Gegen Qinlian Decoction (GQD), a traditional Chinese medicine (TCM) formula, has long been used for the treatment of common metabolic diseases, including type 2 diabetes mellitus. However, the main limitation of its wider application is ingredient complexity of this formula. Thus, it is critically important to identify the major active ingredients of GQD and to illustrate mechanisms underlying its action. Here, we compared the effects of GQD and berberine, a hypothetical key active pharmaceutical ingredient of GQD, on a diabetic rat model by comprehensive analyses of gut microbiota, short-chain fatty acids, proinflammatory cytokines, and ileum transcriptomics. Our results show that berberine and GQD had similar effects on lowering blood glucose levels, modulating gut microbiota, inducing ileal gene expression, as well as relieving systemic and local inflammation. As expected, both berberine and GQD treatment significantly altered the overall gut microbiota structure and enriched many butyrate-producing bacteria, including Faecalibacterium and Roseburia, thereby attenuating intestinal inflammation and lowering glucose. Levels of short-chain fatty acids in rat feces were also significantly elevated after treatment with berberine or GQD. Moreover, concentration of serum proinflammatory cytokines and expression of immune-related genes, including Nfkb1, Stat1, and Ifnrg1, in pancreatic islets were significantly reduced after treatment. Our study demonstrates that the main effects of GQD can be attributed to berberine via modulating gut microbiota. The strategy employed would facilitate further standardization and widespread application of TCM in many diseases.
Keywords:Berberine; Gegen Qinlian Decoction; Gut microbiota; Traditional Chinese medicine; Type 2 diabetes mellitus.
Antidiabetic Effects of Gegen Qinlian Decoction via the Gut Microbiota Are Attributable to Its Key Ingredient Berberine
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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