting licorice and alcoholic liver injury component–target network analysis were analyzed using the String platform (String platform. Available online: https://string-db.org/ (accessed on 27 May 2022)) [18], with the following parameter settings: the species was set to human, the minimum interaction threshold was set to 0.4, free proteins were hidden, and other parameters were kept at default settings [19]. Graphs were then saved, PPI interaction data files were downloaded, and R software was used to select the 30 genes in front of the nodes to draw the bar graph of the PPI network key protein nodes [20].

2.6. Gene Ontology (GO) Analysis

To understand G. uralensis target gene functions, GO analysis was performed, which is widely used to annotate the biological functions of genes and gene products [21]. The GO analysis involves three aspects: cell components (CC), molecular functions (MF), and biological processes (BP). The target list was imported into the David6.8 database for GO analysis [22] for the resultant functional analysis of the targets. Results with p < 0.01 were considered statistically significant [23].

2.7. Kyoto Encyclopedia of Genes and Genomes (KEGG) Analysis

To explain how G. uralensis targets function in biological pathways, KEGG pathway enrichment analysis was performed. The KEGG is a knowledge base for functional analyses; it is used to analyze the relationship between genes and biological pathways [24]. Clue Go, a Cytoscape3.6.1 plug-in, was used to perform KEGG pathway analysis on the corresponding hits of screened licorice targets [25]. The threshold value was set as “Homo sapiens” and the analysis type was restricted to “KEGG,” limited to only the KEGG results with p < 0.01 [3].

2.8. Molecular Docking

After screening, the effective components of G. uralensis and the target proteins related to alcoholic liver injury were computer-simulated, and the proteins with better binding effects were screened by means of molecular docking. AutoDocktools-1.5.6 was used for protein and ligand pre-processing [26], AutoDock4.2.6 (http://autodock.scripps.edu/ (accessed on 29 May 2022)) was used for molecular docking, and PYMOL2.3.0 (https://github.com/schrodinger/ (accessed on 29 May 2022)) and Discovery Studio2020 (https://www.jb51.net/softs/576996.html (accessed on 30 May 2022)) were used for pre-processing and visual analysis [18].

3. Results

3.1. Collection and Screening of Licorice Active Ingredients

A total of 280 active ingredients of G. uralensis were found in the TCMSP database. The oral bioavailability value was used to screen the active ingredients: the higher the reaction value, the better the activity of the ingredients. Furthermore, the drug class value was used to screen the active ingredients, and a total of 92 compounds were obtained. The 92 active ingredients and targets were then screened with a script written using Perl computer programming language, and the names and serial numbers of drug targets were displayed. Based on the findings, the drug component names were transformed into gene labels through the Uniprot database. A total of 2507 drug targets were obtained from the database and 199 effective targets were eliminated after screening.

3.2. Screening of Genes Related to Alcoholic Liver Injury

Disease target genes were obtained from GeneCard and other databases, and a total of 8163 effective targets were processed.

3.3. Target Screening of Licorice Ingredients and Alcoholic Liver Injury Targets

Label information was separated from the target file of G. uralensis data, and then the subsequent label information corresponding to alcoholic liver injury was separated from GeneCard data. The Venn diagram of the corresponding drug–disease data revealed 193 common targets of G. uralensis ingredients and liver injury (Figure 1).

3.4. Construction of the Component–Target Network of G. uralensis and Alcoholic Liver Injury

A script was written using Perl computer programming language to generate the lines corresponding to the active components of drugs and diseases based on the drug–disease data [27]. Cytoscape was used for data visualization, and the interaction network of G. uralensis chemical components–targets was displayed and analyzed. The results showed that 87 nodes and 1337 connections were available. Among them, 87 nodes represented 87 protein targets and 1337 edges represented the interaction between 1337 pairs of proteins. The top 10 targets in this network diagram were PTGS2, ESR1, AR, NOS2, PPARG, GSK3B, ESR2, PRSS1, CCNA2, and MAPK14, with the same number of target molecules in alphabetical order (Figure 2).

3.5. Construction of the PPI Network

The alcoholic liver injury–licorice interacting proteins were imported into the String PPI analysis database, and the human database was selected for reference. After adjustment, the score (confidence) was set to >0.4, free nodes were hidden, and the PPI node graph was generated. A total of 1328 protein interaction wires were generated from the PPI database based on the available data. The results were then enriched and analyzed using R. Among them, the top 20 targets with the strongest interactions were JUN, MAPK3, STAT3, AKT1, MAPK1, RELA, FOS, ESR1, IL6, MAPK14, RXRA, MAPK8, MYC, EGFR, NCOA1, CCND1, IL10, PRKCA, RB1, and STAT 1 (Figure 3).

