Molecular mechanism underlying the hypolipidemic effect of Shanmei Capsule based on network pharmacology and molecular docking

Abstract

BACKGROUND:

Shanmei Capsule is a famous preparation in China. However, the related mechanism of Shanmei Capsule against hyperlipidemia has yet to be revealed.

OBJECTIVE:

To elucidate underlying mechanism of Shanmei Capsule against hyperlipidemia through network pharmacology approach and molecular docking.

METHODS:

Active ingredients, targets of Shanmei Capsule as well as targets for hyperlipidemia were screened based on database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed via Database for Annotation, Visualization, and Integrated Discovery (DAVID) 6.8 database. Ingredient-target-disease-pathway network was visualized utilizing Cytoscape software and molecular docking was performed by Autodock Vina.

RESULTS:

Seventeen active ingredients in Shanmei Capsule were screened out with a closely connection with 34 hyperlipidemia-related targets. GO analysis revealed 40 biological processes, 5 cellular components and 29 molecular functions. A total of 15 signal pathways were enriched by KEGG pathway enrichment analysis. The docking results indicated that the binding activities of key ingredients for PPAR- $\alpha$ are equivalent to that of the positive drug lifibrate.

CONCLUSIONS:

The possible molecular mechanism mainly involved PPAR signaling pathway, Bile secretion and TNF signaling pathway via acting on MAPK8, PPAR $\gamma$ , MMP9, PPAR $\alpha$ , FABP4 and NOS2 targets.

Keywords

Shanmei Capsule hyperlipidemia network pharmacology molecular docking

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1. Introduction

Hyperlipidemia is one of the most prevalent global chronic metabolic diseases, which is closely related to many high-incidence cardiovascular diseases, such as hypertension, diabetes, coronary heart disease and atherosclerosis [1, 2, 3]. It is characterized as elevated contents of total cholesterol (TC), triglyceride (TG), low-density-lipoprotein cholesterol (LDL-C), as well as decreased high-density-lipoprotein cholesterol (HDL-C) levels [4]. With the elevating prevalence and morbidity, hyperlipidemia has become a critical global issue for public health. Regulating lipid metabolism disorders may be a potential approach to decelerating or preventing the progression of hyperlipidemia. At present, statins, such as atorvastatin, lovastatin and simvastatin, are among the most commonly used lipid-lowering drugs to reduce plasma lipids. Statins are generally regarded as safe and tolerable drugs, however, certain controversies remain. A survey analyzed all case reports from the FDA AE Reporting System database linking muscle-related adverse events to statin use. The results show that dose-dependent side effects occur across the statin class [5, 6, 7, 8]. Thus, it is necessary to develop and identify an alternative drug that may be valuable in regulating lipid metabolism. Compound formulae of traditional Chinese medicine (TCM) emphasizes emphasizes the synergism among active ingredients, with proper herbs and relevant dosage to synergize the desirable effects and minimize side effects integrally. Compared with Western medicine, TCM are considered relatively safe and produces fewer adverse reactions [9, 10].

TCM has been used for thousands of years for the treatment of hyperlipidemia in China, with advantages of multitarget mechanisms, remarkable curative effect and safety. They are featured by global regulation of glucose and lipid metabolism, as well as energy homeostasis via active ingredients in a prescription [11]. Shanmei Capsule, a famous preparation in China, is made of a combination of Crataegi Folium (called Shanzhaye in Chinese, the leaves of Grataegus pinnatifida Bge. var. major N.E. Br. or Grataegus pinnatifida Bge.) and Dahurian rose fruit (called Cimeiguo in Chinese, the fruit of Rosa davurica Pall. var. davurica) ethanol extract in proportion [12]. Crataegi Folium and Dahurian rose fruit both are well-known traditional medicinal plants, which have been confirmed to possess various biological and pharmacological activities, such as anti-inflammatory, antioxidant, hypocholesterolaemic and hypolipidaemic effects [13, 14, 15]. In clinical application, they are included in remedies on their own or in combination with other herbs to regulate lipid metabolism disorders induced by cardiovascular diseases. The above evidence suggests that Shanmei Capsule may be an alternative approach for treating hyperlipidemia. However, the hypolipidemic effect and related activity mechanisms of Shanmei Capsule against hyperlipidemia have yet to be revealed. Due to the complexity of active ingredients and the unknown synergistic effects of multiple components, it remains a laborious task to clarify the molecular mechanism of the Shanmei Capsule.

With the booming development of systems biology and bioinformatics, network pharmacology approach has been used successfully for the discovery of the key targets and their potential molecular mechanisms [16, 17]. Contrary to “one drug, one target, one disease” principle of traditional drug design, network pharmacology based on the “drug-disease-target” interaction network, which comprehensively and systematically evaluates the intervention or effect of drugs on diseases. Network pharmacology combines the multi-level information of “ingredient-disease-target-pathway”, which corresponded with the characteristics of TCM. Molecular docking is an important computer-aided drug discovery method, which can be used to predict the interactions between drug ingredients and target proteins, and the possible binding sites of drugs [18, 19]. Interactions between drug molecules and target proteins provide important information on drug discovery, which will help towards the development of drugs to treat hyperlipidemia.

In this study, network pharmacology approach was applied to investigate the relationships between active ingredients, core targets and pathways. Based on the results of network analysis and molecular docking, multi-ingredient, multi-target, and multi-pathway potential mechanisms related to Shanmei Capsule against hyperlipidemia were predicted.

2. Methods

2.1 Collection of active ingredients for Shanmei Capsule

Crataegi Folium and Dahurian rose fruit were used as keywords to screen the candidate ingredients of Shanmei Capsule. All the chemical ingredients were queried based on the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (http://tcmspw.com/tcmsp.php) and literatures. The structural information regarding biological activities of small molecules, including CAS numbers and Canonical SMILES strings, were obtained from the Scifinder database (https://scifinder.cas.org/scifinder/) and PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [20].

2.2 Target acquisition for drug and disease-related

Target prediction of Shanmei Capsule was performed using Swiss Target Prediction (http://www.swisstargetprediction.ch/) [21]. The Canonical SMILES strings obtained in 2.1 was introduced into the Swiss Target Prediction to obtain human-related targets for active ingredients. Using hyperlipidemia as a keyword, the candidate target of hyperlipidemia was searched by the GeneCard database (version 4.14, https://www.genecards.org/). After removing the duplicate targets, the overlapping targets related to active ingredients and hyperlipidemia were selected as the candidate targets, and then imported into the STRING database (http://string-db.org/) for gene interaction analysis.

2.3 GO and KEGG pathway enrichment analysis

Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis were carried out based on the Database for Annotation, Visualization and Integrated Discovery (DAVID, version 6.8, http://www.david.niaid.nih.gov). Results with values of $P<$ 0.01 were selected, and Human was defined as the species. DAVID is a powerful gene functional database, which can provide comprehensive functional annotations of pathways, including biological processes, cellular component and molecular functions. After getting the results, these pathways were visualized as a bubble chart by imageGP (http://www.ehbio.com/ImageGP/), a free online tool for data analysis.

