Identifying key genes for diabetic kidney disease by bioinformatics analysis

Abstract

Abstract Background There are no reliable molecular targets for early diagnosis and effective treatment in the clinical management of diabetic kidney disease (DKD). To identify novel gene factors underlying the progression of DKD. Methods The public transcriptomic datasets of the alloxan-induced DKD model and the streptozotocin-induced DKD model were retrieved to perform an integrative bioinformatic analysis of differentially expressed genes (DEGs) shared by two experimental animal models. The dominant biological processes and pathways associated with DEGs were identified through enrichment analysis. The expression changes of the key DEGs were validated in the classic db/db DKD mouse model. Results The downregulated and upregulated genes in DKD models were uncovered from GSE139317 and GSE131221 microarray datasets. Enrichment analysis revealed that metabolic process, extracellular exosomes, and hydrolase activity are shared biological processes and molecular activity is altered in the DEGs. Importantly, Hmgcs2, angptl4, and Slco1a1 displayed a consistent expression pattern across the two DKD models. In the classic db/db DKD mice, Hmgcs2 and angptl4 were also found to be upregulated while Slco1a1 was downregulated in comparison to the control animals. Conclusions In summary, we identified the common biological processes and molecular activity being altered in two DKD experimental models, as well as the novel gene factors (Hmgcs2, Angptl4, and Slco1a1) which may be implicated in DKD. Future works are warranted to decipher the biological role of these genes in the pathogenesis of DKD.

Keywords

• Diabetic Kidney Disease (DKD)
• Pathogenesis
• mRNA microarray datasets
• Differentially Expressed Genes (DEGs)