Abstract
This chapter describes a methodology for the screening and characterization of functional circRNAs, particularly in the context of neural circuit development. Taking advantage of a primary rat neuron culture model of synaptogenesis, we propose a means of selecting from the plethora of circRNA species based on their expression levels, dendritic localization, conservation, and activity regulation. These candidates are then knocked down with RNAi approaches in a functional screen for their potential role in the formation and maturation of excitatory synapses.
Upon identification of top candidates regulating synaptogenesis, we tie together different “Omics” approaches to explore the molecular mechanisms underlying the phenotypes observed upon circRNA knockdown. We utilized our EnrichMir algorithm to identify overrepresented miRNA binding sites in differentially expressed genes from polyA-RNA-seq following circRNA knockdown. Furthermore, our ScanMiR web tool allows for the miRNA binding prediction of reconstructed internal circular RNA sequences. Small-RNA sequencing is used to monitor changes in miRNA levels in the circRNA knockdown to complement results obtained from EnrichMiR. Finally, the experimental validation of promising miRNA–circRNA pairs sets the stage for in-depth biochemical exploration of the circRNA interactome and mechanism of action.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gokool A, Anwar F, Voineagu I (2020) The landscape of circular RNA expression in the human brain. Biol Psychiatry 87(3):294–304
Rybak-Wolf A, Stottmeister C, Glažar P, Jens M, Pino N, Giusti S, Hanan M, Behm M, Bartok O, Ashwal-Fluss R, Herzog M (2015) Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol Cell 58(5):870–885
Piwecka M, Glažar P, Hernandez-Miranda LR, Memczak S, Wolf SA, Rybak-Wolf A, Filipchyk A, Klironomos F, Cerda Jara CA, Fenske P, Trimbuch T (2017) Loss of a mammalian circular RNA locus causes miRNA deregulation and affects brain function. Science 357(6357):eaam8526
Kleaveland B, Shi CY, Stefano J, Bartel DP (2018) A network of noncoding regulatory RNAs acts in the mammalian brain. Cell 174(2):350–362
Schratt G (2009) microRNAs at the synapse. Nat Rev Neurosci 10(12):842–849
Hollensen AK, Thomsen HS, Lloret-Llinares M, Kamstrup AB, Jensen JM, Luckmann M, Birkmose N, Palmfeldt J, Jensen TH, Hansen TB, Damgaard CK (2020) circZNF827 nucleates a transcription inhibitory complex to balance neuronal differentiation. elife 9:e58478
Colameo D, Rajman M, Soutschek M, Bicker S, von Ziegler L, Bohacek J, Winterer J, Germain PL, Dieterich C, Schratt G (2021) Pervasive compartment-specific regulation of gene expression during homeostatic synaptic scaling. EMBO Rep 22(10):e52094
Nielsen AF, Bindereif A, Bozzoni I, Hanan M, Hansen TB, Irimia M, Kadener S, Kristensen LS, Legnini I, Morlando M, Jarlstad Olesen MT (2022) Best practice standards for circular RNA research. Nat Methods 19:1–13
Soutschek M, Germade T, Germain PL, Schratt G (2022) enrichMiR predicts functionally relevant microRNAs based on target collections. Nucleic Acids Res 50:W280
Soutschek M, Gross F, Schratt G, Germain PL (2022) scanMiR: a biochemically based toolkit for versatile and efficient microRNA target prediction. Bioinformatics 38(9):2466–2473
Jakobi T, Dieterich C (2018) Deep computational circular RNA analytics from RNA-seq data. In: Circular RNAs. Humana Press, New York, NY, pp 9–25
Alexa A, Rahnenfuhrer J (2023) topGO: enrichment analysis for gene ontology (2.52. 0)[Computer software]. Bioconductor version: release (3.17)
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682
Inouye MO, Colameo D, Ammann I, Winterer J, Schratt G (2022) miR-329–and miR-495–mediated Prr7 down-regulation is required for homeostatic synaptic depression in rat hippocampal neurons. Life Sci Alliance 5(12):e202201520
Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, Barnes I, Becker A, Bennett R, Berry A, Bhai J, Bhurji SK (2023) Ensembl 2023. Nucleic Acids Res 51(D1):D933–D941
Glažar P, Papavasileiou P, Rajewsky N (2014) circBase: a database for circular RNAs. RNA 20(11):1666–1670