2.4 Network construction and analysis

Network construction and analysis were performed using Cytoscape 3.3.0 (download from https://cytoscape.org/). The nodes in different shapes and colors of the network represented active ingredients, targets, pathways, and the edges indicated the interaction of them. In this study, degree refers to the number of directly connected nodes with one another. It is considered that the greater the number of degree is, the greater the influence is. The topological parameters of network, such as degree, were calculated by Network Analyzer (Cytoscape plugin).

2.5 Molecular docking

The candidate ingredients of Shanmei Capsule and lifibrate (positive control drug) were chosen for molecular docking, and the results were visualized using PyMOL software (https://www.pymol.org/). The detailed steps were as follows. Firstly, molecular structure of active ingredients and the protein crystal structure of the hyperlipidemia-related target were downloaded from the ZINC 15 (http://zinc.docking.org/) and RCSB PDB database (https://www.rcsb.org/), respectively. Prior to performing the docking process, the target protein structure was pretreated by AutoTools, including removal of water molecules, addition with hydrogens and so on. Secondly, the active pocket on protein structure was predicted by GetBox Plugin (PyMOL plugin) [22, 23]. The values of the utilized parameters in autodock software were presented in Table S1. Finally, molecular docking simulation was performed using the Autodock Vina software. According to the scoring method, the docking models with the lowest score and binding-energy value were selected and visualized for subsequent analysis.

2.6 ADME and toxicology prediction

The 2D structures (in .sdf format) and simplified molecular input line entry specification (SMILES) codes of 17 key active ingredients were downloaded from the PubChem database (http://pubchem.ncbi.nlm.nih.gov). Then, a SwissADME web tool (http://www.swissadme.ch/index.php) was employed to predict ADME (Absorption, Distribution, Metabolism and Excretion) parameters, including bioavailability score, gastrointestinal (GIT) absorption and blood-brain barrier (BBB). Toxicity analysis was done by Toxtree (version 3.1.0.1851), a generic open source application, which is able to estimate toxic hazard by making predictions for a number of toxicological endpoints by different modules, such as carcinogenicity prediction (genotoxic and non-genotoxic) and Ames carcinogenicity tests.

3. Results

3.1 Screening of Shanmei Capsule active ingredients

A total of 59 candidate active ingredients were screened from the TCMSP, including 25 from Crataegi Folium, 29 from Dahurian rose fruit, and 5 shared ingredients. Details are shown in Table 1. The obtained candidate ingredients and all of the potential targets were applied to construct a network of ingredient-target interactions, including 330 nodes (59 ingredients and 271 targets) and 928 edges, as shown in Fig. 1. The degree of targets was adopted as a characteristic topological parameter to define the significance of candidate ingredients. The interactions indicated that one ingredient could regulate numerous targets, such as quercetin, kaempferol, $\beta$ -sitosterol, isorhamnetin, ursolic acid, chlorogenic acid and so on, suggesting that Shanmei Capsule has multi-ingredients and multi-target characteristics for the treatment of hyperlipidemia.

Table 1
A list of the selected compounds in Shanmei Capsule for network analysis

Source	Compound	Molecular formula	CAS number
Crataegi Folium	Tyramine	C ${}_{8}$ H ${}_{11}$ NO	51-67-2
Crataegi Folium	Vitamin c	C ${}_{6}$ H ${}_{8}$ O ${}_{6}$	50-81-7
Crataegi Folium	Cyanidol	C ${}_{15}$ H ${}_{11}$ ClO ${}_{6}$	528-58-5
Crataegi Folium	Heriguard	C ${}_{16}$ H ${}_{18}$ O ${}_{9}$	327-97-9
Crataegi Folium	Caffeic acid	C ${}_{9}$ H ${}_{8}$ O ${}_{4}$	331-39-5
Crataegi Folium	Vitamin-G	C ${}_{6}$ H ${}_{8}$ O ${}_{6}$	83-88-5
Crataegi Folium	Isorhamnetin	C ${}_{16}$ H ${}_{12}$ O ${}_{7}$	480-19-3
Crataegi Folium	Vitexin	C ${}_{21}$ H ${}_{20}$ O ${}_{10}$	3681-93-4
Crataegi Folium	Caffeate	C ${}_{9}$ H ${}_{8}$ O ${}_{4}$	3843-74-1
Crataegi Folium	Nicotinic acid	C ${}_{6}$ H ${}_{5}$ NO ${}_{2}$	59-67-6
Crataegi Folium	Hyperin	C ${}_{21}$ H ${}_{20}$ O ${}_{12}$	482-36-0
Crataegi Folium	Stigmasterol	C ${}_{29}$ H ${}_{50}$ O	68555-08-8
Crataegi Folium	Orientin	C ${}_{21}$ H ${}_{20}$ O ${}_{11}$	28608-75-5
Crataegi Folium	Maslinic acid	C ${}_{30}$ H ${}_{48}$ O ${}_{4}$	4373-41-5
Crataegi Folium	Octacosanol	C ${}_{28}$ H ${}_{58}$ O	557-61-9
Crataegi Folium	Thiamine	C ${}_{12}$ H ${}_{18}$ Cl ${}_{2}$ N ${}_{4}$ OS	70-16-6
Crataegi Folium	Orbitol	C ${}_{6}$ H ${}_{14}$ O ${}_{6}$	50-70-4
Crataegi Folium	Ent-Epicatechin	C ${}_{15}$ H ${}_{14}$ O ${}_{6}$	35323-91-2
Crataegi Folium	p-coumaric acid	C ${}_{9}$ H ${}_{8}$ O ${}_{3}$	501-98-4
Crataegi Folium	Isoorientin	C ${}_{21}$ H ${}_{20}$ O ${}_{11}$	4261-42-1
Crataegi Folium	2-methoxyphenethylamine	C ${}_{9}$ H ${}_{13}$ NO	2045-79-6
Crataegi Folium	(1S)-1-phenylethanamine	C ${}_{8}$ H ${}_{11}$ N	2627-86-3
Crataegi Folium	Vitexin-2-O-rhamnoside	C ${}_{27}$ H ${}_{30}$ O ${}_{14}$	64820-99-1
Crataegi Folium	Diethylamine	C ${}_{4}$ H ${}_{11}$ N	109-89-7
Crataegi Folium	Valamine	C ${}_{4}$ H ${}_{11}$ N	78-81-9
Crataegi Folium/Dahurian rose fruit	Sitosterol	C ${}_{29}$ H ${}_{50}$ O	83-46-5
Crataegi Folium/Dahurian rose fruit	Rutin	C ${}_{27}$ H ${}_{30}$ O ${}_{16}$	153-18-4
Crataegi Folium/Dahurian rose fruit	Kaempferol	C ${}_{15}$ H ${}_{10}$ O ${}_{6}$	520-18-3
Crataegi Folium/Dahurian rose fruit	Ursolic acid	C ${}_{30}$ H ${}_{48}$ O ${}_{3}$	77-52-1
Crataegi Folium/Dahurian rose fruit	Quercetin	C ${}_{15}$ H ${}_{10}$ O ${}_{7}$	117-39-5
Dahurian rose fruit	luteolin-7-O-rutinoside	C ${}_{27}$ H ${}_{30}$ O ${}_{15}$	25694-72-8
Dahurian rose fruit	Hyperoside	C ${}_{21}$ H ${}_{20}$ O ${}_{12}$	482-36-0
Dahurian rose fruit	Isoliquiritigenin	C ${}_{15}$ H ${}_{12}$ O ${}_{4}$	961-29-5
Dahurian rose fruit	$\beta$ -sitosterol	C ${}_{29}$ H ${}_{50}$ O	64997-52-0
Dahurian rose fruit	Tiliroside	C ${}_{30}$ H ${}_{26}$ O ${}_{13}$	20316-62-5
Dahurian rose fruit	Hesperidin	C ${}_{28}$ H ${}_{34}$ O ${}_{15}$	520-26-3
Dahurian rose fruit	Oleanolic acid	C ${}_{30}$ H ${}_{48}$ O ${}_{3}$	508-02-1
Dahurian rose fruit	Stearic acid	C ${}_{18}$ H ${}_{36}$ O ${}_{2}$	57-11-4
Dahurian rose fruit	Palmitic acid	C ${}_{18}$ H ${}_{36}$ O ${}_{2}$	57-10-3
Dahurian rose fruit	Oleic acid	C ${}_{18}$ H ${}_{34}$ O ${}_{2}$	112-80-1
Dahurian rose fruit	Linoleic acid	C ${}_{18}$ H ${}_{32}$ O ${}_{2}$	60-33-3
Dahurian rose fruit	Linolenic acid	C ${}_{18}$ H ${}_{30}$ O ${}_{2}$	463-40-1
Dahurian rose fruit	Eicosadienoic acid	C ${}_{20}$ H ${}_{36}$ O ${}_{2}$	2091-39-6
Dahurian rose fruit	Strictinin	C ${}_{27}$ H ${}_{22}$ O ${}_{18}$	517-46-4
Dahurian rose fruit	Heptacosane	C ${}_{27}$ H ${}_{56}$	593-49-7
Dahurian rose fruit	Cerotic acid	C ${}_{26}$ H ${}_{52}$ O ${}_{2}$	506-46-7
Dahurian rose fruit	Daucosterol	C ${}_{35}$ H ${}_{60}$ O ${}_{6}$	474-58-8
Dahurian rose fruit	Betulinic acid	C ${}_{30}$ H ${}_{48}$ O ${}_{3}$	472-15-1
Dahurian rose fruit	Gallic acid	C ${}_{7}$ H ${}_{6}$ O ${}_{5}$	149-91-7
Dahurian rose fruit	Furfuryl acetate	C ${}_{7}$ H ${}_{8}$ O ${}_{3}$	623-17-6
Dahurian rose fruit	n-hexyl furan	C ${}_{10}$ H ${}_{16}$ O	3777-70-6
Dahurian rose fruit	2-furan-2-yl-pentanal	C ${}_{9}$ H ${}_{12}$ O ${}_{2}$	31681-26-2
Dahurian rose fruit	$\beta$ -sitosterol acetate	C ${}_{31}$ H ${}_{52}$ O ${}_{2}$	915-05-9

