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Article

Exploring the Molecular Mechanism of Zhi Bai Di Huang Wan in the Treatment of Systemic Lupus Erythematosus Based on Network Pharmacology and Molecular Docking Techniques

by
Yanping Zhuang
1,†,
Xuan Zhang
1,†,
Simin Luo
1,
Fangzhi Wei
1,
Yitian Song
1,
Guiling Lin
1,
Minghui Yao
2 and
Aimin Gong
1,*
1
Department of Traditional Chinese Medicine, Hainan Medical University, Haikou 571199, China
2
Shanghai Key Laboratory of Health Identification and Evaluation, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
*
Author to whom correspondence should be addressed.
†
These authors have contributed equally to this work and share first Authorship.
Processes 2022, 10(10), 1914; https://doi.org/10.3390/pr10101914
Submission received: 27 August 2022 / Revised: 16 September 2022 / Accepted: 17 September 2022 / Published: 21 September 2022
(This article belongs to the Special Issue Network Pharmacology Modelling for Drug Discovery)
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Abstract

:
Objective: To investigate the molecular mechanism and simulated validation of Zhi Bai Di Huang Pill (ZBDHP) for the treatment of systemic lupus erythematosus (SLE) using network pharmacology and molecular docking techniques. Methods: The active ingredients of ZBDHP were obtained through the TCMSP database and the Canonical SMILES of the active ingredients were queried through Pubchem. The targets of the active ingredients were predicted in the SwissTarget database based on the SMILES. The SLE-related disease targets were obtained through the GeneCards, OMIM and DisGenets databases, and the intersection targets of ZBDHP and SLE were obtained using the Venny 2.1.0 online platform. Intersection targets build a visual protein interaction network (PPI) through the STRING database, and the core targets were identified by network topology analysis. GO analysis and KEGG pathway enrichment analysis of the intersecting targets were performed using the DAVID database. Finally, the molecular docking of the first four active ingredients and the first four core target genes were verified by Pubchem, the PDB database and CB-Dock online molecular docking technology. Results: ZBDHP screened 91 potential active ingredients and 816 potential targets. Among them, 141 genes were intersected by ZBDHP and SLE. The network topology analysis showed that the main active ingredients were Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, etc., and the key target genes were TNF, AKT1, EGFR, STAT3, etc. GO and KEGG enrichment analysis showed that common targets interfere with biological processes or molecular functions such as signal transduction protein phosphorylation, inflammatory response, transmembrane receptor protein tyrosine kinase activity, etc., through multiple signaling pathways, such as pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, the PI3K-Akt signaling pathway, lipid and atherosclerosis, hepatitis B, etc. Molecular docking results showed that the active components of ZBDHP have good binding activity to the core targets of SLE. Conclusions: This study reveals that the ZBDHP treatment of SLE is a complex mechanistic process with multi-components, multi-targets and multi-pathways, and it may play a therapeutic role in SLE by inhibiting the production, proliferation and apoptosis of inflammatory factors. In conclusion, the present study provides a theoretical basis for further research on ZBDHP.

