GSE106380
IL-17B promotes proinflammatory cytokines expression in mouse lung tissues
Purpose: Explore IL-17B target genes expression via next-generation sequencing (NGS) in mouse lung tissues. Methods: Overexpress IL-17B in mouse lung tissues via intranasal injection of adenovirus encoding IL-17B (Adv-IL-17B) and empty virus (Adv-EV). Three days after infection lung tisses were removed for RNA collection. Five samples per group were mixed to one sample and used for next RNA purification. RNA samples were then used for high-throughput sequencing according to standard operation based on RNA Hiseq 4000. Results: Using an optimized data analysis workflow, we mapped about 13 million sequence reads per sample to the mouse genome (build mm10) and identified 269 upregulated and 99 downregulated genes in lung after IL-17B overexpression. 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. Altered expression of 20 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 lung inflammation and infection. 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 IL-17B induced downstream genes 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. We conclude that RNA-seq based downstream genes characterization would expedite genetic network analyses and permit the dissection of complex biologic functions. Overall design: mRNA profiles of IL-17B overexpressed lung tissue were generated by deep sequencing, in triplicate, using Illumina RNA Hiseq 4000.