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

  • Uri Laserson
  • Hin Hark Gan
  • Tamar Schlick
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 49)

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.

Keywords

Candidate Sequence Archaeal Genome Minimum Energy Structure Natural RNAs Suboptimal Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    D. P. Arya, R. L. Coffee, Jr., and I. Charles. Neomycin-induced hybrid triplex formation. J. Amer. Chem. Soc., 123:11093–11094, 2001.CrossRefGoogle Scholar
  2. [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.CrossRefGoogle Scholar
  3. [3]
    J. P. Bachellerie, J. Cavaille, and A. Huttenhofer. The expanding snoRNA world. Biochimie, 84:775–90, 2002.CrossRefGoogle Scholar
  4. [4]
    A. L. Barabási and E. Bonabeau. Scale-free networks. Sci. Amer., 288:60–69, 2003.CrossRefGoogle Scholar
  5. [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. [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.CrossRefGoogle Scholar
  7. [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.CrossRefGoogle Scholar
  8. [8]
    K. J. Devlin. Mathematics: the New Golden Age. Penguin, London, 1988.MATHGoogle Scholar
  9. [9]
    J. A. Doudna and T. R. Cech. The chemical repertoire of natural ribozymes. Nature, 418:222–228, 2002.CrossRefGoogle Scholar
  10. [10]
    A. D. Ellington and J. W. Szostak. In vitro selection of RNA molecules that bind specific ligands. Nature, 346:818–822, 1990.CrossRefGoogle Scholar
  11. [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.CrossRefGoogle Scholar
  12. [12]
    W. Fontana, D. A. Konings, P. F. Stadler, and P. Schuster. Statistics of RNA secondary structures. Biopolymers, 33:1389–1404, 1993.CrossRefGoogle Scholar
  13. [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.CrossRefGoogle Scholar
  14. [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.CrossRefGoogle Scholar
  15. [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.CrossRefGoogle Scholar
  16. [16]
    W. W. Gibbs. The unseen genome: Gems among the junk. Sci. Amer., 289:46–53, 2003.Google Scholar
  17. [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.CrossRefGoogle Scholar
  18. [18]
    J. Gross and J. Yellen. Graph Theory and its Applications. CRC Press, Boca Raton, FL, 1999.MATHGoogle Scholar
  19. [19]
    F. Harary. The number of homeomorphically irreducible trees and other species. Acta Math., 101:141–162, 1959.MATHMathSciNetGoogle Scholar
  20. [20]
    F. Harary. Graph Theory. Addison-Wesley, Reading, MA, 1969.Google Scholar
  21. [21]
    T. Hermann and D. J. Patel. Adaptive recognition by nucleic acid aptamers. Science, 287:820–825, 2000.CrossRefGoogle Scholar
  22. [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/CrossRefGoogle Scholar
  23. [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.CrossRefGoogle Scholar
  24. [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.CrossRefGoogle Scholar
  25. [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.CrossRefGoogle Scholar
  26. [26]
    N. Kim, N. Shiffeldrim, H. H. Gan, and T. Schlick. Candidates for novel RNA topologies. J. Mol. Biol., 341:1129–1144, 2004.CrossRefGoogle Scholar
  27. [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.CrossRefGoogle Scholar
  28. [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.CrossRefGoogle Scholar
  29. [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.CrossRefGoogle Scholar
  30. [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. [31]
    J. S. Mattick. Non-coding RNAs: the architects of eukaryotic complexity. EMBO Rep., 2:986–991, 2001.CrossRefGoogle Scholar
  32. [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. [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.CrossRefGoogle Scholar
  34. [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.CrossRefGoogle Scholar
  35. [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. [36]
    N. Piganeau and R. Schroeder. Aptamer structures: A preview into regulatory pathways? Chem. Biol., 10:103–104, 2003.CrossRefGoogle Scholar
  37. [37]
    E. Rivas and S. R. Eddy. A dynamic programming algorithm for RNA structure prediction including pseudoknots. J. Mol. Biol., 285:2053–2068, 1999.CrossRefGoogle Scholar
  38. [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.CrossRefGoogle Scholar
  39. [39]
    K. Salehi-Ashtiani and J. W. Szostak. In vitro evolution suggests multiple origins for the hammerhead ribozyme. Nature, 414:82–84, 2001.CrossRefGoogle Scholar
  40. [40]
    M. Sassanfar and J. W. Szostak. An RNA motif that binds ATP. Nature, 364:550–553, 1993.CrossRefGoogle Scholar
  41. [41]
    T. Schlick. Molecular Modeling: An Interdisciplinary Guide. Springer-Verlag, New York, NY, 2002.MATHGoogle Scholar
  42. [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. [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. [44]
    G. A. Soukup and R. R. Breaker. Engineering precision RNA molecular switches. Proc. Natl. Acad. Sci. USA, 96:3584–3589, 1999.CrossRefGoogle Scholar
  45. [45]
    G. A. Soukup and R. R. Breaker. Nucleic acid molecular switches. Trends Biotechnol., 17:469–476, 1999.CrossRefGoogle Scholar
  46. [46]
    G. A. Soukup and R. R. Breaker. Allosteric nucleic acid catalysts. Curr. Opin. Struct. Biol., 10:318–325, 2000.CrossRefGoogle Scholar
  47. [47]
    S. Spiegelman. An approach to the experimental analysis of precellular evolution. Q. Rev. Biophys., 4:213–253, 1971.CrossRefGoogle Scholar
  48. [48]
    G. Storz. An expanding universe of noncoding RNAs. Science, 296:1260–1263, 2002.CrossRefGoogle Scholar
  49. [49]
    V. Tereshko, E. Skripkin, and D. J. Patel. Encapsulating streptomycin within a small 40-mer RNA. Chem. Biol., 10:175–187, 2003.CrossRefGoogle Scholar
  50. [50]
    I. Tinoco, Jr. and C. Bustamante. How RNA folds. J. Mol. Biol., 293:271–281, 1999.CrossRefGoogle Scholar
  51. [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. [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. [53]
    D. S. Wilson and J. W. Szostak. In vitro selection of functional nucleic acids. Ann. Rev. Biochem., 68:611–647, 1999.CrossRefGoogle Scholar
  54. [54]
    W. Winkler, A. Nahvi, and R. R. Breaker. Thiamine derivatives bind messenger RNAs directly to regulate bacterial expression. Nature, 419:952–956, 2002.CrossRefGoogle Scholar
  55. [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.CrossRefGoogle Scholar
  56. [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

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Uri Laserson
    • 1
    • 2
  • Hin Hark Gan
    • 1
  • Tamar Schlick
    • 1
    • 2
  1. 1.Department of ChemistryNew York UniversityNew YorkUSA
  2. 2.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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