Systemic lupus erythematosus (SLE) is a typical autoimmune disease. The lesions can involve multiple tissues, organs and systems [1,2]. Compared to Europe and other Asian countries, China has a higher prevalence of SLE, about 1/1000 [3,4,5], and presents with a more severe condition. Adverse complications such as lupus nephropathy, coronary arteriosclerosis, and thrombocytopenia occur later in life, and the mortality rate is 2~2.9 times higher than normal [6,7,8]. The treatment of SLE mostly uses hormones, antimalarials, immunosuppressants, non-steroidal anti-inflammatory drugs and other symptomatic drugs, but all these drugs can cause different degrees of side effects, and long-term use will cause irreversible damage to the body, such as osteoporosis, premature ovarian failure, etc. [9,10,11]. The efficacy of TCM on SLE is precise, and the combination of Chinese and Western medicine in the treatment of this disease can significantly reduce the hormone-like side effects [12,13] and improve the survival rate and survival quality of SLE patients.
According to Chinese medicine, the kidney is the root of the innate nature and the liver is homologous with it; if the kidney is damaged, the liver will be affected by it. Since ancient times, most doctors believe that the cause and mechanism of SLE lies in congenital deficiency of endowment, deficiency of the liver and kidney, and the imbalance of yin and yang, thus making the righteousness unable to protect the outside, and the external evil attacking and causing the disease to occur due to the combination of internal and external evil. Zhibai Dihuang Pill (ZBDHP) is a common clinical formula for nourishing kidney yin, which can nourish yin and lower fire, and is often used in the treatment of SLE with significant clinical efficacy. This formula is from the ancient medical book “Medical Formulas Kao” and consists of eight herbs: Anemarrhenae Rhizoma (Zhimu, ZM, 6 g), Rehmanniae Radix Praeparata (Shudihuang, SDH, 24 g), Phellodendri Chinrnsis Cortex (Huangbai, HB, 6 g), Cornus Officinalis Sieb. Et Zucc (Shanzhuyu, SZY, 12 g), Rhizoma Dioscoreae (Shanyao, SY, 12 g), Cortex Moutan (Mudanpi, MDP, 9 g), Poria Cocos Wolf (Fuling, FL, 9 g), and Alisma Orientale Juz (Zexie, ZX, 9 g), forming a multi-component, multi-target, and multi-pathway mechanism feature.
Network pharmacology is based on the theory of systems biology to explore the mechanism of action of multiple target drug molecules for the treatment of diseases under a holistic concept. Molecular docking technology is a new technology, based on computer analysis, which mainly studies the interactions between drug molecules, and thus simulates the binding pattern and affinity between molecules, which can greatly improve the efficiency and success rate of drug development. The complementary advantages of the two can reveal the mechanism of action of the herbal compound for the treatment of diseases more completely, and provide a convenient way to explore the active ingredients and mechanism of action of the compound. Therefore, this study will use the network pharmacology approach to find the main active ingredients and core targets of ZBDHP for the treatment of SLE. It will also use molecular docking technology to simulate the binding activity of active ingredients and core targets to provide a theoretical basis for exploring the mechanism of action of ZBDHP for the treatment of SLE. The specific flow chart is shown in Figure 1 below.

1. Materials and Methods

1.1. Collection of Potential Active Ingredients and Relevant Targets of ZBDHP

The screening conditions were oral bioavailability (OB) ≥ 30% and drug-like (DL) ≥ 0.18, and the potential active ingredients of ZBDHP were screened in the TCM Systematic Pharmacology Database and Analysis Platform (TCMSP, https://tcmsp-e.com/, accessed on 1 August 2022). The collected components were used to obtain Canonical SMILES through Pubchem (https://pubchem.ncbi.nlm.nih.gov/, accessed on 1 August 2022) and the targets of the active ingredients were predicted by the SwissTargetPrediction database (http://www.swisstargetprediction.ch/, accessed on 1 August 2022). The results were integrated and duplicate values were removed, and the targets with prediction scores greater than 0.1 were taken as the targets of ZBDHP active ingredients.

1.2. Collecting Targets for SLE and Constructing Veny Diagrams

Using “Systemic lupus erythematosus” as the search term, target genes associated with SLE were retrieved through GeneCards (https://www.genecards.org/, accessed on 3 August 2022), OMIM (https://www.omim.org/, accessed on 3 August 2022) and DisGeNET (https://www.disgenet.org/, accessed on 3 August 2022) databases. Among them, the Genecards database and DisGeNET database were selected for disease targets with scores greater than 10 and 0.1, respectively. The screened targets were pooled and duplicate values were removed to obtain the target genes for SLE. The targets of ZBDHP active ingredients and disease targets of SLE were imported into the online mapping tool Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/, accessed on 3 August 2022) to obtain the Venn diagram of drugs and diseases and common target genes.

1.3. Construction of Protein Interaction Networks

Interactions between the above intersecting targets were analyzed by the STRING database (https://string-db.org/, accessed on 3 August 2022). The “Organism” was selected as “Homo sapiens”, and the “confidence score was set to” was set to ≥ 0.4 by default, hiding the targets that did not produce association in the network, and then obtaining the protein interaction network (PPI) map. The PPI (“tsv” format) was opened with Cytoscape 3.8.2 visualization software (https://www.cytoscape.org/, accessed on 3 August 2022), and the parameters in the PPI network were analyzed to obtain the key targets of ZBDHP for SLE treatment.

1.4. Construction and Drug-Component-Common Target Gene Interaction Network

The herbs, active ingredients and intersection targets in ZBDHP were imported into Cytoscape 3.8.2 software to construct the drug-ingredient-intersection target network graph. The core active ingredients in the network were analyzed according to the degree values of the ingredients.

