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Deciphering the splicing code

Nature volume 465, pages 53–59 (2010)Cite this article

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Abstract

Alternative splicing has a crucial role in the generation of biological complexity, and its misregulation is often involved in human disease. Here we describe the assembly of a ‘splicing code’, which uses combinations of hundreds of RNA features to predict tissue-dependent changes in alternative splicing for thousands of exons. The code determines new classes of splicing patterns, identifies distinct regulatory programs in different tissues, and identifies mutation-verified regulatory sequences. Widespread regulatory strategies are revealed, including the use of unexpectedly large combinations of features, the establishment of low exon inclusion levels that are overcome by features in specific tissues, the appearance of features deeper into introns than previously appreciated, and the modulation of splice variant levels by transcript structure characteristics. The code detected a class of exons whose inclusion silences expression in adult tissues by activating nonsense-mediated messenger RNA decay, but whose exclusion promotes expression during embryogenesis. The code facilitates the discovery and detailed characterization of regulated alternative splicing events on a genome-wide scale.

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Figure 1: Assembling the splicing code.
Figure 2: Predicting tissue-regulated alternative splicing.
Figure 3: Graphical depiction of the splicing code.
Figure 4: Validation of a regulatory feature map.
Figure 5: The code predicts a mechanism for developmental regulation.

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References

  1. Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008)

    Article  ADS  CAS  Google Scholar 

  2. Pan, Q., Shai, O., Lee, L. J., Frey, B. J. & Blencowe, B. J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature Genet. 40, 1413–1415 (2008)

    Article  CAS  Google Scholar 

  3. Wang, G.-S. & Cooper, T. A. Splicing in disease: disruption of the splicing code and the decoding machinery. Nature Rev. Genet. 8, 749–761 (2007)

    Article  CAS  Google Scholar 

  4. Wang, Z. & Burge, C. B. Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. RNA 14, 802–813 (2008)

    Article  CAS  Google Scholar 

  5. Hartmann, B. & Valcarcel, J. Decrypting the genome’s alternative messages. Curr. Opin. Cell Biol. 21, 377–386 (2009)

    Article  CAS  Google Scholar 

  6. Hallegger, M., Llorian, M. & Smith, C. W. Alternative splicing: global insights. FEBS J. 277, 856–866 (2010)

    Article  CAS  Google Scholar 

  7. Nilsen, T. W. & Graveley, B. R. Expansion of the eukaryotic proteome by alternative splicing. Nature 463, 457–463 (2010)

    Article  ADS  CAS  Google Scholar 

  8. Blencowe, B. J. Alternative splicing: new insights from global analyses. Cell 126, 37–47 (2006)

    Article  CAS  Google Scholar 

  9. Black, D. L. Mechanisms of alternative pre-messenger RNA splicing. Annu. Rev. Biochem. 72, 291–336 (2003)

    Article  CAS  Google Scholar 

  10. Fagnani, M. et al. Functional coordination of alternative splicing in the mammalian central nervous system. Genome Biol. 8, R108 (2007)

    Article  Google Scholar 

  11. Shai, O., Morris, Q. D., Blencowe, B. J. & Frey, B. J. Inferring global levels of alternative splicing isoforms using a generative model of microarray data. Bioinformatics 22, 606–613 (2006)

    Article  CAS  Google Scholar 

  12. Sugnet, C. W. et al. Unusual intron conservation near tissue-regulated exons found by splicing microarrays. PLoS Comput. Biol. 2, e4 (2006)

    Article  ADS  Google Scholar 

  13. Das, D. et al. A correlation with exon expression approach to identify cis-regulatory elements for tissue-specific alternative splicing. Nucleic Acids Res. 35, 4845–4857 (2007)

    Article  CAS  Google Scholar 

  14. Castle, J. C. et al. Expression of 24,426 human alternative splicing events and predicted cis regulation in 48 tissues and cell lines. Nature Genet. 40, 1416–1425 (2008)

    Article  CAS  Google Scholar 

  15. Minovitsky, S., Gee, S. L., Schokrpur, S., Dubchak, I. & Conboy, J. G. The splicing regulatory element, UGCAUG, is phylogenetically and spatially conserved in introns that flank tissue-specific alternative exons. Nucleic Acids Res. 33, 714–724 (2005)

    Article  CAS  Google Scholar 

  16. Kawamoto, S. Neuron-specific alternative splicing of nonmuscle myosin II heavy chain-B pre-mRNA requires a cis-acting intron sequence. J. Biol. Chem. 271, 17613–17616 (1996)

    CAS  PubMed  Google Scholar 

  17. Ule, J. et al. An RNA map predicting Nova-dependent splicing regulation. Nature 444, 580–586 (2006)

    Article  ADS  CAS  Google Scholar 

  18. Licatalosi, D. D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464–469 (2008)

    Article  ADS  CAS  Google Scholar 

  19. Chan, R. C. & Black, D. L. The polypyrimidine tract binding protein binds upstream of neural cell-specific c-src exon N1 to repress the splicing of the intron downstream. Mol. Cell. Biol. 17, 4667–4676 (1997)

