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Exploring the Connection Between Synthetic and Natural RNAs in Genomes: A Novel Computational Approach

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New Algorithms for Macromolecular Simulation

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 49))

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Abstract

The central dogma of biology—that DNA makes RNA makes protein—was recently expanded yet again with the discovery of RNAs that carry important regulatory functions (e.g., metabolite-binding RNAs, transcription regulation, chromosome replication). Thus, rather than only serving as mediators between the hereditary material and the cell’s workhorses (proteins), RNAs have essential regulatory roles. This finding has stimulated a search for small functional RNA motifs, either embedded in mRNA molecules or as separate molecules in the cell. The existence of such simple RNA motifs in Nature suggests that the results from experimental in vitro selection of functional RNA molecules may shed light on the scope and functional diversity of these simple RNA structural motifs in vivo. Here we develop a computational method for extracting structural information from laboratory selection experiments and searching the genomes of various organisms for sequences that may fold into similar structures (if transcribed), as well as techniques for evaluating the structural stability of such potential candidate sequences. Applications of our algorithm to several aptamer motifs (that bind either antibiotics or ATP) produce a number of promising candidates in the genomes of selected bacterial and archaeal species. More generally, our approach offers a promising avenue for enhancing current knowledge of RNA’s structural repertoire in the cell.

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References

  1. D. P. Arya, R. L. Coffee, Jr., and I. Charles. Neomycin-induced hybrid triplex formation. J. Amer. Chem. Soc., 123:11093–11094, 2001.

    Article  Google Scholar 

  2. D. P. Arya, R. L. Coffee, Jr., B. Willis, and A. I. Abramovitch. Aminoglycosidenucleic acid interactions: Remarkable stabilization of DNA and RNA triple helices by neomycin. J. Amer. Chem. Soc., 123:5385–5395, 2001.

    Article  Google Scholar 

  3. J. P. Bachellerie, J. Cavaille, and A. Huttenhofer. The expanding snoRNA world. Biochimie, 84:775–90, 2002.

    Article  Google Scholar 

  4. A. L. Barabási and E. Bonabeau. Scale-free networks. Sci. Amer., 288:60–69, 2003.

    Article  Google Scholar 

  5. G. Benedetti and S. Morosetti. A graph-topological approach to recognition of pattern and similarity in RNA secondary structures. Biol. Chem., 59:179–184, 1996.

    Google Scholar 

  6. D. H. Burke, D. C. Hoffman, A. Brown, M. Hansen, A. Pardi, and L. Gold. RNA aptamers to the peptidyl transferase inhibitor chloramphenicol. Chem. Biol., 4:833–843, 1997.

    Article  Google Scholar 

  7. J. H. Chen, S. Y. Le, and J. V. Maizel. Prediction of common secondary structures of RNAs: a genetic algorithm approach. Nucl. Acids Res., 28:991–999, 2000.

    Article  Google Scholar 

  8. K. J. Devlin. Mathematics: the New Golden Age. Penguin, London, 1988.

    MATH  Google Scholar 

  9. J. A. Doudna and T. R. Cech. The chemical repertoire of natural ribozymes. Nature, 418:222–228, 2002.

    Article  Google Scholar 

  10. A. D. Ellington and J. W. Szostak. In vitro selection of RNA molecules that bind specific ligands. Nature, 346:818–822, 1990.

    Article  Google Scholar 

  11. D. Fera, N. Kim, N. Shiffeldrim, J. Zorn, U. Laserson, H. H. Gan, and T. Schlick. RAG: RNA-As-Graphs web resource. BMC Bioinformatics, 5:88, 2004.

    Article  Google Scholar 

  12. W. Fontana, D. A. Konings, P. F. Stadler, and P. Schuster. Statistics of RNA secondary structures. Biopolymers, 33:1389–1404, 1993.

    Article  Google Scholar 

  13. W. Fontana, P. F. Stadler, E. G. Bornberg-Bauer, T. Griesmacher, I. L. Hofacker, M. Tacker, P. Tarazona, E. D. Weinberger, and P. Schuster. RNA folding and combinatory landscapes. Phys. Rev. E, 47:2083–2099, 1993.

    Article  Google Scholar 

  14. H. H. Gan, S. Pasquali, and T. Schlick. Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design. Nucl. Acids Res., 31:2926–2943, 2003.

    Article  Google Scholar 

  15. C. Gaspin and E. Westhof. An interactive framework for RNA secondary structure prediction with a dynamical treatment of constraints. J. Mol. Biol., 254:163–174, 1995.

    Article  Google Scholar 

  16. W. W. Gibbs. The unseen genome: Gems among the junk. Sci. Amer., 289:46–53, 2003.

    Google Scholar 

  17. S. Griffiths-Jones, A. Bateman, M. Marshall, A. Khanna, and S. R. Eddy. Rfam: an RNA family database. Nucl. Acids Res., 31:439–441, 2003.