1.5. GO Functional Analysis and KEGG Pathway Enrichment Analysis

The obtained intersection targets were analyzed by the DAVID database (https://david.ncifcrf.gov/, accessed on 6 August 2022) for Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. “Select Identifier” selects “OFFICIAL_GENE_SMBOL”, “Select species” selects “Homo sapiens”, “List Type” selects “Gene List”, and other parameters are default. After filtering, the results of BP, CC and MF are “GOTERM_BP_ DIRECT”, “GOTERM_CC_DIRECT” and “GOTERM_MF_ DIRECT” in the drop-down list of “Gene_Ontolog”, and the results of KEGG are “KEGG_PATHWAY” in the drop-down list of “Pathways”. After the data results were integrated, the eligible data were filtered by p < 0.05, and then sorted by Count from largest to smallest. GO analysis selected the top 10 for enrichment analysis. In addition, KEGG analysis selected the top 20 for enrichment analysis, and the obtained results were used for the production of enrichment bubble plots through the Microsun online platform (http://www.bioinformatics.com.cn/, accessed on 6 August 2022).

1.6. Molecular Docking Validation

Based on the results of PPI analysis, the top five core targets were selected, and the structure files of the core targets were downloaded from the Protein Data Bank (PDB) (https://www.rcsb.org/, accessed on 7 August 2022) database (with the file name suffix “pdb”). We then selected the top five active ingredients from the “drug-component-target” network diagram according to the degree-value and downloaded the tertiary structure files (the suffix name is “sdf”) of the core active ingredients from the Pubchem database. The core target and the core active ingredient were docked and visualized by CB-Dock online molecular docking technology (http://cao.labshare.cn/cbdock//, accessed on 7 August 2022), with the active ingredient as the ligand and the core target protein as the receptor.

2. Results

2.1. Active Ingredients and Targets of ZBDHP

After screening and integrating and removing duplicates in the TCMSP database, a total of 112 potential active ingredients of ZBDHP were obtained. Among them, the numbers of active ingredients of ZM, SDH, HB, SZY, SY, MDP, FL and ZX were 15, 2, 36, 20, 16, 11, 15 and 10, respectively. The obtained active ingredients were converted into Canonical SMILES by the Pubchem database, and then the targets corresponding to ZBDHP active ingredients were predicted by the SwissTargetPrediction database. The active ingredients that could not be converted into Canonical SMILES and could not be predicted targets were deleted, leaving 91 potential active ingredients. Among them, the number of potential active ingredients for ZM, SDH, HB, SZY, SY, MDP, FL, and ZX were 10, 2, 30, 15, 11, 6, 12, and 8, respectively. The targets with prediction scores greater than 0.1 were selected as drug targets. A total of 816 targets were obtained as the targets of ZBDHP active ingredients.

2.2. Collecting Common Targets of Components and Diseases

The SLE-related targets were obtained from the GeneCards, OMIM and Disgenets databases, and 787, 5, and 26 targets were obtained, respectively. Seven hundred and eighty-eight related targets of SLE were obtained after removing duplicate targets. The targets of ZBDHP active ingredients and the related targets of SLE were imported into the Venny 2.1.0 tool. Next, 141 intersecting targets of ZBDHP-SLE were obtained, which are the targets of ZBDHP action for the treatment of SLE. The visualization of the drug-disease Venn diagram is shown in Figure 2 below.

2.3. Constructing the Interaction Network of Chinese Herbal Medicine—Active Ingredient—Intersection Target

The 91 active ingredients and 141 drug-disease intersection targets in ZBDHP were imported into Cytoscape 3.8.2 software, and the interaction network of “Chinese medicine—active ingredient—intersection target” was drawn, and the results are shown in Figure 3, which contain 240 nodes and 1346 edges. The network topology was then analyzed, and the top 10 active ingredients were ranked according to the degree value of the active ingredients, including Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, niloticin, palmatine, etc., as shown in Table 1 below. The higher the degree value, the greater the number of nodes in the network that interact directly with the node, and thus the greater the importance of the node in the network. In addition, the ingredients in Table 1 are from SZY, ZX, ZM, FL, HB, SY, and MDP, where kaempferol is the active ingredient common to both ZM and MDP.

