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Gene Expression profiling of wild type (WT) and Allograft inflammatory factor-1 deficient (Aif1-/-) immortalized bone marrow derived macrophages by RNA-seq.

ABSTRACT: Purpose: Identification of cellular pathways regulated by AIF1 in bone marrow derived macrophages, based on gene expression patterns identfied by RNA seq. Methods: Imortalized macrophage cell lines were generated from bone marrow of 8 week old WT and Aif1-/- mice. These cells were subject to RNA sequencing in triplicate, using Illumina Nextseq 500, single end read with high output. The sequence reads that passed quality filters were analyzed as follows; Aligned to GRCm38 mouse genome with Tophat (v2.0.13); Gene hits were counted with HTseq(v0.6.1) under default parameters using release 84 of the Mus_musculus.GRCm38; Differential expression analysis was performed in R/Bioconductor following the DESeq2 workflow and annotated with biomaRt. Results: Using an optimized data analysis workflow, we mapped about 35 million sequence reads per sample to the mouse genome (build GRCm38) and identified 26,934 transcripts in WT and Aif1-/- macrophages with a tophat/htseq/DESeq2 workflow. Approximately 4% of the transcripts showed differential expression between the WT and Aif1-/- macrophages, with a fold change ≥1.0 and p value <0.05. Aif1 transcript expression was confirmed in WT and Aif1-/- macrophages. Using qRT–PCR, we validated altered expression of 5 genes involved in the noradrenaline degradation pathway. Unsupervised hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to macrophage AIF1-mediated immune activation and inflammation. Data analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of transcriptomes of WT and Aif1-/- macrophages, with technical 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 Next generation sequencing offers a comprehensive and accurate quantitative and qualitative evaluation of mRNA content within a cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.

ORGANISM(S): Mus musculus

PROVIDER: GSE133278 | GEO | 2022/06/22

REPOSITORIES: GEO

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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 WT and dKO round spermatids transcriptome profiling (RNA-seq) Methods: Adult WT and dKO round spermatids mRNA profiles mice were generated by deep sequencing, in dulplicate. 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 dKO round spermatids, 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. Adult wild type (WT) and dKO mouse round spermatids were generated by deep sequencing, in dulplicate, using Illumina GAIIx.

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Gene Expression profiling of wild type (WT) and Allograft inflammatory factor-1 deficient (Aif1-/-) immortalized bone marrow derived macrophages by RNA-seq.

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