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Table of Contents
Year : 2021  |  Volume : 7  |  Issue : 4  |  Page : 445-455

Effect and Signaling Pathways of Nelumbinis Folium in the Treatment of Hyperlipidemia Assessed by Network Pharmacology

1 Cardiovascular Department, Xiyuan Hospital, China Academy of Chinese Medical Sciences; Department of Pharmacology, School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
2 Department of Pharmacology, School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
3 Cardiovascular department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China

Date of Submission14-May-2021
Date of Acceptance20-Jul-2021
Date of Web Publication21-Oct-2021

Correspondence Address:
Associate Professor Xin-Lou Chai
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2311-8571.328619

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Objective: In this study, the effects and signaling pathways of Nelumbinis folium in the treatment of hyperlipidemia were analyzed based on network pharmacology and molecular docking. Materials and Methods: The main components and targets of Nelumbinis folium were searched through traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), and the active components were selected according to their oral availability and drug-like properties. The main targets of hyperlipidemia were identified using the DisGeNET database. Venny 2.1.0 was used to take the intersection of both targets, which were submitted to the STRING database to construct the protein-protein interaction network model. The Database for Annotation, Visualization and Integrated Discovery 6.7 was used to conduct gene ontology and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses of the targets. Cytoscape 3.7.1 was used to construct the component-target-pathway network. AutoDock Vina molecular docking software was used to study the binding effect and mechanism of the core components and targets of N. folium. Results: Fifteen active components of N. folium and 195 potential targets were selected through TCMSP, whereas 4216 targets for hyperlipidemia were selected from DisGeNET. Further, 138 potential cross-targets of hyperlipidemia were identified. A network of component-target-pathway was constructed. Quercetin, kaempferol, and isorhamnetin were the core components, which played an important role in anti-hyperlipidemia, mainly through the non-alcoholic fatty liver disease and insulin resistance (IR) signaling pathways. Molecular docking results showed that quercetin had the lowest docking energies with peroxisome proliferator activated receptor α, peroxisome proliferator-activated receptor γ, INSR (-6.20,-10.00, and -8.40 (kcal/mol, respectively). The binding mode was mainly hydrogen bonds and van der Waals forces. Conclusions: The active components of N. folium may regulate lipid metabolism by participating in the signaling pathways of non-alcoholic fatty liver disease and IR.

Keywords: Active components, hyperlipidemia, molecular docking, Nelumbinis folium, network pharmacology, signaling pathway

How to cite this article:
Pan Q, Zhang ZQ, Tian CY, Yu T, Yang R, Chai XL. Effect and Signaling Pathways of Nelumbinis Folium in the Treatment of Hyperlipidemia Assessed by Network Pharmacology. World J Tradit Chin Med 2021;7:445-55

How to cite this URL:
Pan Q, Zhang ZQ, Tian CY, Yu T, Yang R, Chai XL. Effect and Signaling Pathways of Nelumbinis Folium in the Treatment of Hyperlipidemia Assessed by Network Pharmacology. World J Tradit Chin Med [serial online] 2021 [cited 2021 Nov 29];7:445-55. Available from: https://www.wjtcm.net/text.asp?2021/7/4/445/328619

  Introduction Top

Hyperlipidemia is a major risk factor for the occurrence and development of atherosclerotic diseases. Cardiovascular diseases caused by hyperlipidemia have become the main cause of death worldwide. In the past 30 years, the incidence of hyperlipidemia in the Chinese population has increased significantly. It has been reported that the overall prevalence of hyperlipidemia in adults in China is as high as 40.4%,[1] and its prevalence in children and adolescents has increased annually, reaching 25.3%.[2] It is estimated that cardiovascular events and related disease burdens will increase significantly in China in the future.