3.6. Enrichment of GO Functional Data

The GO enrichment analysis generates a finite acyclic graph that counts the number or determines the composition of proteins or genes at a certain functional level, including three branches: BP, MF, and CC. The BiocManager data package for R language was used to transform the disease and drug target data and to display their gene IDs, which was convenient for the subsequent data enrichment analysis [28]. The corresponding data package was installed in R language, the corresponding parameters were set, and the function of GO gene was enriched; the results are displayed in bar and bubble charts. Perl computer program language was used to screen the GO enrichment results to obtain functional analysis of the corresponding genes. A total of 188 GO terms were obtained through GO functional enrichment analysis in R language (p < 0.05). As shown in Figure 4, the highest-ranked predicted targets of G. uralensis and alcoholic liver injury corresponded to a response to xenobiotic stimulus, lipopolysaccharide stimulus, and molecules of bacterial origin. DNA-binding, transcription factor-binding, and RNA polymerase II-specific DNA-binding transcription factors were among the enriched MF terms. Raft and micro-domains of membranes accounted for a large proportion of the CC terms (Figure 5).

3.7. KEGG Pathway Enrichment

The data obtained in the previous steps were sorted, the effective genes and proteins were compared using R and Perl languages, and then the signaling pathway map was constructed. A total of 178 signaling pathways were obtained using KEGG pathway enrichment screening (p < 0.05). Pathways with the highest number of targets involved were enriched in lipid and atherosclerosis, chemical carcinogenic receptor activation, Kaposi sarcoma-associated herpesvirus infection, hepatitis B, and human cytomegalovirus infection. The top 20 related pathways are shown in Figure 6 and the associated bubble plot is shown in Figure 7.

3.8. Molecular Docking

To provide further evidence for the potential therapeutic effect of G. uralensis in alcoholic liver injury, the top 10 active components and the top 10 targets with high intermediate values in the 2.5-section of the PPI network were selected. Computer simulation was carried out on the treated small molecules and receptor proteins. AutoDock was used for molecular docking processing. Ten docking models with the associated minimum docking activation energy were obtained. The results showed that the lowest activation energy of docking between the molecule and the protein was less than −5 kcal/mol for all interactions, indicating that the docking effect between the molecule and the receptor protein was strong [29]. The docking model of the five molecules with the lowest activation energies were as follows: the binding energies of shinpterocarpin, 7-methoxy-2-methyl-isoflavone, and MAPK3 were −9.6 kcal/mol. The binding energies of isorhamnetin with MAPK1 and FOS were −9.3 and −9.6 kcal/mol, respectively, whereas that of formononetin with MAPK3 was −9.3 kcal/mol (Figure 8).

4. Discussion

The active components of G. uralensis can inhibit the metabolites of ethanol to a certain extent, and regulate the main toxicological enzymes in liver, so as to reduce the liver injury caused by ethanol metabolites. The above findings showed that 193 intersecting target genes were present between G. uralensis and alcoholic liver injury genes. The following five active components had the most targets: formononetin, 3′-methoxyglabridin, glypallichalcone, 1-methoxyphaseollidin, and glabridin. The main targets of the PPI network analysis were JUN, MAPK3, STAT3, AKT1, and MAPK1. Zhang et al. [30] showed that formononetin combined with the downregulation of Mir-4326 could inhibit the growth and invasion of HCC cells HCCLM3. Zhou et al. [31] have reported that glabridin substantially attenuated the pellet-forming ability of stem cells in the pellet-forming experiment of tumor stem cells. qRT-PCR showed that glabridin reduced the expression of stemness-related genes OCT4, NANOG, and SOX2 in cancer stem cells. Lv [32] showed that Lico A has a good protective effect on LPS/GalN-induced acute liver injury, which may be closely related to Nrf2 signaling and the activation of autophagy. These findings are in agreement with our conclusions to some extent, indicating the possibility that the active components of licorice may be used in the treatment of alcohol-induced liver injury. According to the net drug analysis of Lonicera by Liu et al. [8], its active components may reduce the level of inflammatory factors by inhibiting the expression of key targets of the MAPK signaling pathway, increase the activity of antioxidant enzymes, and inhibit oxidative stress response, in order to improve and prevent liver damage caused by alcohol. This is also consistent with our study findings to some extent, indicating that the symptoms of alcoholic liver can be improved by regulating the MAPK signaling pathway.
The highest-ranked predicted targets of G. uralensis and alcoholic liver injury corresponded to a response to xenobiotic stimulus, lipopolysaccharide stimulus, and molecules of bacterial origin. In addition, lipid and atherosclerosis, chemical carcinogenic receptor activation, and Kaposi sarcoma-associated herpesvirus were the main enriched pathways. We further investigated the possible interactions of these targets using molecular docking, which is a conformation with low ligand and receptor coordination to determine the stability of the ligand. We found that the binding energy of shinpterocarpin, 7-methoxy-2-methyl-isoflavone, and MAPK3 was −9.6 kcal/mol. The binding energies of isorhamnetin with MAPK1 and FOS were −9.3 and −9.6 kcal/mol, respectively, whereas that of formononetin with MAPK3 was −9.3 kcal/mol. These results indicated that the docking effect between the molecules and their corresponding receptor proteins was good.

