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      Data Mining and Network Pharmacology Analysis of Kidney-Tonifying Herbs on the Treatment of Renal Osteodystrophy Based on the Theory of “Kidney Governing Bones” in Traditional Chinese Medicine

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          Abstract

          Background

          Renal osteodystrophy (ROD) secondary to chronic kidney disease is closely associated with osteoporosis and fractures. Based on the theory of “kidney governing bones” in traditional Chinese medicine (TCM), treating bone diseases from the perspective of the kidney has become a basic principle of treating ROD. However, there are many kidney-tonifying herbs and their mechanisms of treating ROD are not clear. Therefore, our study intends to use data mining and network pharmacology to study the commonly used kidney-tonifying herbs, as well as their active ingredients and mechanisms of treating ROD.

          Methods

          We established a clinical ROD database by searching PubMed, CNKI, and other databases and screened out a core herbal combination of treating ROD. Furthermore, by using databases such as Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and GeneCards, we obtained active ingredients and targets of the core herbal combination and ROD targets. The STRING website and Cytoscape software were then used to obtain information on key active ingredients and key targets. Finally, we conducted GO and KEGG analyses using the Metascape website and molecular docking using the AutoDock Vina software.

          Results

          Our study eventually included 58 prescriptions and 116 herbs of treating ROD. Through data mining, we found that yin-yang-huo, du-zhong, and bu-gu-zhi (YDB) constituted a core herbal combination to treat ROD. Network pharmacology showed that YDB mainly acted on targets such as estrogen receptor alpha through active ingredients such as quercetin by mitogen-activated protein kinase and other signaling pathways.

          Conclusion

          Many ingredients, targets, and pathways are involved in the treatment of YDB for ROD. Specifically, the flavonoids contained in YDB have great potential for ROD treatment.

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          Most cited references77

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Paul Shannon, Andrew Markiel, Owen Ozier … (2003)
          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.
          Article 0 comments Cited 11010 times – based on 0 reviews     Review now
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            AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

            Oleg Trott, Arthur Olson (2010)
            AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. Copyright 2009 Wiley Periodicals, Inc.
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              Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

              Yingyao Zhou, Bin Zhou, Lars Pache … (2019)
              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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                Author and article information

                Contributors
                Jianxiong Ma:
                ORCID: https://orcid.org/0000-0001-5875-4508
                Xinlong Ma:
                ORCID: https://orcid.org/0000-0001-8932-3110
                Journal
                Journal ID (nlm-ta): Evid Based Complement Alternat Med
                Journal ID (iso-abbrev): Evid Based Complement Alternat Med
                Journal ID (publisher-id): ECAM
                Title: Evidence-based Complementary and Alternative Medicine : eCAM
                Publisher: Hindawi
                ISSN (Print): 1741-427X
                ISSN (Electronic): 1741-4288
                Publication date Collection: 2022
                Publication date (Electronic): 30 September 2022
                Publication date PMC-release: 30 September 2022
                Volume: 2022
                Electronic Location Identifier: 1116923
                Affiliations
                1School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
                2Tianjin Key Laboratory of Orthopedic Biomechanics and Medical Engineering, Orthopedic Research Institute, Tianjin Hospital, Tianjin, China
                3Tianjin Hospital, Tianjin, China
                4Tianjin Jinnan Traditional Chinese Medicine Hospital, Tianjin, China
                Author notes

                Academic Editor: Atul Kabra

                Author information
                https://orcid.org/0000-0002-9164-5036
                https://orcid.org/0000-0003-3277-4951
                https://orcid.org/0000-0002-6285-8884
                https://orcid.org/0000-0003-0654-4909
                https://orcid.org/0000-0002-0525-7425
                https://orcid.org/0000-0002-4245-1323
                https://orcid.org/0000-0003-1493-2821
                https://orcid.org/0000-0001-5875-4508
                https://orcid.org/0000-0001-8932-3110
                Article
                DOI: 10.1155/2022/1116923
                PMC ID: 9552684
                PubMed ID: 36238608
                SO-VID: 1f070455-8905-4ebc-814c-29b1efd29a18
                Copyright © Copyright © 2022 Xue Tong et al.
                License:

                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
                Date received : 6 May 2022
                Date revision received : 7 August 2022
                Date accepted : 11 August 2022
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Complementary & Alternative medicine
                Data availability:
                ScienceOpen disciplines: Complementary & Alternative medicine

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