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Next Generation Sequencing Facilitates Quantitative Analysis of pancreatic tissue transcriptomes from Wild Type and miR-21-/- mice treated with caerulein

ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of pancreatic tissue transcriptomes from Wild Type and miR-21-/- mice treated with caerulein

PROVIDER: PRJNA417234 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6260455.fastq.gz	Fastqsanger.gz
	SRR6260456.fastq.gz	Fastqsanger.gz
	SRR6260457.fastq.gz	Fastqsanger.gz
	SRR6260458.fastq.gz	Fastqsanger.gz
	SRR6260459.fastq.gz	Fastqsanger.gz

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Project description:Purpose: Purpose: we used next generation sequencing to analyze gene expression profiles of pancreatic tissues from wild-type (WT) and miR-21-/- (miR-21 KO) mice treated with saline(control) or caerulein. The goals of this study are to compare the different gene expression profiles of pancreatic tissue between WT and miR-21 KO mice treated with caerulein. Methods: Female WT and miR-21 KO mice were administered 8-hourly intraperitoneal injection of 50μg/kg caerulein. Mice were sacrificed at 24 hours after the first caerulein injection and total RNA was extracted. mRNArna profiles were generated by deep sequencing WT treated with saline in single, WT treated with caerulein in duplicate, miR-21KO treated with caerulein in duplicate, using High-seq 2000 Illumina sequencing platform. 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: After quality filtering of raw sequencing data, we obtained 32,797,327 (69.9%) out of 46,941,672 tags from WT mice treated with saline, 42,587,312 (87.1%) out of 48,919,213 tags from WT mice treated with caerulein, and 57,139,408 (85.1%) out of 67,111,415 tags from miR-21 KO mice treated with caerulein. We then mapped these tags to the mouse genome. We identified 1457 differential expressed genes (DEGs) between WT mice treated with caerulein and with saline, and 152 genes between WT mice and miR-21 KO mice treated with caerulein with a fold change more than 2.0 (up-regulation) or less than 0.5 (down-regulation). Altered expression of 16 genes was confirmed with qRT-PCR. Conclusions: Our study represents the first detailed analysis of pancreatic transcriptomes generated by RNA-seq technology. By using RNA-seq based transcriptome analysis, we identified 6 miR-21 target genes and 10 downregulated genes involved in pancreatic injury. Specifically, up-regulation of Pias3 and down-regulation of Hmgb1 when miR-21 was ablated coincided with reduced pancreatitis severity. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex pancreatic functions.

Project description:PurposeNext-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.MethodsRetinal 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.ResultsUsing 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 (R(2)) 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.ConclusionsOur 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.

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 Facilitates Quantitative Analysis of pancreatic tissue transcriptomes from Wild Type and miR-21-/- mice treated with caerulein

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