# Effects of Reducing the South and Reinforcing the North Method on Inflammatory Injury Induced by Hyperlipidemia

Authors: Hongjin Wu; Weiwei Dai; Libo Wang; Jie Zhang; Chenglong Wang
Journal: Evidence-Based Complementary and Alternative Medicine
 (2021)

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## Abstract

Inflammation is the pathophysiological basis of hyperlipidemia-related disease (HRD). Reducing the south and reinforcing the north method (RSRN) has a positive effect on HRD. However, the pharmacological mechanisms of RSRN are still unclear in the treatment of HRD. We obtained RSRN compounds from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and identified potential targets of these compounds through target fishing based on the TCMSP databases. Next, we identified the HRD targets by using multiple databases. Then, the overlapping genes between the RSRN potential targets and the HRD targets were used to establish a protein-protein interaction (PPI) network, and we further analyzed their interactions and identified the major hub genes in this network. Subsequently, the Metascape database was utilized to conduct the enrichment of Gene Ontology biological processes (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A total of 187 potential active components and 106 related core targets were obtained and identified overall. Then after the Metascape enrichment analysis, a total of 148 KEGG pathways were screened, which were mainly associated with AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, and NF-kappa B signaling pathway. Furthermore, 34 hub genes, such as AKT1, NF-κBp65(RELA), IκBα(CHUK), MAPK8, and MAPK14, CCND1, were considered potential therapeutic targets. Furthermore, evaluations of protein levels of NF-κBp65, IκBα, TNF-α, IL-1 ß, and IL-6 were performed for experimental validation. RSRN can reduce the expression of NF-κBp65 protein, increase the level of IκBα protein, and reduce the protein levels of TNF-α, IL-1β, and IL-6 in ovariectomized rats. The results indicate that the mechanism of RSRN against inflammation may be related to AKT1, NF-κBp65, IκBα, MAPK8, and MAPK14, as well as TNF, NF-kappa B, PI3K-Akt signaling pathways.


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## Body

## 1. Introduction

The decreased ovarian function and estrogen levels in menopausal women contributes to the increase in the prevalence of dyslipidemia, osteoporosis, and urinary tract infection, of which hyperlipidemia is the most insidious [1]. Previous studies have reported that hyperlipidemia could lead to atherosclerosis (AS), coronary heart disease (CHD), and Alzheimer's disease [2, 3]. There should be necessary interventions to be done for preventing the hyperlipidemia related diseases (HRD), thus to reduce the incidence rate and mortality of AS and CHD in middle-aged and elderly women.Traditional Chinese medicine (TCM) believes that the kidney is the congenital life basis. When women are in the menopausal period, kidney-Qi declines; Tiangui will be exhausted; Chong and Ren become deficient; essence and blood become insufficient; all these conditions can lead to imbalance between Yin and Yang and Zang-fu organs disorders, resulting in menopausal syndromes and hyperlipidemia-related diseases [4]. The pathogenesis of HRD belongs to the syndrome of deficiency of origin and excess of standard, while kidney Yin deficiency is the foundation, and heart fire is excessive, and essence deficiency and blood stasis is the standard [5]. In clinic, method of nourishing kidney and clearing heart can effectively alleviate and control these syndromes. Therefore, reducing the south (clearing heart) and reinforcing the north (nourishing kidney essence) may be the foundation of the HRD treatment. Reducing the south and reinforcing the north formula (RSRN) consist of eight herbs: Epimedium sagittatum (Siebold &Zucc.) Maxim. (ES); Curculigo orchioides Gaerth. (CO); Angelica sinensis (Oliv.) Diels. (AS); Phellodendron chinense Schneid. (PC); Anemarrhena asphodeloides Bge. (AR); Morinda officinalis How (MO); Radix Salvia (RS); and Coptidis Rhizoma (CR). Pharmacological studies on RSRN have assessed its regulation of the sex hormone and lipid metabolism and its prevention of the atherosclerosis and cardiovascular disease during the climacteric period [6, 7]. It was reported [8, 9] that Modified RSRN could enhance the myocardial microvascular density, improve the endothelial secretion function and hemorheology, and regulate the endocrine system and lipid metabolism in ovariectomized rats.Network pharmacology is a kind of new method for investigating the pharmacological mechanisms of TCM based on system biology and multidirectional pharmacology [10]. Due to the complexity of the TCM components and the uncertainty of their targets, conventional pharmacology research methods are difficult to fully elucidate the potential molecular mechanism of TCM compound in the treatment of diseases. In this study, we dissected the mechanisms of RSRN in treating hyperlipidemia and atherosclerosis and identified compounds related to the RSRN with the help of network pharmacology method based on multiple databases; we obtained the compounds potential targets via target fishing and verified the results through experiment. The detailed procedures can be seen in Figure 1.Figure 1
The detailed flowchart of the current study.


## 2. Materials and Methods

### 2.1. Identification of Main Active Components and Related Targets

Herbs were first confirmed by the comprehensive database of traditional Chinese medicine (TCMID), http://119.3.41.228 : 8000/TCMID/prescription search/). Then all of the constituent data of RSRN were obtained from the TCMSP database (http://lsp.nwu.edu.cn/tcmsp.php) [11]. The active components were further identified using the parameters of the oral bioavailability (OB); we set the threshold of OB at ≥30% and the drug likeness (DL) at ≥0.18 [12]. Then we used the TCMSP platform to predict the targets of active ingredients. The target information was obtained by correcting all the retrieved targets to their official names (official symbol) based on the UniprotKB search function in the protein database (UniProt) (http://www.uniprot.org/).


### 2.2. Disease Targets Identification by Multiple Databases

“Hyperlipidemia,” “atherosclerosis,” and “Alzheimer's disease” were selected as the key words for the retrieval of disease targets from the GeneCards database (https://www.genecards.org/), TTD database (Therapeutic Target Database, http://bidd.nus.edu.sg/group/cjttd/), DrugBank database (https://www.durgbank.ca/), OMIM database (Online Mendelian Inheritance in Man (https://www/omim.org/), and then the acquired disease targets were intersected by FunRich3.1.3 software. GSE57691 was screened and download from the GEO database (gene expression omnibus, https://www. ncbi. nlm. nih.gov/geo/) with “hyperlipidemia” and “atherosclerosis” as the key words, including 58 cases of disease group (49 cases of abdominal aortic aneurysm and 9 cases of aortic occlusive disease) and 10 cases of normal control group. We merged the above results and deleted the duplicate genes to obtain the final disease target genes by R 4.0.2 software.


### 2.3. Construction and Analysis of Drug-Disease Target Network

Based on previous steps, drug-disease crossover genes were screened withR software using the Venn Diagram package. The String 11.0 database (http://string-db.org/) was used to analyze the intersecting protein-protein interactions (PPIs) and then Cytoscape3.6.0 was used to determine the drug-disease target network. The “centiscape” plug-in was used to calculate the degree of freedom of drug-disease target. The higher the “degree” value, the greater the probability of playing the main function [13]. Using the Bisogenet and CytoNCA plug-ins of Cytoscape software (version 3.6.0), we set the parameters of degree centrality (DC > 61) and the topology intermediateness (BC > 600) and constructed the core target gene topology network; furthermore, we screened out the important candidate genes.


### 2.4. GO and KEGG Pathway Analysis

The screened drug-disease targets were evaluated by functional enrichment analyses. GO analysis was performed in three categories, namely biological processes (BP), cellular component (CC), and molecular function (MF), and the KEGG signaling pathway analysis was performed withR software using the Bioconductor package. The KEGG pathway was introduced into Cytoscape 3.6.0. According to the degree value, the enrichment degree of pathway and gene was displayed, and the network diagram was drawn.


### 2.5. Herbal Preparation

We prepared RSRN using the following constituents: 30 g ofCurculigo orchioides Gaertn (Xian Mao), 30 g of Epimedii Folium (Yin yang huo), 15 g of Morindae officinalis Radix (Ba ji tian), 15 g of Angelicae Sinensis Radix (Dang gui), 12 g of Phellodendron chinense Cortex (Huang bo), 10 g of Anemarrhenae Rhizoma (Zhi mu), 12 g of Salviae Miltiorrhizae Radix et Rhizoma (Dan shen), and 6 g of Coptidis Rhizoma (Huang lian). We obtained all herbs from Longhua Hospital Shanghai University of TCM, China. The herbs were mixed, soaked in water for 0.5 h, and decocted for 1 h in 5% v/w distilled H2O at 100°C. Subsequently, the filtrate was collected, and the residue decocted for another hour with 5% v/w distilled water. Next, the filtrate was concentrated (RE-3000B, Ya-rong Biochemical Instrument Shanghai Co., Ltd) and lyophilized (LGJ-10D, Four-ring Science Instrument Plant Beijing Co., Ltd), and the resulting RSRN powder was kept at −20°C until use. HPLC was used to determine the components in RSRN, and determination of curculigoside, Dihydrotanshinoen I, Icarisid I, Mangiferin, Sarsasapogenin, Jatrorrhizine in RSRN is shown in Figure 2.Figure 2
HPLC-MS mass specterometry analysis of the RSRN. (a) First-order spectrogram positive ion mode. (b) Second-order spectrogram positive ion mode. (c) Mass spectrometry of the main components; 1. Curculigoside; 2. Dihydrotanshinoen I; 3. Icarisid I; 4. Mangiferin; 5. Sarsasapogenin; 6. Jatrorrhizine.
(a)(b)