2.4. Construction and Analysis of PPI Network

The 141 intersecting targets were imported into the String database for protein interaction network analysis, and the obtained results are shown in Figure 4, with 140 nodes and 2127 edges; each circular node represents a gene or protein, and the connecting lines represent the interaction relationship between two nodes. The nodes were arranged by degree value from largest to smallest, and the top 20 targets were taken to make a histogram, as shown in Figure 5. The top 10 targets were TNF, AKT1, EGFR, STAT3, CTNNB1, MAPK3, CASP3, HSP90AA1, HIF1A, and TLR4, which indicated that ZBDHP might be treating SLE through these targets.

2.5. GO and KEGG Enrichment Analysis

2.5.1. KEGG Enrichment Results

A KEGG pathway analysis was performed on 141 common targets through the DAVID database, and a total of 157 KEGG signaling pathways were obtained. One hundred and fifty-three signaling pathways were obtained by using p < 0.05 as the screening condition, and were then sorted by count value from largest to smallest, and the top 20 signaling pathways were enriched and analyzed through the Microbiology online platform, the results of which are shown in Figure 6. The major enriched signaling pathways are Pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, PI3K-Akt signaling pathway, Lipid and atherosclerosis, Hepatitis B, Protonema spp. MAPK signaling pathway, Proteoglycans in cancer, Prostate cancer, Coronavirus disease—COVID-19, Ras signaling pathway, etc. In the figure below, −log10 (p-value) represents the significance of enrichment. The redder the color, the stronger the significance of enrichment. The size of the circles in the graph represents the number of genes enriched in this pathway. The larger the circle, the more genes that are enriched. We selected the PI3K-Akt signalling pathway that is relevant to the disease mechanism and ranked high to produce the KEGG signalling pathway (Figure 7). The red ones are the core targets, which are distributed in the upstream and downstream of this pathway.

2.5.2. Go Analysis

A GO analysis of 141 common targets was performed by the DAVID database to obtain data of three components: biological process (BP), cellular component (CC) and molecular function (MF). Each item was then filtered with p < 0.05 to find the eligible data, and the top 10 were sorted by count value from largest to smallest to create an enrichment bubble plot on the microbiological letter platform (Figure 8). A total of 616 biological processes, 76 cellular components and 126 molecular functions were obtained from GO enrichment analysis. From Figure 8 we can see that the top five ranked BPs are signal transduction, the positive regulation of transcription from RNA polymerase II promoter, protein phosphorylation, inflammatory response, positive regulation of cell proliferation. The top 5 CCs are plasma membrane, cytoplasm, nucleus, cytosol, and the integral component of membrane. The top 5 MFs are protein binding, identical protein binding, ATP binding, transmembrane receptor protein tyrosine kinase activity, and protein tyrosine kinase activity.

2.6. Molecular Docking Validation

The top five key genes in PPI analysis (Figure 5) and the top five core active ingredients in ZBDHP with degree values (Table 1) were taken for molecular docking simulation validation, as these targets and ingredients may be key to ZBDHP for the treatment of SLE. The results of molecular docking are shown in Table 2 and Figure 9 below. The docking binding energy of the five core targets NF, AKT1, EGFR, STAT3, CTNNB1 and the five core active ingredients Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, niloticin, CTNNB1 are all less than −5 kcal/mol [9,10], indicating that these active ingredients in ZBDHP have strong binding ability to the key targets of the disease, and ZBDHP may be effective in treating SLE through the binding of these ingredients to the key targets.

