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Potent effect of target structure on microRNA function

Nature Structural & Molecular Biology volume 14, pages 287–294 (2007)Cite this article

Abstract

MicroRNAs (miRNAs) are small noncoding RNAs that repress protein synthesis by binding to target messenger RNAs. We investigated the effect of target secondary structure on the efficacy of repression by miRNAs. Using structures predicted by the Sfold program, we model the interaction between an miRNA and a target as a two-step hybridization reaction: nucleation at an accessible target site followed by hybrid elongation to disrupt local target secondary structure and form the complete miRNA-target duplex. This model accurately accounts for the sensitivity to repression by let-7 of various mutant forms of the Caenorhabditis elegans lin-41 3′ untranslated region and for other experimentally tested miRNA-target interactions in C. elegans and Drosophila melanogaster. These findings indicate a potent effect of target structure on target recognition by miRNAs and establish a structure-based framework for genome-wide identification of animal miRNA targets.

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Figure 1: A two-step model for hybridization between a structured mRNA and a partially complementary miRNA, illustrated for a single structural conformation of the target.
Figure 2: Target accessibility profiling by S fold.
Figure 3: The average ΣΔGtotal for miRNAs compared with that calculated for randomers, for positive miRNA-target interactions supported either by genetic epistasis evidence or by nongenetic evidence, and for the set of 12 putative lsy-6–target pairs predicted by conserved seed matching but having negative interactions in vivo27 (Table 2).
Figure 4: Linear regression prediction of in vivo repression sensitivity (measured by β-galactosidase (β-gal) expression ratios in adult and larval stages) by the ΣΔGtotal for the lin-41 3′ UTR mutant constructs (see Table 3).

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References

  1. Ambros, V. The functions of animal microRNAs. Nature 431, 350–355 (2004).

    Article  CAS  Google Scholar 

  2. Bentwich, I. et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat. Genet. 37, 766–770 (2005).

    Article  CAS  Google Scholar 

  3. Boehm, M. & Slack, F. A developmental timing microRNA and its target regulate life span in C. elegans. Science 310, 1954–1957 (2005).

    Article  CAS  Google Scholar 

  4. Calin, G.A. & Croce, C.M. MicroRNA-cancer connection: the beginning of a new tale. Cancer Res. 66, 7390–7394 (2006).

    Article  CAS  Google Scholar 

  5. Cuellar, T.L. & McManus, M.T. MicroRNAs and endocrine biology. J. Endocrinol. 187, 327–332 (2005).

    Article  CAS  Google Scholar 

  6. Brennecke, J., Stark, A., Russell, R.B. & Cohen, S.M. Principles of microRNA-target recognition. PLoS Biol. 3, e85 (2005).

    Article  Google Scholar 

  7. Sethupathy, P., Corda, B. & Hatzigeorgiou, A.G. TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 12, 192–197 (2006).

    Article  CAS  Google Scholar 

  8. Jones-Rhoades, M.W. & Bartel, D.P. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol. Cell 14, 787–799 (2004).

    Article  CAS  Google Scholar 

  9. Lai, E.C. Predicting and validating microRNA targets. Genome Biol. 5, 115 (2004).

    Article  Google Scholar 

  10. Lewis, B.P., Burge, C.B. & Bartel, D.P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005).

    Article  CAS  Google Scholar 

  11. Doench, J.G. & Sharp, P.A. Specificity of microRNA target selection in translational repression. Genes Dev. 18, 504–511 (2004).

    Article  CAS  Google Scholar 

  12. Kiriakidou, M. et al. A combined computational-experimental approach predicts human microRNA targets. Genes Dev. 18, 1165–1178 (2004).

    Article  CAS  Google Scholar 

  13. Lewis, B.P., Shih, I.H., Jones-Rhoades, M.W., Bartel, D.P. & Burge, C.B. Prediction of mammalian microRNA targets. Cell 115, 787–798 (2003).

