Literature DB >> 31106482

Whole transcriptome analysis of diabetic nephropathy in the db/db mouse model of type 2 diabetes.

Li Wen1,2, Zheng Zhang1, Rui Peng3, Luyu Zhang1, Handeng Liu1, Huimin Peng1, Yan Sun1.   

Abstract

Whole-transcriptome analysis using RNA sequencing (RNA-seq) affords broader insights about gene expression regulatory networks in diabetic nephropathy (DN). To better explore the molecular basis of DN, kidney tissue from db/db DN model mice and control mice were submitted to RNA-seq analysis. Thousands of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) were found to be significantly differentially expressed in the DN group relative to the control group. To research the regulatory mechanism of these lncRNAs and mRNAs, the integrated co-expression networks were constructed for 322 mRNAs and 27 lncRNAs that revealed significantly correlated expression patterns in DN. The potential roles of these co-expressed mRNAs were classified by Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The co-expression networks involved 27 lncRNAs interacting with 38 key mRNAs related to metabolic processes, including ND4/4L, Ndufa2/5, Ndufb4/7, Ndufs3, Uqcrc1, Aco2, Alad, Alas1, Alpl, Atp5j2, Coq5, Coq6, Cth, and CytB, all of which are highly related to encoding subunits of the mitochondrial complexes. Thus, mitochondrial dysfunction could result in renal function decline in DN. Seven dysregulated lncRNAs and nine dysregulated mRNAs in the DN model were confirmed by quantitative real-time polymerase chain reaction. The lncRNA-mRNA co-expression network provides novel evidence to support the contention that metabolic changes are associated with metabolic reprogramming in the kidneys, and that these changes play a critical role during the progression of DN.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  RNA sequencing; diabetic nephropathy; long noncoding RNA; network biology

Year:  2019        PMID: 31106482     DOI: 10.1002/jcb.29016

Source DB:  PubMed          Journal:  J Cell Biochem        ISSN: 0730-2312            Impact factor:   4.429


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