Identification of circular RNAs and functional competing endogenous RNA networks in human proximal tubular epithelial cells treated with sodium-glucose cotransporter 2 inhibitor dapagliflozin in diabetic kidney disease

Oxidative Stress: A Culprit in the Progression of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the principal culprit behind chronic kidney disease (CKD), ultimately developing end-stage renal disease (ESRD) and necessitating costly dialysis or kidney transplantation. The limited therapeutic efficiency among individuals with DKD is a result of our finite understanding of its pathogenesis. DKD is the result of complex interactions between various factors. Oxidative stress is a fundamental factor that can establish a link between hyperglycemia and the vascular complications frequently encountered in diabetes, particularly DKD. It is crucial to recognize the essential and integral role of oxidative stress in the development of diabetic vascular complications, particularly DKD. Hyperglycemia is the primary culprit that can trigger an upsurge in the production of reactive oxygen species (ROS), ultimately sparking oxidative stress. The main endogenous sources of ROS include mitochondrial ROS production, NADPH oxidases (Nox), uncoupled endothelial nitric oxide synthase (eNOS), xanthine oxidase (XO), cytochrome P450 (CYP450), and lipoxygenase. Under persistent high glucose levels, immune cells, the complement system, advanced glycation end products (AGEs), protein kinase C (PKC), polyol pathway, and the hexosamine pathway are activated. Consequently, the oxidant-antioxidant balance within the body is disrupted, which triggers a series of reactions in various downstream pathways, including phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), transforming growth factor beta/p38-mitogen-activated protein kinase (TGF-β/p38-MAPK), nuclear factor kappa B (NF-κB), adenosine monophosphate-activated protein kinase (AMPK), and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling. The disease might persist even if strict glucose control is achieved, which can be attributed to epigenetic modifications. The treatment of DKD remains an unresolved issue. Therefore, reducing ROS is an intriguing therapeutic target. The clinical trials have shown that bardoxolone methyl, a nuclear factor erythroid 2-related factor 2 (Nrf2) activator, blood glucose-lowering drugs, such as sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide-1 receptor agonists can effectively slow down the progression of DKD by reducing oxidative stress. Other antioxidants, including vitamins, lipoic acid, Nox inhibitors, epigenetic regulators, and complement inhibitors, present a promising therapeutic option for the treatment of DKD. In this review, we conduct a thorough assessment of both preclinical studies and current findings from clinical studies that focus on targeted interventions aimed at manipulating these pathways. We aim to provide a comprehensive overview of the current state of research in this area and identify key areas for future exploration.

GSK3β: A ray of hope for the treatment of diabetic kidney disease

Diabetic kidney disease (DKD), a major microvascular complication of diabetes, is characterized by its complex pathogenesis, high risk of chronic renal failure, and lack of effective diagnosis and treatment methods. GSK3β (glycogen synthase kinase 3β), a highly conserved threonine/serine kinase, was found to activate glycogen synthase. As a key molecule of the glucose metabolism pathway, GSK3β participates in a variety of cellular activities and plays a pivotal role in multiple diseases. However, these effects are not only mediated by affecting glucose metabolism. This review elaborates on the role of GSK3β in DKD and its damage mechanism in different intrinsic renal cells. GSK3β is also a biomarker indicating the progression of DKD. Finally, the protective effects of GSK3β inhibitors on DKD are also discussed.

The intellectual base and global trends in inflammation of diabetic kidney disease: a bibliometric analysis

Diabetic kidney disease (DKD) is a severe complication of diabetes mellitus (DM). The literature on DKD inflammation research has experienced substantial growth. However, there is a lack of bibliometric analyses. This study aimed to examine the existing research on inflammation in DKD by analyzing articles published in the Web of Science Core Collection (WOSCC) over the past 30 years. We conducted a visualization analysis using several software, including CiteSpace and VOSviewer. We found that the literature on inflammation research in DKD has experienced substantial growth, indicating a rising interest in this developing area of study. In this field, Navarro-Gonzalez, JF is the most frequently cited author, Kidney International is the most frequently cited journal, China had the highest number of publications in the field of DKD inflammation, and Monash University emerged as the institution with the most published research. The research area on inflammation in DKD primarily centers around the investigation of 'Glycation end-products', 'chronic kidney disease', and 'diabetic nephropathy'. The emerging research trends in this field will focus on the 'Gut microbiota', 'NLRP3 inflammasome', 'autophagy', 'pyroptosis', 'sglt2 inhibitor', and 'therapeutic target'. Future research on DKD may focus on further exploring the inflammatory response, identifying specific therapeutic targets, studying biomarkers, investigating stem cell therapy and tissue engineering, and exploring gene therapy and gene editing. In summary, this study examines the main areas of study, frontiers, and trends in DKD inflammation, which have significant implications for future research.

Integrative analyses of biomarkers and pathways for diabetic nephropathy

Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.

An integrated RNA sequencing and network pharmacology approach reveals the molecular mechanism of dapagliflozin in the treatment of diabetic nephropathy

Dapagliflozin, an inhibitor of sodium-glucose cotransporter 2 (SGLT2), is a new type of oral hypoglycemic drugs which can promote glucose excretion in the kidney. Studies have shown that dapagliflozin has renoprotective effect in the treatment of type 2 diabetes. However, the underlying mechanism remains unclear. Here, we combined integrated RNA sequencing and network pharmacology approach to investigate the molecular mechanism of dapagliflozin for diabetic nephropathy (DN). Dapagliflozin significantly relieved glucose intolerance, urinary albumin/creatinine ratio (UACR) and renal pathological injuries of db/db mice. The LncRNA and mRNA expression in kidney tissues from control group (CR), db/db group (DN) and dapagliflozin group (DG) were assessed by RNA sequencing. We identified 7 LncRNAs and 64 mRNAs common differentially expressed in CR vs DN and DN vs DG, which were used to construct co-expression network to reveal significantly correlated expression patterns in DN. In addition, network pharmacology was used to predict the therapeutic targets of dapagliflozin and we constructed component-target-pathway network according to the results of RNA sequencing and network pharmacology. We found that SMAD9, PPARG, CD36, CYP4A12A, CYP4A12B, CASP3, H2-DMB2, MAPK1, MAPK3, C3 and IL-10 might be the pivotal targets of dapagliflozin for treating DN and these genes were mainly enriched in pathways including TGF-β signaling pathway, PPAR signaling pathway, Chemokine signaling pathway, etc. Our results have important implication and provide novel insights into the protective mechanism of dapagliflozin for treating DN.