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Next Generation Sequencing Facilitates Quantitative Analysis of TRPM1 overexpression and vector control in 661W cell Transcriptomes

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of TRPM1 overexpression and vector control in 661W cell Transcriptomes

PROVIDER: PRJNA695467 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR13570959.fastq.gz	Fastqsanger.gz
	SRR13570960.fastq.gz	Fastqsanger.gz

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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived HCT-116 cell transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis. Methods: HCT-116 cell mRNA profiles of HCT-116-GOLPH3-Vector and HCT-116-GOLPH3-Overepression were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays. Results: RNA sequencing (RNA-seq) was used to investigate gene expression in HCT-116-PMSCV-Vector and HCT-116-PMSCV GOLPH3 cells, while Gene Ontology (GO) was used to annotate the various functional genes. By comparing the differentially expressed genes, we noticed that GOLPH3 was associated with EMT. Gene set enrichment analysis (GSEA) analysis of the RNA-seq results based on the GSE77953 dataset was performed to investigate the biological functions of HCT-116-PMSCV-Vector and HCT-116-PMSCV-GOLPH3 cells. The findings suggested that GOLPH3 expression was positively associated with colon cancer cell autophagy. Signal pathway enrichment was next analyzed based on the differential expression of genes between HCT-116-PMSCV-Vector and HCT-116-PMSCV-GOLPH3 cells, as examined by RNA-seq. Notably, GOLPH3 has correlated with the PI3K/Akt signaling pathway. Conclusions: Our study represents the first detailed analysis of HCT-116 cell transcriptomes, with biologic 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 NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

Project description:Purpose: The goal of this study are to investigate the TRPM1-regulated genes using RNAseq to compare the transcriptome profiling between 661W cells expressing TRPM1 and control vectors Methods: RNAs were isolatedusing the RNeasy Mini kit (Qiagen). RNA-seq libraries were prepared using the KAPA mRNA HyperPrep Kit (KAPA Biosystems, Roche, Basel, Switzerland) and validated using the Qsep 100 DNA/RNA Analyzer (BiOptic Inc., Taiwan). Libraries were sequenced on a NovaSeq 6000 sequencer (Illumina, CA, USA). Clean reads were aligned to the mouse genome (GRCm38) using HISAT2 (version 2.1.0) after removing low-quality reads. The differential expression of genes between TRPM1-overexpression and control cells was computed using the fragments per kilobase of transcript per million mapped reads calculated by featureCounts (version 2.0.0). Raw read counts were imported into edgeR (version 3.28.1) and analyzed by using R package of DESeq (version 1.40.0). Genes with false discovery rate (FDR) p-value < 0.05 adjusted by using Benjamini–Hochberg (BH) method were considered as differentially expressed genes (DEGs). Gene set enrichment analysis of the genes differentially expressed upon TRPM1 expression was done using the Gene Set Knowledgebase（GSKB）hallmark gene sets. Results: We had 40,922,040 clean reads in the control group and 44,244,608 clean reads in the TRPM1-overexpressing group. We mapped 43,253, 668 (97.76%) sequence reads in the control group and 39,942,649 (97.6%) sequence reads in the TRPM1-overexpressing group. We identified 16,014 transcripts. 76 transcripts showed differential expression between the vector control group and TRPM1-expressing group, with a fold change ≥1.5 and p value <0.01. Gene set enrichment analysis of the genes differentially expressed upon TRPM1 expression uncovered several TRPM1-regulated genes that may contribute to photoreceptor function, such as retina morphogenesis and JAK-STAT cascade. Conclusions: Our study represents the genes associated with TRPM1 overexpression in 661W photoreceptor cells using RNA-seq approach. The overexpression of TRPM1 may contribute to regulate the photoreceptor morphogenesis and function

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Next Generation Sequencing Facilitates Quantitative Analysis of TRPM1 overexpression and vector control in 661W cell Transcriptomes

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