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Next Generation Sequencing (NGS) for quantitative transcriptomic analysis of colorectal tumor cell lines and their ENU mutants

ABSTRACT: Purpose: To understand differences of gene expression profiles between colorectal cell lines and their ENU mutants. Methods: RNA profiles were generated by deep sequencing using Illumina Next-seq. Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (GRCm38 release-95). Conclusions: Our study represents the detailed differential transcriptomic analysis using ENU mutants of colorectal tumors, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

ORGANISM(S): Mus musculus

PROVIDER: GSE176264 | GEO | 2024/06/07

REPOSITORIES: GEO

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Project description: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.

Project description:Purpose: Identification of cellular pathways regulated by AIF1 in bone marrow derived macrophages, based on gene expression patterns identfied by RNA seq. Methods: Imortalized macrophage cell lines were generated from bone marrow of 8 week old WT and Aif1-/- mice. These cells were subject to RNA sequencing in triplicate, using Illumina Nextseq 500, single end read with high output. The sequence reads that passed quality filters were analyzed as follows; Aligned to GRCm38 mouse genome with Tophat (v2.0.13); Gene hits were counted with HTseq(v0.6.1) under default parameters using release 84 of the Mus_musculus.GRCm38; Differential expression analysis was performed in R/Bioconductor following the DESeq2 workflow and annotated with biomaRt. Results: Using an optimized data analysis workflow, we mapped about 35 million sequence reads per sample to the mouse genome (build GRCm38) and identified 26,934 transcripts in WT and Aif1-/- macrophages with a tophat/htseq/DESeq2 workflow. Approximately 4% of the transcripts showed differential expression between the WT and Aif1-/- macrophages, with a fold change ≥1.0 and p value <0.05. Aif1 transcript expression was confirmed in WT and Aif1-/- macrophages. Using qRT–PCR, we validated altered expression of 5 genes involved in the noradrenaline degradation pathway. Unsupervised hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to macrophage AIF1-mediated immune activation and inflammation. Data analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of transcriptomes of WT and Aif1-/- macrophages, with technical replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that Next generation sequencing offers a comprehensive and accurate quantitative and qualitative evaluation of mRNA content within a cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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Next Generation Sequencing (NGS) for quantitative transcriptomic analysis of colorectal tumor cell lines and their ENU mutants

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