To date, there are more than 140 Chinese herbal medicines with lipid-lowering effects in China.[3] Nelumbinis folium is a traditional Chinese medicine included in the 2020 edition of “Chinese Pharmacopoeia.”[4] N. folium has a long history of clinical application in China. It was recorded in the “key to syndrome differentiation and treatment” that “Nelumbinis folium made people thin.” N. folium has been regarded as an effective medicine for slimming since ancient times in China. Modern studies have shown that N. folium contains alkaloids and flavonoids, which can reduce blood lipid and body weight,[5],[6],[7] resist arteriosclerosis, inhibit smooth muscle proliferation,[8] and inhibit bacteria. Additionally, it can reduce blood glucose,[9] protect the liver,[10] reduce oxidation,[11] inhibit inflammation,[12] and confer anti-aging properties.[5],[13],[14] N. folium is widely used in drugs and auxiliary lipid-regulating health products,[15] but there has been no systematic study on its lipid-lowering mechanism.[16] Network pharmacology is the result of the integration of basic theories and research methods of medicine, biology, computer science, bioinformatics and other disciplines, which can systematically and comprehensively reflect the intervention mechanism of drugs on disease by constructing a “component-target-pathway-disease” network.[17],[18] The organization and integrity of network pharmacology conforms to the multi-component, multi-target, and multi-pathway characteristics of traditional Chinese medicine.[19] Therefore, network pharmacology can provide a new and powerful technical support for the study of the mechanism of TCM, help in discovering drug targets, guide the research and development of new Chinese medicine, and reveal the scientific connotation of TCM theory.

This study adopted the network pharmacology method to analyze the molecular mechanism of N. folium in regulating hyperlipidemia, and carried out molecular docking verification of key targets. This work will help to deepen the understanding of effective substances and mechanisms of N. folium for the treatment of hyperlipidemia, and contribute to its development and application in the future. The detailed workflow is shown in [Figure 1].
Figure 1: The workflow of investigating the lipid-lowering mechanism and active ingredients of folium nelumbinis based on network pharmacology

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  Materials and Methods Top

Screening of targets related to Nelumbinis folium

The study used traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) (http://lsp.nwu.edu.cn/tcmsp.php)[20] to determine the chemical components of N. folium according to the two attribute values of oral availability ≥30% and drug like property ≥0.18. Preliminary screening of active components was carried out to identify the active compounds and their protein targets. After screening, we converted the protein target into the corresponding gene in the UniProt protein database (https://www.uniprot.org) in order to standardize the protein target information.[21]

Screening of targets related to hyperlipidemia

We searched the DisGeNET database (https://www. disgenet. org/) by using “hyperlipidemia,” “hypercholesterolemia,” “hypertriglyceridemia” as key words and set Score ≥0.5 to obtain target information related to hyperlipidemia.[22]

Protein-protein interaction network construction of Nelumbinis folium

Component-hyperlipidemia target

In order to clarify the interaction between N. folium-related targets and hyperlipidemia targets, we used Venny 2.1.0 (https://bioinfogp. cnb. csic. es/tools/venny/) to select the intersection of the two targets and draw a Venn diagram, and then submitted the intersection targets to the STRING 11.0 database (http://string-db. org/cgi/input. pl) to construct a network model of protein interaction,[23] and set the biological species to “Homo sapiens,” the minimum interaction threshold to “highest confidence,”[24] and the rest to the default settings to obtain the protein-protein interaction (PPI) network.

Enrichment analysis of Nelumbinis folium component-hyperlipidemia targets function and pathway

To illustrate the role of target proteins of traditional Chinese medicine compounds in gene function and signaling pathways, the Database for Annotation, Visualization and Integrated Discovery (DAVID 6.7) (https://David.ncifcrf.gov)/) was used to analyze the data. This study performed gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses on the targets. We selected three parameters of biological process (BP), molecular function (MF), and cell composition in GO function for gene enrichment analysis.

Construction of component-target-pathway network of Nelumbinis folium in the treatment of hyperlipidemia

We used Cytoscape 3.7.1 to construct the component-target-pathway network.[25] First, the network topology parameters of the active components were analyzed using the built-in Network Analyzer of Cytoscape.[26] All degrees of betweenness centrality (BC) and closeness centrality (CC) were also analyzed. The screening criteria for the main targets were as follows: 1) the degree of the selected nodes was more than twice the median value of all nodes and 2) the degree of the selected nodes was larger than the median value of BC and CC. As one of the key topological characteristic values, the value of node degree is directly proportional to the importance of the node in the relationship. BC reflects the influence of the node on other nodes in the network. The higher the value, the greater the role of the node in the network. The closer it is to centrality, the closer the relationship between different nodes. Second, the value of the target degree in the PPI network was calculated using the plug-in Cytohubba of Cytoscape. The larger the value, the darker the performance color, and potentially important protein targets can be obtained. Finally, the plug-in MCODE of Cytoscape was used to cluster and select the core module. The function of the core module was described by analyzing its BPs.