    Article  Google Scholar 

  18. J. Gross and J. Yellen. Graph Theory and its Applications. CRC Press, Boca Raton, FL, 1999.

    MATH  Google Scholar 

  19. F. Harary. The number of homeomorphically irreducible trees and other species. Acta Math., 101:141–162, 1959.

    MATH  MathSciNet  Google Scholar 

  20. F. Harary. Graph Theory. Addison-Wesley, Reading, MA, 1969.

    Google Scholar 

  21. T. Hermann and D. J. Patel. Adaptive recognition by nucleic acid aptamers. Science, 287:820–825, 2000.

    Article  Google Scholar 

  22. I. L. Hofacker, W. Fontana, P. F. Stadler, L. S. Bonhoeffer, M. Tacker, and P. Schuster. Fast folding and comparison of RNA secondary structures. Monatsh. Chem., 125:167–188, 1994. www.tbi.univie.ac.-at/~ivo/RNA/

    Article  Google Scholar 

  23. A. Huttenhofer, J. Brosius, and J. P. Bachellerie. RNomics: Identification and function of small, non-messenger RNAs. Curr. Opin. Chem. Biol., 6:835–843, 2002.

    Article  Google Scholar 

  24. A. Huttenhofer, M. Kiefmann, S. Meier-Ewert, J. O’Brien, H. Lehrach, J. P. Bachellerie, and J. Brosius. RNomics: an experimental approach that identifies 201 candidates for novel, small, non-messenger RNAs in mouse. EMBO J., 20:2943–2953, 2001.

    Article  Google Scholar 

  25. L. Jiang, A. Majumdar, W. Hu, T. J. Jaishree, W. Xu, and D. J. Patel. Saccharide-RNA recognition in a complex formed between neomycin B and an RNA aptamer. Structure Fold Des., 7:817–827, 1999.

    Article  Google Scholar 

  26. N. Kim, N. Shiffeldrim, H. H. Gan, and T. Schlick. Candidates for novel RNA topologies. J. Mol. Biol., 341:1129–1144, 2004.

    Article  Google Scholar 

  27. J. Kitagawa, Y. Futamura, and K. Yamamoto. Analysis of the conformational energy landscape of human snRNA with a metric based on tree representation of RNA structures. Nucl. Acids Res., 31:2006–2013, 2004.

    Article  Google Scholar 

  28. S. Y. Le, R. Nussinov, and J. V. Maizel. Tree graphs of RNA secondary structures and their comparisons. Comput. Biomed. Res., 22:461–473, 1989.

    Article  Google Scholar 

  29. T. J. Macke, D. J. Ecker, R. R. Gutell, D. Gautheret, D. A. Case, and R. Sampath. RNAMotif, an RNA secondary structure definition and search algorithm. Nucl. Acids Res., 29:4724–4735, 2001.

    Article  Google Scholar 

  30. H. Margalit, B. A. Shapiro, A. B. Oppenheim, and J. V. Maizel, Jr. Detection of common motifs in RNA secondary structure. Nucl. Acids Res., 17:4829–4845, 1989.

    Google Scholar 

  31. J. S. Mattick. Non-coding RNAs: the architects of eukaryotic complexity. EMBO Rep., 2:986–991, 2001.

    Article  Google Scholar 

  32. J. S. Mattick and M. J. Gagen. The evolution of controlled multitasked gene networks: the role of introns and other noncoding RNAs in the development of complex organisms. Mol. Biol. Evol., 18:1611–1630, 2001.

    Google Scholar 

  33. A. Nahvi, N. Sudarsan, M. S. Ebert, X. Zou, K. L. Brown, and R. R. Breaker. Genetic control by a metabolite binding mRNA. Chem. Biol., 9:1043–1049, 2002.

    Article  Google Scholar 

  34. Y. Okazaki, M. Furuno, et al. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs. Nature, 420:563–573, 2002.

    Article  Google Scholar 

  35. S. Pasquali, H. H. Gan, and T. Schlick. Modular RNA architecture revealed by computational analysis of existing pseudoknots and ribosomal RNAs. Nucl. Acids Res., 2005. In Press.

    Google Scholar 

  36. N. Piganeau and R. Schroeder. Aptamer structures: A preview into regulatory pathways? Chem. Biol., 10:103–104, 2003.

    Article  Google Scholar 

  37. E. Rivas and S. R. Eddy. A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol., 285:2053–2068, 1999.

    Article  Google Scholar 

  38. E. Rivas and S. R. Eddy. Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs. Bioinformatics, 16:583–605, 2000.

    Article  Google Scholar 

  39. K. Salehi-Ashtiani and J. W. Szostak. In vitro evolution suggests multiple origins for the hammerhead ribozyme. Nature, 414:82–84, 2001.

    Article  Google Scholar 

  40. M. Sassanfar and J. W. Szostak. An RNA motif that binds ATP. Nature, 364:550–553, 1993.

    Article  Google Scholar 

  41. T. Schlick. Molecular Modeling: An Interdisciplinary Guide. Springer-Verlag, New York, NY, 2002.

    MATH  Google Scholar 

  42. P. Schuster, W. Fontana, P. F. Stadler, and I. L. Hofacker. From sequences to shapes and back: a case study in RNA secondary structures. Proc. R. Soc. Lond. B. Biol. Sci., 255:279–284, 1994.

