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05 November 2018 : Animal Research  

Tangshen Formula Treatment for Diabetic Kidney Disease by Inhibiting Racgap1-stata5-Mediated Cell Proliferation and Restoring miR-669j-Arntl-Related Circadian Rhythm

Xiuying Wang1ADEF, Hai Zhao2BF, Xingquan Wu3BDF, Guangsheng Xi1BCD, Shengxue Zhou1ADE

DOI: 10.12659/MSM.907412

Med Sci Monit 2018; 24: ANS7914-7928

Abstract

BACKGROUND: The aim of this study was to investigate the underlying mechanisms of Tangshen formula (TSF) for treatment of diabetic kidney disease (DKD).

MATERIAL AND METHODS: Microarray dataset GSE90842 was collected from the Gene Expression Omnibus database, including renal cortical tissues from normal control (NC), DKD, and DKD mice given TSF for 12 weeks (TSF) (n=3). Differentially-expressed genes (DEGs) were identified using LIMMA method. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by module analysis. The Mirwalk2 database was used to predict the underlying miRNAs of DEGs. Function enrichment analysis was performed using the DAVID tool.

RESULTS: A total of 2277 and 2182 genes were identified as DEGs between DKD and NC or TSF groups, respectively. After overlap, 373 DEGs were considered as common in 2 comparison groups. Function enrichment indicated common DEGs were related to cell proliferation (Asf1b, anti-silencing function 1B histone chaperone; Anln, anillin, actin-binding protein; Racgap1, Rac GTPase activating protein 1; and Stat5, signal transducer and activator of transcription 5) and circadian rhythm (Arntl, aryl hydrocarbon receptor nuclear translocator-like). Racgap1 was considered as a hub gene in the PPI network because it could interact with Asf1b, Anln, and Stat5. Arntl was regulated by miR-669j in the miRNA-DEGs network and this miRNA was also a DEG in 2 comparisons.

CONCLUSIONS: TSF may be effective for DKD by inhibiting Racgap1-stata5-mediated cell proliferation and restoring miR-669j-Arntl-related circadian rhythm.

Keywords: Circadian Rhythm

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750