Molecular docking of the active components of Nelumbinis folium with the key targets for hyperlipidemia

AutoDock Vina (http://autodock. scripps. edu/) was used as the molecular docking software[27] for semi-flexible docking. The targets in the KEGG pathway analysis were selected as target proteins. The structure of the target protein was obtained from the RCSB protein database (http://www.rcsb. org/). The two-dimensional structure of the active components (compound) of N. folium was obtained using the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and saved in the mol2 format. The original ligand conformation of the target protein was extracted using PyMOL-2.4.0 (https://pymol. org/2/) software and saved in the PDB format. The target protein was dehydrated, hydrogenated, and charged with AutoDock tools (ADT, http://autodock. scripps. edu/), and saved in the PDBQT format. ADT was used to process the mol2 format ligand file into the PDBQT format. Autodock Vina was used to perform docking with the processed compound and target protein, respectively. PyMOL was used to analyze and observe the docking results of the compound and the target protein.

  Results Top

Active components and targets of Nelumbinis folium

A total of 15 chemical components of N. folium was extracted from 93 chemical components of N. folium after screening, including quercetin, kaempferol, isorhamnetin, armepavine, nuciferin, and o-nornuciferine. There were 209 targets for N. folium components, and 195 targets were obtained by deleting repeats after combination, as shown in [Table 1].
Table 1: The 15 main active components

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Related targets of hyperlipidemia

We obtained 10,198 potential hyperlipidemia targets from DisGeNET after setting Score ≥ 0.5, as a matter of experience. After merging, 4216 targets were obtained by deleting the duplicate values.

The Intersection of N. folium Component Targets and Hyperlipidemia Targets and PPI Network.

The selected targets of active components in N. folium were intersected with the targets of hyperlipidemia, and Venn diagrams were drawn using Venny 2.1.0. Consequently, 138 common targets were obtained. The targets were then submitted to the STRING 11.0 platform. We set the protein interaction score confidence to 0.900 and hid the discrete targets to obtain the PPI network of the intersection targets, as shown in [Figure 2].
Figure 2: (a) Venn diagram (b) The protein-protein interaction network of the intersection targets of nelumbinis folium components and hyperlipidemia

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Gene ontology functional enrichment and Kyoto Encyclopedia of Gene and Genome Pathway analysis of the active components of Nelumbinis folium against hyperlipidemia targets

The DAVID 6.7 data platform was used to analyze the signaling pathways of N. folium in regulating hyperlipidemia-related targets. GO biological function enrichment analysis was performed on 138 common targets. The results showed that N. folium was mainly involved in 650 BPs, including apoptosis, inflammatory response, cell proliferation, hypoxia reaction, redox reaction, protein phosphorylation, ERK1 and ERK2 cascade. N. folium had 66 cellular components, mainly related to cell membrane, nucleus, cytoplasm, exosome, mitochondria, endoplasmic reticulum, and extracellular matrix. N. folium participates in 113 MFs, including binding proteins, enzymes, specific sequence DNA, transcription factors, pantothenate protein ligase, activating protein kinases, serine-type endopeptidases, and protein serine/threonine kinases. The main target enrichment pathways included the PI3K-Akt signaling pathway, TNF signaling pathway, MAPK signaling pathway, HIF-1 signaling pathway, focal adhesion, non-alcoholic fatty liver disease (NAFLD), neuroactive ligand-receptor interaction, insulin resistance (IR), and other signaling pathways. Comprehensive analysis showed that the active components of N. folium may regulate lipid metabolism by participating in the regulation of NAFLD, the IR signal pathway, and other signaling pathways. We focused on two pathways closely related to lipid metabolism, namely the NAFLD and IR signaling pathways [Figure 3] and [Table 2].
Figure 3: Gene ontology functional enrichment and Kyoto Encyclopedia of Gene and Genome pathway analysis of the active components of nelumbinis folium against hyperlipidemia targets (a) Biological process (b) Cellular components (c) Molecular functions (d) Kyoto Encyclopedia of Gene and Genome

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Table 2: Results of kyoto encyclopedia of gene and genome pathway enrichment analysis

