A systematic integrative approach reveals novel microRNAs in diabetic nephropathy

Abstract

Background: Despite huge efforts, the underlying molecular mechanisms of diabetic nephropathy (DN) are yet elusive, and holistic views have rarely been generated. Considering the complexity of DN pathogenesis, the integration of datasets from different molecular types to construct a multilayer map of DN can provide a comprehensive insight toward the disease mechanisms and also can generate new knowledge. Here, we have re-analyzed two mRNA microarray datasets related to glomerular and tubulointerstitial compartments of human diabetic kidneys. Materials and Methods: The quality of the datasets was confirmed by unsupervised hierarchical clustering and principal component analysis. For each dataset, differentially expressed (DE) genes were identified, and transcription factors (TFs) regulating these genes and kinases phosphorylating the TFs were enriched. Furthermore, microRNAs (miRNAs) targeting the DE genes, TFs, and kinases were detected. Based on the harvested genes for glomeruli and tubulointerstitium, key signaling pathways and biological processes involved in diseases pathogenesis were recognized. In addition, the interaction of different elements in each kidney compartment was depicted in multilayer networks, and topology analysis was performed to identify key nodes. Central miRNAs whose target genes were most likely to be related to DN were selected, and their expressions were quantitatively measured in a streptozotocin-induced DN mouse model. Results: Among the examined miRNAs, miR-208a-3p and miR-496a-3p are, for the first time, found to be significantly overexpressed in the cortex of diabetic kidneys compared to controls. Conclusion: We predict that miR-208 is involved in oxygen metabolism and regulation of cellular energy balance. Furthermore, miR-496 potentially regulates protein metabolism and ion transport. However, their exact functions remain to be investigated in future studies. Taken together, starting from transcriptomics data, we have generated multilayer interaction networks and introduced novel players in DN.