    Article  CAS  Google Scholar 

  14. Rajewsky, N. microRNA target predictions in animals. Nat. Genet. 38 Suppl, S8–S13 (2006).

    Article  CAS  Google Scholar 

  15. Vella, M.C., Choi, E.Y., Lin, S.Y., Reinert, K. & Slack, F.J. The C. elegans microRNA let-7 binds to imperfect let-7 complementary sites from the lin-41 3′UTR. Genes Dev. 18, 132–137 (2004).

    Article  CAS  Google Scholar 

  16. Vella, M.C., Reinert, K. & Slack, F.J. Architecture of a validated microRNA:target interaction. Chem. Biol. 11, 1619–1623 (2004).

    Article  CAS  Google Scholar 

  17. Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31, 3406–3415 (2003).

    Article  CAS  Google Scholar 

  18. Robins, H., Li, Y. & Padgett, R.W. Incorporating structure to predict microRNA targets. Proc. Natl. Acad. Sci. USA 102, 4006–4009 (2005).

    Article  CAS  Google Scholar 

  19. Rehmsmeier, M., Steffen, P., Hochsmann, M. & Giegerich, R. Fast and effective prediction of microRNA-target duplexes. RNA 10, 1507–1517 (2004).

    Article  CAS  Google Scholar 

  20. Ding, Y., Chan, C.Y. & Lawrence, C.E. Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res. 32, W135–W141 (2004).

    Article  CAS  Google Scholar 

  21. Ding, Y. & Lawrence, C.E. Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond. Nucleic Acids Res. 29, 1034–1046 (2001).

    Article  CAS  Google Scholar 

  22. Ding, Y. & Lawrence, C.E. A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res. 31, 7280–7301 (2003).

    Article  CAS  Google Scholar 

  23. Hargittai, M.R., Gorelick, R.J., Rouzina, I. & Musier-Forsyth, K. Mechanistic insights into the kinetics of HIV-1 nucleocapsid protein-facilitated tRNA annealing to the primer binding site. J. Mol. Biol. 337, 951–968 (2004).

    Article  CAS  Google Scholar 

  24. Xia, T. et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 37, 14719–14735 (1998).

    Article  CAS  Google Scholar 

  25. Clote, P., Ferre, F., Kranakis, E. & Krizanc, D. Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. RNA 11, 578–591 (2005).

    Article  CAS  Google Scholar 

  26. Seggerson, K., Tang, L. & Moss, E.G. Two genetic circuits repress the Caenorhabditis elegans heterochronic gene lin-28 after translation initiation. Dev. Biol. 243, 215–225 (2002).

    Article  CAS  Google Scholar 

  27. Didiano, D. & Hobert, O. Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat. Struct. Mol. Biol. 13, 849–851 (2006).

    Article  CAS  Google Scholar 

  28. Johnston, R.J. & Hobert, O. A microRNA controlling left/right neuronal asymmetry in Caenorhabditis elegans. Nature 426, 845–849 (2003).

    Article  CAS  Google Scholar 

  29. Ding, Y., Chan, C.Y. & Lawrence, C.E. RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA 11, 1157–1166 (2005).

    Article  CAS  Google Scholar 

  30. Ding, Y., Chan, C.Y. & Lawrence, C.E. Clustering of RNA secondary structures with application to messenger RNAs. J. Mol. Biol. 359, 554–571 (2006).

    Article  CAS  Google Scholar 

  31. Overhoff, M. et al. Local RNA target structure influences siRNA efficacy: a systematic global analysis. J. Mol. Biol. 348, 871–881 (2005).

    Article  CAS  Google Scholar 

  32. Schubert, S., Grunweller, A., Erdmann, V.A. & Kurreck, J. Local RNA target structure influences siRNA efficacy: systematic analysis of intentionally designed binding regions. J. Mol. Biol. 348, 883–893 (2005).