    Article  CAS  Google Scholar 

  20. Ashiya, M. & Grabowski, P. J. A neuron-specific splicing switch mediated by an array of pre-mRNA repressor sites: evidence of a regulatory role for the polypyrimidine tract binding protein and a brain-specific PTB counterpart. RNA 3, 996–1015 (1997)

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Faustino, N. A. & Cooper, T. A. Identification of putative new splicing targets for ETR-3 using sequences identified by systematic evolution of ligands by exponential enrichment. Mol. Cell. Biol. 25, 879–887 (2005)

    Article  CAS  Google Scholar 

  22. Galarneau, A. & Richard, S. Target RNA motif and target mRNAs of the Quaking STAR protein. Nature Struct. Mol. Biol. 12, 691–698 (2005)

    Article  CAS  Google Scholar 

  23. Sorek, R. & Ast, G. Intronic sequences flanking alternatively spliced exons are conserved between human and mouse. Genome Res. 13, 1631–1637 (2003)

    Article  CAS  Google Scholar 

  24. Fairbrother, W. G., Yeh, R. F., Sharp, P. A. & Burge, C. B. Predictive identification of exonic splicing enhancers in human genes. Science 297, 1007–1013 (2002)

    Article  ADS  CAS  Google Scholar 

  25. Zhang, X. H. & Chasin, L. A. Computational definition of sequence motifs governing constitutive exon splicing. Genes Dev. 18, 1241–1250 (2004)

    Article  CAS  Google Scholar 

  26. Stadler, M. B. et al. Inference of splicing regulatory activities by sequence neighborhood analysis. PLoS Genet. 2, e191 (2006)

    Article  Google Scholar 

  27. Yeo, G. W., Nostrand, E. L. & Liang, T. Y. Discovery and analysis of evolutionarily conserved intronic splicing regulatory elements. PLoS Genet. 3, e85 (2007)

    Article  Google Scholar 

  28. Xiao, X., Wang, Z., Jang, M. & Burge, C. B. Coevolutionary networks of splicing cis-regulatory elements. Proc. Natl Acad. Sci. USA 104, 18583–18588 (2007)

    Article  ADS  CAS  Google Scholar 

  29. Shepard, P. J. & Hertel, K. J. Conserved RNA secondary structures promote alternative splicing. RNA 14, 1463–1469 (2008)

    Article  CAS  Google Scholar 

  30. Bishop, C. M. Pattern Recognition and Machine Learning. (Springer, 2006)

    MATH  Google Scholar 

  31. Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)

    Article  MathSciNet  Google Scholar 

  32. Wollerton, M. C., Gooding, C., Wagner, E. J., Garcia-Blanco, M. A. & Smith, C. W. Autoregulation of polypyrimidine tract binding protein by alternative splicing leading to nonsense-mediated decay. Mol. Cell 13, 91–100 (2004)

    Article  CAS  Google Scholar 

  33. Wei, N., Lin, C. Q., Modafferi, E. F., Gomes, W. A. & Black, D. L. A unique intronic splicing enhancer controls the inclusion of the agrin Y exon. RNA 3, 1275–1288 (1997)

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lim, L. P. & Sharp, P. A. Alternative splicing of the fibronectin EIIIB exon depends on specific TGCATG repeats. Mol. Cell. Biol. 18, 3900–3906 (1998)

    Article  CAS  Google Scholar 

  35. Côté, J., Dupuis, S., Jiang, Z. & Wu, J. Y. Caspase-2 pre-mRNA alternative splicing: identification of an intronic element containing a decoy 3′ acceptor site. Proc. Natl Acad. Sci. USA 98, 938–943 (2001)

    Article  ADS  Google Scholar 

  36. Hayakawa, M. et al. Muscle-specific exonic splicing silencer for exon exclusion in human ATP synthase γ-subunit pre-mRNA. J. Biol. Chem. 277, 6974–6984 (2002)

    Article  CAS  Google Scholar 

  37. Jin, Y. et al. A vertebrate RNA-binding protein Fox-1 regulates tissue-specific splicing via the pentanucleotide GCAUG. EMBO J. 22, 905–912 (2003)

    Article  CAS  Google Scholar 

  38. Zhang, C. et al. Defining the regulatory network of the tissue-specific splicing factors Fox-1 and Fox-2. Genes Dev. 22, 2550–2563 (2008)

    Article  CAS  Google Scholar 

  39. Calarco, J. A. et al. Regulation of vertebrate nervous system alternative splicing and development by an SR-related protein. Cell 138, 898–910 (2009)

    Article  CAS  Google Scholar 

  40. Gooding, C. et al. A class of human exons with predicted distant branch points revealed by analysis of AG dinucleotide exclusion zones. Genome Biol. 7, R1 (2006)