5. Conclusions

In summary, based on our network pharmacology and molecular docking simulated analysis, we can infer that licorice may be used for the treatment of alcoholic liver injury due to its multiple components, targets, and means to achieve therapeutic efficacy. However, licorice has not been widely used for the treatment of alcoholic liver damage. If Glycyrrhiza licorice has to be formally used as a therapeutic drug, it still needs to be verified and explored in further trials. This study only provides a basis for the subsequent experiments and drug selection. Thus, the targets, pathways, and mechanisms of treatment should be subjected to further experimental validation in in-depth in vivo studies.

Author Contributions

L.Z.: conceptualization, writing and editing. S.X. and Z.G.: data analysis. X.Y. and Q.Z.: supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Xinjiang Production and Construction Corps Financial Technology Plan Project under Grant (2021CB042); Shihezi University Innovation and Development Special Project under Grant (CXFZ202107); Engineering Research Center for Production Mechanization of Oasis Special Economic Crop, Ministry of Education under Grant (PMOC2021A06).

Institutional Review Board Statement

Ethical review and approval were waived for this study due to no ethical issues involved, just based on software simulation analysis.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Venn diagram of Glycyrrhiza uralensis targets and alcoholic liver disease targets.
Figure 1. Venn diagram of Glycyrrhiza uralensis targets and alcoholic liver disease targets.
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Figure 2. Network analysis diagram of traditional Chinese medicine regulation.
Figure 2. Network analysis diagram of traditional Chinese medicine regulation.
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Figure 3. Construction of the PPI network between Glycyrrhiza uralensis and alcoholic liver disease targets.
Figure 3. Construction of the PPI network between Glycyrrhiza uralensis and alcoholic liver disease targets.
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Figure 4. GO functional analysis of the potential targets for the treatment of alcoholic liver disease by Glycyrrhiza uralensis.
Figure 4. GO functional analysis of the potential targets for the treatment of alcoholic liver disease by Glycyrrhiza uralensis.
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Figure 5. Bubble chart of GO functional analysis of the potential targets for the treatment of alcoholic liver disease with Glycyrrhiza uralensis.
Figure 5. Bubble chart of GO functional analysis of the potential targets for the treatment of alcoholic liver disease with Glycyrrhiza uralensis.
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Figure 6. KEGG enrichment analysis of the potential targets for the treatment of alcoholic liver disease with Glycyrrhiza uralensis.
Figure 6. KEGG enrichment analysis of the potential targets for the treatment of alcoholic liver disease with Glycyrrhiza uralensis.
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Figure 7. KEGG enrichment bubble diagram of potential targets for the treatment of alcoholic liver disease with Glycyrrhiza uralensis.
Figure 7. KEGG enrichment bubble diagram of potential targets for the treatment of alcoholic liver disease with Glycyrrhiza uralensis.
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Figure 8. Schematic representation of molecular docking. (A): The docking activation energy of MAPK3 and shinpterocarpin is −9.8 kcal/mol; (B): The docking activation energy of FOS and isorhamnetin is −9.6 kcal/mol; (C): The docking activation energy of MAPK1 and 2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol is −9.6 kcal/mol; (D): The docking activation energy of MAPK3 and 7-Methoxy-2-methyl-isoflavone is −9.6 kcal/mol; (E): The docking activation energy of MAPK1 and isorhamnetin is −9.3 kcal/mol.
Figure 8. Schematic representation of molecular docking. (A): The docking activation energy of MAPK3 and shinpterocarpin is −9.8 kcal/mol; (B): The docking activation energy of FOS and isorhamnetin is −9.6 kcal/mol; (C): The docking activation energy of MAPK1 and 2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol is −9.6 kcal/mol; (D): The docking activation energy of MAPK3 and 7-Methoxy-2-methyl-isoflavone is −9.6 kcal/mol; (E): The docking activation energy of MAPK1 and isorhamnetin is −9.3 kcal/mol.
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Zhu, L.; Xie, S.; Geng, Z.; Yang, X.; Zhang, Q. Evaluating the Potential of Glycyrrhiza uralensis (Licorice) in Treating Alcoholic Liver Injury: A Network Pharmacology and Molecular Docking Analysis Approach. Processes 2022, 10, 1808. https://doi.org/10.3390/pr10091808

AMA Style

Zhu L, Xie S, Geng Z, Yang X, Zhang Q. Evaluating the Potential of Glycyrrhiza uralensis (Licorice) in Treating Alcoholic Liver Injury: A Network Pharmacology and Molecular Docking Analysis Approach. Processes. 2022; 10(9):1808. https://doi.org/10.3390/pr10091808

Chicago/Turabian Style

Zhu, Lichun, Shuangquan Xie, Zhihua Geng, Xuhai Yang, and Qian Zhang. 2022. "Evaluating the Potential of Glycyrrhiza uralensis (Licorice) in Treating Alcoholic Liver Injury: A Network Pharmacology and Molecular Docking Analysis Approach" Processes 10, no. 9: 1808. https://doi.org/10.3390/pr10091808

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