Expression data comparing CD133+ and CD133- B16-F10 melanoma cells
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PROVIDER: PRJNA534522 | ENA |
REPOSITORIES: ENA
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Project description:We wished to examine the genes regulated by FoxD3 in pigment cells to gain understanding in how FoxD3 represses melanoblast specification in the neural crest. For technical reasons, we could not use neural crest cells, so we used melanoma cells, since they are derived from neural crest cells. To this end, we transfected B16-F10 mouse melanoma cells with constructs expressing FoxD3, or FoxD3-VP16, in which the C-terminal portion of FoxD3 (which contains the transcriptional repression domain) has been replaced by the VP16 transcriptional activation domain. Experiment Overall Design: The base vector used for these studies was pMES, which expresses the gene of interest under control of the chick beta-actin promoter. EGFP is also expressed from the bicistronic mRNA through the use of an IRES. Experiment Overall Design: B16-F10 cells were transfected with either empty pMES, pFoxD3 (which contains FoxD3 inserted into pMES), or pFoxD3-VP16 (similar to pFoxD3, except that the C-terminal portion of FoxD3, which contains the transcriptional repression domain, has been replaced by the transcriptional activation domain of VP16). Experiment Overall Design: 24 hours after transfection, EGFP-positive cells were collected by FACS and those cells were subjected to microarray analysis.
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Project description:Purpose: RNA-sequencing analysis defined a novel role for PRMT7 in melanoma cancer immunotherapy. Methods: PRMT7-deficient cells and control B16.F10 cells treated with mIFN-γ (100ng/ml) for 24 hours. B16.F10 melanoma cells mRNA profiles were generated by deep sequencing, in triplicate using TruSeq Stranded mRNA Sample Prep Kit with TruSeq Unique Dual Indexes (Illumina, Hiseq4000, SR75 platform located at San Diego, CA; UCSD IGM Genomics Facility, La Jolla, CA). Resulting libraries were multiplexed and sequenced with 100 base pair (bp) to a depth of approximately 30 million reads per sample. The sequence reads that passed quality filters were trimmed with Trimmomatic v0.39. STAR v2.7.1a was then used to align the reads to the mouse genome (mm10/GRCm38). Gene expression was quantified across all samples with HOMER v4.11.1, and the normalization was carried out through the regularized logarithm (rlog) transformation of DESeq2 v1.26.0. Differential expression between PRMT7 WT and knockdown samples were calculated through DESeq2 v1.26.0, and the gene expression was considered significantly different if the absolute value of the log-fold-change (LFC) was higher than 2, the base means larger than 10 and the false discovery rate (FDR) less than 0.05. Results: Differential expression analysis between the siLuc and siPRMT7 conditions yielded 185 and 251 genes with statistically significant differences and absolute fold change greater than 2 in the IFN– and IFN+ treatments respectively. Of these, 85% of genes are upregulated in the IFN– treatment, while 72% of genes are downregulated in the IFN+ treatment. Gene ontology analysis using the 185 and 251 lists of differentially expressed genes implies that the downregulated biological processes are enriched for the immune response pathways, while upregulated genes are more involved in the cell cycle process. Altered expression of some genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to better understand PRMT7 function in cancer immunotherapy. Conclusions: Our study represents the first detailed analysis of B16.F10 melanoma transcriptomes without PRMT7 expression, with biologic replicates, generated by RNA-seq technology. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of PRMT7 biologic functions in modulating melanoma transcriptome.
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