No abstract

Diabetic kidney disease (DKD) is a long-term major microvascular complication of uncontrolled hyperglycemia and one of the leading causes of end-stage renal disease (ESDR). The pathogenesis of DKD has not been fully elucidated, and effective therapy to completely halt DKD progression to ESDR is lacking. This study aimed to identify critical molecular signatures and develop novel therapeutic targets for DKD. This study enrolled 10 datasets consisting of 93 renal samples from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). Networkanalyst, Enrichr, STRING, and Cytoscape were used to conduct the differentially expressed genes (DEGs) analysis, pathway enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene screening. The shared DEGs of type 1 diabetic kidney disease (T1DKD) and type 2 diabetic kidney disease (T2DKD) datasets were performed to identify the shared vital pathways and hub genes. Strepotozocin-induced Type 1 diabetes mellitus (T1DM) rat model was prepared, followed by hematoxylin & eosin (HE) staining, and Oil Red O staining to observe the lipid-related morphological changes. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to validate the key DEGs of interest from a meta-analysis in the T1DKD rat. Using meta-analysis, 305 shared DEGs were obtained. Among the top 5 shared DEGs, Tmem43, Mpv17l, and Slco1a1, have not been reported relevant to DKD. Ketone body metabolism ranked in the top 1 in the KEGG enrichment analysis. Coasy, Idi1, Fads2, Acsl3, Oxct1, and Bdh1, as the top 10 down-regulated hub genes, were first identified to be involved in DKD. The qRT-PCR verification results of the novel hub genes were mostly consistent with the meta-analysis. The positive Oil Red O staining showed that the steatosis appeared in tubuloepithelial cells at 6 w after DM onset. Taken together, abnormal ketone body metabolism may be the key factor in the progression of DKD. Targeting metabolic abnormalities of ketone bodies may represent a novel therapeutic strategy for DKD. These identified novel molecular signatures in DKD merit further clinical investigation.

Deep mining of the molecular mechanisms underlying diabetic retinopathy (DR) is critical for the development of novel therapeutic targets. This study aimed to identify key molecular signatures involved in experimental DR on the basis of integrated bioinformatics analysis. Four datasets consisting of 37 retinal samples were downloaded from the National Center of Biotechnology Information Gene Expression Omnibus. After batch-effect adjustment, bioinformatics tools such as Networkanalyst, Enrichr, STRING, and Metascape were used to evaluate the differentially expressed genes (DEGs), perform enrichment analysis, and construct protein-protein interaction networks. The hub genes were identified using Cytoscape software. The DEGs of interest from the meta-analysis were confirmed by quantitative reverse transcription-polymerase chain reaction in diabetic rats and a highglucose-treated retinal cell model, respectively. A total of 743 DEGs related to lens differentiation, insulin resistance, and high-density lipoprotein (HDL) cholesterol metabolism were obtained using the meta-analysis.Alterations of dynamic gene expression in the chloride ion channel, retinol metabolism, and fatty acid metabolism were involved in the course of DR in rats. Importantly, H3K27m3 modifications regulated the expression of