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Visualization network of component target pathway of Nelumbinis folium in the treatment of hyperlipidemia

CytoScape 3.7.1 was used to construct a component-target-pathway network [Figure 4] and [Figure 5]. The network topology parameters were analyzed using a CytoScape-embedded NetworkAnalyzer, and the core components were obtained. Cytoscape network analysis showed that the median degree of each component of N. folium was 12; the medians of BC and CC were 0.03 and 0.36, respectively. The degree, BC, and CC of quercetin were 103, 0.77, and 0.62, respectively, predicting quercetin as the main component of N. folium, followed by kaempferol (with degree, BC, and CC 40, 0.19, and 0.42, respectively). These two components were the core components. The degree, BC, and CC of isorhamnetin were 22, 0.12, and 0.37, respectively, suggesting that isorhamnetin also played an important role in the anti-hyperlipidemia process [Table 3]. The value of targets in the PPI network was calculated using Cytohubba. The degree values of the targets increased gradually from yellow to red. Potentially important targets were obtained according to the degree value. The top 10 targets were AKT1, Jun, TP53, TNF, RELA, EGFR, IL6, MAPK8, FOS, and ESR1. Finally, the MCODE was used to calculate the node information in the network, and seven clusters are obtained. The core cluster was selected according to the MCODE score. It was found that the BPs involved in the core cluster included lipid metabolism-related peroxisome proliferator activated receptor α (PPARα) and inflammation-related targets (RELA, NFKBIA, TNF), etc [Figure 5].
Figure 4: (a) Component-target network of nelumbinis folium in the treatment of hyperlipidemia (b) Component-target-pathway network of nelumbinis folium in the treatment of hyperlipidemia including nonalcoholic fatty liver disease and insulin resistance signaling pathways

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Figure 5: (a) The protein-protein interaction network of the intersection targets of nelumbinis folium components and hyperlipidemia, from yellow to red, the degree value of targets increased gradually. (b) Targe clustering of nelumbinis folium in the treatment of hyperlipidemia

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Table 3: Topological parameters of components in nelumbinis folium

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Results of molecular docking between active components of Nelumbinis folium and main targets

Autodock Vina was used to dock quercetin, kaempferol, isorhamnetin, and other ligands with the major target proteins in NAFLD and IR signaling pathways. Nine dominant configurations of each ligand were obtained by molecular docking, and the optimal configuration was selected according to the binding energy and RMSD value. Compared with kaempferol and isorhamnetin, quercetin had the lowest affinity with PPARα, peroxisome proliferator-activated receptor γ (PPARγ), insulin receptor (INSR), PIK3CG, tumor necrosis factor TNF and AKT1 (−6.20, −10.00, −8.40, −8.60, −6.70, and −7.70 kcal/mol, respectively), while isorhamnetin had the best affinity with PPARδ and RELA (−8.70 and −8.30 kcal/mol, respectively). The binding modes of ligands to target proteins were mainly hydrogen bonds and van der Waals forces [Table 4] and [Figure 6] and [Figure 7].
Figure 6: Two-dimensional structure of effective components in nelumbinis folium

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Figure 7: Docking diagram of quercetin and isorhamnetin with target protein. (a) Quercetin-peroxisome proliferator activated receptor α. (b) Quercetin-peroxisome proliferator activated receptor γ. (c) Isorhamnetin-peroxisome proliferator activated receptor δ. (d) Quercetin-INSR. (e) Quercetin-PIK3CG. (f) Isorhamnetin-RELA. (g) Quercetin- tumor necrosis factor (h) Quercetin-AKT1

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Table 4: Predicted binding energies for active components of nelumbinis folium docked with targets

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  Discussion Top

Hyperlipidemia is a common metabolic disease, and hyperinsulinemia begins to play a role before the occurrence of hyperlipidemia. An increase in fat accumulation eventually leads to IR.[28] Systemic inflammation and oxidative stress caused by high fat can aggravate local liver lesions,[29],[30] which can promote the occurrence and development of NAFLD.[31],[32] In fact, more than 50% of hyperlipidemic patients suffer from NAFLD.[33] Simultaneously, IR can promote the progression of NAFLD to NASH and liver fibrosis.[34] Our study found that the enrichment of the main targets pathways of N. folium regulating hyperlipidemia include NAFLD and IR signaling pathways, especially PPARs and INSR, which play important roles in the NAFLD and IR pathways, respectively.[35]

N. folium is widely distributed and has a high yield. It is a medicinal and edible plant with great development value in China. As a traditional Chinese medicine, N. folium is an important component of many compound preparations. It is necessary to explore and reveal the complex relationships and mechanisms between the effective components and disease targets. Therefore, we constructed an active component-target-pathway network of N. folium in the treatment of hyperlipidemia, and found that N. folium is involved in regulating IR, anti-inflammatory, antioxidant, and other aspects.

