LANDSCAPE RNA PROFILING OF URINARY EXTRACELLULAR VESICLES IN PATIENTS WITH DIABETIC NEPHROPATHY

Abstract BACKGROUND AND AIMS Recent studies have revealed that urinary extracellular vesicles (uEV) are secreted with rich biological information useful for understanding the pathophysiological conditions and identifying potential biomarkers for kidney disease. Diabetic nephropathy (DN) has become the major cause of CKD worldwide, while the features of uEV in DN were poorly understood. The present study aims to provide the landscape and in-depth analysis of the transcriptome characteristics of uEV in DN. METHOD Urine was collected from a total of 40 subjects, including healthy controls (HC, n = 14), type 2 DM (T2DM, n = 16), and renal biopsy-confirmed DN (n = 10). uEV was isolated by differential centrifugation and total RNA was extracted for long RNA sequencing. A model based on CIBERSORT was calculated to trace the genetic source of uEV toward resident cells of urinary system. Differentially expressed genes (DEG) between DN and HC and DN and T2DM were applied to gene ontology (GO), Reactome pathway enrichment, as well as gene set enrichment analysis (GSEA). Correlation of the transcriptome profiling between uEV and kidney tissue in DN was also explored. RESULTS About 67%∼68% of transcripts in RNA-seq were mRNA, 11%∼12% were LncRNA and 7%∼10% were CircRNA, and the abundance of mRNA was greater than other RNA types. By CIBERSORT model applying marker genes of podocytes, epithelial cells of tubule of different segments, loop of Henle, and collecting duct, and bladder transitional epithelial cells. The uEV was shown to originate mainly from the collecting duct epithelial cells of the kidney and bladder transitional epithelial cells (Fig. 1a). Confocal laser microscopy showed the co-localization of CD63 and AQP2 (Fig. 1b) in kidney tissues. GO and Reactome analysis were further performed in upregulated mRNA between DN and T2DM and DN and HC to explore the mRNA profiling in terms of the biological significance. Impressively, ‘extracellular exosome’ as well as other terms associated with constitution of EV, ‘plasma membrane’ and ‘extracellular space’ were enriched by GO cellular component (CC) analysis in DN. Besides, Reactome analysis identified pathways of ‘immune system’, ‘extracellular matrix organization’ and ‘degradation of extracellular matrix’ reflecting the pathogenesis mechanism of DN (Fig. 1c). Additionally, microarray data of DN glomeruli and tubule from the GEO database were analyzed in comparison to uEV RNA-seq data. GSEA showed that great similarities were found in the top 10 terms of Reactome pathways (Fig. 2). CONCLUSION In conclusion, this study provides a landscape transcriptome profile, the genetic trace and the biological function terms of uEV in DN. uEV holds potentials in reflecting the transcriptome characteristics of renal tissue, which is of significance for understanding the pathogenesis of DN and biomarker discovery.