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Network pharmacology approach to evaluate the therapeutic effects of mulberry leaf components for obesity.

ABSTRACT: Obesity is a chronic condition that has become a serious public health challenge globally due to the association with a high incidence of complications. Mulberry leaf is one of the most commonly used medicinal and herbal medicines that has been reported to ameliorate obesity and hyperlipidemia. However, the mechanism remains unclear. In the present study, a network pharmacology approach was used to explore the potential mechanism underlying the effects of mulberry leaf extract on obesity. First, the potential targets of mulberry leaf and obesity were predicted using SwissTargetPrediction, Online Mendelian Inheritance in Man, GeneCards and Comparative Toxicogenomics Database databases, which were then used to construct the protein-protein interaction networks. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyzes were performed using R version 3.6.3. Finally, results of this network analysis were verified by using the mulberry leaf extract to treat high-fat diet-induced obese mice. In total, 24 target genes associated with obesity that could potentially be affected by mulberry leaf treatment were predicted by network pharmacology, using which top seven related pathways were determined by KEGG enrichment analysis. Triglyceride (TG) and total cholesterol (TC) levels in mice serum were detected using TG and TC assay kits. Hepatic fat accumulation was detected by H&E staining whereas liver lipid droplets were detected by Oil red O staining in mice tissues. The expression of IL-1β, NF-κB inhibitor α, inducible nitric oxide synthase, AMP-activated protein kinase (AMPK), sterol regulatory element-binding proteins and fatty acid synthase in the visceral white adipose tissues of mice was analyzed by western blotting. The expression of TNF-α, peroxisome proliferator activated receptor (PPAR)D, PPARG, fatty acid amide hydrolase (FAAH) and hydroxysteroid 11-β dehydrogenase 1 (HSD11B1) in the visceral white adipose tissues of mice was detected by reverse transcription-quantitative PCR. Mulberry leaf extract was found to reduce fat accumulation and hepatic lipid droplet formation. Mulberry leaf also alleviated inflammation and lipogenesis whilst promoting lipid catabolism and fatty acid oxidation by promoting the AMPK signaling pathway. The possible anti-obesity effects of mulberry leaf on the mice may be due to the downregulation of TNF-α, PPARD and PPARG and the upregulation of FAAH and HSD11B1. These results were consistent with the GO enrichment analysis and suggested that mulberry leaf may regulate lipid metabolism and catabolism, fatty acid metabolism and biosynthesis and the inflammatory response to reduce obesity.

SUBMITTER: Wang G

PROVIDER: S-EPMC8630443 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Obesity has become a global issue that affects the emergence of various chronic diseases such as diabetes mellitus, dysplasia, heart disorders, and cancer. In this study, an integration method was developed between the metabolite profile of the active compound of Murraya paniculata and the exploration of the targeting mechanism of adipose tissue using network pharmacology, molecular docking, molecular dynamics simulation, and in vitro tests. Network pharmacology results obtained with the skyline query technique using a block-nested loop (BNL) showed that histone acetyltransferase p300 (EP300), peroxisome proliferator-activated receptor gamma (PPARG), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) are potential targets for treating obesity. Enrichment analysis of these three proteins revealed their association with obesity, thermogenesis, energy metabolism, adipocytokines, fat cell differentiation, and glucose homeostasis. Metabolite profiling of M. paniculata leaves revealed sixteen active compounds, ten of which were selected for molecular docking based on drug-likeness and ADME results. Molecular docking results between PPARG and EP300 with the ten active compounds showed a binding affinity value of ≤ -5.0 kcal/mol in all dockings, indicating strong binding. The stability of the protein-ligand complex resulting from docking was examined using molecular dynamics simulations, and we observed the best average root mean square deviation (RMSD) of 0.99 Å for PPARG with trans-3-indoleacrylic acid, which was lower than with the native ligand BRL (2.02 Å). Furthermore, the RMSD was 2.70 Å for EP300 and the native ligand 99E, and the lowest RMSD with the ligand (1R,9S)-5-[(E)-2-(4-Chlorophenyl)vinyl]-11-(5-pyrimidinylcarbonyl)-7,11-diazatricyclo[7.3.1.02,7]trideca-2,4-dien-6-one was 3.33 Å. The in vitro tests to validate the potential of M. paniculata in treating obesity showed that there was a significant decrease in PPARG and EP300 gene expressions in 3T3-L1 mature adipocytes treated with M. paniculata ethanolic extract starting at concentrations 62.5 μg/ml and 15.625 μg/ml, respectively. These results indicate that M. paniculata can potentially treat obesity by disrupting adipocyte maturation and influencing intracellular lipid metabolism.

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Project description:BACKGROUND: Torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor which raises high-density lipoprotein (HDL) cholesterol and reduces low-density lipoprotein (LDL) cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib. RESULTS: In the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO) analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM) in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs) modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR) beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by Hepatocyte Growth Factor Receptor (HGFR, c-Met) might play decisive characters in the adverse cardiovascular effects associated with torcetrapib. Finally, a reverse docking algorithm in silico between torcetrapib and transmembrane receptors was conducted to identify the potential off-targets. This screening was carried out based on the enriched signaling network analysis. CONCLUSIONS: Our study provided unique insights into the biological processes of torcetrapib-associated off-target adverse effects in a systems biology visual angle. In particular, we highlighted the importance of PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase might be direct off-targets, which were highly related to the unfavorable adverse effects of torcetrapib and worthy of further experimental validation.

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Network pharmacology approach to evaluate the therapeutic effects of mulberry leaf components for obesity.

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