Resource | Published:

Dynamic regulation of RNA editing in human brain development and disease

Nature Neuroscience volume 19, pages 10931099 (2016) | Download Citation

Abstract

RNA editing is increasingly recognized as a molecular mechanism regulating RNA activity and recoding proteins. Here we surveyed the global landscape of RNA editing in human brain tissues and identified three unique patterns of A-to-I RNA editing rates during cortical development: stable high, stable low and increasing. RNA secondary structure and the temporal expression of adenosine deaminase acting on RNA (ADAR) contribute to cis- and trans-regulatory mechanisms of these RNA editing patterns, respectively. Interestingly, the increasing pattern was associated with neuronal maturation, correlated with mRNA abundance and potentially influenced miRNA binding energy. Gene ontology analyses implicated the increasing pattern in vesicle or organelle membrane-related genes and glutamate signaling pathways. We also found that the increasing pattern was selectively perturbed in spinal cord injury and glioblastoma. Our findings reveal global and dynamic aspects of RNA editing in brain, providing new insight into epitranscriptional regulation of sequence diversity.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

BioProject

Sequence Read Archive

Referenced accessions

Gene Expression Omnibus

References

  1. 1.

    , & The ADAR protein family. Genome Biol. 13, 252 (2012).

  2. 2.

    Functions and regulation of RNA editing by ADAR deaminases. Annu. Rev. Biochem. 79, 321–349 (2010).

  3. 3.

    & Proteome diversification by adenosine to inosine RNA editing. RNA Biol. 7, 205–212 (2010).

  4. 4.

    , & Regulation of alternative splicing by RNA editing. Nature 399, 75–80 (1999).

  5. 5.

    , & Regulation of glutamate receptor B pre-mRNA splicing by RNA editing. Nucleic Acids Res. 35, 3723–3732 (2007).

  6. 6.

    & A-to-I RNA editing: effects on proteins key to neural excitability. Neuron 74, 432–439 (2012).

  7. 7.

    & Inosine exists in mRNA at tissue-specific levels and is most abundant in brain mRNA. EMBO J. 17, 1120–1127 (1998).

  8. 8.

    et al. Genome-wide identification of human RNA editing sites by parallel DNA capturing and sequencing. Science 324, 1210–1213 (2009).

  9. 9.

    et al. Identifying RNA editing sites using RNA sequencing data alone. Nat. Methods 10, 128–132 (2013).

  10. 10.

    et al. Point mutation in an AMPA receptor gene rescues lethality in mice deficient in the RNA-editing enzyme ADAR2. Nature 406, 78–81 (2000).

  11. 11.

    & Deciphering the functions and regulation of brain-enriched A-to-I RNA editing. Nat. Neurosci. 16, 1518–1522 (2013).

  12. 12.

    et al. A biochemical landscape of A-to-I RNA editing in the human brain transcriptome. Genome Res. 24, 522–534 (2014).

  13. 13.

    et al. Evolutionary and ontogenetic changes in RNA editing in human, chimpanzee, and macaque brains. RNA 19, 1693–1702 (2013).

  14. 14.

    , , & A survey of RNA editing in human brain. Genome Res. 14, 2379–2387 (2004).

  15. 15.

    et al. mRNA expression, splicing and editing in the embryonic and adult mouse cerebral cortex. Nat. Neurosci. 16, 499–506 (2013).

  16. 16.

    , & Widespread A-to-I RNA editing of Alu-containing mRNAs in the human transcriptome. PLoS Biol. 2, e391 (2004).

  17. 17.

    et al. Widespread RNA editing of embedded Alu elements in the human transcriptome. Genome Res. 14, 1719–1725 (2004).

  18. 18.

    & RADAR: a rigorously annotated database of A-to-I RNA editing. Nucleic Acids Res. 42, D109–D113 (2014).

  19. 19.

    , & Predicting sites of ADAR editing in double-stranded RNA. Nat. Commun. 2, 319 (2011).

  20. 20.

    et al. ADAR regulates RNA editing, transcript stability, and gene expression. Cell Rep. 5, 849–860 (2013).

