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Neural circular RNAs are derived from synaptic genes and regulated by development and plasticity

Nature Neuroscience volume 18, pages 603–610 (2015)Cite this article

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Abstract

Circular RNAs (circRNAs) have re-emerged as an interesting RNA species. Using deep RNA profiling in different mouse tissues, we observed that circRNAs were substantially enriched in brain and a disproportionate fraction of them were derived from host genes that encode synaptic proteins. Moreover, on the basis of separate profiling of the RNAs localized in neuronal cell bodies and neuropil, circRNAs were, on average, more enriched in the neuropil than their host gene mRNA isoforms. Using high-resolution in situ hybridization, we visualized circRNA punctae in the dendrites of neurons. Consistent with the idea that circRNAs might regulate synaptic function during development, many circRNAs changed their abundance abruptly at a time corresponding to synaptogenesis. In addition, following a homeostatic downscaling of neuronal activity many circRNAs exhibited substantial up- or downregulation. Together, our data indicate that brain circRNAs are positioned to respond to and regulate synaptic function.

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Figure 1: Profiling of circRNAs across tissues reveals enrichment in brain.
Figure 2: Brain expressed circRNAs are derived from genes coding for synaptic proteins and are enriched in synaptic tissues.
Figure 3: Regulated expression of brain circRNAs during development.
Figure 4: Regulation of circRNAs by homeostatic plasticity.

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References

  1. Danan, M., Schwartz, S., Edelheit, S. & Sorek, R. Transcriptome-wide discovery of circular RNAs in Archaea. Nucleic Acids Res. 40, 3131–3142 (2012).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  3. Wang, P.L. et al. Circular RNA is expressed across the eukaryotic tree of life. PLoS ONE 9, e90859 (2014).

    Article  Google Scholar 

  4. Hansen, T.B. et al. Natural RNA circles function as efficient microRNA sponges. Nature 495, 384–388 (2013).

    Article  CAS  Google Scholar 

  5. Memczak, S. et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495, 333–338 (2013).

    Article  CAS  Google Scholar 

  6. Hentze, M.W. & Preiss, T. Circular RNAs: splicing's enigma variations. EMBO J. 32, 923–925 (2013).

    Article  CAS  Google Scholar 

  7. Jeck, W.R. & Sharpless, N.E. Detecting and characterizing circular RNAs. Nat. Biotechnol. 32, 453–461 (2014).

    Article  CAS  Google Scholar 

  8. Hanus, C. & Schuman, E.M. Proteostasis in complex dendrites. Nat. Rev. Neurosci. 14, 638–648 (2013).

    Article  CAS  Google Scholar 

  9. Cajigas, I.J. et al. The local transcriptome in the synaptic neuropil revealed by deep sequencing and high-resolution imaging. Neuron 74, 453–466 (2012).

    Article  CAS  Google Scholar 

  10. Zivraj, K.H. et al. Subcellular profiling reveals distinct and developmentally regulated repertoire of growth cone mRNAs. J. Neurosci. 30, 15464–15478 (2010).

    Article  CAS  Google Scholar 

  11. Huntzinger, E. & Izaurralde, E. Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat. Rev. Genet. 12, 99–110 (2011).

    Article  CAS  Google Scholar 

  12. Rinn, J.L. & Chang, H.Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).

    Article  CAS  Google Scholar 

  13. Yu, Y. et al. A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat. Commun. 5, 3230 (2014).

    Article  Google Scholar 

  14. Carlin, R.K., Grab, D.J., Cohen, R.S. & Siekevitz, P. Isolation and characterization of postsynaptic densities from various brain regions: enrichment of different types of postsynaptic densities. J. Cell Biol. 86, 831–845 (1980).

    Article  CAS  Google Scholar 

  15. Dunkley, P.R., Jarvie, P.E. & Robinson, P.J. A rapid Percoll gradient procedure for preparation of synaptosomes. Nat. Protoc. 3, 1718–1728 (2008).