Wu W, Zhao F, Zhang J (2023) circAtlas 3.0: a gateway to 3 million curated vertebrate circular RNAs based on a standardized nomenclature scheme. Nucleic Acids Research, p.gkad770. https://doi.org/10.1093/nar/gkad770
Cheng J, Metge F, Dieterich C (2016) Specific identification and quantification of circular RNAs from sequencing data. Bioinformatics 32(7):1094–1096
Pamudurti NR, Patop IL, Krishnamoorthy A, Ashwal-Fluss R, Bartok O, Kadener S (2020) An in vivo strategy for knockdown of circular RNAs. Cell Discov 6(1):1–11
Panda AC, Dudekula DB, Abdelmohsen K, Gorospe M (2018) Analysis of circular RNAs using the web tool circinteractome. In: Circular RNAs: methods and protocols, pp 43–56
Brummelkamp TR, Bernards R, Agami R (2002) A system for stable expression of short interfering RNAs in mammalian cells. Science 296(5567):550–553
Heumüller AW, Boeckel JN (2018) Characterization and validation of circular RNA and their host gene mRNA expression using PCR. In: Circular RNAs: methods and protocols, pp 57–67
Bates D, Machler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48. https://doi.org/10.18637/jss.v067.i01
Yu F, Zhang Y, Cheng C, Wang W, Zhou Z, Rang W, Yu H, Wei Y, Wu Q, Zhang Y (2020) Poly (A)-seq: a method for direct sequencing and analysis of the transcriptomic poly (A)-tails. PLoS One 15(6):e0234696
Brustikova K, Sedlak D, Kubikova J, Skuta C, Solcova K, Malik R, Bartunek P, Svoboda P (2018) Cell-based reporter system for high-throughput screening of MicroRNA pathway inhibitors and its limitations. Front Genet 9:45
Pandey PR, Rout PK, Das A, Gorospe M, Panda AC (2019) RPAD (RNase R treatment, polyadenylation, and poly (A)+ RNA depletion) method to isolate highly pure circular RNA. Methods 155:41–48
Pandey PR, Munk R, Kundu G, De S, Abdelmohsen K, Gorospe M (2020) Methods for analysis of circular RNAs. Wiley Interdiscip Rev RNA 11(1):e1566
Rahimi K, Venø MT, Dupont DM, Kjems J (2021) Nanopore sequencing of brain-derived full-length circRNAs reveals circRNA-specific exon usage, intron retention and microexons. Nat Commun 12(1):1–15
You X, Vlatkovic I, Babic A, Will T, Epstein I, Tushev G, Akbalik G, Wang M, Glock C, Quedenau C, Wang X (2015) Neural circular RNAs are derived from synaptic genes and regulated by development and plasticity. Nat Neurosci 18(4):603–610
Xin R, Gao Y, Gao Y, Wang R, Kadash-Edmondson KE, Liu B, Wang Y, Lin L, Xing Y (2021) isoCirc catalogs full-length circular RNA isoforms in human transcriptomes. Nat Commun 12(1):1–11
Vromman M, Vandesompele J, Volders PJ (2021) Closing the circle: current state and perspectives of circular RNA databases. Brief Bioinform 22(1):288–297
Mahmoudi E, Kiltschewskij D, Fitzsimmons C, Cairns MJ (2019) Depolarization-associated CircRNA regulate neural gene expression and in some cases may function as templates for translation. Cells 9(1):25.s
Acknowledgments
We would like to thank Christoph Dieterich and Tobias Jacobi for their expertise in circRNA reconstruction from RNA-sequencing datasets and for the statistical assessment of differentially expressed circRNAs in neuronal compartments. We thank Michael Soutschek, Tomás Germade, and Pierre-Luc Germain for their essential development and maintenance of the ScanMir and EnrichMir tools. We would also like to thank David Colameo for the development of automated synapse morphology analysis pipelines and Silvia Bicker for the optimization of circRNA targeting by RNAi. Figures were created with BioRender.com.
This work was funded by a PhD fellowship to D.K. from the Swiss National Science Foundation (SNSF), NCCR “RNA and Disease.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Kelly, D., Schratt, G. (2024). Screening and Characterization of Functional circRNAs in Neuronal Cultures. In: Dieterich, C., Baudet, ML. (eds) Circular RNAs. Methods in Molecular Biology, vol 2765. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3678-7_17
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3678-7_17
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3677-0
Online ISBN: 978-1-0716-3678-7
eBook Packages: Springer Protocols