Table 1, continued
Source	Compound	Molecular formula	CAS number
Dahurian rose fruit	2-amino-propionic acid ethyl ester	C ${}_{5}$ H ${}_{11}$ NO ${}_{2}$	17344-99-9
Dahurian rose fruit	Isobutyl formate	C ${}_{5}$ H ${}_{10}$ O ${}_{2}$	542-55-2
Dahurian rose fruit	Isoamyl acetate	C ${}_{7}$ H ${}_{14}$ O ${}_{2}$	123-92-2
Dahurian rose fruit	Ethyl hexanoate	C ${}_{8}$ H ${}_{16}$ O ${}_{2}$	123-66-0
Dahurian rose fruit	Ethyl 1-acetylcyclopropane-1-carboxylate	C ${}_{8}$ H ${}_{12}$ O ${}_{3}$	32933-03-2
Dahurian rose fruit	Ethyl palmitoleate	C ${}_{18}$ H ${}_{34}$ O ${}_{2}$	56219-10-4

Figure 1.

Establishment of component-target network. Components are shown as red dovetails nodes and target are marked as purple circular nodes.

3.2 Prediction of candidate targets of hyperlipidemia

A total of 1018 potential targets to have correlations with hyperlipidemia were found via the GeneCards database. After merging hyperlipidemia-related targets and active ingredient targets, 34 overlapping targets were considered as candidate targets (Table 2, Fig. 2). Further linking of target ingredients with candidate targets based on Cytoscap 3.3.0 resulted in a network of disease-ingredient-target, with 51 nodes and 301 edges (Fig. 3). Among them, 17 red dovetail nodes represent active ingredients of Shanmei Capsule, and 34 purple ellipse nodes represent candidate targets of hyperlipidemia. After analyzing the topological characteristics of each node in the interaction network, it shows that the node degrees of eight targets, HMGCR, MMP9, MAPK8, PPAR $\alpha$ , PPAR $\gamma$ , FABP4, ALOX5 and NOS2 are more than 13, which implied that these targets might be key targets for hyperlipidemia. Previous study found that these targets were primarily involved in inflammatory reactions, lipid metabolism, etc.

Table 2
Related parameters of potential targets of Shanmei Capsule

No.	Uniprot ID	Target gene	Target protein
1	P15121	AKR1B1	Aldo-keto reductase family 1 member B1
2	O60218	AKR1B10	Aldo-keto reductase family 1 member B10
3	P08253	MMP2	72 kDa type IV collagenase
4	P14780	MMP9	Matrix metalloproteinase-9
5	P03956	MMP1	Interstitial collagenase
6	P08254	MMP3	Stromelysin-1
7	P10636	MAPT	Microtubule-associated protein tau
8	P10275	AR	Androgen receptor
9	P04035	HMGCR	3-hydroxy-3-methylglutaryl-coenzyme A reductase
10	P11511	CYP19A1	Aromatase
11	P01130	LDLR	Low-density lipoprotein receptor
12	P03372	ESR1	Estrogen receptor
13	Q92731	ESR2	Estrogen receptor beta
14	P09917	ALOX5	Arachidonate 5-lipoxygenase
15	P08183	ABCB1	ATP-dependent translocase ABCB1
16	P35869	AHR	Aryl hydrocarbon receptor
17	P04626	ERBB2	Receptor tyrosine-protein kinase erbB-2
18	P00533	EGFR	Epidermal growth factor receptor
19	P05164	MPO	Myeloperoxidase
20	Q9UNQ0	ABCG2	ATP-binding cassette sub-family G member 2
21	P15090	FABP4	Fatty acid-binding protein
22	P07148	FABP1	Fatty acid-binding protein
23	P37231	PPAR $\gamma$	Peroxisome proliferator-activated receptor gamma
24	Q07869	PPAR $\alpha$	Peroxisome proliferator-activated receptor alpha
25	P21554	CNR1	Cannabinoid receptor 1
26	P35354	PTGS2	Prostaglandin G/H synthase 2
27	P00734	F2	Prothrombin
28	Q96RI1	NR1H4	Bile acid receptor
29	P08246	ELANE	Neutrophil elastase
30	P07900	HSP90AA1	Heat shock protein HSP 90-alpha
31	P45983	MAPK8	Mitogen-activated protein kinase 8
32	Q16539	MAPK14	Mitogen-activated protein kinase 14
33	P29474	NOS3	Nitric oxide synthase
34	P35228	NOS2	Nitric oxide synthase

Figure 2.

