| Literature DB >> 27740630 |
A Bonizzato1, E Gaffo2, G Te Kronnie1, S Bortoluzzi2.
Abstract
Cell states in hematopoiesis are controlled by master regulators and by complex circuits of a growing family of RNA species impacting cell phenotype maintenance and plasticity. Circular RNAs (circRNAs) are rapidly gaining the status of particularly stable transcriptome members with distinctive qualities. RNA-seq identified thousands of circRNAs with developmental stage- and tissue-specific expression corroborating earlier suggestions that circular isoforms are a natural feature of the cell expression program. CircRNAs are abundantly expressed also in the hematopoietic compartment. There are a number of studies on circRNAs in blood cells, a specific overview is however lacking. In this review we first present current insight in circRNA biogenesis discussing the relevance for hematopoiesis of the highly interleaved processes of splicing and circRNA biogenesis. Regarding molecular functions circRNAs modulate host gene expression, but also compete for binding of microRNAs, RNA-binding proteins or translation initiation and participate in regulatory circuits. We examine circRNA expression in the hematopoietic compartment and in hematologic malignancies and review the recent breakthrough study that identified pathogenic circRNAs derived from leukemia fusion genes. CircRNA high and regulated expression in blood cell types indicate that further studies are warranted to inform the position of these regulators in normal and malignant hematopoiesis.Entities:
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Year: 2016 PMID: 27740630 PMCID: PMC5098259 DOI: 10.1038/bcj.2016.81
Source DB: PubMed Journal: Blood Cancer J ISSN: 2044-5385 Impact factor: 11.037
Figure 1Linear and circRNAs. CircRNAs are produced by backsplicing, and combinations of exons and introns give rise to different products, including single circularized exons, circRNAs formed by two or more exons, by exon and retained intron sequences (EI-ciRNAs) and by intronic sequences only.
Figure 2Molecular methods for circRNA detection, validation and study. (a) CircRNA detection from RNA-seq data grounds on the identification of sequence reads encompassing the backsplice junction; (b) Backsplice reads map to the genome in chiastic order (two segments of a single read align separately in reverse order) due to the backsplicing in circRNAs biogenesis. (c) Convergent primers (white arrows) designed in adjacent spliced exons amplify both linear and circular isoforms, whereas primers that are divergent in the linear transcripts (black arrows) can be used to specifically amplify the circular isoform; (d) PolyA enrichment protocols deplete circRNAs, whereas ribosome depletion and RNAse R protocols enrich circRNAs; (e) RNAse R digestion before reverse transcription–PCR lowers the amount of false-positive amplicons facilitating circRNA validation; (f) Gel Trap electrophoresis allows isolate the circular and linear fractions of the input RNA, as circRNAs are hold in the well; (g) Two-dimensional acrylamide gel electrophoresis separates the circular RNA fraction in an off-diagonal curve; (h) RNA migration in agarose gel before and after a mild RNAse H treatment resulting in a single cut per molecule shows that circular molecules bearing a ‘backsplice' junction are discriminated from linear ones deriving from a duplication event, as only circRNA results in a single band after being cut once (f–h re-elaborated from[56]).
CircRNA discovery, characterization and quantification from RNA-seq data
| CIRI | BWA-Mem | 1 | No (only for report) | Yes | Yes | Accounts for uncertainty of read mapping to the junction | [ |
| Find_circ | Bowtie | 2 | No | No | Yes | [ | |
| CIRCexplorer | STAR, TopHat | 2 | Yes | Yes | Yes | [ | |
| Testrealign | Segemehl | 1 | No | No | No | Parses segemehl alignments | [ |
| UROBORUS | TopHat, Bowtie | 2 | No | Yes | Yes | Do not underestimate expression; filters spurious alignments | [ |
| NCLscan | BWA, Novoalign, BLAT | 3 | Yes | Yes | No | >98% precision, test on poly(A)±libraries | [ |
| MapSplice | MapSplice | 1 | Yes | Yes | No | Circular RNA explicit detection from MapSplice v2m (2/2013) | [ |
| circRNA_finder | STAR | 1 | No | No | Yes | [ | |
| KNIFE | Bowtie | 2 | Yes | Yes | Yes | Statistical approach to enrich true positives | [ |
| PTESFinder | Bowtie | 1 | No | No | No | [ |
Abbreviation: CircRNA, circular RNA.
