circRNA

circRNA biogenesis

Introduction

After their discovery more than three decades ago, circular RNAs (circRNAs) have been emerging as a large class of generally non-coding RNAs. Originating from the same precursor as linear RNA transcripts, circRNAs are formed through a process called back-splicing, which results in a back-splice junction (BSJ) between a splice donor and an upstream splice acceptor. Due to their circular nature, circRNAs are more resistant to degradation by exonucleases and therefore more stable than linear RNA 1 2. CircRNAs are widespread and abundant in a variety of organisms. Interestingly, the majority of circRNAs seem to be cell-type specific 3 4.

CircRNA detection

CircRNAs are typically identified in RNA sequencing data (RNA-seq) and numerous computational circRNA identification pipelines data have been developed 5678. These pipelines differ in their choice of alignment tool, the use of gene annotation, the ability use of single-end or paired-end data, and in their filtering steps. Our current in-house circRNA pipeline was set up by Jasper Anckaert and is based on both find_circ and CIRCexplorer2. However, these circRNA detection tools are based on short-read sequencing techniques and lack the ability to accurately detect full-length circRNA sequences. In our lab, Jasper Verwilt is looking into long read sequencing methods and their possible application for the detection of circRNAs. As the internal sequence of most circRNAs remains unknown, there is a high demand for such techniques.

An alternative technique to detect circRNAs, is RT-qPCR with divergent primers. For these experiments, Steve Lefever successfully adapted his primer design tool PimerXL9 to design specific circRNA primers.

CircRNA databases

In addition to the circRNA prediction tools, various databases cataloguing circRNAs have been developed as well. In January 2020, Marieke Vromman and colleagues published a review presenting a comprehensive overview of the current circRNA databases and their content, features and usability. Furthermore, in this review they discuss the current issues regarding circRNA databases and come with important suggestions to streamline further research in this growing field 10.

CircRNAs in cancer

Interestingly, circRNAs have been associated with a broad range of diseases, including various types of cancer 11 12. Due to the observed associations between circRNA abundance and cancer, circRNAs may serve as cancer biomarkers with good diagnostic performance 13. Various studies demonstrated that circRNAs are present at relatively high steady state levels in human biofluids, such as saliva, plasma, serum and in exosomes, which makes them attractive candidate biomarkers for non-invasive liquid biopsies 1. In our lab, Eva Hulstaert is looking into the circRNA content in 20 different human biofluids using mRNA capture sequencing 14. Furthermore, Annelien Morlion and Kathleen Schoofs are studying circRNAs in plasma from patients with cancer (26 different cancer types) and plasma from healthy donors.

On the other hand, Lucía Lorenzi set up the RNA Atlas 15, a single nucleotide resolution map of the human transcriptome consisting of matching small, polyA and total RNA seq profiles of a heterogeneous collection of nearly 300 different human tissues and cell types. Using this extensive total RNA seq dataset, circRNA detection was performed and 37,140 circRNAs were discovered in human cell and tissues.

References


  1. Su, M. et al. Circular RNAs in Cancer: Emerging functions in hallmarks, stemness, resistance and roles as potential biomarkers. Molecular Cancer vol. 18 90 (2019). ↩︎

  2. Salzman, J., Gawad, C., Wang, P. L., Lacayo, N. & Brown, P. O. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS One 7, e30733 (2012). ↩︎

  3. Salzman, J., Chen, R. E., Olsen, M. N., Wang, P. L. & Brown, P. O. Cell-Type Specific Features of Circular RNA Expression. PLoS Genet. 9, e1003777 (2013). ↩︎

  4. Hang, R. et al. Comprehensive characterization of circular RNAs in ~1000 human cancer cell lines. Genome Med. 11, 55 (2019). ↩︎

  5. Gao, Y. & Zhao, F. Computational Strategies for Exploring Circular RNAs. Trends Genet. 34, 389–400 (2018). ↩︎

  6. Hansen, T. B., Venø, M. T., Damgaard, C. K. & Kjems, J. Comparison of circular RNA prediction tools. Nucleic Acids Res. 44, e58 (2015). ↩︎

  7. Zeng, X., Lin, W., Guo, M. & Zou, Q. A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput. Biol. 13, e1005420 (2017). ↩︎

  8. Jakobi, T. & Dieterich, C. Computational approaches for circular RNA analysis. Wiley Interdiscip. Rev. RNA 2019, e1528 (2019). ↩︎

  9. Lefever, S. et al. High-throughput PCR assay design for targeted resequencing using primerXL. BMC Bioinformatics 18, 1–9 (2017). ↩︎

  10. Vromman, M., Vandesompele, J. & Volders, P.-J. Closing the circle: current state and perspectives of circular RNA databases. Brief. Bioinform. (2020) doi:10.1093/bib/bbz175. ↩︎

  11. Vo, J. N. et al. The Landscape of Circular RNA in Cancer. Cell 176, 869–881 (2019). ↩︎

  12. Shang, Q., Yang, Z., Jia, R. & Ge, S. The novel roles of circRNAs in human cancer. Mol. Cancer 18, 6 (2019). ↩︎

  13. Tan, H., Gan, L., Fan, X., Liu, L. & Liu, S. Diagnostic value of circular RNAs as effective biomarkers for cancer: A systematic review and meta-analysis. Onco. Targets. Ther. 12, 2623–2633 (2019). ↩︎

  14. Hulstaert, E. et al. Charting extracellular transcriptomes in The Human Biofluid RNA Atlas. bioRxiv (2019) doi:10.1017/CBO9781107415324.004. ↩︎

  15. Lorenzi, L. et al. The RNA Atlas, a single nucleotide resolution map of the human transcriptome. bioRxiv 807529 (2019) doi:10.1101/807529. ↩︎

Marieke Vromman
Marieke Vromman
Doctoral Fellow

CircRNA researcher interested in cancer

Jasper Verwilt
Jasper Verwilt
Doctoral Fellow

The real Jasper

Eva Hulstaert
Eva Hulstaert
Doctoral Fellow

Dermatology resident with an interest in fundamental and translational research

Annelien Morlion
Annelien Morlion
Doctoral Fellow
Kathleen Schoofs
Kathleen Schoofs
Doctoral Fellow
Lucia Lorenzi
Lucia Lorenzi
Doctoral Fellow
Pieter-Jan Volders
Pieter-Jan Volders
PostDoctoral Fellow

LncRNA aficionado working with transcriptomics and proteomics

Jasper Anckaert
Jasper Anckaert
Bioinformatician

The real Jasper

Steve Lefever
Steve Lefever
PostDoctoral Fellow (01/2008-09/2020)
Pieter Mestdagh
Pieter Mestdagh
Professor

Studying non-coding RNAs in cancer.

Jo Vandesompele
Jo Vandesompele
Professor

RNA addict trying to connect all the dots