3. Discussion

SLE is an autoimmune disease that occurs mostly in women of childbearing age. There is no cure for it, and it can only be treated symptomatically by medication, but purely western medicine has certain side effects [11,12,13]. Traditional Chinese medicine or a combination of Chinese and Western medicine can not only achieve the same therapeutic effect, but also greatly reduce the toxic side effects of drugs [14,15]. The name “Systemic lupus erythematosus” is not recorded in traditional Chinese medicine, but according to its clinical manifestations, it can be classified as “yin-yang toxicity”, “red butterfly sore”, and “paralysis” and “sunburn”, etc. Ancient Chinese medicine experts believe that the root cause of SLE lies in the congenital deficiency of endowment and deficiency of liver and kidney. ZBDHP, however, is a formula commonly used clinically for the treatment of SLE and has the effect of nourishing kidney yin. ZBDHP has remarkable clinical efficacy, but its therapeutic mechanism is not yet clear. Therefore, this study used network pharmacology and molecular docking techniques to investigate the possible mechanisms of action of ZBDHP for the treatment of SLE.
ZBDHP was screened by the TCMSP database with 91 potential active ingredients. Among them, the number of potential active ingredients for ZM, SDH, HB, SZY, SY, MDP, FL, and ZX were 10, 2, 30, 15, 11, 6, 12, and 8, respectively. Further screening of targets of action based on these active ingredients yielded 816 potential targets. Among them, the top degree-values include Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, niloticin, etc. Most of these active ingredients are related to anti-inflammation, anti-cancer and immunomodulation. For example, Hydroxygenkwanin has anti-inflammatory, antibacterial, antitumor, and immunomodulatory effects [16,17]. Available studies [18,19] have shown that this active ingredient has good anti-inflammatory effects in both in vitro and in vivo experiments, significantly reducing inflammatory factors such as TNF-α and IL-6. In addition, Hydroxygenkwanin inhibits the levels of overexpressed EGFR and downstream STAT3 and AKT in tumor cells, thus exerting an anti-tumor effect [19]. Alisol B has the function of anti-complement activity [20], and complement plays an important role in the immune injury of kidney. It has been shown that Alisol B can inhibit C3a-mediated mesenchymal transdifferentiation of human renal tubular epithelial cells and has a protective effect on the kidney in immune injury [21]. In addition, Alisol B also inhibits the formation of osteoblasts [22]. The NF-κB pathway is the main oxidative stress response pathway, and Cerevisterol inhibits NF-κB activation by inhibiting NF-κB translocation from cytoplasm to nucleus [23]. Thus, we can find that ZBDHP intervenes in the inflammatory response and immune regulation through a multi-component and multi-pathway approach.
Subsequently, we intersected the targets of ZBDHP with the targets of SLE and obtained 141 intersecting targets. Then, according to the protein interaction network analysis, we found that the targets were closely linked to each other. Among them, the top 20 were TNF, AKT1, EGFR, STAT3, CTNNB1, MAPK3, CASP3, HSP90AA1, HIF1A, TLR4, MMP9, ERBB2, ESR1, IL2, MTOR, PTPRC, PTGS2, ICAM1, PPARG, and JAK2. Most of these targets were associated with inflammatory response, immune regulation, etc. For example, tumor necrosis factor (TNF) is an important pro-inflammatory cytokine with immunomodulatory effects and can be directly involved in apoptosis [24]. It has an important role in the pathogenesis of rheumatic diseases such as rheumatoid arthritis, spondyloarthropathy or SLE [25]. TNF can increase the viability of T cells and neutrophils, stimulate monocytes and macrophages to secrete the inflammatory factor IL-1 and also activate NF-κB signaling to produce IL-1β, IL-6 and IL-8, etc. At the same time, immune complexes in SLE patients can induce macrophages to produce high levels of TNF-α. TNF is an important growth factor for B lymphocytes, and B lymphocytes can produce large amounts of TNF, forming a secretory cycle between the two [26]. Such a vicious cycle can lead to the increased severity of the disease. Clinical studies have shown that high levels of TNF-α expression are positively correlated with disease activity, especially in patients with lesions involving the kidney [27,28,29]. Thus, anti-TNF therapy should be beneficial for SLE patients, but some studies have shown that long-term TNF blockade therapy may increase the risk of serious adverse events. This isecause TNF is again part of the immune defense, involved in the development, differentiation and regulation of immune cells. As part of the defense mechanism, long-term blockade is instead detrimental to the recovery of SLE patients [30,31,32].
