Characterization of three CpG ODN classes in B cells

Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare distinct immunostimulatory activities of three classes of CpG ODNs in B cells using RNA-seq. mRNA profiles of murine B cells induced by three classes of CpG ODNs were generated by RNA-Seq studies. Methods: mRNA profiles of murine B cells induced by three classes of CpG ODNs were generated by deep sequencing, in triplicate, using BGISEQ. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: HISAT based on Burrows-Wheeler transform and Ferragina-Manzini and Bowtie2 followed by RSEM. Results: Using an optimized data analysis workflow, we mapped > 60 million total clean reads per sample to the mouse genome (build mm10), with a clean reads Q20 score >97% and a mapping rate to the reference genome of each sample varying from 89.33% to 93.29%. The Pearson correlation coefficient between each sample revealed that the all samples had a high correlation. In particular, CpG-B and CpG-C have the highest correlation. A total of 28,020 genes were detected as being expressed. More than 10,000 genes were downregulated and 1,000 genes were upregulated in CpG ODNs stimulated groups relative to control group, with a fold change = 2 and Q value <0.001. In addition, only 91 genes were upregulated and 99 genes were downregulated in CpG-C group relative to CpG-B group. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes uncovered several genes that may contribute to characterize the features of three classes of CpG ODNs in B cells. Conclusions: Our study represents the first detailed analysis of B-cell transcriptomes stimulated with three classes of CpG ODNs, with biological replicates, generated by RNA-seq technology. Overall design: mRNA profiles of murine B cells stimulated with or without CpG-A, CpG-B or CpG-C were generated by RNA-Seq studies, in triplicate, using BGISEQ.