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

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. Overall design: mRNA profiles of bone marrow-derived WT and and Aif1l-/- mice were generated by RNA sequencing, in triplicate, using Illumina Next seq 500.