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Single Molecule Cluster Analysis dissects splicing pathway conformational dynamics

Nature Methods volume 12, pages 1077–1084 (2015)Cite this article

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Abstract

We report Single Molecule Cluster Analysis (SiMCAn), which utilizes hierarchical clustering of hidden Markov modeling–fitted single-molecule fluorescence resonance energy transfer (smFRET) trajectories to dissect the complex conformational dynamics of biomolecular machines. We used this method to study the conformational dynamics of a precursor mRNA during the splicing cycle as carried out by the spliceosome. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify the signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified an open conformation adopted late in splicing by a 3′ splice-site mutant, invoking a mechanism for substrate proofreading. SiMCAn enables rapid interpretation of complex single-molecule behaviors and should prove useful for the comprehensive analysis of a plethora of dynamic cellular machines.

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Figure 1: smFRET analysis of pre-mRNA splicing using the HMM.
Figure 2: SiMCAn workflow for sorting and clustering single-molecule-derived HMMs for common dynamic behaviors.
Figure 3: Validation of SiMCAn using a previously analyzed data set describing the transition from the purified Bact complex to the C complex5.
Figure 4: Clustering of clusters to identify 'clades' of similar behavior.
Figure 5: Cluster-occupancy histogram showing the raw fraction of molecules occupying each cluster for the late assembly stages of the splicing cycle.
Figure 6: Dynamic clusters of clade VII enriched in the Prp16DN-WCE conditions showed repeated excursions from the 0.85 state to lower FRET states.

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References

  1. Pitchiaya, S., Heinicke, L.A., Custer, T.C. & Walter, N.G. Single molecule fluorescence approaches shed light on intracellular RNAs. Chem. Rev. 114, 3224–3265 (2014).

    Article  CAS  Google Scholar 

  2. Mustoe, A.M., Brooks, C.L. & Al-Hashimi, H.M. Hierarchy of RNA functional dynamics. Annu. Rev. Biochem. 83, 441–466 (2014).

    Article  CAS  Google Scholar 

  3. Al-Hashimi, H.M. & Walter, N.G. RNA dynamics: it is about time. Curr. Opin. Struct. Biol. 18, 321–329 (2008).

    Article  CAS  Google Scholar 

  4. Cruz, J.A. & Westhof, E. The dynamic landscapes of RNA architecture. Cell 136, 604–609 (2009).

    Article  CAS  Google Scholar 

  5. Krishnan, R. et al. Biased Brownian ratcheting leads to pre-mRNA remodeling and capture prior to first-step splicing. Nat. Struct. Mol. Biol. 20, 1450–1457 (2013).

    Article  CAS  Google Scholar 

  6. Abelson, J. et al. Conformational dynamics of single pre-mRNA molecules during in vitro splicing. Nat. Struct. Mol. Biol. 17, 504–512 (2010).

    Article  CAS  Google Scholar 

  7. Blanco, M. & Walter, N.G. Analysis of complex single-molecule FRET time trajectories. Methods Enzymol. 472, 153–178 (2010).

    Article  CAS  Google Scholar 

  8. Walter, N.G., Huang, C.Y., Manzo, A.J. & Sobhy, M.A. Do-it-yourself guide: how to use the modern single-molecule toolkit. Nat. Methods 5, 475–489 (2008).

    Article  CAS  Google Scholar 

  9. Walter, N.G. & Bustamante, C. Introduction to single molecule imaging and mechanics: seeing and touching molecules one at a time. Chem. Rev. 114, 3069–3071 (2014).

    Article  CAS  Google Scholar 

  10. Crawford, D.J. et al. Single-molecule colocalization FRET evidence that spliceosome activation precedes stable approach of 5′ splice site and branch site. Proc. Natl. Acad. Sci. USA 110, 6783–6788 (2013).

    Article  CAS  Google Scholar 

  11. Brody, E. & Abelson, J. The “spliceosome”: yeast pre-messenger RNA associates with a 40S complex in a splicing-dependent reaction. Science 228, 963–967 (1985).