Target-target interaction network between active ingredients of Shanmei Capsule against hyperlipidemia.

Figure 3.

Component-target-diseas interactive network of Shanmei Capsule. Red dovetails nodes are the main active ingredients of Shanmei Capsule, and purple circular nodes are the potential targets for treating hyperlipidemia.

3.3 GO and KEGG pathway enrichment analysis

To further clarify relevant functions and pathways, GO functional analyses and KEGG pathway enrichment were performed for above 34 core targets using DAVID database. GO enrichment analysis was annotated in the following three categories, including biological process, cellular component and molecular function, with $P<$ 0.01 serving as the threshold. There were 40 biological processes involved, including oxidation-reduction processes, intracellular receptor signaling pathway, regulation of inflammatory response, positive regulation of MAP kinase activity, etc. (Fig. 4A). In the cellular component group, GO terms were mainly included nucleus, nucleoplasm, extracellular space, perinuclear region of cytoplasm and basolateral plasma membrane (Fig. 4B). Figure 4C shows that the enriched molecular functions of targets are mainly associated with RNA polymerase II transcription factor activity, enzyme binding, steroid hormone receptor activity, etc. These results indicated that the potential mechanism of Shanmei Capsule against hyperlipidemia was related to oxidation-reduction processes, intracellular receptor signaling, and inflammatory response. A total of 15 signal pathways were enriched by KEGG pathway enrichment analysis (Table 3), including PPAR signaling pathway, Bile secretion and TNF signaling pathway, et al. Among them, Pathway in cancer is a cancer-related signaling pathway. Recent studies have reported that a carcinogenic role of the metabolic syndrome in variety of cancers [24]. Based on these results, Shanmei Capsule may attenuate hyperlipidemia partially by regulating inflammation or apoptosis, lipid metabolism and decrease oxidative stress during hyperlipidemia progression.

Table 3
Pathway enrichment of potential targets based on KEGG pathway analysis

No.	Pathway description	$P$ value	Nr. Genes	Ratio
1	Pathways in cancer	0.00001	11	32.4
2	Estrogen signaling pathway	0.00001	7	20.6
3	Bladder cancer	0.00004	5	14.7
4	PPAR signaling pathway	0.00024	5	14.7
5	Bile secretion	0.00027	5	14.7
6	TNF signaling pathway	0.00140	5	14.7
7	Ovarian steroidogenesis	0.00150	4	11.8
8	Toxoplasmosis	0.00160	5	14.7
9	Proteoglycans in cancer	0.00210	6	17.6
10	Hepatitis C	0.00320	5	14.7
11	Prolactin signaling pathway	0.00420	4	11.8
12	Transcriptional misregulation in cancer	0.00710	5	14.7
13	Prostate cancer	0.00770	4	11.8
14	GnRH signaling pathway	0.00840	4	11.8
15	HIF-1 signaling pathway	0.00970	4	11.8

Figure 4.

Go and KEGG pathway enrichment bubble diagrams for candidate targets ( $P\leqslant$ 0.01). (A) GO analysis of Biological processes, (B) GO analysis of cellular components, (C) GO analysis of molecular functions, (D) KEGG analysis.

Figure 5.

The component-target-pathway network for Shanmei Capsule. Purple circular nodes represent the candidate targets, red dovetails nodes represent the active ingredients and rose red parallelogram nodes represent the KEGG pathways. The size of nodes was proportional with the degree and larger sizes represent greater associations with hyperlipidemia treatment.

At last, in order to elucidate the connection between the core targets and corresponding pathways, a component-target-pathway interaction network map was generated by Cytoscape 3.3.0 (Fig. 5). The 17 red dovetails nodes represent the active pharmaceutical ingredient, the 34 purple circular nodes denote the potential targets, and the 15 rose red parallelogram nodes represent the pathways. And the node sizes depend on degree value. The larger the node is, the larger the degree value is. There were 66 nodes and 340 edges were involved in the association between these targets, which reflected the biological process of the key targets. These results were revealed that Shanmei Capsule take effect in hyperlipidemia treatment by multi-components, multi-targets and multi-pathways.

3.4 Docking results analysis

Molecular docking was performed among 17 ingredients and 34 potential targets using the Autodock Vina software, and detailed results are presented in Fig. 6. Affinity was the binding score for the molecular docking, and when the docking score value was smaller, the binding affinity to target protein was stronger. The docking score $<-$ 7 indicated a high binding activity [25]. The molecular docking results revealed that the docking scores of 47% active ingredients were lower than $-$ 7, and the ingredients with the lowest scores among these targets were quercetin and chlorogenic acid (heriguard) (Fig. 6). PPAR- $\alpha$ is a key target of lifibrate (positive drug), which belongs to the nuclear hormone receptor family, which is linked to dyslipidemia and diabetes. The docking results showed that the binding activity of quercetin, kaempferol, isorhamsin and chlorogenic acid for PPAR- $\alpha$ are equivalent to, and even better than that of the positive drug lifibrate. The molecular docking models are illustrated in Fig. 7. These findings indirectly verified that Shanmei Capsule had a regulatory effect on hyperlipidemia targets. At the same time, the above molecular docking results were consistent with those of previous network screening, which verified the reliability of network pharmacology.

Figure 6.

The heat map of molecule docking scores.

3.5 Results of drug ADME and toxicity estimations

The ADME properties of 17 active ingredients were calculated by SwissADME web tool. As is can be observed in Table 4, all the ingredients, besides heriguard, have bioavailability score above the threshold of 0.5, which might suggest good oral bioavailability. The majority of the tested ingredients have no ability to penetrate the blood-brain barrier, thus presenting a low risk for central nervous system side effects. Based on toxicity tests, it appears that all the ingredients have no nongenetic carcinogen properties, with only vitaminG was predicted to be genetic carcinogen. The results of SwissADME calculations and toxicity tests provided useful information about the selected ingredients and found to be free from high risks of undesired effects.

