Skip to main content
NCBI home page
Evidence-based Complementary and Alternative Medicine : eCAM logoLink to Evidence-based Complementary and Alternative Medicine : eCAM
. 2021 Sep 20;2021:1860508. doi: 10.1155/2021/1860508

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

Hongjin Wu 1,✉, Weiwei Dai 1, Libo Wang 1, Jie Zhang 1, Chenglong Wang 1
PMCID: PMC8478564  PMID: 34594388

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.

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.

Figure 1

Open in a new tab

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 with R 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 with R 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 of Curculigo 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.

Figure 2

Open in a new tab

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.

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 as p < 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 of Curculigo 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 ID Components OB% DL Herbs
MOL001506 Supraene 33.6 0.42 Ba ji tian
MOL002879 Diop 43.6 0.39 Ba ji tian
MOL002883 Ethyl oleate (NF) 32.4 0.19 Ba ji tian
MOL000358 Beta-sitosterol 36.9 0.75 Ba ji tian
MOL000359 Sitosterol 36.9 0.75 Ba ji tian
MOL006147 Alizarin-2-methylether 32.8 0.21 Ba ji tian
MOL009495 2-Hydroxy-1,5-dimethoxy-6-(methoxymethyl)-9,10-anthraquinone 95.9 0.37 Ba ji tian
MOL009496 1,5,7-Trihydroxy-6-methoxy-2-methoxymethylanthracenequinone 80.4 0.38 Ba ji tian
MOL009500 1,6-Dihydroxy-5-methoxy-2-(methoxymethyl)-9,10-anthraquinone 105 0.34 Ba ji tian
MOL009503 1-Hydroxy-3-methoxy-9,10-anthraquinone 104 0.21 Ba ji tian
MOL009504 1-Hydroxy-6-hydroxymethylanthracenequinone 81.8 0.21 Ba ji tian
MOL009513 2-Hydroxy-1,8-dimethoxy-7-methoxymethylanthracenequinone 112 0.37 Ba ji tian
MOL009519 (2R,3S) -(+)-3′,5-dihydroxy-4,7-dimethoxydihydroflavonol 77.2 0.33 Ba ji tian
MOL009524 3beta,20(R),5-alkenyl-stigmastol 36.9 0.75 Ba ji tian
MOL009525 3beta-24S(R)-butyl-5-alkenyl-cholestol 35.4 0.82 Ba ji tian
MOL009537 Americanin A 46.7 0.35 Ba ji tian
MOL009541 Asperuloside tetraacetate 45.5 0.82 Ba ji tian
MOL009551 Isoprincepin 49.1 0.77 Ba ji tian
MOL009558 2-Hydroxyethyl 5-hydroxy-2-(2-hydroxybenzoyl)-4-(hydroxymethyl)benzoate 62.3 0.26 Ba ji tian
MOL009562 Ohioensin-A 38.1 0.76 Ba ji tian
MOL000358 Beta-sitosterol 36.9 0.75 Dang gui
MOL000449 Stigmasterol 43.8 0.76 Dang gui
MOL001454 Berberine 36.9 0.78 Huang bo
MOL001458 Coptisine 30.7 0.86 Huang bo
MOL002636 Kihadalactone A 34.2 0.82 Huang bo
MOL013352 Obacunone 43.3 0.77 Huang bo
MOL002641 Phellavin_qt 35.9 0.44 Huang bo
MOL002643 Delta 7-stigmastenol 37.4 0.75 Huang bo
MOL002644 Phellopterin 40.2 0.28 Huang bo
MOL002651 Dehydrotanshinone II A 43.8 0.4 Huang bo
MOL002652 delta7-dehydrosophoramine 54.5 0.25 Huang bo
MOL002656 Dihydroniloticin 36.4 0.81 Huang bo
MOL002659 Kihadanin A 31.6 0.7 Huang bo
MOL002660 Niloticin 41.4 0.82 Huang bo
MOL002662 Rutaecarpine 40.3 0.6 Huang bo
MOL002663 Skimmianin 40.1 0.2 Huang bo
MOL002666 Chelerythrine 34.2 0.78 Huang bo
MOL000449 Stigmasterol 43.8 0.76 Huang bo
MOL002668 Worenine 45.8 0.87 Huang bo
MOL002670 Cavidine 35.6 0.81 Huang bo
MOL002671 Candletoxin A 31.8 0.69 Huang bo
MOL002672 Hericenone H 39 0.63 Huang bo
MOL002673 Hispidone 36.2 0.83 Huang bo
MOL000358 Beta-sitosterol 36.9 0.75 Huang bo
MOL000622 Magnograndiolide 63.7 0.19 Huang bo
MOL000762 Palmidin A 35.4 0.65 Huang bo
MOL000785 Palmatine 64.6 0.65 Huang bo