    Article  CAS  Google Scholar 

  12. Egecioglu, D.E. & Chanfreau, G. Proofreading and spellchecking: a two-tier strategy for pre-mRNA splicing quality control. RNA 17, 383–389 (2011).

    Article  CAS  Google Scholar 

  13. Semlow, D.R. & Staley, J.P. Staying on message: ensuring fidelity in pre-mRNA splicing. Trends Biochem. Sci. 37, 263–273 (2012).

    Article  CAS  Google Scholar 

  14. Staley, J.P. & Guthrie, C. Mechanical devices of the spliceosome: motors, clocks, springs, and things. Cell 92, 315–326 (1998).

    Article  CAS  Google Scholar 

  15. Wahl, M.C., Will, C.L. & Luhrmann, R. The spliceosome: design principles of a dynamic RNP machine. Cell 136, 701–718 (2009).

    Article  CAS  Google Scholar 

  16. Abelson, J., Hadjivassiliou, H. & Guthrie, C. Preparation of fluorescent pre-mRNA substrates for an smFRET study of pre-mRNA splicing in yeast. Methods Enzymol. 472, 31–40 (2010).

    Article  CAS  Google Scholar 

  17. Gopich, I.V. & Szabo, A. Theory of the energy transfer efficiency and fluorescence lifetime distribution in single-molecule FRET. Proc. Natl. Acad. Sci. USA 109, 7747–7752 (2012).

    Article  CAS  Google Scholar 

  18. Keller, B.G. et al. Complex RNA folding kinetics revealed by single-molecule FRET and hidden Markov models. J. Am. Chem. Soc. 136, 4534–4543 (2014).

    Article  CAS  Google Scholar 

  19. Greenfeld, M., Pavlichin, D.S., Mabuchi, H. & Herschlag, D. Single Molecule Analysis Research Tool (SMART): an integrated approach for analyzing single molecule data. PLoS One 7, e30024 (2012).

    Article  CAS  Google Scholar 

  20. Bruno, A.E. et al. Comparing chemistry to outcome: the development of a chemical distance metric, coupled with clustering and hierarchal visualization applied to macromolecular crystallography. PLoS One 9, e100782 (2014).

    Article  Google Scholar 

  21. Mall, R., Langone, R. & Suykens, J.A. Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks. PLoS One 9, e99966 (2014).

    Article  Google Scholar 

  22. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Series B Stat. Methodol. 63, 411–423 (2001).

    Article  Google Scholar 

  23. Bronson, J.E. et al. Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data. Biophys. J. 97, 3196–3205 (2009).

    Article  CAS  Google Scholar 

  24. Kim, S.H. & Lin, R.J. Spliceosome activation by PRP2 ATPase prior to the first transesterification reaction of pre-mRNA splicing. Mol. Cell. Biol. 16, 6810–6819 (1996).

    Article  CAS  Google Scholar 

  25. Warkocki, Z. et al. Reconstitution of both steps of Saccharomyces cerevisiae splicing with purified spliceosomal components. Nat. Struct. Mol. Biol. 16, 1237–1243 (2009).

    Article  CAS  Google Scholar 

  26. Koodathingal, P., Novak, T., Piccirilli, J.A. & Staley, J.P. The DEAH box ATPases Prp16 and Prp43 cooperate to proofread 5′ splice site cleavage during pre-mRNA splicing. Mol. Cell 39, 385–395 (2010).

    Article  CAS  Google Scholar 

  27. Schneider, S., Hotz, H.R. & Schwer, B. Characterization of dominant-negative mutants of the DEAH-box splicing factors Prp22 and Prp16. J. Biol. Chem. 277, 15452–15458 (2002).

    Article  CAS  Google Scholar 

  28. Rymond, B.C. & Rosbash, M. Cleavage of 5′ splice site and lariat formation are independent of 3′ splice site in yeast mRNA splicing. Nature 317, 735–737 (1985).

    Article  CAS  Google Scholar 

  29. Ohrt, T. et al. Molecular dissection of step 2 catalysis of yeast pre-mRNA splicing investigated in a purified system. RNA 19, 902–915 (2013).