### 2.6. Animals and Administration

We obtained 40 healthy eight-week-old female Sprague-Dawley (SD) rats (mean weight: 200 ± 20 g) from Shanghai Slack Laboratory Animals Co., Ltd. [license number: SCXK (Hu) 2012–0002]. All the rats were housed in an air-conditioned room with 12 h light-dark cycles at a constant temperature (22–26°C) and humidity (50% ± 10%). Further, the rats were provided with rodent chow and tap water ad libitum. The animal model of climacteric atherosclerosis was established by bilateral ovariectomy and high-fat diet [14]. After acclimation for one week, the rats underwent either ovariectomy (n = 30) or sham operation (n = 10) under anesthesia using an intraperitoneal injection of 30 mg/kg pentobarbital sodium. Nine days after surgery, we randomly divided 30 OVX rats into the following three groups: OVX (high-fat emulsion [15] with an equal volume of ddH2O; 1 mL/100 g/day), RSRN (high-fat emulsion with RSRN; 5.46 g/kg/day), and EV groups (high-fat emulsion with estrogen valerate; 0.1 mg/kg/day). The sham-operated rats (SHAM group) received a normal diet with an equal volume of ddH2O; 1 mL/100 g/day. During the experimental period, the rats underwent weekly weight measurements. All procedures were approved by the Department of Laboratory Animal Science Longhua Hospital Shanghai University of TCM. The animal welfare and experimental procedures were conducted in strict accordance with the guidelines for the care and use of experimental animals and the ethics of Shanghai University of TCM. For the next six weeks after the last administration, the rats were food- and water deprived for 12 h. The rats were anaesthetized by diethyl ether inhalation. The brain and the blood were taken for further use.


### 2.7. Immunohistochemistry Analysis

Immunohistrochemistry procedure was performed as previously reported protocol [16]. The sections were incubated with the primary antibody for NF-κB p65 overnight at 4°C (#8242; 1 : 100, Cell Signalling Technology, MA,USA). After washing, the sections were incubated with the secondary antibody (#MR-R100; MR Biotech, Shanghai, China) for 1 h and then stained with 3,3′-diaminobenzidine (DAB) (Biyuntian Institute of Biotechnology, Jiangsu, China). Signals were visualized by light microscopic observation. The results were analyzed by using Image J software version1.50i (National Institute of Health, USA). The investigators used the software to measure the ratio of positive area (A%).


### 2.8. Western Blotting Analysis

The protein samples were separated by 10% SDS-PAGE transferred to PVDF membrane. The membrane was blocked with 1% BSA in TBST for 30 min, the primary antibodies including NF-κBp65 (#8242; 1 : 1000; Cell Signalling Technology, MA, USA), IκB α (#4812; 1 : 1000; Cell Signalling Technology, MA, USA), TNF-α (#sc-52746; 1 : 1000; Santa Curz Biptechnology, USA), IL-1β (#sc-52012; 1 : 1000; Santa Curz Biptechnology, USA), IL-6 (#sc-32296; 1 : 1000; Santa Curz Biptechnology, USA) were incubated at 4°C overnight (≥12 h), and secondary antibodies for GAPDH (#5174T; 1 : 5000; Cell Signalling Technology, MA, USA) and m-IgGk BP-HRP (#sc-516101; 1 : 5000; Santa Curz Biptechnology, USA) were used as the internal reference antibodies. Washed with TBST 5 min × 3 times, chemiluminescence imaging was performed with ECL luminescent liquid.


### 2.9. Statistical Analyses

Using SPSS20.0 statistical analysis software, the data were expressed as means ± standard deviation (SD). The significant difference was expressed asp<0.05. One-way ANOVA was used to compare the experimental results among groups. LSD test (meeting the requirement of homogeneity of variance) or Dunnett's T3 (not meeting the requirement of homogeneity of variance) was used for further pairwise comparison.


## 2.1. Identification of Main Active Components and Related Targets

Herbs were first confirmed by the comprehensive database of traditional Chinese medicine (TCMID), http://119.3.41.228 : 8000/TCMID/prescription search/). Then all of the constituent data of RSRN were obtained from the TCMSP database (http://lsp.nwu.edu.cn/tcmsp.php) [11]. The active components were further identified using the parameters of the oral bioavailability (OB); we set the threshold of OB at ≥30% and the drug likeness (DL) at ≥0.18 [12]. Then we used the TCMSP platform to predict the targets of active ingredients. The target information was obtained by correcting all the retrieved targets to their official names (official symbol) based on the UniprotKB search function in the protein database (UniProt) (http://www.uniprot.org/).


## 2.2. Disease Targets Identification by Multiple Databases

“Hyperlipidemia,” “atherosclerosis,” and “Alzheimer's disease” were selected as the key words for the retrieval of disease targets from the GeneCards database (https://www.genecards.org/), TTD database (Therapeutic Target Database, http://bidd.nus.edu.sg/group/cjttd/), DrugBank database (https://www.durgbank.ca/), OMIM database (Online Mendelian Inheritance in Man (https://www/omim.org/), and then the acquired disease targets were intersected by FunRich3.1.3 software. GSE57691 was screened and download from the GEO database (gene expression omnibus, https://www. ncbi. nlm. nih.gov/geo/) with “hyperlipidemia” and “atherosclerosis” as the key words, including 58 cases of disease group (49 cases of abdominal aortic aneurysm and 9 cases of aortic occlusive disease) and 10 cases of normal control group. We merged the above results and deleted the duplicate genes to obtain the final disease target genes by R 4.0.2 software.


## 2.3. Construction and Analysis of Drug-Disease Target Network

Based on previous steps, drug-disease crossover genes were screened withR software using the Venn Diagram package. The String 11.0 database (http://string-db.org/) was used to analyze the intersecting protein-protein interactions (PPIs) and then Cytoscape3.6.0 was used to determine the drug-disease target network. The “centiscape” plug-in was used to calculate the degree of freedom of drug-disease target. The higher the “degree” value, the greater the probability of playing the main function [13]. Using the Bisogenet and CytoNCA plug-ins of Cytoscape software (version 3.6.0), we set the parameters of degree centrality (DC > 61) and the topology intermediateness (BC > 600) and constructed the core target gene topology network; furthermore, we screened out the important candidate genes.


## 2.4. GO and KEGG Pathway Analysis

The screened drug-disease targets were evaluated by functional enrichment analyses. GO analysis was performed in three categories, namely biological processes (BP), cellular component (CC), and molecular function (MF), and the KEGG signaling pathway analysis was performed withR software using the Bioconductor package. The KEGG pathway was introduced into Cytoscape 3.6.0. According to the degree value, the enrichment degree of pathway and gene was displayed, and the network diagram was drawn.


## 2.5. Herbal Preparation

We prepared RSRN using the following constituents: 30 g ofCurculigo orchioides Gaertn (Xian Mao), 30 g of Epimedii Folium (Yin yang huo), 15 g of Morindae officinalis Radix (Ba ji tian), 15 g of Angelicae Sinensis Radix (Dang gui), 12 g of Phellodendron chinense Cortex (Huang bo), 10 g of Anemarrhenae Rhizoma (Zhi mu), 12 g of Salviae Miltiorrhizae Radix et Rhizoma (Dan shen), and 6 g of Coptidis Rhizoma (Huang lian). We obtained all herbs from Longhua Hospital Shanghai University of TCM, China. The herbs were mixed, soaked in water for 0.5 h, and decocted for 1 h in 5% v/w distilled H2O at 100°C. Subsequently, the filtrate was collected, and the residue decocted for another hour with 5% v/w distilled water. Next, the filtrate was concentrated (RE-3000B, Ya-rong Biochemical Instrument Shanghai Co., Ltd) and lyophilized (LGJ-10D, Four-ring Science Instrument Plant Beijing Co., Ltd), and the resulting RSRN powder was kept at −20°C until use. HPLC was used to determine the components in RSRN, and determination of curculigoside, Dihydrotanshinoen I, Icarisid I, Mangiferin, Sarsasapogenin, Jatrorrhizine in RSRN is shown in Figure 2.Figure 2
HPLC-MS mass specterometry analysis of the RSRN. (a) First-order spectrogram positive ion mode. (b) Second-order spectrogram positive ion mode. (c) Mass spectrometry of the main components; 1. Curculigoside; 2. Dihydrotanshinoen I; 3. Icarisid I; 4. Mangiferin; 5. Sarsasapogenin; 6. Jatrorrhizine.
(a)(b)


## 2.6. Animals and Administration

We obtained 40 healthy eight-week-old female Sprague-Dawley (SD) rats (mean weight: 200 ± 20 g) from Shanghai Slack Laboratory Animals Co., Ltd. [license number: SCXK (Hu) 2012–0002]. All the rats were housed in an air-conditioned room with 12 h light-dark cycles at a constant temperature (22–26°C) and humidity (50% ± 10%). Further, the rats were provided with rodent chow and tap water ad libitum. The animal model of climacteric atherosclerosis was established by bilateral ovariectomy and high-fat diet [14]. After acclimation for one week, the rats underwent either ovariectomy (n = 30) or sham operation (n = 10) under anesthesia using an intraperitoneal injection of 30 mg/kg pentobarbital sodium. Nine days after surgery, we randomly divided 30 OVX rats into the following three groups: OVX (high-fat emulsion [15] with an equal volume of ddH2O; 1 mL/100 g/day), RSRN (high-fat emulsion with RSRN; 5.46 g/kg/day), and EV groups (high-fat emulsion with estrogen valerate; 0.1 mg/kg/day). The sham-operated rats (SHAM group) received a normal diet with an equal volume of ddH2O; 1 mL/100 g/day. During the experimental period, the rats underwent weekly weight measurements. All procedures were approved by the Department of Laboratory Animal Science Longhua Hospital Shanghai University of TCM. The animal welfare and experimental procedures were conducted in strict accordance with the guidelines for the care and use of experimental animals and the ethics of Shanghai University of TCM. For the next six weeks after the last administration, the rats were food- and water deprived for 12 h. The rats were anaesthetized by diethyl ether inhalation. The brain and the blood were taken for further use.


