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Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument

Nature Biotechnology volume 29, pages 659–664 (2011)Cite this article

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Abstract

Several methods for characterizing DNA-protein interactions are available1,2,3,4,5,6, but none have demonstrated both high throughput and quantitative measurement of affinity. Here we describe 'high-throughput sequencing'-'fluorescent ligand interaction profiling' (HiTS-FLIP), a technique for measuring quantitative protein-DNA binding affinity at unprecedented depth. In this approach, the optics built into a high-throughput sequencer are used to visualize in vitro binding of a protein to sequenced DNA in a flow cell. Application of HiTS-FLIP to the protein Gcn4 (Gcn4p), the master regulator of the yeast amino acid starvation response7, yielded ∼440 million binding measurements, enabling determination of dissociation constants for all 12-mer sequences having submicromolar affinity. These data revealed a complex interdependency between motif positions, allowed improved discrimination of in vivo Gcn4p binding sites and regulatory targets relative to previous methods and showed that sets of genes with different promoter affinities to Gcn4p have distinct functions and expression kinetics. Broad application of this approach should increase understanding of the interactions that drive transcription.

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Figure 1: HiTS-FLIP method.
Figure 2: Positional interdependencies in the DNA binding affinity of Gcn4p.
Figure 3: HiTS-FLIP accurately predicts in vivo binding and regulation and reveals impact of lower-affinity binding sites.

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References

  1. Klug, S.J. & Famulok, M. All you wanted to know about SELEX. Mol. Biol. Rep. 20, 97–107 (1994).

    Article  CAS  PubMed  Google Scholar 

  2. Ren, B. et al. Genome-wide location and function of DNA binding proteins. Science 290, 2306–2309 (2000).

    Article  CAS  PubMed  Google Scholar 

  3. Mukherjee, S. et al. Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays. Nat. Genet. 36, 1331–1339 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Johnson, D.S., Mortazavi, A., Myers, R.M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Zhao, Y., Granas, D. & Stormo, G.D. Inferring binding energies from selected binding sites. PLOS Comput. Biol. 5, e1000590 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fordyce, P.M. et al. De novo identification and biophysical characterization of transcription-factor binding sites with microfluidic affinity analysis. Nat. Biotechnol. 28, 970–975 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Natarajan, K. et al. Transcriptional profiling shows that Gcn4p is a master regulator of gene expression during amino acid starvation in yeast. Mol. Cell. Biol. 21, 4347–4368 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Noyes, M.B. et al. Analysis of homeodomain specificities allows the family-wide prediction of preferred recognition sites. Cell 133, 1277–1289 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Badis, G. et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhu, C. et al. High-resolution DNA-binding specificity analysis of yeast transcription factors. Genome Res. 19, 556–566 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Gottardo, R. Modeling and analysis of ChIP-chip experiments. Methods Mol. Biol. 567, 133–143 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Bentley, D.R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Harbison, C.T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chua, G. et al. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl. Acad. Sci. USA 103, 12045–12050 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, H., Johnston, M. & Mitra, R.D. Calling cards for DNA-binding proteins. Genome Res. 17, 1202–1209 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shi, Y., Klutstein, M., Simon, I., Mitchell, T. & Bar-Joseph, Z. A combined expression-interaction model for inferring the temporal activity of transcription factors. J. Comput. Biol. 16, 1035–1049 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Herzog, B., Streckfuss-Bomeke, K. & Braus, G.H. A feedback circuit between transcriptional activation and self-destruction of Gcn4 separates its metabolic and morphogenic response in diploid yeasts. J. Mol. Biol. 405, 909–925 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Hope, I.A. & Struhl, K. Functional dissection of a eukaryotic transcriptional activator protein, GCN4 of yeast. Cell 46, 885–894 (1986).

    Article  CAS  PubMed  Google Scholar 

  19. Oliphant, A.R., Brandl, C.J. & Struhl, K. Defining the sequence specificity of DNA-binding proteins by selecting binding sites from random-sequence oligonucleotides: analysis of yeast GCN4 protein. Mol. Cell. Biol. 9, 2944–2949 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jorgensen, P. et al. The size of the nucleus increases as yeast cells grow. Mol. Biol. Cell 18, 3523–3532 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hill, D.E., Hope, I.A., Macke, J.P. & Struhl, K. Saturation mutagenesis of the yeast his3 regulatory site: requirements for transcriptional induction and for binding by GCN4 activator protein. Science 234, 451–457 (1986).