    Article  CAS  Google Scholar 

  30. Umen, J.G. & Guthrie, C. Prp16p, Slu7p, and Prp8p interact with the 3′ splice site in two distinct stages during the second catalytic step of pre-mRNA splicing. RNA 1, 584–597 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Mayas, R.M., Maita, H. & Staley, J.P. Exon ligation is proofread by the DExD/H-box ATPase Prp22p. Nat. Struct. Mol. Biol. 13, 482–490 (2006).

    Article  CAS  Google Scholar 

  32. Woese, C.R. & Fox, G.E. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc. Natl. Acad. Sci. USA 74, 5088–5090 (1977).

    Article  CAS  Google Scholar 

  33. Hoskins, A.A. et al. Ordered and dynamic assembly of single spliceosomes. Science 331, 1289–1295 (2011).

    Article  CAS  Google Scholar 

  34. Stevens, S.W. & Abelson, J. Yeast pre-mRNA splicing: methods, mechanisms, and machinery. Methods Enzymol. 351, 200–220 (2002).

    Article  CAS  Google Scholar 

  35. Roy, R., Hohng, S. & Ha, T. A practical guide to single-molecule FRET. Nat. Methods 5, 507–516 (2008).

    Article  CAS  Google Scholar 

  36. Widom, J.R., Dhakal, S., Heinicke, L.A. & Walter, N.G. Single-molecule tools for enzymology, structural biology, systems biology and nanotechnology: an update. Arch. Toxicol. 88, 1965–1985 (2014).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank A. Price (University of California, San Francisco) for providing native gel analysis of CC2 formation using Ubc4; D.R. Semlow and J.P. Staley (University of Chicago) for providing the dominant-negative Prp16 protein expression plasmid; N.N. Vo for compiling all Matlab scripts of SiMCAn into a GUI; and C. Guthrie, D.R. Semlow, J.P. Staley and A.A. Hoskins for providing valuable comments on the manuscript. The authors acknowledge funding from the US National Institutes of Health (grant R01GM098023 to N.G.W. and J.A.), the National Heart, Lung and Blood Institute (grant R01HL111527-01 to A.L.) and the National Science Foundation through the National Evolutionary Synthesis Center (NESCent) (grant NSF\#EF-0905606 to J.S.M.).

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Author notes

  1. Mario R Blanco & Joshua S Martin

    Present address: Present addresses: Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA (M.R.B.); National Evolutionary Synthesis Center, Durham, North Carolina, USA (J.S.M.).,

  2. Mario R Blanco, Joshua S Martin and Matthew L Kahlscheuer: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Chemistry, Single Molecule Analysis Group, University of Michigan, Ann Arbor, Michigan, USA

    Mario R Blanco, Matthew L Kahlscheuer, Ramya Krishnan & Nils G Walter

  2. Cellular and Molecular Biology, University of Michigan, Ann Arbor, Michigan, USA

    Mario R Blanco

  3. Biology Department, University of North Carolina, Chapel Hill, North Carolina, USA

    Joshua S Martin & Alain Laederach

  4. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA

    John Abelson

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Contributions

M.L.K. and R.K. performed in vitro splicing verification assays. M.L.K. and M.R.B. performed single-molecule experiments and performed data analysis. M.L.K. expressed and purified the Prp16DN protein. M.R.B. and J.S.M. wrote and developed the Matlab scripts for SiMCAn. M.L.K. prepared all fluorescent substrates and yeast whole-cell extracts. M.R.B., J.S.M., M.L.K., J.A., A.L. and N.G.W. jointly wrote the manuscript.

Corresponding author

Correspondence to Nils G Walter.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19, Supplementary Tables 1–3 and Supplementary Notes 1–5 (PDF 11322 kb)

Supplementary Software

SiMCAn MATLAB scripts (ZIP 1156 kb)

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Blanco, M., Martin, J., Kahlscheuer, M. et al. Single Molecule Cluster Analysis dissects splicing pathway conformational dynamics. Nat Methods 12, 1077–1084 (2015). https://doi.org/10.1038/nmeth.3602

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