## 2.7. Immunohistochemistry Analysis

Immunohistrochemistry procedure was performed as previously reported protocol [16]. The sections were incubated with the primary antibody for NF-κB p65 overnight at 4°C (#8242; 1 : 100, Cell Signalling Technology, MA,USA). After washing, the sections were incubated with the secondary antibody (#MR-R100; MR Biotech, Shanghai, China) for 1 h and then stained with 3,3′-diaminobenzidine (DAB) (Biyuntian Institute of Biotechnology, Jiangsu, China). Signals were visualized by light microscopic observation. The results were analyzed by using Image J software version1.50i (National Institute of Health, USA). The investigators used the software to measure the ratio of positive area (A%).


## 2.8. Western Blotting Analysis

The protein samples were separated by 10% SDS-PAGE transferred to PVDF membrane. The membrane was blocked with 1% BSA in TBST for 30 min, the primary antibodies including NF-κBp65 (#8242; 1 : 1000; Cell Signalling Technology, MA, USA), IκB α (#4812; 1 : 1000; Cell Signalling Technology, MA, USA), TNF-α (#sc-52746; 1 : 1000; Santa Curz Biptechnology, USA), IL-1β (#sc-52012; 1 : 1000; Santa Curz Biptechnology, USA), IL-6 (#sc-32296; 1 : 1000; Santa Curz Biptechnology, USA) were incubated at 4°C overnight (≥12 h), and secondary antibodies for GAPDH (#5174T; 1 : 5000; Cell Signalling Technology, MA, USA) and m-IgGk BP-HRP (#sc-516101; 1 : 5000; Santa Curz Biptechnology, USA) were used as the internal reference antibodies. Washed with TBST 5 min × 3 times, chemiluminescence imaging was performed with ECL luminescent liquid.


## 2.9. Statistical Analyses

Using SPSS20.0 statistical analysis software, the data were expressed as means ± standard deviation (SD). The significant difference was expressed asp<0.05. One-way ANOVA was used to compare the experimental results among groups. LSD test (meeting the requirement of homogeneity of variance) or Dunnett's T3 (not meeting the requirement of homogeneity of variance) was used for further pairwise comparison.


## 3. Results

### 3.1. Active Components of RSRN

A total of 187 active components were identified from the TCMSP databases by the ADME thresholds (OB ≥ 30%, DL ≥ 0.18), including 8 components ofCurculigo orchioides Gaertn (Xian Mao), 22 components of Epimedii Folium (Yin yang huo), 21 components of Morindae officinalis Radix (Ba ji tian), 2 components of Angelicae Sinensis Radix (Dang gui), 38 components of Phellodendron chinense Cortex (Huang bo), 17 components of Anemarrhenae Rhizoma (Zhi mu), 65 components of Salviae Miltiorrhizae Radix et Rhizoma (Dan shen), and 14 components of Coptidis Rhizoma (Huang lian) (Table 1).Table 1
Detailed information on 106 active compounds from RSRN.
Mol IDComponentsOB%DLHerbsMOL001506Supraene33.60.42Ba ji tianMOL002879Diop43.60.39Ba ji tianMOL002883Ethyl oleate (NF)32.40.19Ba ji tianMOL000358Beta-sitosterol36.90.75Ba ji tianMOL000359Sitosterol36.90.75Ba ji tianMOL006147Alizarin-2-methylether32.80.21Ba ji tianMOL0094952-Hydroxy-1,5-dimethoxy-6-(methoxymethyl)-9,10-anthraquinone95.90.37Ba ji tianMOL0094961,5,7-Trihydroxy-6-methoxy-2-methoxymethylanthracenequinone80.40.38Ba ji tianMOL0095001,6-Dihydroxy-5-methoxy-2-(methoxymethyl)-9,10-anthraquinone1050.34Ba ji tianMOL0095031-Hydroxy-3-methoxy-9,10-anthraquinone1040.21Ba ji tianMOL0095041-Hydroxy-6-hydroxymethylanthracenequinone81.80.21Ba ji tianMOL0095132-Hydroxy-1,8-dimethoxy-7-methoxymethylanthracenequinone1120.37Ba ji tianMOL009519(2R,3S) -(+)-3′,5-dihydroxy-4,7-dimethoxydihydroflavonol77.20.33Ba ji tianMOL0095243beta,20(R),5-alkenyl-stigmastol36.90.75Ba ji tianMOL0095253beta-24S(R)-butyl-5-alkenyl-cholestol35.40.82Ba ji tianMOL009537Americanin A46.70.35Ba ji tianMOL009541Asperuloside tetraacetate45.50.82Ba ji tianMOL009551Isoprincepin49.10.77Ba ji tianMOL0095582-Hydroxyethyl 5-hydroxy-2-(2-hydroxybenzoyl)-4-(hydroxymethyl)benzoate62.30.26Ba ji tianMOL009562Ohioensin-A38.10.76Ba ji tianMOL000358Beta-sitosterol36.90.75Dang guiMOL000449Stigmasterol43.80.76Dang guiMOL001454Berberine36.90.78Huang boMOL001458Coptisine30.70.86Huang boMOL002636Kihadalactone A34.20.82Huang boMOL013352Obacunone43.30.77Huang boMOL002641Phellavin_qt35.90.44Huang boMOL002643Delta 7-stigmastenol37.40.75Huang boMOL002644Phellopterin40.20.28Huang boMOL002651Dehydrotanshinone II A43.80.4Huang boMOL002652delta7-dehydrosophoramine54.50.25Huang boMOL002656Dihydroniloticin36.40.81Huang boMOL002659Kihadanin A31.60.7Huang boMOL002660Niloticin41.40.82Huang boMOL002662Rutaecarpine40.30.6Huang boMOL002663Skimmianin40.10.2Huang boMOL002666Chelerythrine34.20.78Huang boMOL000449Stigmasterol43.80.76Huang boMOL002668Worenine45.80.87Huang boMOL002670Cavidine35.60.81Huang boMOL002671Candletoxin A31.80.69Huang boMOL002672Hericenone H390.63Huang boMOL002673Hispidone36.20.83Huang boMOL000358Beta-sitosterol36.90.75Huang boMOL000622Magnograndiolide63.70.19Huang boMOL000762Palmidin A35.40.65Huang boMOL000785Palmatine64.60.65Huang boMOL000787Fumarine59.30.83Huang boMOL000790Isocorypalmine35.80.59Huang boMOL000098Quercetin46.40.28Huang boMOL001131Phellamurin_qt56.60.39Huang boMOL001455(S)-canadine53.80.77Huang boMOL001771Poriferast-5-en-3beta-ol36.90.75Huang boMOL0016011,2,5,6-Tetrahydrotanshinone38.750.36Dan shenMOL001659Poriferasterol43.830.76Dan shenMOL001771Poriferast-5-en-3beta-ol36.910.75Dan shenMOL001942Isoimperatorin45.460.23Dan shenMOL002222Sugiol36.110.28Dan shenMOL002651Dehydrotanshinone II A43.760.4Dan shenMOL002776Baicalin40.120.75Dan shenMOL000569Digallate61.850.26Dan shenMOL000006Luteolin36.160.25Dan shenMOL006824α-Amyrin39.510.76Dan shenMOL0070365,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one33.770.29Dan shenMOL0070412-Isopropyl-8-methylphenanthrene-3,4-dione40.860.23Dan shenMOL0070453α-Hydroxytanshinone?a44.930.44Dan shenMOL007048(E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid48.240.31Dan shenMOL0070494-Methylenemiltirone34.350.23Dan shenMOL0070502-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde62.780.4Dan shenMOL0070516-o-Syringyl-8-o-acetyl shanzhiside methyl ester46.690.71Dan shenMOL007058Formyltanshinone73.440.42Dan shenMOL0070593-Beta-hydroxymethyllenetanshiquinone32.160.41Dan shenMOL007061Methylenetanshinquinone37.070.36Dan shenMOL007063Przewalskin a37.110.65Dan shenMOL007064Przewalskin b110.320.44Dan shenMOL007068Przewaquinone B62.240.41Dan shenMOL007069Przewaquinone c55.740.4Dan shenMOL007070(6S,7 R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-dione41.310.45Dan shenMOL007071Przewaquinone f40.310.46Dan shenMOL007077Sclareol43.670.21Dan shenMOL007079Tanshinaldehyde52.470.45Dan shenMOL007081Danshenol B57.950.56Dan shenMOL007082Danshenol A56.970.52Dan shenMOL007085Salvilenone30.380.38Dan shenMOL007088Cryptotanshinone52.340.4Dan shenMOL007093Dan-shexinkum d38.880.55Dan shenMOL007094Danshenspiroketallactone50.430.31Dan shenMOL007098Deoxyneocryptotanshinone49.40.29Dan shenMOL007100Dihydrotanshinlactone38.680.32Dan shenMOL007101Dihydrotanshinone I45.040.36Dan shenMOL007105Epidanshenspiroketallactone68.270.31Dan shenMOL002331N-Methylflindersine32.40.18Huang boMOL002894Berberrubine35.70.73Huang boMOL005438Campesterol37.60.71Huang boMOL006392Dihydroniloticin36.40.82Huang boMOL006401Melianone40.50.78Huang boMOL006413Phellochin35.40.82Huang boMOL006422Thalifendine44.40.73Huang boMOL001607ZINC0398245436.90.76Xian MaoMOL003578Cycloartenol38.70.78Xian MaoMOL000358Beta-sitosterol36.90.75Xian MaoMOL0041143,2′,4′,6′-tetrahydroxy-4,3′-dimethoxy chalcone52.70.28Xian MaoMOL004125Curculigoside B_qt83.40.19Xian MaoMOL004146Curculigosaponin C39.30.19Xian MaoMOL000449Stigmasterol43.80.76Xian MaoMOL00151024-Epicampesterol37.60.71Xian MaoMOL001645Linoleyl acetate42.10.2Yin yang huoMOL001771Poriferast-5-en-3beta-ol36.90.75Yin yang huoMOL001792DFV32.80.18Yin yang huoMOL003044Chryseriol35.90.27Yin yang huoMOL0035428-Isopentenyl-kaempferol380.39Yin yang huoMOL000359Sitosterol36.90.75Yin yang huoMOL000422Kaempferol41.90.24Yin yang huoMOL004367Olivil62.20.41Yin yang huoMOL004373Anhydroicaritin45.40.44Yin yang huoMOL004380C-Homoerythrinan,1,6-didehydro-3,15,16-trimethoxy-, (3. Beta)-39.10.49Yin yang huoMOL004382Yin yang huo A570.77Yin yang huoMOL004384Yin yang huo C45.70.5Yin yang huoMOL004386Yin yang huo E51.60.55Yin yang huoMOL0043886-Hydroxy-11,12-dimethoxy-2,2-dimethyl-1,8-dioxo-2,3,4,8-tetrahydro-1h-isochromeno[3,4-h] isoquinolin-2-ium60.60.66Yin yang huoMOL0043918-(3-Methylbut-2-enyl)-2-phenyl-chromone48.50.25Yin yang huoMOL004394Anhydroicaritin-3-O-alpha-L-rhamnoside41.60.61Yin yang huoMOL0043961,2-bis(4-hydroxy-3-methoxyphenyl) propan-1,3-diol52.30.22Yin yang huoMOL004425Icariin41.60.61Yin yang huoMOL004427Icariside A731.90.86Yin yang huoMOL000006Luteolin36.20.25Yin yang huoMOL000622Magnograndiolide63.70.19Yin yang huoMOL000098Quercetin46.40.28Yin yang huoMOL001677Asperglaucide580.52Zhi muMOL001944Marmesin50.30.18Zhi muMOL003773Mangiferolic acid36.20.84Zhi muMOL000422Kaempferol41.90.24Zhi muMOL004373Anhydroicaritin45.40.44Zhi muMOL004489Anemarsaponin F_qt60.10.79Zhi muMOL004492Chrysanthemaxanthin38.70.58Zhi muMOL004497Hippeastrine51.70.62Zhi muMOL004514Timosaponin B III_qt35.30.87Zhi muMOL000449Stigmasterol43.80.76Zhi muMOL004528Icariin I41.60.61Zhi muMOL004540Anemarsaponin C_qt35.50.87Zhi muMOL004542Anemarsaponin E_qt30.70.86Zhi muMOL000483(Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl) ethyl] acrylamide1180.26Zhi muMOL000546Diosgenin80.90.81Zhi muMOL000631Coumaroyltyramine1130.2Zhi muMOL007107C0909236.070.25Dan shenMOL007108Isocryptotanshi-none54.980.39Dan shenMOL007111Isotanshinone II49.920.4Dan shenMOL007115Manool45.040.2Dan shenMOL007118Microstegiol39.610.28Dan shenMOL007119Miltionone I49.680.32Dan shenMOL007120Miltionone II71.030.44Dan shenMOL007121Miltipolone36.560.37Dan shenMOL007122Miltirone38.760.25Dan shenMOL007123Miltirone II44.950.24Dan shenMOL007124Neocryptotanshinone ii39.460.23Dan shenMOL007125Neocryptotanshinone52.490.32Dan shenMOL0071271-Methyl-8,9-dihydro-7h-naphtho[5,6-g] benzofuran-6,10,11-trione34.720.37Dan shenMOL007130Prolithospermic acid64.370.31Dan shenMOL007132(2R)-3-(3,4-dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl) acryloyl] oxy-propionic acid109.380.35Dan shenMOL007140(Z)-3-[2-[(E)-2-(3,4-dihydroxyphenyl) vinyl]-3,4-dihydroxy-phenyl] acrylic acid88.540.26Dan shenMOL007141Salvianolic acid g45.560.61Dan shenMOL007142Salvianolic acid j43.380.72Dan shenMOL007143Salvilenone I32.430.23Dan shenMOL007145Salviolone31.720.24Dan shenMOL007149NSC 12242134.490.28Dan shenMOL007150(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-quinone75.390.46Dan shenMOL007151Tanshindiol B42.670.45Dan shenMOL007152Przewaquinone E42.850.45Dan shenMOL007154Tanshinone iia49.890.4Dan shenMOL007155(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-dione65.260.45Dan shenMOL007156Tanshinone VI45.640.3Dan shenMOL002897Epiberberine43.090.78Huang lianMOL002903(R)-canadine55.370.77Huang lianMOL002904Berlambine36.680.82Huang lianMOL002907Corchoroside A_qt104.950.78Huang lianMOL000622Magnograndiolide63.710.19Huang lianMOL000762Palmidin A35.360.65Huang lianMOL000785Palmatine64.60.65Huang lianMOL000098Quercetin46.430.28Huang lianMOL001458Coptisine30.670.86Huang lianMOL002668Worenine45.830.87Huang lianMOL008647Moupinamide86.710.26Huang lian


### 3.2. Analysis of “RSRN-Compound-Target” Network

We conducted target fishing for these 187 active ingredients using the TCMSP databases based on chemical similarity and obtained 225 related targets. We evaluated the relationships between the components and targets with a constructed “RSRN-compound-target” network, which had a total of 365 nodes and 4841 edges (Figure3(a)). The inverted triangles represent the active compounds, and the circles represent their targets. The network topology was analyzed by using centiscape plug-in, and the degree value of the topological network was 26.53, the betweenness value was 469.26, the closeness value was 0.0012, and the eigenvector value was 0.039. The top 10 compounds and top 6 key target of RSRN are shown in Table 2. The top six targets were PTGS2 (degree = 231), PTGS1 (degree = 148), SCN5A (degree = 137), HSP90AA1 (degree = 131), NCOA2 (degree = 130), and ADRB2 (degree = 127) (Figure 3(b) and 3(c)).Figure 3
The “RSRN-compound-target” network and the key targets: (a) the RSRN-active component-target network, the red rectangle represents the name of drug, the blue inverted triangle represents the components, the green circle represents the targets; (b) the active component-key target network, the blue rectangle represents the key targets, the green inverted triangle represents the components.
(a)(b)(c)Table 2
The active component and key target of RSRN.
CompoundTypeBetweennessClosenessDegreeEigenvectorQuercetinMol6956.3670.0016164260.172085KaempferolMol1074.9720.0014221140.093268StigmasterolMol234.99620.0013511120.071736LuteolinMol966.5660.0014141100.077567Beta-sitosterolMol188.02140.001361880.076787AnhydroicaritinMol173.25520.001372660.083024Danshinone II AMol1130.8710.00122410.073208Dehydrotanshinone II AMol26.813710.001348400.06437PalmatineMol16.990110.001297340.054885C-homoerythrinan, 1,6-didehydro-3,15,16-trimethoxy-, (3. Beta.)-Mol292.79470.001333320.070996PTGS2Gene6853.5240.0015972310.158477PTGS1Gene1835.1120.0015041480.126258SCN5AGene1382.6290.0014971370.127246HSP90AA1Gene2411.6880.0014971310.107942NCOA2Gene2395.6280.0014811300.098189ADRB2Gene1468.8980.0014951270.12509


### 3.3. Disease Targets Acquisition and Analysis

There were 1373 disease targets related to hyperlipidemia, 4481 targets related to atherosclerosis, and 9553 targets related to “hyperlipidemia,” which were selected from GeneCards, TTD, DrugBank, and OMIM database. The above disease targets were analyzed by Venn map (Figure4(a)). The data set GSE57691 was selected from the GEO database for screening differentially expressed genes. Then differential expression analysis on the data was performed using the limma software package. Compared with control samples, a total of 269 genes were significantly differentially expressed in hyperlipidemia samples, 47 were upregulated, and 221 were downregulated. The differentially expressed genes are shown in the cluster diagram (Figure 4(b)) and volcano map (Figure 4(c)). A total of 974 disease targets were obtained by merging above disease targets in the end.Figure 4
Disease targets analysis: (a) “hyperlipidemia,” “atherosclerosis,” and “Alzheimer's disease” were selected as the key words for the retrieval of disease targets; (b, c) the cluster diagram and volcano map of the differentially expressed genes in GSE57691 data set, red represents upregulated and green represents downregulated.
(a)(b)(c)


### 3.4. Construction of Drug-Disease Target Network

After the construction of the Venn diagram, 106 targets between the 974 disease targets and 225 related targets of RSRN were selected as the potential targets in the treatment of hyperlipidemia-related diseases (Figure5(a)). The protein interaction relationship was obtained by using BisoGenet plug-in of Cytoscape software (Figure 5(b)). Using the CytoNCA plug-in of Cytoscape software, the core disease-drug targets was analyzed and confirmed. The selection criteria were set as follows: DC value > 61 (Figure 5(c)) (yellow part in the figure was qualified), and BC value > 600 (Figure 5(d)). The red rectangle in the topological network represents the last selected target genes, including AKT1, AR, NF-κB, CASP3, mTOR, ERBB2, CHUK, CAV1, MAPK8, MAPK14, HIF1A, PPARG, RELA, NR3C1, ESR2, FOS, CDK4, GSK3B, HSPB1, MYC, MDM2, EGFR, HSP90AA1, HSPA5, NOS2, ADRB2, VCAM1, APP, ESR1, XIAP, CASP8, Bax, ICAM1, SOD1.Figure 5
The drug-disease target network. (a) The Venn diagram for drug and disease targets. The overlap targets mean the potential therapeutic gene for RSRN when treating hyperlipidemia-related disease; (b) there were 6664 nodes and 146555 edges in the network; (c) the first screening threshold was DC >61, which resulted in 1350 nodes and 58513 edges; (d) the second screening threshold was BC >600, and 609 nodes and 25745 edges remained.


### 3.5. Functional Enrichment Analysis

The 106 potential targets were then subjected to GO and KEGG analysis to explore the links between the functional units, their potential significance in the biological systems network. The GO terms were determined in the following categories (Figures6(a) and 6(b)): 1900 biological processes (BP), 43 cellular components (CC), and 140 molecular functions (MF) branches. In the category BP, the genes were associated with response to metal ion (GO:0010038), response to nutrient levels (GO:0031667), response to lipopolysaccharide (GO:0032496), and response to molecular of bacterial origin (GO:0002237). In the category CC, the genes were associated with cell components such as membrane raft (GO:0045121), membrane micro domain (GO:0098857), membrane region (GO:0098589), and transcription regulator complex (GO:0005667). In the category MF, the genes were associated with DNA binding transcription factor binding (GO:0140297) and RNA polymerase II specific DNA binding transcription factor binding (GO:0061629). The KEGG enrichment result indicated that the genes were associated with AGE-RAGE signaling pathway (hsa04933), fluid shear stress and atherosclerosis (hsa05418), PI3K-Akt signaling pathway (hsa04151), TNF signaling pathway (hsa04668), and NF-kappa B signaling pathway (hsa04064) (Figures 6(c) and 6(d)). The KEGG network included 83 nodes and 360 edges (Figure 7(a)). The top six target genes were AKT1 (protein-serine-threonine kinase 1) (degree = 17), RELA (nuclear factor kappa B p65, NF-κB p65) (degree = 16), CHUK (conserved helix-loop-helix ubiquitous kinase, also known as IκB kinase α, IKKα, or IKK1) (degree = 14), CCND1 (Cyclin D1) (degree = 13), MAPK8 (mitogen-activated protein kinase 8) (degree = 12), and MAPK14 (mitogen-activated protein kinase 14) (degree = 11) (Figure 7(b)). The PI3K-Akt signaling pathway and NF-kappa B signaling pathway were performed by R software (Figures 7(c) and 7(d)), and the red mark represents the potential target of RSRN intervention.Figure 6
Functional enrichment analysis. (a, b) GO functional enrichment analysis; (c, d) KEGG functional enrichment analysis.
(a)(b)(c)(d)Figure 7
KEGG network and key pathways: (a) KEGG pathways target genes network, blue inverted triangle represents KEGG pathways, green rectangle represents KEGG pathway-related genes; (b) top 25 pathways and target genes; (c) PI3K-AKT signal pathway; (d) NF-KAPPA B signal pathway.
(a)(b)(c)(d)


### 3.6. RSRN Downregulated the Expression of NF-κBp65 Protein in Hypothalamus of Ovariectomized Rats

Theimmunohistochemistry results showed that the positive expression of NF-κBp65 cells included glial cells, which were located in cytoplasm or nucleus (Figure 8(a)–8(e)). Compared with the SHAM group, the expression of NF-κBp65 protein in hypothalamus in OVX group was significantly increased (p<0.05), with a large number of positive expression cells and dark brown color. Compared with the OVX group, the expression of NF-κBp65 protein in hypothalamus in RSRN group and EV group was significantly decreased (p<0.05).Figure 8
The expression of NF-κBp65 in hypothalamus. (a–d) The expressions of NF-kBp65 in hypothalamus tissue were analyzed by immunohistochemistry (×200). (e) Values are presented as the mean ± standard deviation (SD), n = 3 per group. ★p<0.05, compared with the SHAM group; #p<0.05, compared with the OVX group.
(a)(b)(c)(d)(e)


### 3.7. RSRN Regulated the Expression of NF-κBp65, IκBα, TNFα, IL-1β, and IL-6 in Brain of Ovariectomized Rats

Western blotting results showed that the expression levels of NF-κBp65, TNFα, IL-1β, and IL-6 in OVX group were significantly higher than those of the SHAM group (p<0.05), while the expression of IκBα was significantly decreased (p<0.05). Compared with the OVX group, the protein expression levels of NF-κBp65, TNFα, IL-1β, and IL-6 in RSRN group were significantly decreased (p<0.05), while the expression of IκBα protein was significantly increased (p<0.05) (Figure 9(a)–9(c); Figure 10(a)–10(c)).Figure 9
Protein expression of IκBα and NF-κBp65 by western blot: (a) gene levels of NF-κBp65, IκBα in hypothalamus and hippocampus by western blot; (b) semi-quantitative analysis of NF-κBp65, IκBα proteins expression compared with GAPDH. Values are presented as the means ± standard deviation (SD), n = 3 per group. ★p<0.05, compared with the SHAM group; #p<0.05, compared with the OVX group.
(a)(b)(c)Figure 10
Protein expression of TNFα, IL-1β, and IL-6 by western blot: (a) gene levels of TNFα, IL-1β, and IL-6 in hypothalamus and hippocampus by western blot; (b) semi-quantitative analysis of TNFα, IL-1β, and IL-6 proteins expression compared with GAPDH. Values are presented as the means ± standard deviation (SD), n = 3 per group. ★p<0.05, compared with the SHAM group; #p<0.05, compared with the OVX group.
(a)(b)(c)


