Search

Circular RNAs (circRNAs) are abundantly expressed in the haematopoietic compartment, but knowledge on their diversity among blood cell types is still limited. Nevertheless, emerging data indicate an array of circRNA functions exerted through interactions with other RNAs and proteins, by translation into peptides, and circRNA involvement as regulatory molecules in many biological processes and cancer mechanisms. Interestingly, the role of specific circRNAs in leukemogenesis has been disclosed by a few studies, mostly in acute myeloid leukemia. In this study, circRNA expression in B-cells, T-cells and monocytes of healthy subjects is described, including putative new circRNA genes. Expression comparison considered 6,228 circRNAs and highlighted cell population-specific expression and exon usage patterns. Differential expression has been confirmed by qRT-PCR for circRNAs specific of B-cells (circPAX5, circAFF3, circIL4R, and circSETBP1) or T-cells (circIKZF1, circTNIK, circTXK, and circFBXW7), and for circRNAs from intronic (circBCL2) and intergenic regions that were overexpressed in lymphocytes. Starting from this resource of circRNA expression in mature blood cell populations, targeted examination identified striking and generalized upregulated expression of circPAX5, circPVT1 and circHIPK3 in pediatric B-precursor acute lymphoblastic leukemia, and disclosed circRNAs with variable expression across cytogenetic subtypes.

Circular RNAs (circRNAs) are generated by backsplicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and posttranscriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNAseq data using appropriate computational methods to identify the sequence reads spanning backsplice junctions that do not colinearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here,we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNAseq data using in parallel four different methods for backsplice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression level. We applied our method to RNAseqdata of monocyte and macrophage samples in relation to haploinsufficiency of the RNAbinding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies.