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Next Generation Sequencing (NGS) Facilitates Quantitative Analysis of Non-silencing Control and EP300 Knockdown Transcriptomes

ABSTRACT: Purpose: The goal of this study is to identify p300-regulated genes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE114742 | GEO | 2026/05/11

REPOSITORIES: GEO

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Project description:Purpose: The goal of this study is to demonstrate the gene profiles of ASGR1 knock down mediated by siRNA compared with siControl in Huh7 cells. Methods: The Huh7 cells were transfected with small interference RNA (siRNA) targeting scramble (WT) or ASGR1 (AS2) for 8 hours, then cells were refreshed with Dulbecco's Modified Eagle Medium (DMEM) supplemented with 100 units ml-1 penicillin, 100 μg ml-1 streptomycin sulfate, and 10% fetal bovine serum (FBS). After 72 hours, the cells were harvested for RNA isolation and followed by high-throughput sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcriptome with Hisat2(v2.0.1). qRT–PCR validation was performed using SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 45 million sequence reads per sample to the human genome in the WT and AS2 cell lines Hisat2 (v2.0.1). RNA-seq data confirmed stable expression of the known housekeeping genes, and these genes were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than a goodness of fit (R2) of 0.9. Approximately 4.2% of the transcripts showed significantly differential expression between the WT and AS2 cell lines, with a log2 fold change ≥1.0 and p value <0.05. Altered expression of 5 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Conclusions: Our study represents the first detailed transcriptomes analysis of ASGR1 knock down in Huh7 cells, 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 the WT and AS2 cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

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 retinal 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: Retinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl-/-) mice 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: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl−/− retina, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 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 retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of retinal 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. Retinal mRNA profiles of 21-day old wild type (WT) and Nrl-/- mice were generated by deep sequencing, in triplicate, using Illumina GAIIx.

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Next Generation Sequencing (NGS) Facilitates Quantitative Analysis of Non-silencing Control and EP300 Knockdown Transcriptomes

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