MOL000787 Fumarine 59.3 0.83 Huang bo
MOL000790 Isocorypalmine 35.8 0.59 Huang bo
MOL000098 Quercetin 46.4 0.28 Huang bo
MOL001131 Phellamurin_qt 56.6 0.39 Huang bo
MOL001455 (S)-canadine 53.8 0.77 Huang bo
MOL001771 Poriferast-5-en-3beta-ol 36.9 0.75 Huang bo
MOL001601 1,2,5,6-Tetrahydrotanshinone 38.75 0.36 Dan shen
MOL001659 Poriferasterol 43.83 0.76 Dan shen
MOL001771 Poriferast-5-en-3beta-ol 36.91 0.75 Dan shen
MOL001942 Isoimperatorin 45.46 0.23 Dan shen
MOL002222 Sugiol 36.11 0.28 Dan shen
MOL002651 Dehydrotanshinone II A 43.76 0.4 Dan shen
MOL002776 Baicalin 40.12 0.75 Dan shen
MOL000569 Digallate 61.85 0.26 Dan shen
MOL000006 Luteolin 36.16 0.25 Dan shen
MOL006824 α-Amyrin 39.51 0.76 Dan shen
MOL007036 5,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one 33.77 0.29 Dan shen
MOL007041 2-Isopropyl-8-methylphenanthrene-3,4-dione 40.86 0.23 Dan shen
MOL007045 3α-Hydroxytanshinone?a 44.93 0.44 Dan shen
MOL007048 (E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid 48.24 0.31 Dan shen
MOL007049 4-Methylenemiltirone 34.35 0.23 Dan shen
MOL007050 2-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde 62.78 0.4 Dan shen
MOL007051 6-o-Syringyl-8-o-acetyl shanzhiside methyl ester 46.69 0.71 Dan shen
MOL007058 Formyltanshinone 73.44 0.42 Dan shen
MOL007059 3-Beta-hydroxymethyllenetanshiquinone 32.16 0.41 Dan shen
MOL007061 Methylenetanshinquinone 37.07 0.36 Dan shen
MOL007063 Przewalskin a 37.11 0.65 Dan shen
MOL007064 Przewalskin b 110.32 0.44 Dan shen
MOL007068 Przewaquinone B 62.24 0.41 Dan shen
MOL007069 Przewaquinone c 55.74 0.4 Dan shen
MOL007070 (6S,7 R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-dione 41.31 0.45 Dan shen
MOL007071 Przewaquinone f 40.31 0.46 Dan shen
MOL007077 Sclareol 43.67 0.21 Dan shen
MOL007079 Tanshinaldehyde 52.47 0.45 Dan shen
MOL007081 Danshenol B 57.95 0.56 Dan shen
MOL007082 Danshenol A 56.97 0.52 Dan shen
MOL007085 Salvilenone 30.38 0.38 Dan shen
MOL007088 Cryptotanshinone 52.34 0.4 Dan shen
MOL007093 Dan-shexinkum d 38.88 0.55 Dan shen
MOL007094 Danshenspiroketallactone 50.43 0.31 Dan shen
MOL007098 Deoxyneocryptotanshinone 49.4 0.29 Dan shen
MOL007100 Dihydrotanshinlactone 38.68 0.32 Dan shen
MOL007101 Dihydrotanshinone I 45.04 0.36 Dan shen
MOL007105 Epidanshenspiroketallactone 68.27 0.31 Dan shen
MOL002331 N-Methylflindersine 32.4 0.18 Huang bo
MOL002894 Berberrubine 35.7 0.73 Huang bo
MOL005438 Campesterol 37.6 0.71 Huang bo
MOL006392 Dihydroniloticin 36.4 0.82 Huang bo
MOL006401 Melianone 40.5 0.78 Huang bo
MOL006413 Phellochin 35.4 0.82 Huang bo
MOL006422 Thalifendine 44.4 0.73 Huang bo
MOL001607 ZINC03982454 36.9 0.76 Xian Mao
MOL003578 Cycloartenol 38.7 0.78 Xian Mao
MOL000358 Beta-sitosterol 36.9 0.75 Xian Mao
MOL004114 3,2′,4′,6′-tetrahydroxy-4,3′-dimethoxy chalcone 52.7 0.28 Xian Mao
MOL004125 Curculigoside B_qt 83.4 0.19 Xian Mao
MOL004146 Curculigosaponin C 39.3 0.19 Xian Mao
MOL000449 Stigmasterol 43.8 0.76 Xian Mao
MOL001510 24-Epicampesterol 37.6 0.71 Xian Mao
MOL001645 Linoleyl acetate 42.1 0.2 Yin yang huo
MOL001771 Poriferast-5-en-3beta-ol 36.9 0.75 Yin yang huo
MOL001792 DFV 32.8 0.18 Yin yang huo
MOL003044 Chryseriol 35.9 0.27 Yin yang huo
MOL003542 8-Isopentenyl-kaempferol 38 0.39 Yin yang huo
MOL000359 Sitosterol 36.9 0.75 Yin yang huo
MOL000422 Kaempferol 41.9 0.24 Yin yang huo
MOL004367 Olivil 62.2 0.41 Yin yang huo
MOL004373 Anhydroicaritin 45.4 0.44 Yin yang huo
MOL004380 C-Homoerythrinan,1,6-didehydro-3,15,16-trimethoxy-, (3. Beta)- 39.1 0.49 Yin yang huo
