Quantitative Analysis of PPARD Transcriptomes in Colon Cancer Cells by Next Generation Sequencing (NGS)

Purpose: NGS has revolutionized systems-based analysis of cell signaling pathways. The goal of this study is to determine the effects of PPARD in colon cancer cell transcriptomes in relation to the metastatic potential. Methods: NGS-derived colon cancer cell mRNA transcriptome profiles of HCT116 WT (HCT116) and HCT116 with genetic PPARD-knockout (KO1) cells were generated by deep sequencing, in quadruplicate, using Illumina HiSeq2000 .The transcriptomes of HCT116 and KO1 cells will be compared to determine the differentially expressed genes between HCT116 and KO1 cells. Differentially expressed genes will be examined in relation to the metastatic potential and validated by qRT-PCR. Results: Using an optimized data analysis workflow Tophat2, we mapped about 25 million sequence reads per sample to the human genome. Out of 22229 genes, we identified 12118 transcripts with >50 reads in at least one sample of HCT116 and KO1 cells with edgeR package and identified 6668 differentailly expressed genes with FDR 0.001 and P value cutoff 0.0022 using GLM tests fitted with BUM model. We further fltered the genes with both p-value and fold change and identified 416 genes with FDR 0.001 and fold change larger than 2. Among the differentially expressed genes, 311 were downregulated and 105 were upregulated in the KO1 cells compared with the WT cells. Twenty-three of the differentially expressed genes had significant association (i.e., a tendency towards co-occurrence) with PPARD expression (P < 0.05; log odds ratio > 1.5) in the TCGA colorectal adenocarcinoma database. Of these 23 genes, 7 were linked to metastasis by PubMed literature searches: GJA1, VIM, SPARC, NRG1, CXCL8 (IL-8), STC1, and SNCG, which were validated by q-RT-PCR. Conclusions: Our study represents the detailed analysis of PPARD transcriptomes in colon cancer cells, generated by mRNA-seq technology. Our results show that NGS offers a comprehensive and accurate quantitative and qualitative evaluations of mRNA contents in cells. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions. The transcriptome profiles of HCT116 WT and KO1 colon cancer cells were generated by deep sequencing, in quadruplicate, using Illumina HiSeq2000.