Through TCMSP, we screened 15 active components of N. folium that met the standards of oral bioavailability and drug-like properties. Among them, quercetin and kaempferol were the core components, while isorhamnetin also played an important role according to the degree value. In addition, armepavine, nuciferin, o-nornuciferine, and others play a synergistic role in lipid regulation. We mainly analyzed the pathways and targets of quercetin, kaempferol, and isorhamnetin, and found that there are many targets involved in lipid-lowering, especially PPAR-and IR-related targets, which may be the key to the lipid-lowering effect of N. folium. Since its discovery, PPARs have been extensively studied because of their key roles in glucose and lipid metabolism, inflammation, and energy balance.[36],[37],[38],[39] PPARs exist in three forms: PPARα, PPARβ/δ, and PPARγ.[40] PPARα is mainly expressed in tissues with high fatty acid catabolism, which plays an important role in the development of IR.[41] Activation of PPARγ can cause insulin sensitivity and promote glucose metabolism.[40] Activation of PPARβ/δ can promote fatty acid metabolism.[42] PPARs and retinoic acid X receptor form heterodimers, which regulate the gene expression of downstream genes depending on the presence of co-repressors or co-activators.[42] Our study found that quercetin, kaempferol, and isorhamnetin had different effects on PPARs, and cluster analysis showed that PPARα is one of the targets of core clustering. We studied another important signaling pathway, the IR pathway. Insulin combines with the INSR to promote metabolism and growth. Loss of function or mutation of two INSR alleles leads to extreme IR.[43] Therefore, INSR is very important for insulin signal transduction and IR.

Flavonoids are the main active components in N. folium, and quercetin is the core structure of most of them. Quercetin has anti-inflammatory effects and can reduce lipid peroxidation, platelet aggregation, and capillary permeability,[44] and has been widely studied to improve lipid metabolism disorders.[45],[46],[47] Kaempferol is a natural flavonoid with various metabolic functions.[48] Kaempferol can improve steatohepatitis, which is related to energy metabolism, lipid metabolism, oxidative stress, and inflammation-related pathways.[49] Kaempferol reduces TG levels by inhibiting AKT and activating PPARα and PPARδ.[50],[51] Isorhamnetin is also a natural flavonoid and a direct metabolite of quercetin.[52] Isorhamnetin can resist inflammation, antioxidation, inhibit apoptosis and autophagy, and prevent obesity, and its related mechanisms involve PI3K/Akt/PKB, NF-κB, MAPK, p38/PPARα, and other signaling pathways.[53],[54]

We found that in the NAFLD signaling pathway, quercetin was the first component of N. folium involved for lipid lowering. Quercetin mainly regulated PPARs (PPARα, RXRa), INSR, inflammation (IL1B, IL6, PIK3CG, RELA, tumor necrosis factor [TNF]), and apoptosis (AKT1, BAX, CASP3, CASP8). Kaempferol was the second component of N. folium involved in lipid lowering in the NAFLD signaling pathway. Kaempferol plays a role in regulating cell apoptosis (AKT1, BAX, CASP3, IKKB), INSR, and inflammation (PIK3CG, RELA, TNF). The anti-oxidative stress effect of kaempferol was not reflected in this pathway. In the IR signaling pathway, quercetin plays an important role through insulin receptor and glucose transport (INSR, SLC2A4), inflammation (IL6, NFKBIA, PIK3CG, RELA, TNF), apoptosis (AKT1), PPARα, and other targets. Kaempferol acts through insulin receptors and glucose transport (SLC2A4), inflammation (GSK3B, PIK3CG, RELA, TNF), and apoptosis (IKBKB and AKT1). Isorhamnetin mainly plays a role in regulating the targets related to muscle glycogen decomposition, negative regulation of glucose homeostasis, insulin signal transduction (PYGM, GSK3B, PTPN1), and inflammation (PIK3CG, RELA).