AKT1 (a serine/threonine kinase) is a core factor on the PI3K/AKT signaling pathway and plays an important role in B cell differentiation and T cell homeostasis. Studies have shown that AKT kinase and the corresponding AKT1 gene expression are significantly upregulated in SLE patients [33,34] and show correlations with the expression levels of various genes such as Th17 (RORC), Treg (TGFB2), IL-5, MAPK1 and so on. Moreover, upregulation of AKT1 can activate AKT/mTOR signaling pathway, which is involved in the pathogenesis of SLE. Therefore, S Chen [35] et al. suggested that AKT1 is a potential target for the treatment of SLE and verified that their conjecture was correct in an in vitro experiment.
Epidermal growth factor receptor (EGFR) is a transmembrane protein that stimulates cell growth and differentiation upon binding to specific ligands [36]. The expression of EGFR is significantly elevated in cancers such as gastric, breast, and bladder cancers, etc. [37,38]. Huang CM [39] et al. investigated the relationship between EGFR gene Bsr I polymorphisms and the disease in SLE patients in Taiwan, China, and found that polymorphisms of EGFR gene Bsr I were associated with certain developmental factors of SLE (e.g., photosensitivity), but none have been found to be associated with the severity of SLE. Bollee G [40,41] et al., also found that EGFR promotes the development of SLE and that lowering the level of this receptor may be beneficial for disease control.
STAT3 encodes a protein that is a member of the STAT protein family and plays a key role in cell proliferation, differentiation, migration, inflammation, and apoptosis [42]. Existing studies have shown that STAT3 gene expression is significantly upregulated in SLE patients [43], which promotes the expression of cytokines such as IL-10, IL-17A, IL-17, and IFN [43,44,45], and that the level of this gene expression correlates with disease activity. In addition, several scholars have found [44,45,46,47] that inhibition of STAT3 expression can lead to a decrease in inflammatory factors such as IL-10, IL-17A and so on. And this target may be a major target for the future treatment of SLE. In summary, we can find that the above targets are the key targets for the treatment of SLE, and ZBDHP is the treatment of SLE by regulating these targets. This reflects the characteristics of “multi-target” treatment in TCM.
In molecular docking simulations, we found that the binding energies of five core components (Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, niloticin) to four core targets (TNF, AKT1, EGFR, STAT3) were all <−5 kcal/mol [9,10], indicating a good binding affinity between these core components and the targets, further validating the reliability of our network pharmacology predictions.
GO enrichment analysis was performed on 141 intersecting targets, and we found that ZBDHP acts as a treatment for SLE by affecting signal transduction protein phosphorylation, inflammatory response, and transmembrane receptor protein tyrosine kinase activity, etc. In the KEGG enrichment analysis we found that the main signaling pathways involved in ZBDHP are Pathways in cancer, Kaposi sarcoma-associated herpesvirus infection, the PI3K-Akt signaling pathway, Lipid and atherosclerosis, Hepatitis B, MAPK signaling pathway, etc. According to previous scholars’ studies [48,49,50,51], we found that SLE has several signaling pathways showing abnormalities, such as the PI3K-Akt signaling pathway, the AKT-mTOR signaling pathway, the MAPK/ERK signaling pathway, etc. In this study, we selected the signaling pathway of PI3K-Akt, a signal mainly involved in ZBDHP, to make a signaling pathway map (Figure 7). From the figure, we can see that the intersecting genes of ZBDHP and SLE are widely distributed in this pathway, and the pathway contains the core targets of AKT1 and EGFR that we screened earlier. AKT1 activates AKT through PI3K, but AKT is the core factor in the PI3K/AKT signaling pathway, and its activation inevitably activates the PI3K/AKT signaling pathway. After the activation of this signaling pathway, the downstream mTOR is stimulated to play a key regulatory function in protein synthesis, cell growth, cell proliferation, immune function and many other processes. Previous studies have also shown [33,34,35] that the inhibition of PI3K or AKT1 gene expression in this pathway can be effective in the treatment of SLE.
In this study, we elucidated the main active division of ZBDHP for the treatment of SLE and the key targets of action. Further enrichment analysis of the intersecting genes revealed that the intersecting targets were involved in multiple signaling pathways, such as the PI3K-Akt signaling pathway and the AKT-mTOR signaling pathway, et al. It indicates that the ZBDHP treatment of SLE may be achieved through these pathways. Finally, we performed molecular docking validation of the five screened active ingredients and four targets, and the binding activity between each target was good, which further confirmed the reliability of our prediction.