    Article  CAS  Google Scholar 

  16. Squire, L.R. Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. Psychol. Rev. 99, 195–231 (1992).

    Article  CAS  Google Scholar 

  17. Brakeman, P.R. et al. Homer: a protein that selectively binds metabotropic glutamate receptors. Nature 386, 284–288 (1997).

    Article  CAS  Google Scholar 

  18. Guo, J.U., Agarwal, V., Guo, H. & Bartel, D.P. Expanded identification and characterization of mammalian circular RNAs. Genome Biol. 15, 409 (2014).

    Article  Google Scholar 

  19. Ingolia, N.T., Brar, G.A., Rouskin, S., McGeachy, A.M. & Weissman, J.S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).

    Article  CAS  Google Scholar 

  20. Turrigiano, G.G., Leslie, K.R., Desai, N.S., Rutherford, L.C. & Nelson, S.B. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391, 892–896 (1998).

    Article  CAS  Google Scholar 

  21. Jeck, W.R. et al. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19, 141–157 (2013).

    Article  CAS  Google Scholar 

  22. Westholm, J.O. et al. Genome-wide analysis of Drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation. Cell Reports 9, 1966–1980 (2014).

    Article  CAS  Google Scholar 

  23. Tai, H.C. & Schuman, E.M. MicroRNA: microRNAs reach out into dendrites. Curr. Biol. 16, R121–R123 (2006).

    Article  CAS  Google Scholar 

  24. 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).

    Article  CAS  Google Scholar 

  25. Guttman, M. et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 477, 295–300 (2011).

    Article  CAS  Google Scholar 

  26. Mercer, T.R. et al. Long noncoding RNAs in neuronal-glial fate specification and oligodendrocyte lineage maturation. BMC Neurosci. 11, 14 (2010).

    Article  Google Scholar 

  27. Ng, S.Y., Bogu, G.K., Soh, B.S. & Stanton, L.W. The long noncoding RNA RMST interacts with SOX2 to regulate neurogenesis. Mol. Cell 51, 349–359 (2013).

    Article  CAS  Google Scholar 

  28. Bottai, D. et al. Synaptic activity-induced conversion of intronic to exonic sequence in Homer 1 immediate early gene expression. J. Neurosci. 22, 167–175 (2002).

    Article  CAS  Google Scholar 

  29. Hu, J.H. et al. Homeostatic scaling requires group I mGluR activation mediated by Homer1a. Neuron 68, 1128–1142 (2010).

    Article  CAS  Google Scholar 

  30. Roberts, A., Pimentel, H., Trapnell, C. & Pachter, L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics 27, 2325–2329 (2011).

    Article  CAS  Google Scholar 

  31. Li, B. & Dewey, C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

  32. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  33. Yeo, G. & Burge, C.B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).

    Article  CAS  Google Scholar 

  34. Sun, W. et al. Ultra-deep profiling of alternatively spliced Drosophila Dscam isoforms by circularization-assisted multi-segment sequencing. EMBO J. 32, 2029–2038 (2013).

    Article  CAS  Google Scholar 

  35. Huang, D.W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  Google Scholar 

  36. Huang, D.W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  Google Scholar 

  37. Aakalu, G., Smith, W.B., Nguyen, N., Jiang, C. & Schuman, E.M. Dynamic visualization of local protein synthesis in hippocampal neurons. Neuron 30, 489–502 (2001).

    Article  CAS  Google Scholar 

  38. Yamashita, K., Kusakabe, M. & Sano, M. A simple and rapid method of dissociating hepatocytes from fixed liver of the mouse. Stain Technol. 56, 29–33 (1981).

    Article  CAS  Google Scholar 

  39. Schlogl, A., Jonas, P., Schmidt-Hieber, C. & Guzman, S.J. Stimfit: a fast visualization and analysis environment for cellular neurophysiology. Biomed. Tech. (Berl) published online, doi:10.1515/bmt-2013-4181 (7 September 2013).