    Article  Google Scholar 

  41. Wu, J. I., Reed, R. B., Grabowski, P. J. & Artzt, K. Function of quaking in myelination: regulation of alternative splicing. Proc. Natl Acad. Sci. USA 99, 4233–4238 (2002)

    Article  ADS  CAS  Google Scholar 

  42. Oberstrass, F. C. et al. Structure of PTB bound to RNA: specific binding and implications for splicing regulation. Science 309, 2054–2057 (2005)

    Article  ADS  CAS  Google Scholar 

  43. Markovtsov, V. et al. Cooperative assembly of an hnRNP complex induced by a tissue-specific homolog of polypyrimidine tract binding protein. Mol. Cell. Biol. 20, 7463–7479 (2000)

    Article  CAS  Google Scholar 

  44. Xie, J., Jan, C., Stoilov, P., Park, J. & Black, D. L. A consensus CaMK IV-responsive RNA sequence mediates regulation of alternative exons in neurons. RNA 11, 1825–1834 (2005)

    Article  CAS  Google Scholar 

  45. Pérez, I., Lin, C. H., McAfee, J. G. & Patton, J. G. Mutation of PTB binding sites causes misregulation of alternative 3′ splice site selection in vivo. RNA 3, 764–778 (1997)

    PubMed  PubMed Central  Google Scholar 

  46. Lipowsky, G. et al. Exportin 4: a mediator of a novel nuclear export pathway in higher eukaryotes. EMBO J. 19, 4362–4371 (2000)

    Article  CAS  Google Scholar 

  47. Gontan, C. et al. Exportin 4 mediates a novel nuclear import pathway for Sox family transcription factors. J. Cell Biol. 185, 27–34 (2009)

    Article  CAS  Google Scholar 

  48. Lefebvre, V., Dumitriu, B., Penzo-Méndez, A., Han, Y. & Pallavi, B. Control of cell fate and differentiation by Sry-related high-mobility-group box (Sox) transcription factors. Int. J. Biochem. Cell Biol. 39, 2195–2214 (2007)

    Article  CAS  Google Scholar 

  49. Zender, L. et al. An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 135, 852–864 (2008)

    Article  CAS  Google Scholar 

  50. Ray, D. et al. RNACompete: Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nature Biotechnol. 27, 667–670 (2009)

    Article  CAS  Google Scholar 

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Acknowledgements

We thank D. L. Black, D. Botstein, S. E. Brenner, C. B. Burge, B. Chabot, R. Durbin, X.-D. Fu, B. R. Graveley, T. R. Hughes, N. Jojic, C. W. J. Smith, S. Tavazoie and members of our various laboratories for discussions or comments on the manuscript; D. D. Licatalosi and R. B. Darnell for communicating unpublished results; and S. Chaudhry for RT–PCR work. B.J.F. also thanks C. M. Bishop and D. J. C. MacKay for hosting him during his sabbatical in Cambridge. This research was funded by a grant from Genome Canada through the OGI to B.J.B., B.J.F. and others; an NSERC/CFI/OIT CRC grant to B.J.F.; CIHR grants to B.J.F. and B.J.B.; an NCIC grant to B.J.B.; and NSERC EWR Steacie and Canadian Institute for Advanced Research Fellowships to B.J.F.

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Author notes

  1. Yoseph Barash and John A. Calarco: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Biomedical Engineering, University of Toronto, 10 King’s College Road, Toronto M5S 3G4, Canada,

    Yoseph Barash, Weijun Gao, Xinchen Wang, Ofer Shai & Brendan J. Frey

  2. Banting and Best Department of Medical Research and Department of Molecular Genetics, Donnelly Centre, University of Toronto, 160 College Street, Toronto M5S 3E1, Canada,

    Yoseph Barash, John A. Calarco, Qun Pan, Xinchen Wang, Benjamin J. Blencowe & Brendan J. Frey

  3. Microsoft Research, 7 J. J. Thomson Avenue, Cambridge CB3 0FB, UK,

    Brendan J. Frey

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Contributions

Y.B. and B.J.F. developed the predictive framework and code assembly algorithms, analysed validation rates, and with B.J.B. and J.A.C. extracted predictions for regulatory mechanisms. Y.B., B.J.B. and B.J.F. produced the feature compendium. J.A.C. performed wet laboratory experiments. Q.P. generated exon and intron datasets. W.G. and Y.B. developed the web tool with input from the other authors. X.W. analysed exons from neurological disorder-associated genes. O.S. estimated the percentage inclusion values. B.J.F., B.J.B. and Y.B. designed the study and wrote the manuscript with input from the other authors.

Corresponding authors

Correspondence to Benjamin J. Blencowe or Brendan J. Frey.

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The authors declare no competing financial interests.

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Barash, Y., Calarco, J., Gao, W. et al. Deciphering the splicing code. Nature 465, 53–59 (2010). https://doi.org/10.1038/nature09000

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