Differential gene expression in murine olfactory epithelium: effect of aging and loss of APP, APLP2 and PSEN2.
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ABSTRACT:
PROVIDER: PRJNA616336 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11452800_1.fastq.gz | Fastqsanger.gz | |||
| SRR11452800_2.fastq.gz | Fastqsanger.gz | |||
| SRR11452782_1.fastq.gz | Fastqsanger.gz | |||
| SRR11452782_2.fastq.gz | Fastqsanger.gz | |||
| SRR11452783_1.fastq.gz | Fastqsanger.gz |
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Project description:RNAseq differential gene expression profiling of olfactory mucosa in young wild type, aged wild type, APP deficient, APLP2 deficient and PSEN2 deficient mice. Purpose: Next-generation gene expression profiling has revolutionized analysis of molecular pathways. The goals of this study were to compare NGS-derived olfactory mucosa transcriptome profiles (RNAseq) of aged wild-type mice, APP knockout mice, APLP2 knockout mice and PSEN2 knockout mice with young wild-type controls. Selected genes and pathways will be analyze further by low-throughput techniques such as real-time RT-PCR. Methods: Olfactory mucosa mRNA profiles of 2 months old wild-type (WT), 2 years old wild-type, APP knockout, APLP2 knockout and PSEN2 knockout mice were generated by deep sequencing, in triplicate, using Illumina NovaSeq6000 . For inspecting the quality of RNA-Seq data, the 100 most abundant genes are taken from all the samples and heatmaps were generated to observe the relation between samples/conditions. The sequence reads that passed the quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks. Results: Using our data analysis workflow, we mapped at least 30 million sequence reads per sample to the mouse genome (build mm10) and identified approximately 25,867 transcripts in the olfactory epithelium of WT and genetically modified mice with TopHat workflow. RNAseq data confirmed stable expression of 20 known housekeeping genes, and 3 of them were validated with qRT-PCR. Approximately 20% of transcripts showed differential expression between WT and aged samples and 0.1-1.0 % showed differential expression between WT and genetically modified lines (fold change >1.5; p value < 0.05). Altered expression of 20 genes for aged samples and 10 genes for PS-deficient samples was confirmed by qRT-PCR, demonstrating the high degree of sensitivity of RNAseq approach. Conlusion: Our study represent the first detailed analysis of differential gene expression by RNAseq technology in murine olfactory mucosa in aged animals. It is also the first study examining effects of gene knockout for APP, APLP2 and PSEN2 on gene expression profile in murine olfactory mucosa. We conclude that these data would help future studies in olfactory mucosa cells aimed to reveal molecular mechanisms associated with aging and biological function of APP, APLP2 and PSEN2 genes. RNAseq differential gene expression profiling of olfactory mucosa in young wild type, aged wild type, APP deficient, APLP2 deficient and PSEN2 deficient mice. Purpose: Next-generation gene expression profiling has revolutionized analysis of molecular pathways. The goals of this study were to compare NGS-derived olfactory mucosa transcriptome profiles (RNAseq) of aged wild-type mice, APP knockout mice, APLP2 knockout mice and PSEN2 knockout mice with young wild-type controls. Selected genes and pathways will be analyze further by low-throughput techniques such as real-time RT-PCR. Methods: Olfactory mucosa mRNA profiles of 2 months old wild-type (WT), 2 years old wild-type, APP knockout, APLP2 knockout and PSEN2 knockout mice were generated by deep sequencing, in triplicate, using Illumina NovaSeq6000 . For inspecting the quality of RNA-Seq data, the 100 most abundant genes are taken from all the samples and heatmaps were generated to observe the relation between samples/conditions. The sequence reads that passed the quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks. Results: Using our data analysis workflow, we mapped at least 30 million sequence reads per sample to the mouse genome (build mm10) and identified approximately 24,000 transcripts in the olfactory epithelium of WT and genetically modified mice with TopHat workflow. RNAseq data confirmed stable expression of 20 known housekeeping genes, and 3 of them were validated with qRT-PCR. Approximately 20% of transcripts showed differential expression between WT and aged samples and 0.1-1.0 % showed differential expression between WT and genetically modified lines (fold change >1.5; p value < 0.05). Altered expression of 20 genes for aged samples and 10 genes for PS-deficient samples was confirmed by qRT-PCR, demonstrating the high degree of sensitivity of RNAseq approach. Conlusion: Our study represent the first detailed analysis of differential gene expression by RNAseq technology in murine olfactory mucosa in aged animals. It is also the first study examining effects of gene knockout for APP, APLP2 and PSEN2 on gene expression profile in murine olfactory mucosa. We conclude that these data would help future studies in olfactory mucosa cells aimed to reveal molecular mechanisms associated with aging and biological function of APP, APLP2 and PSEN2 genes. This RNAseq project has been supported by a grant of Polish National Science Centre (UMO-2013/09/NZ3/02359).
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