MOL004382 Yin yang huo A 57 0.77 Yin yang huo
MOL004384 Yin yang huo C 45.7 0.5 Yin yang huo
MOL004386 Yin yang huo E 51.6 0.55 Yin yang huo
MOL004388 6-Hydroxy-11,12-dimethoxy-2,2-dimethyl-1,8-dioxo-2,3,4,8-tetrahydro-1h-isochromeno[3,4-h] isoquinolin-2-ium 60.6 0.66 Yin yang huo
MOL004391 8-(3-Methylbut-2-enyl)-2-phenyl-chromone 48.5 0.25 Yin yang huo
MOL004394 Anhydroicaritin-3-O-alpha-L-rhamnoside 41.6 0.61 Yin yang huo
MOL004396 1,2-bis(4-hydroxy-3-methoxyphenyl) propan-1,3-diol 52.3 0.22 Yin yang huo
MOL004425 Icariin 41.6 0.61 Yin yang huo
MOL004427 Icariside A7 31.9 0.86 Yin yang huo
MOL000006 Luteolin 36.2 0.25 Yin yang huo
MOL000622 Magnograndiolide 63.7 0.19 Yin yang huo
MOL000098 Quercetin 46.4 0.28 Yin yang huo
MOL001677 Asperglaucide 58 0.52 Zhi mu
MOL001944 Marmesin 50.3 0.18 Zhi mu
MOL003773 Mangiferolic acid 36.2 0.84 Zhi mu
MOL000422 Kaempferol 41.9 0.24 Zhi mu
MOL004373 Anhydroicaritin 45.4 0.44 Zhi mu
MOL004489 Anemarsaponin F_qt 60.1 0.79 Zhi mu
MOL004492 Chrysanthemaxanthin 38.7 0.58 Zhi mu
MOL004497 Hippeastrine 51.7 0.62 Zhi mu
MOL004514 Timosaponin B III_qt 35.3 0.87 Zhi mu
MOL000449 Stigmasterol 43.8 0.76 Zhi mu
MOL004528 Icariin I 41.6 0.61 Zhi mu
MOL004540 Anemarsaponin C_qt 35.5 0.87 Zhi mu
MOL004542 Anemarsaponin E_qt 30.7 0.86 Zhi mu
MOL000483 (Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl) ethyl] acrylamide 118 0.26 Zhi mu
MOL000546 Diosgenin 80.9 0.81 Zhi mu
MOL000631 Coumaroyltyramine 113 0.2 Zhi mu
MOL007107 C09092 36.07 0.25 Dan shen
MOL007108 Isocryptotanshi-none 54.98 0.39 Dan shen
MOL007111 Isotanshinone II 49.92 0.4 Dan shen
MOL007115 Manool 45.04 0.2 Dan shen
MOL007118 Microstegiol 39.61 0.28 Dan shen
MOL007119 Miltionone I 49.68 0.32 Dan shen
MOL007120 Miltionone II 71.03 0.44 Dan shen
MOL007121 Miltipolone 36.56 0.37 Dan shen
MOL007122 Miltirone 38.76 0.25 Dan shen
MOL007123 Miltirone II 44.95 0.24 Dan shen
MOL007124 Neocryptotanshinone ii 39.46 0.23 Dan shen
MOL007125 Neocryptotanshinone 52.49 0.32 Dan shen
MOL007127 1-Methyl-8,9-dihydro-7h-naphtho[5,6-g] benzofuran-6,10,11-trione 34.72 0.37 Dan shen
MOL007130 Prolithospermic acid 64.37 0.31 Dan shen
MOL007132 (2R)-3-(3,4-dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl) acryloyl] oxy-propionic acid 109.38 0.35 Dan shen
MOL007140 (Z)-3-[2-[(E)-2-(3,4-dihydroxyphenyl) vinyl]-3,4-dihydroxy-phenyl] acrylic acid 88.54 0.26 Dan shen
MOL007141 Salvianolic acid g 45.56 0.61 Dan shen
MOL007142 Salvianolic acid j 43.38 0.72 Dan shen
MOL007143 Salvilenone I 32.43 0.23 Dan shen
MOL007145 Salviolone 31.72 0.24 Dan shen
MOL007149 NSC 122421 34.49 0.28 Dan shen
MOL007150 (6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-quinone 75.39 0.46 Dan shen
MOL007151 Tanshindiol B 42.67 0.45 Dan shen
MOL007152 Przewaquinone E 42.85 0.45 Dan shen
MOL007154 Tanshinone iia 49.89 0.4 Dan shen
MOL007155 (6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7h-naphtho[8,7-g] benzofuran-10,11-dione 65.26 0.45 Dan shen
MOL007156 Tanshinone VI 45.64 0.3 Dan shen
MOL002897 Epiberberine 43.09 0.78 Huang lian
MOL002903 (R)-canadine 55.37 0.77 Huang lian
MOL002904 Berlambine 36.68 0.82 Huang lian
MOL002907 Corchoroside A_qt 104.95 0.78 Huang lian
MOL000622 Magnograndiolide 63.71 0.19 Huang lian
MOL000762 Palmidin A 35.36 0.65 Huang lian
MOL000785 Palmatine 64.6 0.65 Huang lian
MOL000098 Quercetin 46.43 0.28 Huang lian
MOL001458 Coptisine 30.67 0.86 Huang lian
MOL002668 Worenine 45.83 0.87 Huang lian
MOL008647 Moupinamide 86.71 0.26 Huang lian
Open in a new tab

