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      The Mechanisms of Bushen-Yizhi Formula as a Therapeutic Agent against Alzheimer’s Disease

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          Abstract

          Bushen-Yizhi prescription (BSYZ) has been an effective traditional Chinese medicine (TCM) prescription in treating Alzheimer’s disease (AD) for hundreds of years. However, the underlying mechanisms have not been fully elucidated yet. In this work, a systems pharmacology approach was developed to reveal the underlying molecular mechanisms of BSYZ in treating AD. First, we obtained 329 candidate compounds of BSYZ by in silico ADME/T filter analysis and 138 AD-related targets were predicted by our in-house WEGA algorithm via mapping predicted targets into AD-related proteins. In addition, we elucidated the mechanisms of BSYZ action on AD through multiple network analysis, including compound-target network analysis and target-function network analysis. Furthermore, several modules regulated by BSYZ were incorporated into AD-related pathways to uncover the therapeutic mechanisms of this prescription in AD treatment. Finally, further verification experiments also demonstrated the therapeutic effects of BSYZ on cognitive dysfunction in APP/PS1 mice, which was possibly via regulating amyloid-β metabolism and suppressing neuronal apoptosis. In conclusion, we provide an integrative systems pharmacology approach to illustrate the underlying therapeutic mechanisms of BSYZ formula action on AD.

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          Most cited references36

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          DAVID: Database for Annotation, Visualization, and Integrated Discovery.

          Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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            Genetic enhancement of learning and memory in mice.

            Hebb's rule (1949) states that learning and memory are based on modifications of synaptic strength among neurons that are simultaneously active. This implies that enhanced synaptic coincidence detection would lead to better learning and memory. If the NMDA (N-methyl-D-aspartate) receptor, a synaptic coincidence detector, acts as a graded switch for memory formation, enhanced signal detection by NMDA receptors should enhance learning and memory. Here we show that overexpression of NMDA receptor 2B (NR2B) in the forebrains of transgenic mice leads to enhanced activation of NMDA receptors, facilitating synaptic potentiation in response to stimulation at 10-100 Hz. These mice exhibit superior ability in learning and memory in various behavioural tasks, showing that NR2B is critical in gating the age-dependent threshold for plasticity and memory formation. NMDA-receptor-dependent modifications of synaptic efficacy, therefore, represent a unifying mechanism for associative learning and memory. Our results suggest that genetic enhancement of mental and cognitive attributes such as intelligence and memory in mammals is feasible.
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              The road to restoring neural circuits for the treatment of Alzheimer's disease.

              Alzheimer's disease is a progressive loss of memory and cognition, for which there is no cure. Although genetic studies initially suggested a primary role for amyloid-in Alzheimer's disease, treatment strategies targeted at reducing amyloid-have failed to reverse cognitive symptoms. These clinical findings suggest that cognitive decline is the result of a complex pathophysiology and that targeting amyloid-alone may not be sufficient to treat Alzheimer's disease. Instead, a broad outlook on neural-circuit-damaging processes may yield insights into new therapeutic strategies for curing memory loss in the disease.
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                Author and article information

                Contributors
                fangjs@gzucm.edu.cn
                wangqi@gzucm.edu.cn
                junxu@biochemomes.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 February 2018
                15 February 2018
                2018
                : 8
                : 3104
                Affiliations
                [1 ]ISNI 0000 0000 8848 7685, GRID grid.411866.c, Institute of Clinical Pharmacology, , Guangzhou University of Chinese Medicine, ; Guangzhou, 510405 China
                [2 ]ISNI 0000 0001 2360 039X, GRID grid.12981.33, Research Center for Drug Discovery, School of Pharmaceutical Sciences, , Sun Yat-sen University, ; Guangzhou, 510006 China
                [3 ]ISNI 0000 0000 8848 7685, GRID grid.411866.c, Department of Neurology & Psychology, Fourth Clinical Medical College, , Guangzhou University of Chinese Medicine, ; Shenzhen, 518033 China
                Article
                21468
                10.1038/s41598-018-21468-w
                5814461
                29449587
                55ac48a2-029e-45ba-8b8e-224d1fc64922
                © The Author(s) 2018

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 October 2017
                : 5 February 2018
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