Table 4
ADME and toxicity prediction results of 17 key ingredients in Shanmei Capsule

No.	Compound	GI absorption	BBB permeant	Bioavailability score	Genetic carcinogen	Nongenetic carcinogen	Ames
1	$\beta$ -Sitosterolacetate	Low	No	0.55	$-$	$-$	$-$
2	Eicosadienoicacid	High	No	0.56	$-$	$-$	$-$
3	Ergosterol	Low	No	0.55	$-$	$-$	$-$
4	2-amino-propionic acid ethyl ester	High	No	0.55	$-$	$-$	$-$
5	Ethylpalmitoleate	High	Yes	0.55	$-$	$-$	$-$
6	Heriguard	Low	No	0.11	$-$	$-$	$-$
7	Isoliquiritigenin	High	Yes	0.55	$-$	$-$	$+$
8	Isorhamnetin	High	No	0.55	$-$	$-$	$+$
9	Kaempferol	High	No	0.55	$-$	$-$	$-$
10	Linoleicacid	High	Yes	0.56	$-$	$-$	$-$
11	Linolenicacid	High	Yes	0.56	$-$	$-$	$-$
12	Oleicacid	High	No	0.56	$-$	$-$	$-$
13	Quercetin	High	No	0.55	$-$	$-$	$+$
14	Sitosterol	Low	No	0.55	$-$	$-$	$-$
15	Stigmasterol	Low	No	0.55	$-$	$-$	$-$
16	Ursolicacid	Low	No	0.56	$-$	$-$	$-$
17	VitaminG	Low	No	0.55	$+$	$-$	$+$

Notes: ( $-$ ) negative; ( $+$ ) positive.

Figure 7.

Molecular models of bioactive ingredients and positive drug binding to the predicted PPAR $\alpha$ target. (A) Quercetin, (B) Heriguard, (C) Lifibrate.

4. Discussion

Hyperlipidemia is one of the most prevalent global chronic metabolic diseases, which usually involved in a disorder of lipid metabolism and accompanied by elevated contents of TC, TG, LDL-C, as well as decreased HDL-C levels. During this process, blood lipid level, oxidative stress, inflammation, and other factors might be the key link of the development of dyslipidemia-induced hyperlipidemia. Shanmei Capsule, a famous preparation in China, is made of a combination of Crataegi Folium and Dahurian rose fruit ethanol extract in proportion. Crataegi Folium and Dahurian rose fruit both are well-known traditional medicinal plants, which have been confirmed to possess various biological and pharmacological activities, such as anti-inflammatory, antioxidant and hypolipidaemic effects. Therefore, in this study, network pharmacology approach and molecular docking were applied to investigate the potential mechanism of multi-ingredient, multi-targets, and multi-pathway on Shanmei Capsule against hyperlipidemia (Fig. 8) [26, 27, 28, 29, 30].

Figure 8.

Detailed signaling pathway of Shanmei Capsule in the treatment of hyperlipidemia.

Via network pharmacology analysis, a total of 17 key active ingredients were regarded to be effective on hyperlipidemia, including quercetin, kaempferol, isorhamnetin, chlorogenic acid, ursolic acid and $\beta$ -sitosterol, etc. Quercetin, one of antioxidant substances, has been reported that it could upregulate the expression of PPAR $\alpha$ . Quercetin and its metabolites induced eNOS activity by AMPK phosphorylation, which resulted in an improvement in vessel function. They were also inhibited the formation of foam cell through activating PPAR $\gamma$ -ABCA1 pathway, and thereby improving lipid levels [31, 32]. Foam cell accumulation in atherosclerotic lesions is a critical early stage process of atherosclerosis. Treatment with kaempferol markedly suppresses oxidized low-density lipoprotein-induced macrophage foam cell formation, which promotes an increase in cholesterol efflux and a decrease in lipid accumulation in foam cells [33]. Zhang et al. [34] indicated that isorhamnetin is a dietary source of PPAR $\gamma$ antagonist. Isorhamnetin treatment inhibited the adipocyte differentiation induced by the PPAR $\gamma$ agonist rosiglitazone, and ameliorated insulin resistance, which may be beneficial to prevent obesity development and hepatic steatosis. Several researchers reported that anti-lipidemic and Anti-diabetic effects of chlorogenic acid are mediated through the activation of AMPK (AMP-activated protein kinase). Chronic administration of chlorogenic acid inhibited hepatic glucose-6-phosphatase expression and activity, improved lipid homeostasis and skeletal muscle glucose uptake, attenuated hepatic steatosis, which in turn improved glucose tolerance, fasting glucose level, dyslipidemia and insulin sensitivity in Leprdb/db mice [35, 36]. Ursolic acid is a triterpenoid compound commonly present in fruits, vegetables and TCM, and that have a wide range of biological functions, such as anti-inflammatory, antihyperlipidemic, hypoglycemic and antioxidant properties [37]. He et al. [38] showed that ursolic acid inhibited 3T3-L1 preadipocyte differentiation and adipogenesis via the LKB1/AMPK pathway, so as to suppress 3T3-L1 preadipocyte differentiation and lipid accumulation by regulating the transcriptional factors and their downstream lipogenic targets. $\beta$ -sitosterol is a plant sterol. Experimental and clinical studies have shown that $\beta$ -sitosterol has hypolipidemic, anti-diabetic, hepatoprotective, anti-cancer, and anti-arthritic role [39]. In summary, the main active ingredients (e.g., quercetin, kaempferol, isorhamnetin, chlorogenic acid, ursolic acid and $\beta$ -sitosterol) of Shanmei Capsule in the treatment of hyperlipidemia could regulate multiple cellular signaling pathways, thereby involving in the regulation of lipid synthesis and metabolism, lowering cholesterol levels, and preventing or delaying the development of dyslipidemia.