Computational methods available for circRNAs prediction. In the past reads mapping to the reference genome with non-collinear exon junctions were often considered artifacts and discarded; with the rise of interest on fusion transcripts produced from rearranged genomes, trans-splicing and circRNAs, RNA-seq aligners were improved to consider also these ‘exotic' events, where transcript sequence parts correspond to regions that are remote in the normal reference genome (fusion and trans-spliced transcripts) or that map to the genome in chiastic way (that is, two segments of a single read align separately in reverse order due to the backsplicing in circRNA biogenesis; see also Figure 2b). The table presents 10 computational methods that allow blacksplice events identification using different strategies and read aligners; some approaches are specific to predict circRNAs, whereas others were developed with more general purposes, such as read alignment and/or detection of fusion events, and allow also circRNAs detection. For each method, the table indicates the read aligners and number of mapping steps implemented in the prediction strategy, and clarify whether the method requires genome annotations in input, whether it provides an annotation of predicted backsplice junctions in terms of overlapping genes and whether it has been designed explicitly for circRNA identification or is a more general purpose software; the last two columns report additional notes on specific software features and references. Recently, five circRNA prediction tools (circRNA finder, Find_circ, CIRCexplorer, CIRI and MapSplice) have been compared by evaluating the levels of bona fide and false-positive circRNAs based on RNase R resistance data, showing that not in all cases the most abundant circRNAs are true positives, that circRNAs identified by a single method only are in general less reliable and that the combination of at least two methods might increase specificity.[94]
Web resources dedicated to circRNAs
| CircBase | Simple circRNA database that provides a searchable table of circRNAs identified by several studies (five on human data) that can be useful to sort newly identified from known circRNAs | [ | |
| starBase v2.0 | Focuses on RNA–RNA and protein–RNA interaction networks inferred from CLIP-Seq data sets. Among others, in the miRNA–lncRNAs section, it includes predicted miRNA–circRNA interactions and can be searched to identify known circRNA that potentially sponge a specific miRNA | [ | |
| Circ2Traits | Useful to explore potential associations of circRNAs with diseases based on predicted interactions of circRNAs with disease-associated miRNAs and on the overlap between disease-associated SNPs to circRNA loci | [ | |
| CircNet | Provides expression profiles of circRNAs in 464 RNA-seq samples, with circRNA sequences and annotations in term of overlapping genes and interactions | [ |
The table describes four websites that store circRNA expression data and provide useful resources to explore possible circRNA functions.
Figure 3Expression variation of enzymes involved in circRNA expression. Gene expression intensities of ADAR1, MBNL1 and QKI in samples of normal bone marrow and six B-cell leukemia subtypes carrying specific genetic aberrations (according to Haferlach et al.[87]); expression data obtained with HG-U133 Plus 2.0 (Affymetrix, Santa Clara, CA, USA).
Figure 4CircRNA functions. Elucidated circRNA functions include the ability to sponge miRNAs thus regulating the silencing of canonical targets (for example, ciRS-7/CDR1-as harbors 63 binding sites for miR-7) and participating to ceRNA networks; similarly circRNAs could decoy RBP ultimately regulating the functions in which RBP are implicated (for example, circ-Foxo3 forms a ternary complex with p21 and CDK2 arresting cell cycle progression); circRNAs can also regulate in cis the expression if the gene from which they derive through interactions with the U1 RNA in the U1 RNP in the nucleus (for example, circEIF3J); moreover, circRNAs harboring an IRES could be translated to produce peptides or compete with mRNA translation (for example, circFMN contains an active translation start site not leading to the protein synthesis).
Figure 5f-circRNAs derived from chromosomal translocations have oncogenic role. (a) Transcription of fusion genes generated by cancer-associated chromosomal translocation could generate both linear mRNA coding for oncogenic fusion proteins and f-circRNAs.[73] The figure depicts the example of f-CircM9_1 expressed in cells harboring the well-known acute myeloid leukemia MLL/AF9 t(9;11)) translocation: f-CircM9_1 includes two sequences not present in the normal genome, the MLL exon 8 and AF9 exon 6 fusion junction derived from the chromosomal translocation, and the backsplice junction connecting MLL exon 7 with AF9 exon 6; (b) f-CircM9 was demonstrated to be proto-oncogenic in vitro (increasing proliferation rate and foci forming ability in mouse embryonic fibroblasts, MEF), and required for leukemic cell (THP1) viability. f-CircM9 alone resulted not sufficient to trigger leukemia in vivo when expressed in HSC xenografted in mice. Concurrent expression of f-circM9 and MLL/AF9 fusion protein contributed to leukemia progression in vivo and ex vivo cells expressing f-circM9 and MLL/AF9 displayed increased ability to proliferate and to form colonies. Furthermore, f-circM9 expression in MLL/AF9 mouse model cells increased the resistance to leukemia treatments suggesting that f-circM9 impacts to pre-clinical therapeutic outcome.