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      Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

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          Abstract

          Background

          Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF.

          Methods

          The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools.

          Results

          We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list.

          Conclusion

          This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

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          Most cited references47

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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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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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                ECAM
                Evidence-based Complementary and Alternative Medicine : eCAM
                Hindawi
                1741-427X
                1741-4288
                2021
                17 April 2021
                17 April 2021
                : 2021
                : 6623010
                Affiliations
                1Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
                2Graduate College, Beijing University of Traditional Chinese Medicine, Beijing 100029, China
                3Laboratory of Molecular and Biology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
                4Department of Endocrinology, Affiliated Hospital to Changchun University of Chinese Medicine, Changchun 130021, China
                Author notes

                Academic Editor: Daniel Dias Rufino Arcanjo

                Author information
                https://orcid.org/0000-0002-9367-4471
                https://orcid.org/0000-0002-0644-6930
                https://orcid.org/0000-0001-7447-0556
                https://orcid.org/0000-0002-0362-7365
                https://orcid.org/0000-0002-9873-4154
                Article
                10.1155/2021/6623010
                8081615
                1e1973cd-3ef7-4a13-a4e5-7771cf2c23f5
                Copyright © 2021 Xinmiao Wang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 3 October 2020
                : 19 February 2021
                : 12 April 2021
                Funding
                Funded by: Beijing Municipal Natural Science Foundation
                Award ID: 7212189
                Funded by: Fundamental Research Funds for the Central Public Welfare Research Institutes
                Award ID: ZZ13-ZD-06
                Funded by: China Academy of Chinese Medical Sciences
                Award ID: ZZ0808004
                Categories
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine

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