Different components of N. folium have different binding abilities with hyperlipidemia-related targets. Compared with kaempferol and isorhamnetin, quercetin had the lowest docking ability with PPARα, PPARγ, INSR, PIK3CG, TNF, and AKT1, suggesting that quercetin, as the core component of N. folium, could achieve lipid-lowering effects from lipid catabolism, anti-inflammatory effects, and regulation of apoptosis. The results of molecular docking showed that quercetin had more advantages than kaempferol when docking with the same targets, but their docking affinities were close to each other. Kaempferol could assist quercetin to a certain extent and enhance the regulation of the target. The results of molecular docking showed that isorhamnetin had the best affinity for PPARδ and RELA. A report on the bioavailability of quercetin showed that most of the absorbed quercetin was metabolized into isorhamnetin in the form of methylation, and the latter was maintained in plasma for a longer time than quercetin.[52],[55] Therefore, isorhamnetin may enhance the intensity and duration of action of quercetin.

Inflammation is an important part of lipid metabolism. Hyperlipidemia can cause an increase in reactive oxygen species and pro-inflammatory cytokines, which may be important factors leading to lipotoxicity.[56],[57] Many studies have been devoted to finding a new way to restore lipid homeostasis, which is beneficial for reducing adipose tissue inflammation.[58] We found that inflammation-related targets accounted for a high proportion of the selected targets in this study, suggesting that N. folium plays an indispensable role in improving lipid metabolism and inhibiting inflammation. Quercetin and kaempferol may affect the overall inflammatory response by inhibiting the expression of TNF upstream.[59] Quercetin may participate in the NF-κB signaling pathway, downregulate NFKBIA, and directly or indirectly inhibit the release of NFκB, thereby inhibiting the expression of downstream inflammatory factors.[60] In this study, PIK3CG was the target of multiple active components of N. folium. Data showed that the knockout of PIK3CG could specifically inhibit the infiltration of M1 macrophages, thereby inhibiting the inflammatory response in adipose tissue induced by a high-fat diet and ultimately improving insulin sensitivity.[61] These targets were consistent with the inflammation-related targets found in this study. Based on this, we performed molecular docking between the active components of N. folium and inflammatory targets. The docking results indicated that quercetin had the best docking affinity with PIK3CG and TNF compared with kaempferol and isorhamnetin, while isorhamnetin had the best docking affinity with RELA. It is worth noting that the inflammatory response involves different targets and pathways, suggesting that N. folium has a wide range of anti-inflammatory targets, and the intervention effects of N. folium on different inflammatory targets need to be studied separately. The proposed mechanism of N. folium treatment of hyperlipidemia is shown in [Figure 8].
Figure 8: The proposed mechanism of nelumbinis folium in treatment of hyperlipidemia

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  Conclusions Top

In this study, network pharmacology was used to explore the mechanism of N. folium in the treatment of hyperlipidemia from the perspective of multiple components, multiple targets, and multiple pathways. The results showed that the same component of N. folium could regulate different targets, and the same target could interfere with different BPs and signaling pathways. We focused on quercetin, kaempferol, isorhamnetin, PPARs, IR, inflammation, and other targets to reveal the effective components and mechanism of the lipid-lowering effect of N. folium. The results of molecular docking indicated that quercetin and isorhamnetin had the greatest affinity for target proteins. This study is mainly based on the results of bioinformatics and massive data calculation, and will be verified by animal or cell experiments on this basis, to clarify the main regulatory targets of N. folium. The molecular docking results need to be further studied to enhance the affinity of compounds in order to improve the therapeutic effect of hyperlipidemia and other metabolic diseases.

Data availability

The data used to support the findings of this study can be found online at https://figshare.com/articles/dataset/Data_for_network_pharmacology/13366013.


The authors thank Professor Wei Wang for his help and guidance in this research. The authors also thank Dr. Yi-yu Chen for her help with language editing.

Financial support and sponsorship

This work was financially supported by the National Science and Technology Major Project (2019ZX09201004-001-021) and the National Natural Science Foundation of China (No. 81403368).

Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8]

  [Table 1], [Table 2], [Table 3], [Table 4]


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