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      Exploration of the molecular targets and mechanisms of suxiao xintong dropping pills for myocardial infarction by network pharmacology method

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          Abstract

          Background: Suxiao Xintong dropping pills (SXXTDP), a traditional Chinese medicine, is widely applied for treating myocardial infarction (MI). However, its therapy mechanisms are still unclear. Therefore, this research is designed to explore the molecular mechanisms of SXXTDP in treating MI.

          Methods: The active ingredients of SXXTDP and their corresponding genes of the active ingredients were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. MI-related genes were identified via analyzing the expression profiling data (accession number: GSE97320). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to study the shared genes of drug and disease. Through protein–protein interaction (PPI) network and the Cytoscape plugin cytoHubba, the hub genes were screened out. The compounds and hub targets binding were simulated through molecular docking method.

          Results: We obtained 21 active compounds and 253 corresponding target genes from TCMSP database. 1833 MI-related genes were identified according to P<0.05 and |log 2FC| ≥ 0.5. 27 overlapping genes between drug and disease were acquired. GO analysis indicated that overlapping genes were mainly enriched in MAP kinase activity and antioxidant activity. KEGG analysis indicated that overlapping genes were mainly enriched in IL-17 signaling pathway and TNF signaling pathway. We obtained 10 hub genes via cytoHubba plugin. Six of the 10 hub genes, including PTGS2, MAPK14, MMP9, MAPK1, NFKBIA, and CASP8, were acted on molecular docking verification with their corresponding compounds of SXXTDP.

          Conclusion: SXXTDP may exert cardioprotection effect through regulating multiple targets and multiple pathways in MI.

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          Most cited references47

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                Role: Data curationRole: SoftwareRole: MethodologyRole: Writing—original draft
                Role: Supervision
                Role: Validation
                Role: Formal analysis
                Role: Visualization
                Role: MethodologyRole: Funding
                Role: Methodology
                Journal
                Biosci Rep
                Biosci Rep
                bsr
                Bioscience Reports
                Portland Press Ltd.
                0144-8463
                1573-4935
                27 August 2021
                05 August 2021
                : 41
                : 8
                : BSR20204211
                Affiliations
                Department of Cardiology, Affiliated Nanping First Hospital, Fujian Medical University, Nanping 353000, Fujian Province, China
                Author notes
                Correspondence: Shanghua Xu ( 13365998866@ 123456163.com ) or Shunxiang Luo ( 15959755605@ 123456139.com )
                [*]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-1707-0027
                Article
                BSR20204211
                10.1042/BSR20204211
                8350434
                34286341
                a13db4c2-8d7f-426f-903e-921b62eab533
                © 2021 The Author(s).

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).

                History
                : 09 December 2020
                : 17 July 2021
                : 19 July 2021
                : 21 July 2021
                Page count
                Pages: 14
                Categories
                Bioinformatics
                Cardiovascular System & Vascular Biology
                Gene Expression & Regulation
                Therapeutics & Molecular Medicine
                Research Articles

                Life sciences
                chinese traditional,medicine,molecular docking simulation,myocardial infarction,pharmacology

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