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 (Figure 3(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.

Figure 3

Open in a new tab

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.

Table 2.

The active component and key target of RSRN.

Compound Type Betweenness Closeness Degree Eigenvector
Quercetin Mol 6956.367 0.001616 426 0.172085
Kaempferol Mol 1074.972 0.001422 114 0.093268
Stigmasterol Mol 234.9962 0.001351 112 0.071736
Luteolin Mol 966.566 0.001414 110 0.077567
Beta-sitosterol Mol 188.0214 0.001361 88 0.076787
Anhydroicaritin Mol 173.2552 0.001372 66 0.083024
Danshinone II A Mol 1130.871 0.00122 41 0.073208
Dehydrotanshinone II A Mol 26.81371 0.001348 40 0.06437
Palmatine Mol 16.99011 0.001297 34 0.054885
C-homoerythrinan, 1,6-didehydro-3,15,16-trimethoxy-, (3. Beta.)- Mol 292.7947 0.001333 32 0.070996
PTGS2 Gene 6853.524 0.001597 231 0.158477
PTGS1 Gene 1835.112 0.001504 148 0.126258
SCN5A Gene 1382.629 0.001497 137 0.127246
HSP90AA1 Gene 2411.688 0.001497 131 0.107942
NCOA2 Gene 2395.628 0.001481 130 0.098189
ADRB2 Gene 1468.898 0.001495 127 0.12509
Open in a new tab

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 (Figure 4(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.

Figure 4

Open in a new tab

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.

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 (Figure 5(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.

Figure 5

Open in a new tab

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 (Figures 6(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.

Figure 6

Open in a new tab

Functional enrichment analysis. (a, b) GO functional enrichment analysis; (c, d) KEGG functional enrichment analysis.

Figure 7.

Figure 7

Open in a new tab

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.

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

The immunohistochemistry 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.