    Article  CAS  Google Scholar 

  33. Zhao, Y., Samal, E. & Srivastava, D. Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature 436, 214–220 (2005).

    Article  CAS  Google Scholar 

  34. Farh, K.K. et al. The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310, 1817–1821 (2005).

    Article  CAS  Google Scholar 

  35. Enright, A.J. et al. MicroRNA targets in Drosophila. Genome Biol. 5, R1 (2003).

    Article  Google Scholar 

  36. Krek, A. et al. Combinatorial microRNA target predictions. Nat. Genet. 37, 495–500 (2005).

    Article  CAS  Google Scholar 

  37. Miranda, K.C. et al. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell 126, 1203–1217 (2006).

    Article  CAS  Google Scholar 

  38. Cullen, B.R. Viruses and microRNAs. Nat. Genet. 38 Suppl, S25–S30 (2006).

    Article  CAS  Google Scholar 

  39. Paillart, J.C., Skripkin, E., Ehresmann, B., Ehresmann, C. & Marquet, R. A loop-loop “kissing” complex is the essential part of the dimer linkage of genomic HIV-1 RNA. Proc. Natl. Acad. Sci. USA 93, 5572–5577 (1996).

    Article  CAS  Google Scholar 

  40. Reynaldo, L.P., Vologodskii, A.V., Neri, B.P. & Lyamichev, V.I. The kinetics of oligonucleotide replacements. J. Mol. Biol. 297, 511–520 (2000).

    Article  CAS  Google Scholar 

  41. Milner, N., Mir, K.U. & Southern, E.M. Selecting effective antisense reagents on combinatorial oligonucleotide arrays. Nat. Biotechnol. 15, 537–541 (1997).

    Article  CAS  Google Scholar 

  42. Kolb, F.A. et al. Bulged residues promote the progression of a loop-loop interaction to a stable and inhibitory antisense-target RNA complex. Nucleic Acids Res. 29, 3145–3153 (2001).

    Article  CAS  Google Scholar 

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Acknowledgements

We acknowledge the Computational Molecular Biology and Statistics Core at the Wadsworth Center for providing computing resources. This work was supported in part by US National Science Foundation grants DMS-0200970 and DBI-0650991 and US National Institutes of Health grant GM068726 to Y.D., and by US National Institutes of Health grants GM34028 and GM066826 to V.A. We thank F. Slack of Yale University for gifts of plasmids, and A. Lee, G. Ambros and members of the Ambros lab for technical help and advice.

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Authors and Affiliations

  1. New York State Department of Health, Wadsworth Center, 150 New Scotland Avenue, Albany, 12208, New York, USA

    Dang Long, Chi Yu Chan & Ye Ding

  2. Department of Genetics, Dartmouth Medical School, Hanover, 03755, New Hampshire, USA

    Rosalind Lee, Peter Williams & Victor Ambros

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  1. Dang Long
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Contributions

D.L. and Y.D. designed the algorithm and performed computational modeling of RNA structure and thermodynamics, R.L. and V.A. analyzed lin-41 reporter genes in C. elegans, P.W. performed computational modeling and C.Y.C. developed the web interface.

Corresponding authors

Correspondence to Victor Ambros or Ye Ding.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Analysis of alternative initiation energy values. (PDF 65 kb)

Supplementary Table 1

Open blocks of nucleotides in lin-41 UTR constructs. (PDF 83 kb)

Supplementary Table 2

Analysis of published microRNA-target interactions. (PDF 54 kb)

Supplementary Table 3

Comparison of folding programs. (PDF 37 kb)

Supplementary Table 4

UTR sequences of lac-Z reporter constructs. (PDF 31 kb)

Supplementary Table 5

Spacer sequences. (PDF 26 kb)

Supplementary Data (PDF 112 kb)

Supplementary Methods (PDF 147 kb)

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Long, D., Lee, R., Williams, P. et al. Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14, 287–294 (2007). https://doi.org/10.1038/nsmb1226

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