  40. Bartel, D.P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).

    Article  CAS  Google Scholar 

  41. Griffiths-Jones, S. The microRNA registry. Nucleic Acids Res. 32, D109–D111 (2004).

    Article  CAS  Google Scholar 

  42. Cook, K.B., Kazan, H., Zuberi, K., Morris, Q. & Hughes, T.R. RBPDB: a database of RNA-binding specificities. Nucleic Acids Res. 39, D301–D308 (2011).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank I. Bartnik, N. Fuerst and A. Staab for the preparation of cultured hippocampal neurons and M. Feldkamp and C. Langnick for their excellent technical assistance on sequencing. We thank C. Schanzenbaecher and J. Langer for generating mass spectrometry data and for the bioinformatic evaluation of circRNA translation. E.M.S. is funded by the Max Planck Society, an Advanced Investigator award from the European Research Council, DFG CRC 902: Molecular Principles of RNA-based Regulation, DFG CRC 1080: Molecular and Cellular Mechanisms of Neural Homeostasis, and the DFG Cluster of Excellence for Macromolecular Complexes, Goethe University. As part of the Berlin Institute for Medical Systems Biology at the MDC, the research group of W.C. is funded by the Federal Ministry for Education and Research (BMBF) and the Senate of Berlin, Germany (BIMSB 0315362A, 0315362C). H.L., T.C. and W.S. are supported by the Chinese Scholarship Council (CSC).

Author information

Author notes

  1. Xintian You, Irena Vlatkovic, Ana Babic, Tristan Will, Irina Epstein, Georgi Tushev and Güney Akbalik: These authors contributed equally to this work.

  2. Erin M Schuman and Wei Chen: These authors jointly directed this work.

Authors and Affiliations

  1. Berlin Institute for Medical Systems Biology, Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany

    Xintian You, Ana Babic, Claudia Quedenau, Xi Wang, Jingyi Hou, Hongyu Liu, Wei Sun, Tao Chen & Wei Chen

  2. Department of Synaptic Plasticity, Max Planck Institute for Brain Research, Frankfurt, Germany

    Irena Vlatkovic, Tristan Will, Irina Epstein, Georgi Tushev, Güney Akbalik, Mantian Wang, Caspar Glock, Sivakumar Sambandan & Erin M Schuman

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Contributions

X.Y. and G.T. designed and performed all of the bioinformatic analysis and edited the manuscript. I.V., A.B., T.W., I.E. and G.A. designed, conducted and analyzed the experiments and edited the manuscript, with the help of M.W., C.G., C.Q., T.C., X.W., J.H., H.L. and W.S. S.S. performed and analyzed the electrophysiology experiments. W.C. and E.M.S. conceived and supervised the project, and wrote the manuscript.

Corresponding authors

Correspondence to Erin M Schuman or Wei Chen.

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Integrated supplementary information

Supplementary Figure 1 circRNAs are poly(A) tail–depleted, RNAse R–resistant and enriched in brain

