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      Investigation of Cardioprotective Activity of Silybin: Network Pharmacology, Molecular Docking, and In Vivo Studies

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          Abstract

          The abundant health benefits of silybin are known to benefit people with myocardial infarction (MI). However, their mechanisms of action are not precise. To address this problem, network pharmacology was used to identify the various components that can be utilized to treat this condition, and an in vivo study was conducted to evaluate the cardioprotective effect in MI rats. Genes associated with silybin and MI targets were extracted, and overlapping genes between silybin‐associated genes and MI targets were identified using Venn diagrams. Using Cytoscape, we built, visualized, and analyzed a network of compounds and genes with pathways. Protein‐protein interaction network (PPI), gene ontology (GO) function enrichment, and Kyoto Encyclopedia of Genes, and Genomes (KEGG) pathway enrichment analyses of the core targets were performed to predict its mechanism. A molecular docking study assessed the affinity between silybin and the top three genes. ECG pattern, serum CK‐MB, LDH, serum and heart tissue antioxidants, SOD and catalase in isoproterenol‐induced MI rats were used to test the cardioprotective effect of silybin. Silybin‐related genes (114) and MI‐related genes (1800) were identified, and 74 genes overlapped, in which the degrees of AKT1, TNF‐α and IL‐6 were higher than those of other targets are the disease target precisely. The enrichment of the gene set‐based indicated that the PI3K‐Akt, TNF‐α, IL‐17, VEGF, and HIF‐1 signaling pathways were significantly involved in the mechanisms of silybin against MI. The QRS complex of the ECG of silybin‐treated MI rats was restored to normal ECG and significantly increased serum (p<0.0001***) and heart tissue (p<0.0001***) SOD and serum (p<0.001 **) and heart tissue (p<0.001 **) catalase compared to MI rats. This study embodies the complex network relationship of multi‐target and multiple pathways of silybin in the treatment of MI and provides a novel method for further research on the mechanism of silybin. It has been suggested that silybin alleviates the symptoms of MI by improving antioxidant levels through the PI3K‐Akt/HIF‐1 pathway.

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          Most cited references40

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            cytoHubba: identifying hub objects and sub-networks from complex interactome

            Background Network is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks. Results We introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network. Conclusions CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.
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              KEGG: integrating viruses and cellular organisms

              Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                ChemistrySelect
                ChemistrySelect
                Wiley
                2365-6549
                2365-6549
                May 25 2023
                May 22 2023
                May 25 2023
                : 8
                : 20
                Affiliations
                [1 ] Department of Pharmacology Santhiram College of Pharmacy JNTUA Andhra Pradesh India
                [2 ] Department of Pharmaceutical Analysis CES College of Pharmacy JNTUA Andhra Pradesh India
                [3 ] Department of Pharmacognosy KVSR Siddhartha College of Pharmaceutical Sciences Vijayawada Andhra Pradesh India
                [4 ] Department of Pharmaceutical Sciences School of Biotechnology and Pharmaceutical Sciences Vignan's Foundation for Science Technology &amp; Research (Deemed to be University) Guntur Andhra Pradesh India
                [5 ] EQRX International Inc. Massachusetts Boston USA
                [6 ] Department of Pharmacology College of Pharmacy JNTUA Andhra Pradesh India
                Article
                10.1002/slct.202300148
                bff3ce02-5127-48e2-99a2-cad59d8953c3
                © 2023

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