    Article  CAS  PubMed  Google Scholar 

  22. Sellers, J.W., Vincent, A.C. & Struhl, K. Mutations that define the optimal half-site for binding yeast GCN4 activator protein and identify an ATF/CREB-like repressor that recognizes similar DNA sites. Mol. Cell. Biol. 10, 5077–5086 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hollenbeck, J.J. & Oakley, M.G. GCN4 binds with high affinity to DNA sequences containing a single consensus half-site. Biochemistry 39, 6380–6389 (2000).

    Article  CAS  PubMed  Google Scholar 

  24. Cranz, S., Berger, C., Baici, A., Jelesarov, I. & Bosshard, H.R. Monomeric and dimeric bZIP transcription factor GCN4 bind at the same rate to their target DNA site. Biochemistry 43, 718–727 (2004).

    Article  CAS  PubMed  Google Scholar 

  25. Man, T.K. & Stormo, G.D. Non-independence of Mnt repressor-operator interaction determined by a new quantitative multiple fluorescence relative affinity (QuMFRA) assay. Nucleic Acids Res. 29, 2471–2478 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bulyk, M.L. Analysis of sequence specificities of DNA-binding proteins with protein binding microarrays. Methods Enzymol. 410, 279–299 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Genome Res. 16, 962–972 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Prill, R.J. et al. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE 5, e9202 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gertz, J., Siggia, E.D. & Cohen, B.A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).

    Article  CAS  PubMed  Google Scholar 

  30. Gaudet, J. & Mango, S.E. Regulation of organogenesis by the Caenorhabditis elegans FoxA protein PHA-4. Science 295, 821–825 (2002).

    Article  CAS  PubMed  Google Scholar 

  31. Cranz, S., Berger, C., Baici, A., Jelesarov, I. & Bosshard, H.R. Monomeric and dimeric bZIP transcription factor GCN4 bind at the same rate to their target DNA site. Biochemistry 43, 718–727 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Hellman, L.M. & Fried, M.G. Electrophoretic mobility shift assay (EMSA) for detecting protein-nucleic acid interactions. Nat. Protoc. 2, 1849–1861 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank G. Braus for providing the GCN4 containing plasmids, N. Clarke for sharing amino acid starvation microarray time course data, A. Keating and A. Reinke for advice on EMSA experiments, T. Theara, S. Levine and the Biomicro Center at Massachusetts Institute of Technology for help with Illumina sequencing technology and initial experiments, and L. Boyer, N. Clarke, W. Gilbert, P. Sharp and members of the Burge laboratory for helpful discussion and comments on the manuscript. This work was supported by a Human Frontiers Science Program long-term fellowship (R.N.), a Computational Science Graduate Fellowship from the Office of Science in the Department of Energy under contract DE-FG02-97ER25308 (R.C.F.), by a major equipment grant from the National Science Foundation (no. 0821391) and by grants from the US National Institutes of Health (C.B.B.).

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Author notes

  1. Razvan Nutiu and Robin C Friedman: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Razvan Nutiu, Robin C Friedman & Christopher B Burge

  2. Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Robin C Friedman & Christopher B Burge

  3. Illumina, Inc., Hayward, California, USA

    Shujun Luo, Irina Khrebtukova, David Silva, Robin Li, Lu Zhang & Gary P Schroth

  4. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Christopher B Burge

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Contributions

R.N., initial idea, experimental design and initial implementation, Gcn4p purification and EMSA experiments; R.C.F., experimental design and initial implementation, computational analysis of image data, all bioinformatic analyses; S.L. and G.P.S., experimental design and method optimization; I.K., D.S., R.L., L.Z., method optimization; C.B.B., initial idea, coordination of project, contributions to bioinformatics analyses.

Corresponding author

Correspondence to Christopher B Burge.

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Competing interests

S.L., I.K., D.S., R.L., L.Z. and G.P.S. are employees of Illumina.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 6, 8, Supplementary Methods, Discussion and Supplementary Figures 1–11 (PDF 1463 kb)

Supplementary Table 1

HiTS-FLIP dissociation constants for 8mers. (TXT 9 kb)

Supplementary Table 2

HiTS-FLIP dissociation constants for 9mers. (TXT 48 kb)

Supplementary Table 3

HiTS-FLIP dissociation constants for 10mers. (TXT 203 kb)

Supplementary Table 4

HiTS-FLIP dissociation constants for 11mers. (TXT 881 kb)

Supplementary Table 5

HiTS-FLIP dissociation constants for 12mers. (TXT 3887 kb)

Supplementary Table 7

Predicted promoter occupancies using 8mer Kd values or PBM data. (TXT 235 kb)

Supplementary Data (ZIP 17 kb)

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Nutiu, R., Friedman, R., Luo, S. et al. Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat Biotechnol 29, 659–664 (2011). https://doi.org/10.1038/nbt.1882

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