Thirty-four potential targets consist of HMGCR, MMP9, MAPK, PPAR $\alpha$ , PPAR $\gamma$ , FABP4, NOS2, etc., were screened as effective targets of Shanmei Capsule against hyperlipidemia. HMGCR (HMG-CoA reductase), is a rate-limiting enzyme of cholesterol biosynthesis. HMGCR inhibitors (e.g., statins) are currently the mainstay of treatment for dyslipidemia, which can reduce the levels of cholesterol via blocking mevalonate synthesis [40]. MMP9, belongs to matrix metalloproteinase (MMP) molecular family, has been considered as a key target for treating metabolic diseases. Studies have shown that MMP inhibitor was closely associated with calcific aortic valve disease (CAVD) among high fat induced mice, and that these enzymes might be important targets for preventing CAVD development [41]. Intracellular mitogen-activated protein kinases (MAPK) are an important class of proline-dependent protein kinases, which involved in the regulation of various biological functions, such as inflammation, cell proliferation and differentiation. Aouadi et al. [42] demonstrated that p38MAPK increased adipogenesis via inhibition of C/EBP $\beta$ and PPAR $\gamma$ activations. PPAR, a member of the nuclear receptor transcription factor family, has 3 isoforms: PPAR $\alpha$ , PPAR $\beta$ and PPAR $\gamma$ . It is known that lipid metabolism could be regulated by PPAR, which were shown to decrease lipoprotein secretion, accelerate triglyceride catabolism, and influences serum total- and LDL-cholesterol levels. On the other hand, PPAR $\gamma$ stimulates the fatty acids storage in adipocytes and regulates glucose metabolism, thereby improving insulin sensitivity [43]. FABP4, also known as Adipocyte fatty acid–binding protein (A-FABP), is a major cytoplasmic protein in adipocytes. It is shown that FABP4 is expressed at high levels in adipose tissues, especially in adipocyte differentiation [44, 45, 46]. NO is generated from L-arginine catalyzed by a class of nitric oxide synthases (NOS), including neuronal NOS (nNOS), inducible NOS (iNOS) and endothelial NOS (eNOS). All three NOS isoforms were expressed in atherosclerotic plaque. Once the enzyme is expressed, iNOS is regulated by the transcription fact and catalyzes the high expression of NO. On the contrary, eNOS and nNOS produce a small number of NO in a highly regulated way [47]. The above results indicated that Shanmei Capsule treatment may play its role of hypolipidemic via multi-targets.

The KEGG enrichment analysis indicated that the main pathways involved in the hyperlipidemia treatment process are PPAR signaling pathway, Bile secretion and TNF signaling pathway. PPAR signaling pathway participate in many physiological processes implicated in cardiovascular diseases associated with hyperlipidemia and diabetes, including lipid metabolisms, energy metabolisms, and inflammatory reactions, etc. PPAR $\alpha$ is an essential transcription factor involved in lipid metabolism and inflammation, and there is predominantly expressed in $\beta$ -oxidation activities and tissues with high mitochondrial (e.g., liver) [48]. While PPAR $\gamma$ is mainly expressed in immune system and adipose tissue, where it modulates genes involved in cell differentiation, cell cycle regulation, insulin sensitivity and glucose uptake [49]. Bile acids are famous for their effects on lipid digestion and cholesterol homeostasis. The conversion of cholesterol to bile acids (i.e., bile acid synthesis) in the liver is the major pathway of cholesterol removal from the body [50], which itself is endogenous ligands of the farnesoid X receptor (FXR). FXR, together with small heterodimer partner (SHP), plays an important role in linking bile acids regulation with lipid, lipoprotein, and glucose metabolism [51, 52]. As for TNF signaling pathway, it has direct effect on inflammation, whereas many cytokines such as TNF- $\alpha$ are able to increase lipolysis, which in turn contributes to a chronic state of insulin resistance and increases circulating free fatty acids. It has been reported that expanding adipose tissue results in chronic low-grade inflammation that leads to the development of metabolic disorders, including insulin resistance, dyslipidemia, and type 2 diabetes [53, 54].

The potential binding sites and intermolecular interaction of active ingredients for target proteins were further elaborated by docking analysis. PPAR $\alpha$ is commonly considered as a key target of lifibrate, and at the same time it is also an important target screened from the network pharmacology of Shanmei Capsule. The melocule docking results confirmed the potential hypolipidemic mechanism of Shanmei Capsule, showing excellent affinity towards PPAR $\alpha$ target protein. The above findings also support the network pharmacology analysis results of Shanmei Capsule. Therefore, such compounds could provide a molecular foundation for the subsequent development of novel pharmacological inhibitors against hyperlipidemia.

5. Conclusion

In the present study, a network pharmacology approach in combination with molecular docking was proposed to explore the underlying mechanism of action of Shanmei Capsule against hyperlipidemia. The active ingredients of Shanmei Capsule in hyperlipidemia treatment are composed of 59 ingredients. Among them, quercetin, kaempferol, isorhamsin and chlorogenic acid are main active ingredients. The possible molecular mechanism mainly involved PPAR signaling pathway, Bile secretion and TNF signaling pathway via acting on MAPK8, PPAR $\gamma$ , MMP9, PPAR $\alpha$ , FABP4 and NOS2 targets. In conclusion, Shanmei Capsule, as a traditional Chinese medicine preparation, has multiple constituents, multiple targets and multiple pathways properties, which could serve as a promising therapeutic drug for hyperlipidemia.

Footnotes

Acknowledgments

This work was supported by the Key Research and Development Program of Shanxi Province (201903D321205).

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Supplementary material

Table S1

Grid parameters of the protein-ligand interaction for the key targets

Protein	PDB ID	Grid parameters of the protein-ligand interaction
		Center x		Center y		Center z		Size x	Size y	Size z
AKR1B10	1ZUA	$-$ 24.	9	23.	9	15.	0	60.0	60.0	60.0
AR	2AX6	26.	9	3.	5	4.	4	17.8	18.5	12.5
CNR1	5TGZ	32.	3	19.	0	292.	2	39.8	73.1	70.4
CYP19A1	3EQM	85.	2	51.	3	42.	8	21.9	24.7	21.4
EGFR	5U8L	13.	4	$-$ 4.	7	$-$ 15.	9	21.7	29.1	47.6
ELANE	1H1B	10.	5	8.	9	15.	9	39.2	52.8	31.0
ERBB2	3PP0	16.	4	17.	4	26.	2	17.4	23.9	17.2
ESR1	3UUC	$-$ 10.	1	$-$ 16.	2	$-$ 3.	9	18.1	26.0	25.5
ESR2	3OLL	$-$ 10.	6	45.	0	10.	2	20.4	32.2	16.6
F2	3K65	22.	9	0.	9	2.	4	16.9	22.7	14.6
FABP4	5Y0F	5.	6	$-$ 7.	5	$-$ 19.	2	17.4	16.1	18.6
LDLR	1IJQ	$-$ 3.	1	39.	3	47.	6	50.0	48.0	52.0
MAPK8	4L7F	$-$ 5.	1	53.	8	4.	3	19.1	17.3	27.5
MAPK14	6SFI	53.	4	69.	4	17.	4	19.7	23.8	21.7
MMP1	966C	8.	8	$-$ 10.	9	38.	9	20.4	18.4	18.8
MMP3	1D7X	$-$ 7.	4	19.	8	29.	6	40.0	40.0	40.0
MMP9	4H1Q	29.	2	6.	0	18.	9	37.5	22.8	25.2
MPO	5MFA	$-$ 27.	5	4.	9	0.	3	64.4	65.2	84.7
NOS2	4NOS	3.	1	95.	2	17.	2	26.8	19.1	30.0
NR1H4	6HL1	10.	9	$-$ 14.	7	12.	3	17.9	23.7	14.5
PPARA	5HYK	8.	7	33.	9	19.	3	14.5	18.0	19.8
PPARG	6T9C	14.	8	17.	3	17.	7	21.9	17.8	18.8
PTGS2	5F19	28.	1	41.	0	56.	6	54.1	75.2	45.6
AKR1B1	5OUK	4.	5	2.	7	4.	8	27.0	24.7	20.5
FABP1	3STK	$-$ 0.	9	$-$ 8.	8	19.	4	24.2	18.4	28.0
HAP90AA1	4LWE	$-$ 0.	7	$-$ 36.	0	$-$ 25.	3	17.1	17.9	17.3

References

Nelson

. Hyperlipidemia as a risk factor for cardiovascular disease. Primary Care. 2013; 40: 195-211.

