RNA-sequencing reveals genome-wide long non-coding RNAs profiling associated with early development of diabetic nephropathy
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Wanxin Tang1,*, Dongmei Zhang1 and Xiaoke Ma2,*
1Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
2School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, China
*These authors have contributed equally to this work
Xiaoke Ma, email: firstname.lastname@example.org
Keywords: long non-coding RNA; kidney; nephropathy; gene expression; network biology
Received: June 15, 2017 Accepted: September 08, 2017 Published: November 11, 2017
Background: Diabetic nephropathy (DN) seriously threatens the lives of patients, and the mechanism of DN remains largely unknown because of the complex regulation between long non-coding RNA (lncRNA) and protein-coding genes. In early development of diabetic nephropathy (DN), pathogenesis remains largely unknown.
Results: We used RNA-sequencing to profile protein-coding and lncRNA gene transcriptome of mouse kidney proximal tubular cells during early stage of DN at various time points. Over 7000 protein-coding and lncRNA genes were differentially expressed, and most of them were time-specific. Nearly 40% of lncRNA genes overlapped with functional element signals using CHIP-Seq data from ENCODE database. Disease progression was characterized by lncRNA expression patterns, rather than protein-coding genes, indicating that the lncRNA genes are potential biomarkers for DN. For gene ontologies related to kidney, enrichment was observed in protein-coding genes co-expressed with neighboring lncRNA genes. Based on protein-coding and lncRNA gene profiles, clustering analysis reveals dynamic expression patterns for kidney, suggesting that they are highly correlated during disease progression. To evaluate translation of mouse model to human conditions, we experimentally validated orthologous genes in human cells in vitro diabetic model. In mouse model, most gene expression patterns were repeated in human cell lines.
Conclusions: These results define dynamic transcriptome and novel functional roles for lncRNAs in diabetic kidney cells; these roles may result in lncRNA-based diagnosis and therapies for DN.
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