## 3.1. Active Components of RSRN

A total of 187 active components were identified from the TCMSP databases by the ADME thresholds (OB ≥ 30%, DL ≥ 0.18), including 8 components ofCurculigo orchioides Gaertn (Xian Mao), 22 components of Epimedii Folium (Yin yang huo), 21 components of Morindae officinalis Radix (Ba ji tian), 2 components of Angelicae Sinensis Radix (Dang gui), 38 components of Phellodendron chinense Cortex (Huang bo), 17 components of Anemarrhenae Rhizoma (Zhi mu), 65 components of Salviae Miltiorrhizae Radix et Rhizoma (Dan shen), and 14 components of Coptidis Rhizoma (Huang lian) (Table 1).Table 1
Detailed information on 106 active compounds from RSRN.
Mol IDComponentsOB%DLHerbsMOL001506Supraene33.60.42Ba ji tianMOL002879Diop43.60.39Ba ji tianMOL002883Ethyl oleate (NF)32.40.19Ba ji tianMOL000358Beta-sitosterol36.90.75Ba ji tianMOL000359Sitosterol36.90.75Ba ji tianMOL006147Alizarin-2-methylether32.80.21Ba ji tianMOL0094952-Hydroxy-1,5-dimethoxy-6-(methoxymethyl)-9,10-anthraquinone95.90.37Ba ji tianMOL0094961,5,7-Trihydroxy-6-methoxy-2-methoxymethylanthracenequinone80.40.38Ba ji tianMOL0095001,6-Dihydroxy-5-methoxy-2-(methoxymethyl)-9,10-anthraquinone1050.34Ba ji tianMOL0095031-Hydroxy-3-methoxy-9,10-anthraquinone1040.21Ba ji tianMOL0095041-Hydroxy-6-hydroxymethylanthracenequinone81.80.21Ba ji tianMOL0095132-Hydroxy-1,8-dimethoxy-7-methoxymethylanthracenequinone1120.37Ba ji tianMOL009519(2R,3S) -(+)-3′,5-dihydroxy-4,7-dimethoxydihydroflavonol77.20.33Ba ji tianMOL0095243beta,20(R),5-alkenyl-stigmastol36.90.75Ba ji tianMOL0095253beta-24S(R)-butyl-5-alkenyl-cholestol35.40.82Ba ji tianMOL009537Americanin A46.70.35Ba ji tianMOL009541Asperuloside tetraacetate45.50.82Ba ji tianMOL009551Isoprincepin49.10.77Ba ji tianMOL0095582-Hydroxyethyl 5-hydroxy-2-(2-hydroxybenzoyl)-4-(hydroxymethyl)benzoate62.30.26Ba ji tianMOL009562Ohioensin-A38.10.76Ba ji tianMOL000358Beta-sitosterol36.90.75Dang guiMOL000449Stigmasterol43.80.76Dang guiMOL001454Berberine36.90.78Huang boMOL001458Coptisine30.70.86Huang boMOL002636Kihadalactone A34.20.82Huang boMOL013352Obacunone43.30.77Huang boMOL002641Phellavin_qt35.90.44Huang boMOL002643Delta 7-stigmastenol37.40.75Huang boMOL002644Phellopterin40.20.28Huang boMOL002651Dehydrotanshinone II A43.80.4Huang boMOL002652delta7-dehydrosophoramine54.50.25Huang boMOL002656Dihydroniloticin36.40.81Huang boMOL002659Kihadanin A31.60.7Huang boMOL002660Niloticin41.40.82Huang boMOL002662Rutaecarpine40.30.6Huang boMOL002663Skimmianin40.10.2Huang boMOL002666Chelerythrine34.20.78Huang boMOL000449Stigmasterol43.80.76Huang boMOL002668Worenine45.80.87Huang boMOL002670Cavidine35.60.81Huang boMOL002671Candletoxin A31.80.69Huang boMOL002672Hericenone H390.63Huang boMOL002673Hispidone36.20.83Huang boMOL000358Beta-sitosterol36.90.75Huang boMOL000622Magnograndiolide63.70.19Huang boMOL000762Palmidin A35.40.65Huang boMOL000785Palmatine64.60.65Huang boMOL000787Fumarine59.30.83Huang boMOL000790Isocorypalmine35.80.59Huang boMOL000098Quercetin46.40.28Huang boMOL001131Phellamurin_qt56.60.39Huang boMOL001455(S)-canadine53.80.77Huang boMOL001771Poriferast-5-en-3beta-ol36.90.75Huang boMOL0016011,2,5,6-Tetrahydrotanshinone38.750.36Dan shenMOL001659Poriferasterol43.830.76Dan shenMOL001771Poriferast-5-en-3beta-ol36.910.75Dan shenMOL001942Isoimperatorin45.460.23Dan shenMOL002222Sugiol36.110.28Dan shenMOL002651Dehydrotanshinone II A43.760.4Dan shenMOL002776Baicalin40.120.75Dan shenMOL000569Digallate61.850.26Dan shenMOL000006Luteolin36.160.25Dan shenMOL006824α-Amyrin39.510.76Dan shenMOL0070365,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one33.770.29Dan shenMOL0070412-Isopropyl-8-methylphenanthrene-3,4-dione40.860.23Dan shenMOL0070453α-Hydroxytanshinone?a44.930.44Dan shenMOL007048(E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid48.240.31Dan shenMOL0070494-Methylenemiltirone34.350.23Dan shenMOL0070502-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde62.780.4Dan shenMOL0070516-o-Syringyl-8-o-acetyl shanzhiside methyl ester46.690.71Dan shenMOL007058Formyltanshinone73.440.42Dan shenMOL0070593-Beta-hydroxymethyllenetanshiquinone32.160.41Dan shenMOL007061Methylenetanshinquinone37.070.36Dan shenMOL007063Przewalskin a37.110.65Dan shenMOL007064Przewalskin b110.320.44Dan shenMOL007068Przewaquinone B62.240.41Dan shenMOL007069Przewaquinone c55.740.4Dan shenMOL007070(6S,7 R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-dione41.310.45Dan shenMOL007071Przewaquinone f40.310.46Dan shenMOL007077Sclareol43.670.21Dan shenMOL007079Tanshinaldehyde52.470.45Dan shenMOL007081Danshenol B57.950.56Dan shenMOL007082Danshenol A56.970.52Dan shenMOL007085Salvilenone30.380.38Dan shenMOL007088Cryptotanshinone52.340.4Dan shenMOL007093Dan-shexinkum d38.880.55Dan shenMOL007094Danshenspiroketallactone50.430.31Dan shenMOL007098Deoxyneocryptotanshinone49.40.29Dan shenMOL007100Dihydrotanshinlactone38.680.32Dan shenMOL007101Dihydrotanshinone I45.040.36Dan shenMOL007105Epidanshenspiroketallactone68.270.31Dan shenMOL002331N-Methylflindersine32.40.18Huang boMOL002894Berberrubine35.70.73Huang boMOL005438Campesterol37.60.71Huang boMOL006392Dihydroniloticin36.40.82Huang boMOL006401Melianone40.50.78Huang boMOL006413Phellochin35.40.82Huang boMOL006422Thalifendine44.40.73Huang boMOL001607ZINC0398245436.90.76Xian MaoMOL003578Cycloartenol38.70.78Xian MaoMOL000358Beta-sitosterol36.90.75Xian MaoMOL0041143,2′,4′,6′-tetrahydroxy-4,3′-dimethoxy chalcone52.70.28Xian MaoMOL004125Curculigoside B_qt83.40.19Xian MaoMOL004146Curculigosaponin C39.30.19Xian MaoMOL000449Stigmasterol43.80.76Xian MaoMOL00151024-Epicampesterol37.60.71Xian MaoMOL001645Linoleyl acetate42.10.2Yin yang huoMOL001771Poriferast-5-en-3beta-ol36.90.75Yin yang huoMOL001792DFV32.80.18Yin yang huoMOL003044Chryseriol35.90.27Yin yang huoMOL0035428-Isopentenyl-kaempferol380.39Yin yang huoMOL000359Sitosterol36.90.75Yin yang huoMOL000422Kaempferol41.90.24Yin yang huoMOL004367Olivil62.20.41Yin yang huoMOL004373Anhydroicaritin45.40.44Yin yang huoMOL004380C-Homoerythrinan,1,6-didehydro-3,15,16-trimethoxy-, (3. Beta)-39.10.49Yin yang huoMOL004382Yin yang huo A570.77Yin yang huoMOL004384Yin yang huo C45.70.5Yin yang huoMOL004386Yin yang huo E51.60.55Yin yang huoMOL0043886-Hydroxy-11,12-dimethoxy-2,2-dimethyl-1,8-dioxo-2,3,4,8-tetrahydro-1h-isochromeno[3,4-h] isoquinolin-2-ium60.60.66Yin yang huoMOL0043918-(3-Methylbut-2-enyl)-2-phenyl-chromone48.50.25Yin yang huoMOL004394Anhydroicaritin-3-O-alpha-L-rhamnoside41.60.61Yin yang huoMOL0043961,2-bis(4-hydroxy-3-methoxyphenyl) propan-1,3-diol52.30.22Yin yang huoMOL004425Icariin41.60.61Yin yang huoMOL004427Icariside A731.90.86Yin yang huoMOL000006Luteolin36.20.25Yin yang huoMOL000622Magnograndiolide63.70.19Yin yang huoMOL000098Quercetin46.40.28Yin yang huoMOL001677Asperglaucide580.52Zhi muMOL001944Marmesin50.30.18Zhi muMOL003773Mangiferolic acid36.20.84Zhi muMOL000422Kaempferol41.90.24Zhi muMOL004373Anhydroicaritin45.40.44Zhi muMOL004489Anemarsaponin F_qt60.10.79Zhi muMOL004492Chrysanthemaxanthin38.70.58Zhi muMOL004497Hippeastrine51.70.62Zhi muMOL004514Timosaponin B III_qt35.30.87Zhi muMOL000449Stigmasterol43.80.76Zhi muMOL004528Icariin I41.60.61Zhi muMOL004540Anemarsaponin C_qt35.50.87Zhi muMOL004542Anemarsaponin E_qt30.70.86Zhi muMOL000483(Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl) ethyl] acrylamide1180.26Zhi muMOL000546Diosgenin80.90.81Zhi muMOL000631Coumaroyltyramine1130.2Zhi muMOL007107C0909236.070.25Dan shenMOL007108Isocryptotanshi-none54.980.39Dan shenMOL007111Isotanshinone II49.920.4Dan shenMOL007115Manool45.040.2Dan shenMOL007118Microstegiol39.610.28Dan shenMOL007119Miltionone I49.680.32Dan shenMOL007120Miltionone II71.030.44Dan shenMOL007121Miltipolone36.560.37Dan shenMOL007122Miltirone38.760.25Dan shenMOL007123Miltirone II44.950.24Dan shenMOL007124Neocryptotanshinone ii39.460.23Dan shenMOL007125Neocryptotanshinone52.490.32Dan shenMOL0071271-Methyl-8,9-dihydro-7h-naphtho[5,6-g] benzofuran-6,10,11-trione34.720.37Dan shenMOL007130Prolithospermic acid64.370.31Dan shenMOL007132(2R)-3-(3,4-dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl) acryloyl] oxy-propionic acid109.380.35Dan shenMOL007140(Z)-3-[2-[(E)-2-(3,4-dihydroxyphenyl) vinyl]-3,4-dihydroxy-phenyl] acrylic acid88.540.26Dan shenMOL007141Salvianolic acid g45.560.61Dan shenMOL007142Salvianolic acid j43.380.72Dan shenMOL007143Salvilenone I32.430.23Dan shenMOL007145Salviolone31.720.24Dan shenMOL007149NSC 12242134.490.28Dan shenMOL007150(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-quinone75.390.46Dan shenMOL007151Tanshindiol B42.670.45Dan shenMOL007152Przewaquinone E42.850.45Dan shenMOL007154Tanshinone iia49.890.4Dan shenMOL007155(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-dione65.260.45Dan shenMOL007156Tanshinone VI45.640.3Dan shenMOL002897Epiberberine43.090.78Huang lianMOL002903(R)-canadine55.370.77Huang lianMOL002904Berlambine36.680.82Huang lianMOL002907Corchoroside A_qt104.950.78Huang lianMOL000622Magnograndiolide63.710.19Huang lianMOL000762Palmidin A35.360.65Huang lianMOL000785Palmatine64.60.65Huang lianMOL000098Quercetin46.430.28Huang lianMOL001458Coptisine30.670.86Huang lianMOL002668Worenine45.830.87Huang lianMOL008647Moupinamide86.710.26Huang lian