Navar-Boggan

Peterson

D’Agostino

RBSr

Neely

Sniderman

Pencina

. Hyperlipidemia in early adulthood increases long-term risk of coronary heart disease. Circulation. 2015; 131(5): 451-458.

Genest

McPherson

Frohlich

Anderson

Campbell

Carpentier

, et al. 2009 canadian cardiovascular society/canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult-2009 recommendations. Canadian Journal of Cardiology. 2009; 25(10): 567-579.

Zeng

Huang

Luo

Chen

, et al. Nontargeted urine metabolomics analysis of the protective and therapeutic effects of citri reticulatae chachiensis pericarpium on high-fat feed-induced hyperlipidemia in rats. Biomedical Chromatography. 2020; 34: 4795.

Mancini

Baker

Bergeron

Fitchett

Frohlich

Genest

, et al. Diagnosis, prevention, and management of statin adverse effects and intolerance: Canadian consensus working group update (2016). Canadian Journal of Cardiology. 2016; 32: S35-65.

Hippisley-Cox

Coupland

. Unintended effects of statins in men and women in England and Wales: population based cohort study using the QResearch database. BMJ. 2010; 340: 2197.

Du Souich

Roederer

Dufour

. Myotoxicity of statins: mechanism of action. Pharmacology & Therapeutics. 2017; 175: 1-16.

Hoffman

Kraus

Dimbil

Golomb

. A survey of the FDA’s AERS database regarding muscle and tendon adverse events linked to the statin drug class. PloS One. 2012; 7(8): 42866.

Chu

Shih

Yang

Chen

Chu

. Use of traditional Chinese medicine in patients with hyperlipidemia: a population-based study in Taiwan. Journal of Ethnopharmacology. 2015; 168: 129-135.

10.

El-Tantawy

Temraz

. Natural products for controlling hyperlipidemia: review. Archives of Physiology and Biochemistry. 2019; 125(2): 128-135.

11.

Fang

Shi

Zhang

. Baicalin and its aglycone: a novel approach for treatment of metabolic disorders. Pharmacological Reports. 2020; 72(1): 13-23.

12.

Editorial Committee of Chinese Pharmacopoeia. Chinese Pharmacopoeia. Medical Science and Technology Press 2020.

13.

Yin

Zhang

Gao

Ying

, et al. Tissue distribution comparison between healthy and fatty liver rats after oral administration of hawthorn leaf extract. Biomedical Chromatography. 2014; 28: 637-647.

14.

Zheng

Shang

Liu

. Hawthorn leaves flavonoids decreases inflammation related to acute myocardial ischemia/reperfusion in anesthetized dogs. Chinese Journal of Integrative Medicine. 2013; 19(8): 582-588.

15.

Jung

Song

Shim

Park

Lim

. Anti-Inflammatory, anti-angiogenic, and anti-nociceptive activities of the chloroform fraction of a methanol extract from Rosa Davurica Pall. leaves in experimental animal models. Immunopharmacology and Immunotoxicology. 2011; 33: 186-192.

16.

Huang

Zhang

Peng

, et al. A network pharmacology-based strategy for predicting active ingredients and potential targets of Liuwei Dihuang Pill in treating type 2 diabetes mellitus. Drug Design Development and Therapy. 2019; 13: 3989-4005.

17.

Song

Zhang

Dai

Wang

. Prediction of triptolide targets in rheumatoid arthritis using network pharmacology and molecular docking. International Immunopharmacology. 2020; 80: 106179.

18.

Elokely

Doerksen

. Docking challenge: protein sampling and molecular docking performance. Journal of Chemical Information and Modeling. 2013; 53(8): 1934-1945.

19.

Comeau

Gatchell

Vajda

Camacho

. C lusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics. 2004; 20(1): 45-50.

20.

Wang

Cheng

Bryant

. PubChem BioAssay: a decade’s development toward open high-throughput screening data sharing. SLAS Discovery. 2017; 22(6): 655-666.

21.

Gfeller

Michielin

Zoete

. Shaping the interaction landscape of bioactive molecules. Bioinformatics. 2013; 29(23): 3073-3079.

22.

Parikesit

Nurdiansyah

. The predicted structure for the anti-sense siRNA of the RNA polymerase enzyme (RdRp) gene of the SARS-CoV-2. Berita Biologi. 2020; 19: 97-108.

23.

Tambunan

USF

Bramantya

Parikesit

. In silico modification of suberoylanilide hydroxamic acid (SAHA) as potential inhibitor for class II histone deacetylase (HDAC). BMC Bioinformatics. 2011; 12(Suppl 13): S23.

24.

Uzunlulu

Telci Caklili

Oguz

. Association between metabolic syndrome and cancer. Annals of Nutrition and Metabolism. 2016; 68(3): 173-179.

25.

Trott

Olson

. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. 2010; 31(2): 455-461.

26.

Chinetti

Fruchart

Staels

. Peroxisome proliferator-activated receptors (PPARs): nuclear receptors at the crossroads between lipid metabolism and inflammation. Inflammation Research. 2000; 49(10): 497-505.

27.

Schoonjans

Peinado-Onsurbe

Lefebvre

Heyman

Briggs

Deeb

, et al. PPARα and PPARγ activators direct a distinct tissue-specific transcriptional response via a PPRE in the lipoprotein lipase gene. Embo Journal. 1996; 15(19): 5336-5348.

28.

Hotamisligil

Peraldi

Budavari

Ellis

White

Spiegelman

. IRS-1-Mediated Inhibition of Insulin Receptor Tyrosine Kinase Activity in TNF-α- and Obesity-Induced Insulin Resistance. Science. 1996; 271(5249): 665-668.

29.

Enslen

Raingeaud

Davis

. Selective activation of p38 mitogen-activated protein (MAP) kinase isoforms by the MAP kinase kinases MKK3 and MKK6. Journal of Biological Chemistry. 1998; 273(3): 1741-1748.

30.

Pineda Torra

Claudel

Duval

Kosykh

Fruchart

Staels

. Bile acids induce the expression of the human peroxisome proliferator-activated receptor alpha gene via activation of the farnesoid X receptor. Molecular Endocrinology. 2003; 17(2): 259-272.

31.

Wang

Zhang

Hassan

Liu

Shang

. Amelioration of free fatty acid-induced fatty liver by quercetin-3-O-β-D-glucuronide through modulation of peroxisome proliferator-activated receptor-alpha/sterol regulatory element-binding protein-1c signaling. Hepatology Research. 2016; 46(2): 225-238.

32.

