Mechanism of Yi-Qi-Bu-Shen Recipe for the Treatment of Diabetic Nephropathy Complicated with Cognitive Dysfunction Based on Network Pharmacology and Experimental Validation

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Abstract

Context

Diabetic nephropathy (DN) and cognitive dysfunction (CD) are common complications of diabetes. Yi-Qi-Bu-Shen Recipe (YQBS) can effectively reduce blood glucose, improve insulin resistance, and delay the progression of diabetic complications. The underlying mechanisms of its effects need to be further studied.

Objective

This study elucidates the mechanism of YQBS in DN with CD through network pharmacology and experimental validation.

Materials and Methods

Protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Male Sprague-Dawley (SD) rats were divided into 6 groups: model, YQBS (2, 4, 8 g/kg), positive control (metformin, 200 mg/kg), and control; the DN model was established by high sugar and high fat diet combined with intraperitoneal streptozotocin injection. After the DN model was established, the rats were gavaged for 10 weeks. Serum, kidneys, and hippocampus tissues were collected to measure the expression levels of TLR4, NF-κB, TNF-α, and IL-6.

Results

The network pharmacology analysis showed that quercetin and kaempferol were the main active components of YQBS. TNF and IL-6 were the key targets, and TLR4/NF-κB pathway was crucial to YQBS in treating DN complicated with CD. Experimental validation showed that the intervention of YQBS can reduce TNF-α and IL-6 in serum, and also significantly decreases the protein expression of TLR4 and NF-κB.

Conclusion

YQBS exerts anti-inflammatory effects on DN with CD through TLR4/NF-κB pathway. This study provides a biological basis for the scientific usage of YQBS in inflammation diseases and supplies experimental evidence for future traditional Chinese medicine development.

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Most cited references 73

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

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

Da Huang, Brad T. Sherman, Richard A. Lempicki (2009)

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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AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

Oleg Trott, Arthur J 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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Author and article information

Journal

Journal ID (nlm-ta): Diabetes Metab Syndr Obes

Journal ID (iso-abbrev): Diabetes Metab Syndr Obes

Journal ID (publisher-id): dmso

Title: Diabetes, Metabolic Syndrome and Obesity

Publisher: Dove

ISSN (Electronic): 1178-7007

Publication date (Electronic): 23 October 2024

Publication date Collection: 2024

Volume: 17

Pages: 3943-3963

Affiliations

[1 ]First Clinical Medical College, Shandong University of Traditional Chinese Medicine , Jinan, People’s Republic of China

[2 ]Research Center for Basic Medical Sciences, Qilu Hospital of Shandong University , Jinan, People’s Republic of China

[3 ]Traditional Chinese Medicine College, Shandong University of Traditional Chinese Medicine , Jinan, People’s Republic of China

[4 ]Department of Traditional Chinese Medicine, Qilu Hospital of Shandong University , Jinan, People’s Republic of China

Author notes

Correspondence: Deshan Liu, Department of Traditional Chinese Medicine, Qilu Hospital of Shandong University , Jinan, Shandong, 250012, People’s Republic of China, Tel +86 18560087381, Email liudeshan@sdu.edu.cn

Article

Publisher ID: 481740

DOI: 10.2147/DMSO.S481740

PMC ID: 11512782

PubMed ID: 39465123

SO-VID: 2c6d5e75-dd83-403f-b332-16a4d8fe83f3

License:

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

History

Date received : 01 August 2024

Date accepted : 05 October 2024

Page count

Figures: 9, Tables: 4, References: 73, Pages: 21

Funding

Funded by: supported by the Qilu Geriatric Diseases Chinese and Western Academic School Inheritance Workshop Project (No. 2022-93-1-10);

This study was supported by the Qilu Geriatric Diseases Chinese and Western Academic School Inheritance Workshop Project (No. 2022-93-1-10).