  21. 21.

    et al. Multiple knockout mouse models reveal lincRNAs are required for life and brain development. eLife 2, e01749 (2013).

  22. 22.

    et al. Laminar and temporal expression dynamics of coding and noncoding RNAs in the mouse neocortex. Cell Rep. 6, 938–950 (2014).

  23. 23.

    et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285–7290 (2015).

  24. 24.

    et al. CORTECON: a temporal transcriptome analysis of in vitro human cerebral cortex development from human embryonic stem cells. Neuron 83, 51–68 (2014).

  25. 25.

    , , & Longitudinal RNA sequencing of the deep transcriptome during neurogenesis of cortical glutamatergic neurons from murine ESCs. F1000Res. 2, 35 (2013).

  26. 26.

    et al. Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol. Cell 58, 870–885 (2015).

  27. 27.

    , & Control of cortical neuronal migration by glutamate and GABA. Front. Cell. Neurosci. 9, 4 (2015).

  28. 28.

    & Glutamate induces de novo growth of functional spines in developing cortex. Nature 474, 100–104 (2011).

  29. 29.

    , & Organelles in developing neurons: essential regulators of neuronal morphogenesis and function. Int. J. Dev. Biol. 53, 19–27 (2009).

  30. 30.

    & Adenosine-to-inosine RNA editing and human disease. Genome Med. 5, 105 (2013).

  31. 31.

    et al. RNA-seq characterization of spinal cord injury transcriptome in acute/subacute phases: a resource for understanding the pathology at the systems level. PLoS One 8, e72567 (2013).

  32. 32.

    , , , & Prenatal expression patterns of genes associated with neuropsychiatric disorders. Am. J. Psychiatry 171, 758–767 (2014).

  33. 33.

    et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  34. 34.

    , , & Alu elements shape the primate transcriptome by cis-regulation of RNA editing. Genome Biol. 15, R28 (2014).

  35. 35.

    , , , & A distant cis acting intronic element induces site-selective RNA editing. Nucleic Acids Res. 40, 9876–9886 (2012).

  36. 36.

    , , , & A high-throughput screen to identify enhancers of ADAR-mediated RNA-editing. RNA Biol. 10, 192–204 (2013).

  37. 37.

    & RNA editing in regulating gene expression in the brain. Biochim. Biophys. Acta 1779, 459–470 (2008).

  38. 38.

    et al. Requirement of the RNA-editing enzyme ADAR2 for normal physiology in mice. J. Biol. Chem. 286, 18614–18622 (2011).

  39. 39.

    , , & A-to-I pre-mRNA editing in Drosophila is primarily involved in adult nervous system function and integrity. Cell 102, 437–449 (2000).

  40. 40.

    et al. Engineered alterations in RNA editing modulate complex behavior in Drosophila: regulatory diversity of adenosine deaminase acting on RNA (ADAR) targets. J. Biol. Chem. 286, 8325–8337 (2011).

  41. 41.

    , , & Activity-regulated RNA editing in select neuronal subfields in hippocampus. Nucleic Acids Res. 41, 1124–1134 (2013).

  42. 42.

    , , , & Activity-dependent A-to-I RNA editing in rat cortical neurons. Genetics 192, 281–287 (2012).

  43. 43.

    , & Mammalian conserved ADAR targets comprise only a small fragment of the human editosome. Genome Biol. 15, R5 (2014).

  44. 44.

    & Influences of primary cilia on cortical morphogenesis and neuronal subtype maturation. Neuroscientist 21, 136–151 (2015).

  45. 45.

    , , & Primary cilia in neurodevelopmental disorders. Nat. Rev. Neurol. 10, 27–36 (2014).

  46. 46.

    et al. The genomic landscape and clinical relevance of A-to-I RNA editing in human cancers. Cancer Cell 28, 515–528 (2015).

  47. 47.

    et al. Modulation of dendritic AMPA receptor mRNA trafficking by RNA splicing and editing. Nucleic Acids Res. 41, 617–631 (2013).

  48. 48.

    , , & Altered RNA editing in 3′ UTR perturbs microRNA-mediated regulation of oncogenes and tumor-suppressors. Sci. Rep. 6, 23226 (2016).