Sun

Wang

Qin

Niu

, et al. Quercetin increases macrophage cholesterol efflux to inhibit foam cell formation through activating PPARγ-ABCA1 pathway. International Journal of Clinical and Experimental Pathology. 2015; 8(9): 10854-10860.

33.

Kong

Zhou

. Kaempferol suppresses lipid accumulation in macrophages through the downregulation of cluster of differentiation 36 and the upregulation of scavenger receptor class B type I and ATP-binding cassette transporters A1 and G1. International Journal of Molecular Medicine. 2013; 31(2): 331-338.

34.

Zhang

Cai

Feng

Zhang

, et al. Dietary component isorhamnetin is a PPARγ antagonist and ameliorates metabolic disorders induced by diet or leptin deficiency. Surface Science Reports. 2016; 18(6): 19288.

35.

Ong

Hsu

Tan

BKH

. Anti-diabetic and anti-lipidemic effects of chlorogenic acid are mediated by ampk activation. Biochemical Pharmacology. 2013; 85: 1341-1351.

36.

McCarty

. A chlorogenic acid-induced increase in GLP-1 production may mediate the impact of heavy coffee consumption on diabetes risk. Medical Hypotheses. 2005; 64: 848-853.

37.

Rao

De Melo

Queiroz

MGR

Lemos

TLG

Menezes

Melo

, et al. Ursolic acid, a pentacyclic triterpene from sambucus australis, prevents abdominal adiposity in mice fed a high-fat diet. Journal of Medicinal Food. 2011; 14(11): 1375-1382.

38.

Zhao

Wang

Sun

. Ursolic acid inhibits adipogenesis in 3T3-L1 adipocytes through LKB1/AMPK pathway. PLoS One. 2013; 8(7): 70135.

39.

Ponnulakshmi

Shyamaladevi

Vijayalakshmi

Selvaraj

. In silico and in vivo analysis to identify the antidiabetic activity of beta sitosterol in adipose tissue of high fat diet and sucrose induced type-2 diabetic experimental rats. Toxicology Mechanisms and Methods. 2019; 29(4): 276-290.

40.

Osea

Budinski

Hounslow

Arnesonc

. Long-term treatment with pitavastatin is effective and well tolerated by patients with primary hypercholesterolemia or combined dyslipidemia. Atherosclerosis. 2010; 210(1): 202-208.

41.

Jung

Razavian

Kim

Golestani

Toczek

, et al. Matrix metalloproteinase inhibitor, doxycycline and progression of calcific aortic valve disease in hyperlipidemic mice. Scientific Reports; 2016; 194(10): 225-231.

42.

Aouadi

Laurent

Prot

Marchand-Brustel

Binétruy

Bost

. Inhibition of p38MAPK increases adipogenesis from embryonic to adult stages. Diabetes. 2006; 55(2): 281-289.

43.

Yilmaz-Aydogan

Kurnaz

Kucukhuseyin

Akadam-Teker

Kurt

Eronat

, et al. Different effects of PPARA, PPARG and ApoE SNPs on serum lipids in patients with coronary heart disease based on the presence of diabetes. Gene. 2013; 523(1): 20-26.

44.

Tso

AWK

Cheung

BMY

Wang

Wat

NMS

Fong

CHY

, et al. Circulating adipocyte-fatty acid binding protein levels predict the development of the metabolic syndrome: a 5-year prospective study. Circulation. 2007; 115(12): 1537-1543.

45.

Tso

AWK

Sham

Wat

NMS

Wang

Fong

CHY

, et al. Serum adipocyte fatty acid binding protein as a new biomarker predicting the development of type 2 diabetes: a 10-year prospective study in a Chinese cohort. Diabetes Care. 2007; 30(10): 2667-2672.

46.

Coll

Cabre

Alonso-Villaverde

Lazaro

Aragonés

Parra

, et al. The fatty acid binding protein-4 (FABP4) is a strong biomarker of metabolic syndrome and lipodystrophy in HIV-infected patients. Atherosclerosis. 2008; 199(1): 147-153.

47.

Wilcox

Subramanian

Sundell

Tracey

Pollock

Harrison

, et al. Expression of multiple isoforms of nitric oxide synthase in normal and atherosclerotic vessels. Arteriosclerosis Thrombosis and Vascular Biology. 1997; 17: 2479-2488.

48.

Schoonjans

Staels

Auwerx

. Role of the peroxisome proliferator-activated receptor (PPAR) in mediating the effects of fibrates and fatty acids on gene expression. Journal of Lipid Research. 1996; 37(5): 907-925.

49.

Guan

Breyer

. Peroxisome proliferator-activated receptors (PPARs): novel therapeutic targets in renal disease. Kidney International. 2001; 60: 14-30.

50.

Asgharpour

Kumar

Sanyal

. Bile acids: emerging role in management of liver diseases. Hepatology International. 2015; 9(4): 527-533.

51.

Zheng

Zhao

Jiang

Lin

, et al. Altenusin, a non-steroidal microbial metabolite, attenuates non-alcoholic fatty liver disease by activating the farnesoid X receptor. Molecular Pharmacology. 2017; 92(4): 425-436.

52.

Kim

Choi

Zhou

Kim

Lee

Saha

, et al. Hepatic FXR/SHP axis modulates systemic glucose and fatty acid homeostasis in aged mice. Hepatology. 2017; 66(2): 498-509.

53.

Croft

Benedict

Ware

. Clinical targeting of the TNF and TNFR superfamilies. Nature Reviews Drug Discovery. 2013; 12(2): 147-168.

54.

Oliveira

Menezes-garcia

Henriques

MCC

Soriani

Pinho

Faria

AMC

, et al. Acute and sustained inflammation and metabolic dysfunction induced by high refined carbohydrate-containing diet in mice. Obesity. 2013; 21(9): 396-406.

Molecular mechanism underlying the hypolipidemic effect of Shanmei Capsule based on network pharmacology and molecular docking

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Methods

2.1 Collection of active ingredients for Shanmei Capsule

2.2 Target acquisition for drug and disease-related

2.3 GO and KEGG pathway enrichment analysis

2.4 Network construction and analysis

2.5 Molecular docking

2.6 ADME and toxicology prediction

3. Results

3.1 Screening of Shanmei Capsule active ingredients

Table 1 A list of the selected compounds in Shanmei Capsule for network analysis

Table 2 Related parameters of potential targets of Shanmei Capsule

Table 3 Pathway enrichment of potential targets based on KEGG pathway analysis

Table 4 ADME and toxicity prediction results of 17 key ingredients in Shanmei Capsule

Footnotes

Acknowledgments

Conflict of interest

Supplementary material

References

Table 1
A list of the selected compounds in Shanmei Capsule for network analysis

Table 2
Related parameters of potential targets of Shanmei Capsule

Table 3
Pathway enrichment of potential targets based on KEGG pathway analysis

Table 4
ADME and toxicity prediction results of 17 key ingredients in Shanmei Capsule