Biomedical Research

Research Article - Biomedical Research (2017) Volume 28, Issue 1

De Novo transcriptome assembly for analysis and functional annotation of genes expressed in Alport syndrome iPSCs

Alport syndrome (AS) is an inherited disorder of collagen that affects the kidney, eye and cochlea. About 85% of AS cases are caused by a mutation in X-linked COL4A5, which encodes the alpha 5 chain of type IV collagen. AS patients inevitably develop end-stage renal disease and need replacement therapy. The mechanism by which the gene mutation results in AS is not completely known, in part because of a lack of genomic and transcriptome information about AS. In this study, an AS family contained three generations was subjected to comprehensively analyse. We performed high-throughput transcriptome sequencing on induced pluripotent stem cells (iPSCs) from AS renal tubular cells. Transcript sequences were used for gene analysis and functional characterization. Using an Illumina sequencing platform, 26,886,745 raw reads were acquired from AS cells and 29,252,903 from normal control (NC) cells. After quality control and filtering of raw reads, we obtained 26,021,874 clean reads from AS cells and 27,551,343 from NCs. Clean reads were analyzed for differences in gene expression, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, alternative splicing, and novel transcript prediction. Analyses showed 1168 differentially expressed genes between AS and NC samples, with 786 upregulated and 382 down regulated. GO analysis showed that the largest proportions of differentially expressed genes were in membranes and membrane components. The mitogen-activated protein kinase (MAPK) signalling pathway had the most differentially expressed genes by KEGG analysis. We predicted 881 novel transcripts in AS cells and 963 in NCs. Novel transcripts were assessed for protein-coding potential using a coding potential calculator. We used SOAP splice to detect alternative splicing of mRNA. This study lays a foundation for further research on population genetics and gene function analysis in AS.

Author(s): Wenbiao Chen, Jianrong Huang, Yong Dai

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