Figure 8

Open in a new tab

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.

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.

Figure 9

Open in a new tab

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.

Figure 10.

Figure 10

Open in a new tab

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.

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.

Acknowledgments

This study was supported by grant from Natural Science Foundation of Shanghai, China (Project no. 16ZR1447300).

Abbreviations

ADME:

Absorption, distribution, metabolism, and excretion

OB:

Oral bioavailability

DL:

Drug-likeness

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

CC:

Cellular components

MF:

Molecular functions

BP:

Biological processes

TCMSP:

Traditional Chinese medicine systems pharmacology database and analysis platform.

Data Availability

All the data generated or analyzed during this study are included within the paper.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors' Contributions

Hongjin Wu conceived and designed the experiment. Weiwei Dai analyzed the data and edited the manuscript. Jie Zhang, Libo Wang, and Chenglong Wang were responsible for figure drawing and table design and the manuscript revising. All the authors have read and approved the final manuscript.

References

  • 1.Liu W., Fu S., Fan G. W. Treatment progress of dyslipidemia in postmenopausal women. Chinese Journal of Gerontology . 2016;36(16):p. 4110. [Google Scholar]
  • 2.Wang N., Chen P., Hong Y. F., Sun J., Qin Y., Du C. H. Progress of hyperlipidemia treated by traditional Chinese Medicine. Journal of Practical traditional Chinese Medicine . 2019;35(2):247–249. [Google Scholar]
  • 3.Li Y., Sun K. H., Bai F., Luo H. M., Wu Z. Z. Progress on pathogenesis of hyperlipidemia related diseases. Journal of Liaoning University of Traditional Chinese Medicine . 2019;21(4):84–87. [Google Scholar]
  • 4.Zhou Y. Z., Lu J. Q. Progress in the treatment of atherosclerosis with Chinese Medicine. Hunan Journal of Traditional Chinese medicine . 2017;33(4):169–171. [Google Scholar]
  • 5.Qi X. Probe and analysis on pathogenic mechanism of climacterium cobined with hyperlipemia. Jilin Journal of Traditional Chinese Medicine . 2007;27(1):4–6. [Google Scholar]
  • 6.Wu H., Wei-wei D., Wang L., Zhang J., Wang C. Effects of reducing the south and reinforcing the north method on sex hormones and lipid metabolism of ovariectomized female rats. SCJTCMP . 2018;33(9):4114–4117. [Google Scholar]
  • 7.Wu H. J., Zhang Z. F., Xu J. T., Zhang T. T., Xu L. W., Li S. G. Changes of pulse diagram parameter and sex hormone level before and after the Chinese medicine treatment in perimenopausal syndrome with syndrome of yin deficiency of liver and kidney. CJTCMP . 2017;32(4):1870–1873. [Google Scholar]
  • 8.Jiang Y. H., Wang Y. H., Liu Y. J., Xiang L. H., Zhang Z. G., Chen Y. J. Effect of erxian decoction on myocardial microvessels and hemorheology in ovariectomized rats. Chinese journal of experimental Traditional Medical formulae . 2020;26(24):59–67. [Google Scholar]
  • 9.Wang Y. H. Changes of Cardiac Function and Glucose and Lipid Metabolism in Ovariectomized Rats and Intervention of Erxian Tang . Beijing, China: Chinese Academy of traditional Chinese Medicine; 2020. [Google Scholar]
  • 10.Chen G., Huang C., Liu Y., et al. A network pharmacology approach to uncover the potential mechanism of yinchensini decoction. Evidence-based Complementary and Alternative Medicine . 2018;2018:14. doi: 10.1155/2018/2178610.2178610 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Ru J., Li P., Wang J., et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. Journal of Cheminformatics . 2014;6(1):p. 13. doi: 10.1186/1758-2946-6-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Shen X., Zhao Z., Wang H., Guo Z., Hu B., Zhang G. Elucidation of the anti-inflammatory mechanisms of Bupleuri and Scutellariae radix using system pharmacological analyses. Mediators of Inflammation . 2017;2017:10. doi: 10.1155/2017/3709874.3709874 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Wu N., Mao X. K., Wei W., Yu N., Rao Z. Y., Li H. S. Comparative study on the molecular mechanism of the treatment of ulcerative colitis by Four prescription for recuperating intestines based on network pharmacology and molecular docking. Journal of Chinese Medicinal Materials . 2020;1:186–192. [Google Scholar]
