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      Deciphering the mechanisms of Yinlan Tiaozhi capsule in treating hyperlipidemia by combining network pharmacology, molecular docking and experimental verification

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          Abstract

          Yinlan Tiaozhi capsule (YLTZC) has been widely used to treat hyperlipidemia (HLP). However, its material basis and underlying pharmacological effects remain unclean. The current study aimed to explore the mechanisms involved in the treatment of YLTZC on HLP based on network pharmacology, molecular docking, and experimental verification. Firstly, UPLC-Q-TOF–MS/MS was used to comprehensively analyze and identify the chemical constituents in YLTZC. A total of 66 compounds, mainly including flavonoids, saponins, coumarins, lactones, organic acids, and limonin were characterized and classified. Simultaneously, the mass fragmentation pattern of different types of representative compounds was further explored. By network pharmacology analysis, naringenin and ferulic acid may be the core constituents. The 52 potential targets of YLTZC, including ALB, IL-6, TNF, and VEGFA, were considered potential therapeutic targets. Molecular docking results showed that the core active constituents of YLTZC (naringenin and ferulic acid) have a strong affinity with the core targets of HLP. Lastly, animal experiments confirmed that naringenin and ferulic acid significantly upregulated the mRNA expression of ALB and downregulated the mRNA expression of IL-6, TNF, and VEGFA. In sum, the constituents of YLTZC, such as naringenin and ferulic acid, might treat HLP by regulating the mechanism of angiogenesis and inhibiting inflammatory responses. Furthermore, our data fills the gap in the material basis of YLTZC.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

            To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.
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              KEGG for taxonomy-based analysis of pathways and genomes

              KEGG ( https://www.kegg.jp ) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups.
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                Author and article information

                Contributors
                zyfxyjs@gzucm.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 April 2023
                19 April 2023
                2023
                : 13
                : 6424
                Affiliations
                [1 ]GRID grid.484195.5, Guangdong Province Engineering and Technology Research Institute of Traditional Chinese Medicine/Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine, ; Guangzhou, People’s Republic of China
                [2 ]GRID grid.411866.c, ISNI 0000 0000 8848 7685, School of the Fifth Clinical Medicine, , Guangzhou University of Chinese Medicine, ; Guangzhou, 510405 People’s Republic of China
                Article
                33673
                10.1038/s41598-023-33673-3
                10115829
                37076581
                64f03bc2-afe4-43ae-adbf-0ef5187fb537
                © 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/.

                History
                : 26 December 2022
                : 17 April 2023
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                © The Author(s) 2023

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                computational biology and bioinformatics,drug discovery,diseases,chemistry
                Uncategorized
                computational biology and bioinformatics, drug discovery, diseases, chemistry

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