2,8-dihydroxyadenine nephrolithiasis induces developmental stage-specific alterations in gene expression in mouse kidney

Discovery of 2,8-dihydroxyadenine in HUA patients with uroliths and biomarkers for its associated nephropathy

Currently, it is acknowledged that gout is caused by uric acid (UA). However, some studies have revealed no correlation between gout and UA levels, and growing evidence suggests that 2,8-dihydroxyadenine (2,8-DHA), whose structural formula is similar to UA but is less soluble, may induce gout. Hence, we hypothesized that uroliths from hyperuricemia (HUA) patients, which is closely associated with gout, may contain 2,8-DHA. In this study, 2,8-DHA in uroliths and serum of HUA patients were determined using HPLC. Moreover, bioinformatics was used to investigate the pathogenic mechanisms of 2,8-DHA nephropathy. Subsequently, a mouse model of 2,8-DHA nephropathy established by the gavage administration of adenine, as well as a model of injured HK-2 cells induced by 2,8-DHA were used to explore the pathogenesis of 2,8-DHA nephropathy. Interestingly, 2,8-DHA could readily deposit in the cortex of the renal tubules, and was found in the majority of these HUA patients. Additionally, the differentially expressed genes between 2,8-DHA nephropathy mice and control mice were found to be involved in inflammatory reactions. Importantly, CCL2 and IL-1β genes had the maximum degree, closeness, and betweenness centrality scores. The expressions of CCL2 and IL-1β genes were significantly increased in the serum of 24 HUA patients with uroliths, indicating that they may be significant factors for 2,8-DHA nephropathy. Further analysis illustrated that oxidative damage and inflammation were the crucial processes of 2,8-DHA renal injury, and CCL2 and IL-1β genes were verified to be essential biomarkers for 2,8-DHA nephropathy. These findings revealed further insights into 2,8-DHA nephropathy, and provided new ideas for its diagnosis and treatment.

Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics

PURPOSE

Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets.

METHODS

The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene-miRNA network and drug-DEG interaction network.

RESULTS

In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT-PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group.

CONCLUSION

The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.

Identification of Candidate Genes Involved in Renal Ischemia/Reperfusion Injury

Renal ischemia/reperfusion injury (IRI) is a main risk factor for the occurrence of delayed graft function or primary graft nonfunction of kidney transplantation. However, it lacks ideal molecular markers for indicating IRI in kidney transplantation. The present study is to explore novel candidate genes involved in renal IRI. Experimental renal IRI mouse models were constructed, and the differentially expressed genes were screened using a microarray assay. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed. The expression of genes was detected using real-time qPCR assay. Western blotting and immunohistochemistry staining assays were performed for protein determination. We identified that renal IRI induced the upregulation of SPRR2F, SPRR1A, MMP-10, and long noncoding RNA (lncRNA) Malat1 in kidney tissues for 479.3-, 4.98-, 238.1-, and 3.79-fold, respectively. The expression of miR-139-5p in kidney tissues of IRI-treated mice was decreased to 40.4% compared with the sham-operated mice. These genes are associated with keratinocyte differentiation, regeneration and repair of kidney tissues, extracellular matrix degradation and remodeling, inflammation, and cell proliferation in renal IRI. Identification of novel biomarkers involved in renal IRI may provide evidences for the diagnosis and treatment of renal IRI.