  • 14.Yao Y., Wu Y., Ma Z., Zhang Y. Y., Li Y., Bian H. M. Effect of Liuwei Dihuang Formula and its compatibility on rats with ovariectomy-induced atherosclerosis. Chinese Traditional Patent Medicine . 2016;38(10):2111–2117. [Google Scholar]
  • 15.Ni H. C., Li J., Jin Y., Zang H. M., Peng L. The experimental animal model of hyperlipidemia and hyperlipidemic fatty liver in rats. Chinese Pharmacological Bulletin . 2004;20(6):703–706. [Google Scholar]
  • 16.Sun Q., Zhong W., Zhang W., Zhou Z. Defect of mitochondrial respiratory chain is a mechanism of ROS overproduction in a rat model of alcoholic liver disease: role of zinc deficiency. American Journal of Physiology - Gastrointestinal and Liver Physiology . 2016;310(3):G205–G214. doi: 10.1152/ajpgi.00270.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Imran M., Rauf A., Shah Z. A., et al. Chemo-preventive and therapeutic effect of the dietary flavonoid kaempferol: a comprehensive review. Phytotherapy Research . 2019;33(2):263–275. doi: 10.1002/ptr.6227. [DOI] [PubMed] [Google Scholar]
  • 18.Chang C., Tzeng T.-F., Liou S.-S., Chang Y.-S., Liu I.-M. Kaempferol regulates the lipid-profile in high-fat diet-fed rats through an increase in hepatic PPARαLevels. Planta Medica . 2011;77(17):1876–1882. doi: 10.1055/s-0031-1279992. [DOI] [PubMed] [Google Scholar]
  • 19.Cui Y., Hou P., Li F., et al. Quercetin improves macrophage reverse cholesterol transport in apolipoprotein E-deficient mice fed a high-fat diet. Lipids in Health and Disease . 2017;16(1):p. 9. doi: 10.1186/s12944-016-0393-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Lee E.-J., Oh S.-Y., Sung M.-K. Luteolin exerts anti-tumor activity through the suppression of epidermal growth factor receptor-mediated pathway in MDA-MB-231 ER-negative breast cancer cells. Food and Chemical Toxicology . 2012;50(11):4136–4143. doi: 10.1016/j.fct.2012.08.025. [DOI] [PubMed] [Google Scholar]
  • 21.Chen C.-Y., Peng W.-H., Tsai K.-D., Hsu S.-L. Luteolin suppresses inflammation-associated gene expression by blocking NF-κB and AP-1 activation pathway in mouse alveolar macrophages. Life Sciences . 2007;81(23-24):1602–1614. doi: 10.1016/j.lfs.2007.09.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wong H. S., Chen N., Leong P. K., Ko K. M. β-Sitosterol enhances cellular glutathione redox cycling by reactive oxygen species generated from mitochondrial respiration: protection against oxidant injury in H9c2 cells and rat hearts. Phytotherapy Research . 2014;28(7):999–1006. doi: 10.1002/ptr.5087. [DOI] [PubMed] [Google Scholar]
  • 23.Lampronti I., Dechecchi M. C., Rimessi A., et al. β-Sitosterol reduces the expression of chemotactic cytokine genes in cystic fibrosis bronchial epithelial cells. Frontiers in Pharmacology . 2017;8:p. 236. doi: 10.3389/fphar.2017.00236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Vasudevan K. M., Garraway L. A. Akt signaling in physiology and disease. Current Topics in Microbiology and Immunology . 2010;347(1):105–133. doi: 10.1007/82_2010_66. [DOI] [PubMed] [Google Scholar]
  • 25.Quan J.-H., Cha G.-H., Zhou W., Chu J.-Q., Nishikawa Y., Lee Y.-H. Involvement of PI 3 kinase/Akt-dependent Bad phosphorylation in Toxoplasma gondii-mediated inhibition of host cell apoptosis. Experimental Parasitology . 2013;133(4):462–471. doi: 10.1016/j.exppara.2013.01.005. [DOI] [PubMed] [Google Scholar]
  • 26.Ngoc T., Park M. A., Jacqueline C. S., et al. HnRNP U enhances caspase-9 splicing and is modulated by AKT-dependent phosphorylation of HnRNP L. Journal of Biological Chemistry . 2013;288(12):8575–8584. doi: 10.1074/jbc.M112.443333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Yang L., Deng J., Deng Z. H. Progress of NF-κB and its gene polymorphism in relation to inflammation and tumor. Guizhou Medical Journal . 2016;40(10):1098–1099. [Google Scholar]
