FB2025_01 , released February 20, 2025
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Cao, X., La, X., Zhang, B., Wang, Z., Li, Y., Bo, Y., Chang, H., Gao, X., Tian, C., Wu, C., Li, J.A. (2022). Sanghuang Tongxie Formula Ameliorates Insulin Resistance in Drosophila Through Regulating PI3K/Akt Signaling.  Front. Pharmacol. 13(): 874180.
FlyBase ID
FBrf0253817
Publication Type
Research paper
Abstract
Insulin resistance (IR) is a pivotal pathological characteristic that affects the occurrence and development of type 2 diabetes mellitus (T2DM). Thus, the effective control of IR is of great significance for diabetes prevention and treatment. Traditional Chinese medicine (TCM) represents a valuable tool handed down to the world by the Chinese nation and has a long history of use for diabetes clinical therapy. In this study, we focused on a self-drafted TCM-patented formula, Sanghuang Tongxie Formula (SHTXF), which exhibits clinical efficacy in the treatment of diabetes. To explore the effect and molecular mechanism of SHTXF on IR in vivo, Drosophila melanogaster was used and a (Collagen) Cg > InRK1409A diabetic IR fly model was established. SHTXF water extract was found to contribute toward carbohydrate clearance from the circulating system by converting it into triglycerides (TAG), not glycogen, for nutrient storage. In addition, SHTXF activated phosphatidylinositol-3-kinase (PI3K) activity and improved protein kinase B (PKB, also termed Akt) phosphorylation. Finally, SHTXF promoted Drosophila Forkhead Box O (dFoxO) cytoplasmic localization and inhibited its transcriptional activity. Taken together, these findings not only highlight the positive role of SHTXF in ameliorating IR via the PI3K/Akt pathway but also provide potential drug targets and key insights for use in T2DM clinical treatment strategies.
PubMed ID
PubMed Central ID
PMC9207506 (PMC) (EuropePMC)
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    Language of Publication
    English
    Additional Languages of Abstract
    Parent Publication
    Publication Type
    Journal
    Abbreviation
    Front. Pharmacol.
    Title
    Frontiers in pharmacology
    ISBN/ISSN
    1663-9812
    Data From Reference
    Alleles (4)
    Genes (4)
    Human Disease Models (2)
    Insertions (1)
    Transgenic Constructs (3)