Uncovering the relationship and mechanisms of Tartary buckwheat ( Fagopyrum tataricum ) and Type II diabetes, hypertension, and hyperlipidemia using a network pharmacology approach

PeerJ

article

Chao-Long Lu¹  Qi Zheng¹  Qi Shen²  Chi Song²  Zhi-Ming Zhang¹ 

[1] Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region, Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University;Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences;Guizhou Rapeseed Institute, Guizhou Province of Academy of Agricultural Sciences

关键词: Network pharmacology;    Fagopyrum tataricum;    Hyperglycemia;    Hypertension;    Hyperlipidemia;    Type II diabetes;    Tartary buckwheat;   

DOI  :  10.7717/peerj.4042

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundTartary buckwheat (TB), a crop rich in protein, dietary fiber, and flavonoids, has been reported to have an effect on Type II diabetes (T2D), hypertension (HT), and hyperlipidemia (HL). However, limited information is available about the relationship between Tartary buckwheat and these three diseases. The mechanisms of how TB impacts these diseases are still unclear.MethodsIn this study, network pharmacology was used to investigate the relationship between the herb as well as the diseases and the mechanisms of how TB might impact these diseases.ResultsA total of 97 putative targets of 20 compounds found in TB were obtained. Then, an interaction network of 97 putative targets for these compounds and known therapeutic targets for the treatment of the three diseases was constructed. Based on the constructed network, 28 major nodes were identified as the key targets of TB due to their importance in network topology. The targets of ATK2, IKBKB, RAF1, CHUK, TNF, JUN, and PRKCA were mainly involved in fluid shear stress and the atherosclerosis and PI3K-Akt signaling pathways. Finally, molecular docking simulation showed that 174 pairs of chemical components and the corresponding key targets had strong binding efficiencies.ConclusionFor the first time, a comprehensive systemic approach integrating drug target prediction, network analysis, and molecular docking simulation was developed to reveal the relationships and mechanisms between the putative targets in TB and T2D, HT, and HL.

【 授权许可】

CC BY