  • 28.Basson J., de Las Fuentes L., Rao D..C. Single nucleotide polymorphism-single nucleotide polymorphism interactions among inflammation genes in the genetic architecture of blood pressure in the framingham heart study. American Journal of Hypertension . 2015;28(2):248–255. doi: 10.1093/ajh/hpu132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Asselbergs F. W., Guo Y., van Iperen E. P., et al. Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. The American Journal of Human Genetics . 2012;91(5):823–838. doi: 10.1016/j.ajhg.2012.08.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wei Q. G., Wu Y. L., Chen Z. W., Zhou J. Y., Xie J. J., Gu L. Influence of Helix-loop-helix domain diffusion kinase (CHUK) gene polymorphism on lipid metabolism of Ischemic stroke and phlegm stasis resistance syndrome. Yunnan zhongyi Zhongyao Zazhi . 2015;36(12):61–64. [Google Scholar]
  • 31.Ray P. D., Huang B.-W., Tsuji Y. Reactive oxygen species (ROS) homeostasis and redox regulation in cellular signaling. Cellular Signalling . 2012;24(5):981–990. doi: 10.1016/j.cellsig.2012.01.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wang W., Liu Z. Y., Chen G. R. Progress in studies of endotoxin receptor and anti-endotoxin. Chinese archives of traditional Chinese Medicine . 2016;34(10):2367–2370. [Google Scholar]
  • 33.Hayashi C., Madrigal A. G., Liu X., et al. Pathogen-mediated inflammatory atherosclerosis is mediated in part via Toll-like receptor 2-induced inflammatory responses. Journal of Innate Immunity . 2010;2(4):334–343. doi: 10.1159/000314686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Unkelbach K., Gardemann A., Kostrzewa M., Philipp M., Tillmanns H., Haberbosch W. A new promoter polymorphism in the gene of lipopolysaccharide receptor CD14 is associated with expired myocardial infarction in patients with low atherosclerotic risk profile. Arteriosclerosis, Thrombosis, and Vascular Biology . 1999;19(4):932–938. doi: 10.1161/01.atv.19.4.932. [DOI] [PubMed] [Google Scholar]
  • 35.Hegab Z., Gibbons S., Ludwig N., Mamas M. A. Role of advanced glycation end products in cardiovascular disease. World Journal of Cardiology . 2012;4(4):90–102. doi: 10.4330/wjc.v4.i4.90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Simmons R. D., Kumar S., Jo H. The role of endothelial mechanosensitive genes in atherosclerosis and omics approaches. Archives of Biochemistry and Biophysics . 2016;591:111–131. doi: 10.1016/j.abb.2015.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Adam J., Teng Z., Evans P. C., Gillard J. H., Samady H., Bennett M. R. Role of biomechanical forces in the natural history of coronary atherosclerosis. Nature Reviews Cardiology . 2016;13(4):210–220. doi: 10.1038/nrcardio.2015.203. [DOI] [PubMed] [Google Scholar]
  • 38.Sopasakis V. R., Liu P., Suzuki R., et al. Specific roles of the p110alpha isoform of phosphatidylinsositol 3-kinase in hepatic insulin signaling and metabolic regulation. Cell Metabolism . 2010;11(3):220–230. doi: 10.1016/j.cmet.2010.02.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Taniguchi C. M., Kondo T., Sajan M., et al. Divergent regulation of hepatic glucose and lipid metabolism by phosphoinositide 3-kinase via Akt and PKCλ/ζ. Cell Metabolism . 2006;3(5):343–353. doi: 10.1016/j.cmet.2006.04.005. [DOI] [PubMed] [Google Scholar]
  • 40.Xie S., Chen M., Yan B., He X., Chen X., Li D. Identification of a role for the PI3K/AKT/mTOR signaling pathway in innate immune cells. PloS One . 2014;9(4) doi: 10.1371/journal.pone.0094496.e94496 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Gong Y., Shi X., Niu J., et al. Hypothalamic inflammation and mechanism of pathogenesis for obesity. Journal of Liaoning University of TCM . 2014;16(7):85–88. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All the data generated or analyzed during this study are included within the paper.

Articles from Evidence-based Complementary and Alternative Medicine : eCAM are provided here courtesy of Wiley

RESOURCES