    Google Scholar 

  43. B. A. Shapiro and K. Z. Zhang. Comparing multiple RNA secondary structures using tree comparisons. Comput. Appl. Biosci., 6:309–318, 1990.

    Google Scholar 

  44. G. A. Soukup and R. R. Breaker. Engineering precision RNA molecular switches. Proc. Natl. Acad. Sci. USA, 96:3584–3589, 1999.

    Article  Google Scholar 

  45. G. A. Soukup and R. R. Breaker. Nucleic acid molecular switches. Trends Biotechnol., 17:469–476, 1999.

    Article  Google Scholar 

  46. G. A. Soukup and R. R. Breaker. Allosteric nucleic acid catalysts. Curr. Opin. Struct. Biol., 10:318–325, 2000.

    Article  Google Scholar 

  47. S. Spiegelman. An approach to the experimental analysis of precellular evolution. Q. Rev. Biophys., 4:213–253, 1971.

    Article  Google Scholar 

  48. G. Storz. An expanding universe of noncoding RNAs. Science, 296:1260–1263, 2002.

    Article  Google Scholar 

  49. V. Tereshko, E. Skripkin, and D. J. Patel. Encapsulating streptomycin within a small 40-mer RNA. Chem. Biol., 10:175–187, 2003.

    Article  Google Scholar 

  50. I. Tinoco, Jr. and C. Bustamante. How RNA folds. J. Mol. Biol., 293:271–281, 1999.

    Article  Google Scholar 

  51. C. Tuerk and L. Gold. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science, 249:505–510, 1990.

    Google Scholar 

  52. S. T. Wallace and R. Schroeder. In vitro selection and characterization of streptomycin-binding RNAs: Recognition discrimination between antibiotics. RNA, 4:112–123, 1998.

    Google Scholar 

  53. D. S. Wilson and J. W. Szostak. In vitro selection of functional nucleic acids. Ann. Rev. Biochem., 68:611–647, 1999.

    Article  Google Scholar 

  54. W. Winkler, A. Nahvi, and R. R. Breaker. Thiamine derivatives bind messenger RNAs directly to regulate bacterial expression. Nature, 419:952–956, 2002.

    Article  Google Scholar 

  55. W. C. Winkler, S. Cohen-Chalamish, and R. R. Breaker. An mRNA structure that controls gene expression by binding FMN. Proc. Natl. Acad. Sci. USA, 99:15908–15913, 2002.

    Article  Google Scholar 

  56. M. Zuker, D. H. Mathews, and D. H. Turner. Algorithms and thermodynamics for RNA secondary structure prediction: A practical guide. In J. Barciszewski and B. F. C. Clark, editors, RNA Biochemistry and Biotechnology, NATO ASI Series, pages 11–43. Klewer Academic Publishers, Dordrecht, NL, 1999.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Chemistry, New York University, 251 Mercer Street, New York, New York, 10012, USA

    Uri Laserson, Hin Hark Gan & Tamar Schlick

  2. Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, New York, 10012, USA

    Uri Laserson & Tamar Schlick

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  1. Uri Laserson
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  2. Hin Hark Gan
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  3. Tamar Schlick
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Leicester, University Road, Leicester, LE1 7RH, UK

    Benedict Leimkuhler

  2. Institut nancéien de chimie moléculaire, Université Henri Poincaré - Nancy I, B.P. 239, 54506, Vandoeuvre-lès-Nancy, France

    Christophe Chipot

  3. Department of Computer Science, Cornell University, 4130 Upson Hall, Ithaca, NY, 14853-7501, USA

    Ron Elber

  4. Arrhenius Laboratory Division of Physical Chemistry, Stockholm University, 106 91, Stockholm, Sweden

    Aatto Laaksonen

  5. Laboratory of Biophysical Chemistry, University of Groningen, Nijenborgh 4, 9747 AG, Groningen, The Netherlands

    Alan Mark

  6. Department of Chemistry, Courant Institute of Mathematical Sciences, New York University, Mercer Street 251, New York, NY, 10012, USA

    Tamar Schlick

  7. FB Mathematik und Informatik, Freie Universität Berlin, Arnimallee 2-6, 14195, Berlin, Germany

    Christoph Schütte

  8. Department of Computer Science, Purdue University, N. University Street 250, West Lafayette, IN, 47907-2066, USA

    Robert Skeel

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Laserson, U., Gan, H.H., Schlick, T. (2006). Exploring the Connection Between Synthetic and Natural RNAs in Genomes: A Novel Computational Approach. In: Leimkuhler, B., et al. New Algorithms for Macromolecular Simulation. Lecture Notes in Computational Science and Engineering, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31618-3_3

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  • DOI: https://doi.org/10.1007/3-540-31618-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25542-0

  • Online ISBN: 978-3-540-31618-3

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