4. Conclusions

In conclusion, this study initially investigated the mechanism of action of ZBDHP in the treatment of SLE based on network pharmacology and molecular docking techniques. The main active ingredients of ZBDHP are Hydroxygenkwanin, Alisol B, asperglaucide, Cerevisterol, niloticin, and so on. It is mainly used to treat SLE by regulating multiple signaling pathways such as PI3K-Akt through multiple targets such as TNF, AKT1, EGFR and STAT3, interfering with processes or functions such as signal transduction protein phosphorylation, inflammatory response and transmembrane receptor protein tyrosine kinase activity. In addition, we also verified that the active components of ZBDHP have good binding activity to the targets of SLE using the molecular docking technique. It shows that ZBDHP is therapeutic for SLE through a multi-component, multi-target and multi-pathway approach. This study provides a theoretical reference for further research on the mechanism of action of ZBDHP in the treatment of SLE, but its mechanism study still needs to be supported and validated by experimental studies.

Author Contributions

Conceptualization, methodology and software, Y.Z., X.Z. and A.G.; visualization, S.L., F.W. and Y.S.; data curation and formal analysis, Y.Z. and X.Z.; validation and investigation, G.L. and M.Y.; writing—original draft preparation, Y.Z., X.Z. and A.G.; supervision, project administration and writing—review and editing, A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The National Science Foundation of China, grant number 82160874; Hainan Provincial Science and Technology Department Key R&D Projects, grant number ZDYF2022SHFZ078; Hainan Province High Level Talent Project, grant number 2019RC206; Hainan postgraduate innovation and Entrepreneurship Project, grant number Qhys2021-359.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors thank the members of their laboratory and their collaborators for their research work. This article was funded by the National Natural Science Foundation of China (82160874), Hainan Provincial Science and Technology Department Key R&D Projects (ZDYF2022SHFZ078), Hainan Province High Level Talent Project (2019RC206) and Hainan postgraduate innovation and Entrepreneurship Project (Qhys2021-359).

