Establishment of microRNA, transcript and protein regulatory networks in Alport syndrome induced pluripotent stem cells

Chen,Wenbiao; Tang,Donge; Dai,Yong; Diao,Hongyan

doi:10.3892/mmr.2018.9672

Establishment of microRNA, transcript and protein regulatory networks in Alport syndrome induced pluripotent stem cells

Authors:
- Wenbiao Chen
- Donge Tang
- Yong Dai
- Hongyan Diao
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Affiliations: State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, P.R. China, Clinical Medical Research Center, The Second Clinical Medical College of Jinan University (Shenzhen People's Hospital), Shenzhen, Guangdong 518020, P.R. China
Published online on: November 20, 2018 https://doi.org/10.3892/mmr.2018.9672
Pages: 238-250
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Alport syndrome (AS) is an inherited progressive disease caused by mutations in genes encoding for the α3, α4 and α5 chains, which are an essential component of type IV collagen and are required for formation of the glomerular basement membrane. However, the underlying etiology of AS remains largely unknown, and the aim of the present study was to examine the genetic mechanisms in AS. Induced pluripotent stem cells (iPSCs) were generated from renal tubular cells. The Illumina HiSeq™ 2000 system and iTRAQ‑coupled 2D liquid chromatography‑tandem mass spectrometry were used to generate the sequences of microRNAs (miRNAs), transcripts and proteins from AS‑iPSCs. Integration of miRNA, transcript and protein expression data was used to construct regulatory networks and identify specific miRNA targets amongst the transcripts and proteins. Relative quantitative proteomics using iTRAQ technology revealed 383 differentially abundant proteins, and high‑throughput sequencing identified 155 differentially expressed miRNAs and 1,168 differentially expressed transcripts. Potential miRNA targets were predicted using miRanda, TargetScan and Pictar. All target proteins and transcripts were subjected to network analysis with miRNAs. Gene ontology analysis of the miRNAs and their targets revealed functional information on the iPSCs, including biological process and cell signaling. Kyoto Encyclopedia of Genes and Genomes pathways analysis revealed that the transcripts and proteins were primarily enriched in metabolic and cell adhesion molecule pathways. In addition, the network maps identified hsa‑miRNA (miR)‑4775 as a prominent miRNA that was associated with a number of targets. Similarly, the prominent ELV‑like protein 1‑A and epidermal growth factor receptor (EGFR)‑associated transcripts were identified. Reverse transcription‑quantitative polymerase chain reaction analysis was used to confirm the upregulation of hsa‑miR‑4775 and EGFR. The integrated approach used in the present study provided a comprehensive molecular characterization of AS. The results may also further understanding of the genetic pathogenesis of AS and facilitate the identification of candidate biomarkers for AS.

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