A. circRNAs are depleted in the polyA-enriched RNA-seq library (n = 1 from 1 mouse). Each circle represents one circRNA. Values on X/Y axis denote the abundance of circRNAs in poly(A)-enriched library (Y) and in rRNA-depleted library (X). Inset shows that circRNAs are strongly depleted in the poly(A)-enriched library. Whiskers show extreme data points no more than 1.5 times the interquartile range from the box. B. circRNAs from mouse and rat are at least 5-fold more RNase R resistant than their linear transcripts. C. circRNAs are enriched in mouse brain. rRNA depleted RNA-seq were performed with five tissues from one wild type C57B6 male mice at the age of 14 weeks. Left. The percentage of circular junction reads from all the reads mapped on the genome is shown for different tissues, with the highest value in brain, followed by testis. Middle. The number of circRNA host genes that are exclusively expressed in one tissue is shown across different tissues, with the highest value in brain, Right. The relative contribution of circRNA to the total transcription output of the same gene locus i.e. the ratio between the abundance of each circRNA and the total transcriptional output (TTO) of the hosting gene loci (measured in TPM, transcripts per million) is significantly higher in brain compared to all the other tissues, The ratios of the relative contribution between brain and other four tissues are significantly larger than one (two-sided one-sample t-test, brain/heart: n=1176, p< 2.2e-16; brain/liver: n=579, p< 2.2e-16; brain/lung: n=919, p< 2.2e-16; brain/testis: n=1860, p< 2.2e-16). D CircRNAs are enriched in rat brain. RNA-Seq data from the five rat tissues, each with four biological replicates from rat with matched age (6 weeks), two from males and two from females (with the exception of testis), was downloaded from GEO (Accession No. GSE53960, Ying Yu et al. Nat Commun. 2014;5:3230). We applied our computational pipeline to this dataset. Twenty to 60 million 50nt-long reads were obtained from each sample. Due to the short read length, the number of circRNAs identified with this dataset is much smaller. Nevertheless, compared to other tissues, the enrichment of circRNA in brain is still obvious,. Left. The percentage of circular junction reads from all the reads mapped on the genome is shown for different tissues, with the highest value in brain, followed by testis. Middle. The number of circRNA host genes that are exclusively expressed in one tissue is shown across different tissues, with the highest value in brain. Right. The relative contribution of circRNA to the total transcription output of the same gene locus i.e. the ratio between the abundance of each circRNA and the total transcriptional output (TTO) of the hosting gene loci (measured in TPM, transcripts per million) is significantly higher in brain compared to all the other tissues. The ratios of the relative contribution between brain and other four tissues are significantly larger than one (two-sided one-sample t-test, brain/heart: n=293, p=1.412e-09; brain/liver: n=130, p=0.0001614; brain/lung: n=330, p=3.153e-16; brain/testis: n=327, p=1.806e-07).

Supplementary Figure 2 circRNAs are enriched in synaptic tissue

A. Gene ontology enrichment analysis of the genes producing brain expressed circRNAs. Functional groups related to synaptic function were overrepresented regardless the background gene set used for comparison. We chose a background gene set consisting of the 1000, 2000, 3000, 5000, or 10000 most highly expressed genes (top1k, top2k, top3k, top5k, top10k), and then tested for the enrichment of GO terms for the circRNA-hosting genes within the same gene set, respectively. B. The abundance of circRNA and total transcriptional output of protein-coding gene loci (measured in TPM) were compared between mouse hippocampal synaptosomes (X-axis) and mouse hippocampal homogenates (Y-axis). Each red dot represents one circRNA, and each dark dot represents one protein-coding gene (n=1 for homogenate prepared from 10 mice and n=1 for synaptosomes prepared from 10 mice). C. The abundance of circRNA and total transcriptional output of protein-coding gene loci (measured in TPM) were compared between neuropil (X-axis) and the somatic layer of the hippocampus (Y axis) in rat. Each red dot represents one circRNA, and each dark dot represents one protein-coding gene locus (n=1 for the somata sample prepared from 7 rats and n=1 for the neuropil sample prepared from 7 rats). Insets in B and C show that the abundance of circRNAs, but not total transcriptional output, is significantly higher in the synaptosome and neuropil fractions (two-side unpaired Student's T-test with Welch's correction, ***p < 2.2E-16). Whiskers show the extreme data points no more than 1.5 times the interquartile range from the box. D. Overlap between circRNAs detected in mouse synaptosomes and neuropil. E. Overlap between circRNAs identified in mouse and rat neuropil. F. Cumulative distribution of the circRNA abundance shown in E. The common circRNAs are of higher abundance (blue circles for mouse, orange x for rat) than circRNAs only detected in either mouse (green circles) or rat (pink x).

