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      Network pharmacology to unveil the mechanism of suanzaoren decoction in the treatment of alzheimer’s with diabetes

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          Abstract

          Background

          Suanzaoren Decoction (SZRD), a well-known formula from traditional Chinese medicine, has been shown to have reasonable cognitive effects while relaxing and alleviating insomnia. Several studies have demonstrated significant therapeutic effects of SZRD on diabetes and Alzheimer’s disease (AD). However, the active ingredients and probable processes of SZRD in treating Alzheimer’s with diabetes are unknown. This study aims to preliminarily elucidate the potential mechanisms and potential active ingredients of SZRD in the treatment of Alzheimer’s with diabetes.

          Methods

          The main components and corresponding protein targets of SZRD were searched on the TCMSP database. Differential gene expression analysis for diabetes and Alzheimer’s disease was conducted using the Gene Expression Omnibus database, with supplementation from OMIM and genecards databases for differentially expressed genes. The drug-compound-target-disease network was constructed using Cytoscape 3.8.0. Disease and SZRD targets were imported into the STRING database to construct a protein-protein interaction network. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the intersection of genes. Molecular docking and molecular dynamics simulations were conducted on the Hub gene and active compounds. Gene Set Enrichment Analysis was performed to further analyze key genes.

          Results

          Through the Gene Expression Omnibus database, we obtained 1977 diabetes related genes and 622 AD related genes. Among drugs, diabetes and AD, 97 genes were identified. The drug-compound-target-disease network revealed that quercetin, kaempferol, licochalcone a, isorhamnetin, formononetin, and naringenin may be the core components exerting effects. PPI network analysis identified hub genes such as IL6, TNF, IL1B, CXCL8, IL10, CCL2, ICAM1, STAT3, and IL4. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that SZRD in the treatment of Alzheimer’s with diabetes is mainly involved in biological processes such as response to drug, aging, response to xenobiotic, and enzyme binding; as well as signaling pathways such as Pathways in cancer, Chemical carcinogenesis - receptor activation, and Fluid shear stress and atherosclerosis. Molecular docking results showed that licochalcone a, isorhamnetin, kaempferol, quercetin, and formononetin have high affinity with CXCL8, IL1B, and CCL2. Molecular dynamics simulations also confirmed a strong interaction between CXCL8 and licochalcone a, isorhamnetin, and kaempferol. Gene Set Enrichment Analysis revealed that CXCL8, IL1B, and CCL2 have significant potential in diabetes.

          Conclusion

          This study provides, for the first time, insights into the active ingredients and potential molecular mechanisms of SZRD in the treatment of Alzheimer’s with diabetes, laying a theoretical foundation for future basic research.

          Graphical Abstract

          Highlights

          • SZRD may improve Alzheimer’s with diabetes through potential active ingredients and hub genes.

          • licochalcone a, isorhamnetin, kaempferol, quercetin, and formononetin are potential active ingredients of SZRD for the treatment of Alzheimer’s with diabetes.

          • IL6, TNF, IL1B, CXCL8, IL10, CCL2, ICAM1, STAT3 and IL4 are hub genes and have a strong binding capacity to potential active ingredients.

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          Most cited references56

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

              Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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                Author and article information

                Contributors
                gongxiaoming@whu.edu.cn
                zhangry@hbust.edu.cn
                Journal
                Hereditas
                Hereditas
                Hereditas
                BioMed Central (London )
                0018-0661
                1601-5223
                3 January 2024
                3 January 2024
                2024
                : 161
                : 2
                Affiliations
                [1 ]GRID grid.470508.e, ISNI 0000 0004 4677 3586, Department of Pharmacy, Xianning Central Hospital, , The First Affiliate Hospital of Hubei University of Science and Technology, ; Xianning, 437100 Hubei China
                [2 ]Hubei Key Laboratory of Diabetes and Angiopathy, Hubei University of Science and Technology, ( https://ror.org/018wg9441) Xianning, 437100 China
                [3 ]School of Basic Medical Sciences, Xianning Medical College, Hubei University of Science and Technology, ( https://ror.org/018wg9441) Xianning, 437100 China
                Author information
                http://orcid.org/0000-0001-6469-998X
                Article
                301
                10.1186/s41065-023-00301-z
                10762922
                38167125
                ed2d83cb-4f85-4901-8edc-1f5f5524eae2
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 8 August 2023
                : 26 October 2023
                Funding
                Funded by: The Xiannning Central Hospital Fund project
                Award ID: 2021XYB033
                Funded by: FundRef http://dx.doi.org/10.13039/100012554, Hubei Provincial Department of Education;
                Award ID: T201921
                Award ID: B2022190
                Funded by: Hubei University of Science and Technology
                Award ID: 2022TNB02
                Award ID: 2022TNB10
                Categories
                Research
                Custom metadata
                © Mendelian Society of Lund and BioMed Central Ltd. 2024

                network pharmacology,molecular dynamics simulation,alzheimer’s with diabetes,licochalcone a,isorhamnetin,kaempferol

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