## 3.2. Analysis of “RSRN-Compound-Target” Network

We conducted target fishing for these 187 active ingredients using the TCMSP databases based on chemical similarity and obtained 225 related targets. We evaluated the relationships between the components and targets with a constructed “RSRN-compound-target” network, which had a total of 365 nodes and 4841 edges (Figure3(a)). The inverted triangles represent the active compounds, and the circles represent their targets. The network topology was analyzed by using centiscape plug-in, and the degree value of the topological network was 26.53, the betweenness value was 469.26, the closeness value was 0.0012, and the eigenvector value was 0.039. The top 10 compounds and top 6 key target of RSRN are shown in Table 2. The top six targets were PTGS2 (degree = 231), PTGS1 (degree = 148), SCN5A (degree = 137), HSP90AA1 (degree = 131), NCOA2 (degree = 130), and ADRB2 (degree = 127) (Figure 3(b) and 3(c)).Figure 3
The “RSRN-compound-target” network and the key targets: (a) the RSRN-active component-target network, the red rectangle represents the name of drug, the blue inverted triangle represents the components, the green circle represents the targets; (b) the active component-key target network, the blue rectangle represents the key targets, the green inverted triangle represents the components.
(a)(b)(c)Table 2
The active component and key target of RSRN.
CompoundTypeBetweennessClosenessDegreeEigenvectorQuercetinMol6956.3670.0016164260.172085KaempferolMol1074.9720.0014221140.093268StigmasterolMol234.99620.0013511120.071736LuteolinMol966.5660.0014141100.077567Beta-sitosterolMol188.02140.001361880.076787AnhydroicaritinMol173.25520.001372660.083024Danshinone II AMol1130.8710.00122410.073208Dehydrotanshinone II AMol26.813710.001348400.06437PalmatineMol16.990110.001297340.054885C-homoerythrinan, 1,6-didehydro-3,15,16-trimethoxy-, (3. Beta.)-Mol292.79470.001333320.070996PTGS2Gene6853.5240.0015972310.158477PTGS1Gene1835.1120.0015041480.126258SCN5AGene1382.6290.0014971370.127246HSP90AA1Gene2411.6880.0014971310.107942NCOA2Gene2395.6280.0014811300.098189ADRB2Gene1468.8980.0014951270.12509


## 3.3. Disease Targets Acquisition and Analysis

There were 1373 disease targets related to hyperlipidemia, 4481 targets related to atherosclerosis, and 9553 targets related to “hyperlipidemia,” which were selected from GeneCards, TTD, DrugBank, and OMIM database. The above disease targets were analyzed by Venn map (Figure4(a)). The data set GSE57691 was selected from the GEO database for screening differentially expressed genes. Then differential expression analysis on the data was performed using the limma software package. Compared with control samples, a total of 269 genes were significantly differentially expressed in hyperlipidemia samples, 47 were upregulated, and 221 were downregulated. The differentially expressed genes are shown in the cluster diagram (Figure 4(b)) and volcano map (Figure 4(c)). A total of 974 disease targets were obtained by merging above disease targets in the end.Figure 4
Disease targets analysis: (a) “hyperlipidemia,” “atherosclerosis,” and “Alzheimer's disease” were selected as the key words for the retrieval of disease targets; (b, c) the cluster diagram and volcano map of the differentially expressed genes in GSE57691 data set, red represents upregulated and green represents downregulated.
(a)(b)(c)


## 3.4. Construction of Drug-Disease Target Network

After the construction of the Venn diagram, 106 targets between the 974 disease targets and 225 related targets of RSRN were selected as the potential targets in the treatment of hyperlipidemia-related diseases (Figure5(a)). The protein interaction relationship was obtained by using BisoGenet plug-in of Cytoscape software (Figure 5(b)). Using the CytoNCA plug-in of Cytoscape software, the core disease-drug targets was analyzed and confirmed. The selection criteria were set as follows: DC value > 61 (Figure 5(c)) (yellow part in the figure was qualified), and BC value > 600 (Figure 5(d)). The red rectangle in the topological network represents the last selected target genes, including AKT1, AR, NF-κB, CASP3, mTOR, ERBB2, CHUK, CAV1, MAPK8, MAPK14, HIF1A, PPARG, RELA, NR3C1, ESR2, FOS, CDK4, GSK3B, HSPB1, MYC, MDM2, EGFR, HSP90AA1, HSPA5, NOS2, ADRB2, VCAM1, APP, ESR1, XIAP, CASP8, Bax, ICAM1, SOD1.Figure 5
The drug-disease target network. (a) The Venn diagram for drug and disease targets. The overlap targets mean the potential therapeutic gene for RSRN when treating hyperlipidemia-related disease; (b) there were 6664 nodes and 146555 edges in the network; (c) the first screening threshold was DC >61, which resulted in 1350 nodes and 58513 edges; (d) the second screening threshold was BC >600, and 609 nodes and 25745 edges remained.


## 3.5. Functional Enrichment Analysis

The 106 potential targets were then subjected to GO and KEGG analysis to explore the links between the functional units, their potential significance in the biological systems network. The GO terms were determined in the following categories (Figures6(a) and 6(b)): 1900 biological processes (BP), 43 cellular components (CC), and 140 molecular functions (MF) branches. In the category BP, the genes were associated with response to metal ion (GO:0010038), response to nutrient levels (GO:0031667), response to lipopolysaccharide (GO:0032496), and response to molecular of bacterial origin (GO:0002237). In the category CC, the genes were associated with cell components such as membrane raft (GO:0045121), membrane micro domain (GO:0098857), membrane region (GO:0098589), and transcription regulator complex (GO:0005667). In the category MF, the genes were associated with DNA binding transcription factor binding (GO:0140297) and RNA polymerase II specific DNA binding transcription factor binding (GO:0061629). The KEGG enrichment result indicated that the genes were associated with AGE-RAGE signaling pathway (hsa04933), fluid shear stress and atherosclerosis (hsa05418), PI3K-Akt signaling pathway (hsa04151), TNF signaling pathway (hsa04668), and NF-kappa B signaling pathway (hsa04064) (Figures 6(c) and 6(d)). The KEGG network included 83 nodes and 360 edges (Figure 7(a)). The top six target genes were AKT1 (protein-serine-threonine kinase 1) (degree = 17), RELA (nuclear factor kappa B p65, NF-κB p65) (degree = 16), CHUK (conserved helix-loop-helix ubiquitous kinase, also known as IκB kinase α, IKKα, or IKK1) (degree = 14), CCND1 (Cyclin D1) (degree = 13), MAPK8 (mitogen-activated protein kinase 8) (degree = 12), and MAPK14 (mitogen-activated protein kinase 14) (degree = 11) (Figure 7(b)). The PI3K-Akt signaling pathway and NF-kappa B signaling pathway were performed by R software (Figures 7(c) and 7(d)), and the red mark represents the potential target of RSRN intervention.Figure 6
Functional enrichment analysis. (a, b) GO functional enrichment analysis; (c, d) KEGG functional enrichment analysis.
(a)(b)(c)(d)Figure 7
KEGG network and key pathways: (a) KEGG pathways target genes network, blue inverted triangle represents KEGG pathways, green rectangle represents KEGG pathway-related genes; (b) top 25 pathways and target genes; (c) PI3K-AKT signal pathway; (d) NF-KAPPA B signal pathway.
(a)(b)(c)(d)


## 3.6. RSRN Downregulated the Expression of NF-κBp65 Protein in Hypothalamus of Ovariectomized Rats

Theimmunohistochemistry results showed that the positive expression of NF-κBp65 cells included glial cells, which were located in cytoplasm or nucleus (Figure 8(a)–8(e)). Compared with the SHAM group, the expression of NF-κBp65 protein in hypothalamus in OVX group was significantly increased (p<0.05), with a large number of positive expression cells and dark brown color. Compared with the OVX group, the expression of NF-κBp65 protein in hypothalamus in RSRN group and EV group was significantly decreased (p<0.05).Figure 8
The expression of NF-κBp65 in hypothalamus. (a–d) The expressions of NF-kBp65 in hypothalamus tissue were analyzed by immunohistochemistry (×200). (e) Values are presented as the mean ± standard deviation (SD), n = 3 per group. ★p<0.05, compared with the SHAM group; #p<0.05, compared with the OVX group.
(a)(b)(c)(d)(e)