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Supplementary Figure 3 circRNA localization in neuronal compartments

A. Schematics of circRNA probe for in situ hybridization. Probe is shown as connected rectangles targeted at the circRNA junction (black line). Probe originates from first 10nt of one exon (red) and last 10nt of the other exon (blue) that form the head-to-tail junction. B. Exon control probe consists of start and end of exons that do not form circular junction. C-J. High resolution in situ hybridization experiments in cultured hippocampal neurons using probe sets designed to detect the indicated circRNA (E-J), no probe (C) or control probe (D). In each case, many circRNA-positive particles (green) are apparent in the cell bodies, but also in the dendritic processes, identified using an anti-MAP2 antibody (red). The absence of a probe (no probe) or a control (scrambled) probe designed to lack detection of any known exonic sequence (see Methods) yielded just a few background particles. Scale bar = 20 microns.

Supplementary Figure 4 circRNA in situ hybridization is specific

A. Relative expression (measured as TPM) of circRmst, circKlhl2 and circGigyf2 in brain, liver and lung based on RNA-seq experiments. Error bars represent standard deviation. B. Validation of expression level of the circRNAs in liver and lung using in situ hybridization. No probe control indicates background levels of fluorescence. circRmst and circKlhl2 are not expressed while circGigyf2, by comparison, is moderately expressed, as expected based on RNA-sequencing data. Nuclei were stained by DAPI (blue); circRNA in situ hybridization puncta are shown in green and anti- antibody staining (albumin for liver and Heme-Oxygenase 1 for lung) is shown in red. Scale bar = 10 microns in large images and 5 microns in zoomed images.

Supplementary Figure 5 circRims1 and its host gene mRNA do not colocalize

In situ hybridization of circRims1 (purple) and linear Rims1 (green) transcript in the same neuron shows lack of co-localization. Whole image and blow up of soma and dendrites are shown. Scale bar = 20μm for whole image and 10μm for zoom and dendrite.

Supplementary Figure 6 circRNA: no enrichment of miRNA/RBP binding and translation

A. Based on nucleotide sequences, circRNAs (red) as a population do not possess a higher density of miRNA binding sites than either the 3′ UTR (black) or CDS (coding sequence, yellow) of the mRNAs. This trend persists when circRNAs of different abundances are examined. B. CircRNAs (red) have a lower density of RBP binding sites than either 3′ UTR (black) and CDS (yellow) of the mRNAs. This trend also persists when circRNAs of different abundances are examined. C. CircRNAs are enriched in the non-ribosome fraction and are depleted drastically from the mono-/poly-ribosome fraction. Values on Y-axis denote the percentage of circRNAs in the RNA fractions marked on X-axis. n=2 whole brains. D. circRNAs (red dots) are enriched in the non-ribosome fraction, whilst mRNAs (black dots) are enriched in the poly-ribosome fraction. Classical noncoding RNAs such as snRNAs and snoRNAs are shown in blue. The inset shows that circRNAs show significantly less association with ribosomes than those classical noncoding RNAs (two-sided unpaired Student's t-test with Welch's correction, ****p < 2.2E-16 and ***p = 3.999e-10). Whiskers show extreme data points no more than 1.5 times the interquartile range from the box; n = 1 from 10 mice).

Supplementary Figure 7 RNA-sequencing of the circRNAs is reproducible

A-E. Sequencing replicates for mouse tissues: brain (A), heart (B), liver (C), lung (D) and testis (E). F-I: Sequencing replicates for mouse developmental stages: E18 (F), P1 (G), P10 (H), P30 (I). J-K Sequencing replicates for control (J) and bicuculline treatment (K). Black dots represent mRNAs and lincRNAs, and red dots represent circRNAs. Values on X/Y axis denote the abundance of the genes (in TPM) in biological replicates (n = 6 − 7, 3, 3 and 3 − 4 in each of two replicates for developmental stages E18, P1, P10 and P30, respectively).

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You, X., Vlatkovic, I., Babic, A. et al. Neural circular RNAs are derived from synaptic genes and regulated by development and plasticity. Nat Neurosci 18, 603–610 (2015). https://doi.org/10.1038/nn.3975

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