  49. 49.

    et al. ADAR1 regulates ARHGAP26 gene expression through RNA editing by disrupting miR-30b-3p and miR-573 binding. RNA 19, 1525–1536 (2013).

  50. 50.

    et al. Developmental regulation of human cortex transcription and its clinical relevance at single base resolution. Nat. Neurosci. 18, 154–161 (2015).

  51. 51.

    et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  52. 52.

    , & HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  53. 53.

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

  54. 54.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  55. 55.

    et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 1–33 (2013).

  56. 56.

    et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

  57. 57.

    & Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335, 1302 author reply 1302 (2012).

  58. 58.

    et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

  59. 59.

    et al. MicroRNA targets in Drosophila. Genome Biol. 5, R1 (2003).

  60. 60.

    , , & miRBase: tools for microRNA genomics. Nucleic Acids Res. 36, D154–D158 (2008).

  61. 61.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164–e164 (2010).

  62. 62.

    et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat. Neurosci. 19, 40–47 (2016).

  63. 63.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  64. 64.

    et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

  65. 65.

    , , & A resource of ribosomal RNA-depleted RNA-Seq data from different normal adult and fetal human tissues. Sci. Data 2, 150063 (2015).

  66. 66.

    et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

Download references

Acknowledgements

We are thankful for the vision and generosity of the Lieber and Maltz families who founded the Lieber Institute for Brain Development. We dedicate this work to the memory of Constance Lieber, our inspirational founder and director. We also gratefully acknowledge and thank the families of the donors whose tissues were used in this study. This work was supported by the Lieber Institute for Brain Development, and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A09057171) in Korea. A.K.K.L. thanks the Johns Hopkins Bloomberg School of Public Health start-up fund.

Author information

Author notes

Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Taeyoung Hwang
  2. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.

    • Taeyoung Hwang
    • , Yuan Gao
    • , Thomas M Hyde
    • , Joel E Kleinman
    • , Anandita Rajpurohit
    • , Ran Tao
    • , Joo Heon Shin
    •  & Daniel R Weinberger
  3. Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea.

    • Chul-Kee Park
  4. Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.

    • Anthony K L Leung
  5. Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Anthony K L Leung
  6. Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Thomas M Hyde
    •  & Daniel R Weinberger
  7. Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Thomas M Hyde
    •  & Daniel R Weinberger
  8. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Thomas M Hyde
    •  & Daniel R Weinberger
  9. Department of Biological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Thomas M Hyde
  10. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Daniel R Weinberger

Authors

  1. Search for Taeyoung Hwang in:

  2. Search for Chul-Kee Park in:

  3. Search for Anthony K L Leung in:

  4. Search for Yuan Gao in:

  5. Search for Thomas M Hyde in:

  6. Search for Joel E Kleinman in:

  7. Search for Anandita Rajpurohit in:

  8. Search for Ran Tao in:

  9. Search for Joo Heon Shin in:

  10. Search for Daniel R Weinberger in:

Contributions

J.H.S. and D.R.W. designed the project and oversaw all aspects of the studies. T.H. developed the computational pipeline for identifying RNA editing sites and performed the computational analyses as well as exome and targeted DNA sequencing experiments. C.-K.P. provided glioblastoma patient samples, generated RNA-seq on them and assisted the clinical interpretations. A.K.L.L. consulted on the molecular experiments and assisted in the biological interpretation of the computational findings. Y.G. designed the project and oversaw the RNA-seq data generation. T.M.H. and J.E.K. provided brain tissue and demographic data and assisted in the biological interpretation of fetal samples. A.R. helped with sequencing experiments. R.T. performed RNA extractions. T.H. and D.R.W. wrote the manuscript with inputs from all the authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Joo Heon Shin or Daniel R Weinberger.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary figures 1–15

  2. 2.

    Supplementary Methods Checklist

  3. 3.

    Supplementary Table legends

Excel files

  1. 1.

    Supplementary Tables 1–16

Zip files

  1. 1.

    Supplementary Software

    R and python source codes that used to analyze RNA editing in 33 human brain samples

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nn.4337

Further reading