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Figure 1. Flow chart of the study.
Figure 1. Flow chart of the study.
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Figure 2. Venn diagram of intersection targets of Zhibai Dihuang pill and SLE.
Figure 2. Venn diagram of intersection targets of Zhibai Dihuang pill and SLE.
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Figure 3. Chinese herbal medicine—active ingredient—intersection target. (Square: common target of active ingredient and disease; Round: drug active ingredient; Diamond: drug of ZBDHP).
Figure 3. Chinese herbal medicine—active ingredient—intersection target. (Square: common target of active ingredient and disease; Round: drug active ingredient; Diamond: drug of ZBDHP).
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Figure 4. PPI network of common targets of ZBDHP and SLE.
Figure 4. PPI network of common targets of ZBDHP and SLE.
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Figure 5. Top 20 Key Targets in PPI Network.
Figure 5. Top 20 Key Targets in PPI Network.
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Figure 6. KEGG pathway enrichment of 141 intersection targets. (The top 20 pathways were identified. Color represented p value and size of the spot represented count of genes).
Figure 6. KEGG pathway enrichment of 141 intersection targets. (The top 20 pathways were identified. Color represented p value and size of the spot represented count of genes).
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Figure 7. PI3K-Akt signaling pathway. (Red represents the intersection targets of ZBDHP against SLE in the signaling pathway).
Figure 7. PI3K-Akt signaling pathway. (Red represents the intersection targets of ZBDHP against SLE in the signaling pathway).
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Figure 8. The top 10 GO functional terms of 141 intersection targets.
Figure 8. The top 10 GO functional terms of 141 intersection targets.
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Figure 9. Molecular docking. ((A)—a, b, c, d, e: Hydroxygenkwanin-TNF, AKT1, EGFR, STAT3, CTNNB1) ((B)—a, b, c, d, e: Alisol B-TNF, AKT1, EGFR, STAT3, CTNNB1) ((C)—a, b, c, d, e: asperglaucide-TNF, AKT1, EGFR, STAT3, CTNNB1) ((D)—a, b, c, d, e: Cerevisterol-TNF, AKT1, EGFR, STAT3, CTNNB1) ((E)—a, b, c, d, e: niloticin-TNF, AKT1, EGFR, STAT3, CTNNB1).
Figure 9. Molecular docking. ((A)—a, b, c, d, e: Hydroxygenkwanin-TNF, AKT1, EGFR, STAT3, CTNNB1) ((B)—a, b, c, d, e: Alisol B-TNF, AKT1, EGFR, STAT3, CTNNB1) ((C)—a, b, c, d, e: asperglaucide-TNF, AKT1, EGFR, STAT3, CTNNB1) ((D)—a, b, c, d, e: Cerevisterol-TNF, AKT1, EGFR, STAT3, CTNNB1) ((E)—a, b, c, d, e: niloticin-TNF, AKT1, EGFR, STAT3, CTNNB1).
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Table
Table 1. Active ingredients of Zhibai Dihuang Pill. (top 10).
Table 1. Active ingredients of Zhibai Dihuang Pill. (top 10).
Mol IDMolecule NameDegreeOB (%)DLType
MOL005530Hydroxygenkwanin2836.470.27SZY
MOL000830Alisol B2834.470.82ZX
MOL001677asperglaucide2858.020.52ZM
MOL000279Cerevisterol2737.960.77FL
MOL002660niloticin2741.410.82HB
MOL000785palmatine2764.60.65HB
MOL000285(2R)-2-[(5R,10S,13R,14R,16R,17R)-16-hydroxy-3-keto-4,4,10,13,14-pentamethyl-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren- 17-yl]-5-isopropyl-hex-5-enoic acid2538.260.82FL
MOL000310Denudatin B2561.470.38SY
MOL000322Kadsurenone2554.720.38SY
MOL000422kaempferol2541.880.24ZM, MDP
Table
Table 2. Molecular docking results of active components and target genes of ZBDHP.
Table 2. Molecular docking results of active components and target genes of ZBDHP.
DockMolecule NameTargetBinding Energy
(kcal/mol)
MOL005530HydroxygenkwaninTNF−6.8
MOL000830Alisol BTNF−7.4
MOL001677asperglaucideTNF−6.2
MOL000279CerevisterolTNF−7.7
MOL002660niloticinTNF−7.5
MOL005530HydroxygenkwaninAKT1−6.6
MOL000830Alisol BAKT1−6.6
MOL001677asperglaucideAKT1−6.0
MOL000279CerevisterolAKT1−7.2
MOL002660niloticinAKT1−7.4
MOL005530HydroxygenkwaninEGFR−7.4
MOL000830Alisol BEGFR−7.8
MOL001677asperglaucideEGFR−6.6
MOL000279CerevisterolEGFR−8.3
MOL002660niloticinEGFR−7.8
MOL005530HydroxygenkwaninSTAT3−7.5
MOL000830Alisol BSTAT3−8.2
MOL001677asperglaucideSTAT3−7.5
MOL000279CerevisterolSTAT3−7.7
MOL002660niloticinSTAT3−7.4
MOL005530HydroxygenkwaninCTNNB1−8.2
MOL000830Alisol BCTNNB1−7.4
MOL001677asperglaucideCTNNB1−8.5
MOL000279CerevisterolCTNNB1−6.7
MOL002660niloticinCTNNB1−7.8
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Zhuang, Y.; Zhang, X.; Luo, S.; Wei, F.; Song, Y.; Lin, G.; Yao, M.; Gong, A. Exploring the Molecular Mechanism of Zhi Bai Di Huang Wan in the Treatment of Systemic Lupus Erythematosus Based on Network Pharmacology and Molecular Docking Techniques. Processes 2022, 10, 1914. https://doi.org/10.3390/pr10101914

AMA Style

Zhuang Y, Zhang X, Luo S, Wei F, Song Y, Lin G, Yao M, Gong A. Exploring the Molecular Mechanism of Zhi Bai Di Huang Wan in the Treatment of Systemic Lupus Erythematosus Based on Network Pharmacology and Molecular Docking Techniques. Processes. 2022; 10(10):1914. https://doi.org/10.3390/pr10101914

Chicago/Turabian Style

Zhuang, Yanping, Xuan Zhang, Simin Luo, Fangzhi Wei, Yitian Song, Guiling Lin, Minghui Yao, and Aimin Gong. 2022. "Exploring the Molecular Mechanism of Zhi Bai Di Huang Wan in the Treatment of Systemic Lupus Erythematosus Based on Network Pharmacology and Molecular Docking Techniques" Processes 10, no. 10: 1914. https://doi.org/10.3390/pr10101914

APA Style

Zhuang, Y., Zhang, X., Luo, S., Wei, F., Song, Y., Lin, G., Yao, M., & Gong, A. (2022). Exploring the Molecular Mechanism of Zhi Bai Di Huang Wan in the Treatment of Systemic Lupus Erythematosus Based on Network Pharmacology and Molecular Docking Techniques. Processes, 10(10), 1914. https://doi.org/10.3390/pr10101914

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