## 3.7. RSRN Regulated the Expression of NF-κBp65, IκBα, TNFα, IL-1β, and IL-6 in Brain of Ovariectomized Rats

Western blotting results showed that the expression levels of NF-κBp65, TNFα, IL-1β, and IL-6 in OVX group were significantly higher than those of the SHAM group (p<0.05), while the expression of IκBα was significantly decreased (p<0.05). Compared with the OVX group, the protein expression levels of NF-κBp65, TNFα, IL-1β, and IL-6 in RSRN group were significantly decreased (p<0.05), while the expression of IκBα protein was significantly increased (p<0.05) (Figure 9(a)–9(c); Figure 10(a)–10(c)).Figure 9
Protein expression of IκBα and NF-κBp65 by western blot: (a) gene levels of NF-κBp65, IκBα in hypothalamus and hippocampus by western blot; (b) semi-quantitative analysis of NF-κBp65, IκBα proteins expression compared with GAPDH. Values are presented as the means ± standard deviation (SD), n = 3 per group. ★p<0.05, compared with the SHAM group; #p<0.05, compared with the OVX group.
(a)(b)(c)Figure 10
Protein expression of TNFα, IL-1β, and IL-6 by western blot: (a) gene levels of TNFα, IL-1β, and IL-6 in hypothalamus and hippocampus by western blot; (b) semi-quantitative analysis of TNFα, IL-1β, and IL-6 proteins expression compared with GAPDH. Values are presented as the means ± standard deviation (SD), n = 3 per group. ★p<0.05, compared with the SHAM group; #p<0.05, compared with the OVX group.
(a)(b)(c)


## 4. Discussion

The method of reducing the south and reinforcing the north (RSRN) can nourish kidney essence and purging the heart. It is also called the method of purging fire and replenishing water. The method of RSRN in this study was used to coordinate yin and yang and to prevent postmenopausal-related diseases. There were 187 active components in RSRN, of which quercetin, kaempferol, Stigmasterol, luteolin, beta-sitosterol, and Anhydroicaritin were the main active components. Kaempferol and quercetin may have hypoglycemic, lipid-lowering, anti-inflammatory, antioxidant, and anticancer effects [17]. It was reported that Kaempferol can increase lipid metabolism by increasing PPARα level, decreasing SREBPs level, and promoting expression of ACO and CYP4A1, so as to reduce visceral fat accumulation and improve hyperlipidemia in obese rats fed with high-fat diet [18]. Quercetin can improve cholesterol reverse transport by upregulating the expression of ABCA1 and ABCG1 protein and enhancing the cholesterol acceptance of HDL and ApoA1 by reducing oxidation so as to reduce lipid accumulation [19]. Research [20] showed that luteolin could reduce the activation of PI3K/Akt induced by EGF and reduce the phosphorylation of EGFR, Akt, p38, and extracellular signal regulated kinase (ERK). Studies [21] found that pretreatment with luteolin could reduce the production of proinflammatory cytokines such as TNF-α, IL-6, and inflammatory mediator nitric oxide (NO) which produced by lipopolysaccharide (LPS)-stimulated MH-S cells of mouse alveolar macrophages. Studies have illustrated that ß-sitosterol exerts cholesterol-lowering, antioxidant, and anti-inflammatory effects [22, 23]. The above studies showed that the main active ingredients of RSRN had anti-inflammatory, antioxidation stress, hypoglycemic and lipid-lowering effects, and cardiovascular system protection.A total of 106 drug-disease core targets were obtained, and the genes were used to constructed the core target gene topology network and the KEGG network. The top six target genes from the KEGG network were merged with the candidate genes from the core target gene topology network; we obtained the key target genes including AKT1, RELA, CHUK, MAPK8, MAPK14, and CCND1. The key targets are mainly involved in NF-κB/MAPK signaling pathway, PI3K-Akt signaling pathway, atherosclerosis, and inflammation-related signaling pathways. Akt is the central link of PI3K/Akt signaling pathway, and it plays an important role in regulating cell survival, protein synthesis, angiogenesis, and insulin-dependent metabolic cell response [24]. Akt can inhibit apoptosis, phosphorylate caspase-9 precursor [25, 26]. Nuclear factor κB (NF-κB) plays an important role in the regulation of gene transcription related to inflammation, cell proliferation, differentiation and apoptosis, immune response, and tumor formation; the human NF-κB family consists of five members: P50/P105, p52/P100, p65/RELA, RELB, and c-Rel, which are encoded by NF-κB1, NF-κB2, RELA, RELB, and REL genes [27]. CHUK (also known as IKK α, IKK α) is the upstream component of signal transduction pathway that directly enters the nucleus to regulate gene expression, and it is also a component of activating cytokine protein complex, studies have shown that gene mutation of CHUK is associated with hypertension and lipid abnormality [28–30]. These studies indicated that the key targets have the functions of regulating lipid metabolism, glucose metabolism, and immune regulation, which were of great significance for the prevention and treatment of hyperlipidemia, and also had certain effects on the complications of atherosclerosis and Alzheimer's disease.The core targets of RSRN-active ingredients in the treatment of hyperlipidemia-related diseases may involve in the response to lipopolysaccharide, oxidative stress, and DNA binding transcription factor binding. Oxidative stress plays an important role in the pathogenesis of atherosclerosis (AS), hypertension, metabolic syndrome, hypercholesterolemia, Alzheimer's disease, aging, and cancer [31]. Lipopolysaccharide (LPS) activates downstream target genes by activating TLR4/NF-κB signaling pathways, thus to release inflammatory factors such as TNF-α, IL-8, IL-1, and IL-6 [32]. Endotoxin is an inflammatory reaction promoter of LPS in the outer membrane of Gram-negative bacteria; it is the main ligand of Toll like receptor; and it has been confirmed that endotoxin plays an important role in the process and progress of AS [33, 34].The main pathways of RSRN may be involved in AGE-RAGE signaling pathway, cell fluid shear stress and atherosclerosis, and PI3K-Akt signaling pathway. Advanced glycosylated compounds (AGEs) are complex compounds produced by nonenzymatic glycosylation and oxidation of proteins, lipids, and nucleic acids; they can activate AGE-RAGE signaling pathway, MAPK signaling pathway, and NF-κB signaling pathway, leading to the expression of proinflammatory cytokines such as IL-1, IL-6, and TNF–α, and the release of VCAM1, VEGF, and RAGE, so as to promote the development of atherosclerosis [35]. Cell fluid shear stress and atherosclerotic pathway play an important role in the development of dyslipidemia to atherosclerosis; it has been reported that the wall concentration of lipid in the slow flow area was higher than that in the high-speed laminar flow area, and the action time of lipid and arterial wall was prolonged, which was easy to cause atherosclerosis, and it also can promote oxidative stress, increase the production of ox LDL, upregulate the expression of NF-κB, thus to promote inflammatory response [36, 37]. PI3K/Akt signaling pathway plays an important role in lipid metabolism and inflammation regulation; inhibition of PI3K/Akt signaling pathway can significantly reduce serum-free fatty acids, cholesterol, and triglyceride [38, 39], and it also inhibited the secretion of proinflammatory mediators such as TNF-α and IL-1 ß [40]. It can be speculated that RSRN may regulate dyslipidemia and prevent or delay the occurrence and development of AS through regulation of endocrine, metabolic, and inflammatory pathways.Studies have found that hyperlipidemia is closely related to hypothalamic inflammatory response. Hypothalamic inflammation leads to the occurrence of obesity-based metabolic diseases. A short-term high-fat diet can increase the expression of biomarkers and promote inflammatory response in the basal hypothalamus to form a transient inflammation. Under the condition of long-term high-fat diet, hypothalamic glial hyperplasia, and nerve injury can promote the occurrence of hypothalamic inflammation [41]. In this experimental study, the results showed that the expression of NF-κB p65, TNF-α, IL-1β, IL-6 in hypothalamic nucleus of OVX group was significantly increased, which indicated that NF-κB signal pathway and inflammatory cytokines were activated under the stimulation of intracellular and extracellular signals; after treatment with RSRN, the expression of activated NF-κ B (p65) in nucleus was significantly reduced, and the expression of TNF-α, IL-1β, and IL-6 was also significantly reduced in RSRN group; the results showed that RSRN could inhibit the activity of NF-κB and reduce the release of inflammatory cytokines. In the future, it is necessary to further explore the relationship between target protein and the upstream and downstream molecules of the signaling pathway and the specific regulatory mechanisms and confirm the curative effect through clinical trials.


## 5. Conclusion

In this study, a total of 187 potential active components and 106 related core targets were obtained and identified overall. Then after the Metascape enrichment analysis, RSRN may regulate AKT1, NF-κBp65, IKK α, TNF-α, IL-1 β, IL-6 through TNF signaling pathway, PI3K-Akt signaling pathway, and NF-kappa B signaling pathway, so as to regulate lipid metabolism, inflammatory response, and prevent or delay the development of atherosclerotic diseases. This study suggests that RSRN may be used in the treatment of hyperlipidemia and related diseases. Due to the limitation of database data and corresponding analysis algorithms, the results may be biased, and further in vitro and in vivo studies are needed to verify the results.



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*Source: 1860508-2021-09-21.xml*