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Genomic footprinting

Nature Methods volume 13, pages 213–221 (2016)Cite this article

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Abstract

The advent of DNA footprinting with DNase I more than 35 years ago enabled the systematic analysis of protein-DNA interactions, and the technique has been instrumental in the decoding of cis-regulatory elements and the identification and characterization of transcription factors and other DNA-binding proteins. The ability to analyze millions of individual genomic cleavage events via massively parallel sequencing has enabled in vivo DNase I footprinting on a genomic scale, offering the potential for global analysis of transcription factor occupancy in a single experiment. Genomic footprinting has opened unique vistas on the organization, function and evolution of regulatory DNA; however, the technology is still nascent. Here we discuss both prospects and challenges of genomic footprinting, as well as considerations for its application to complex genomes.

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Figure 1: Principles of a DNase I footprinting experiment.
Figure 2: Resolving cis-regulatory architecture at nucleotide resolution in individual regulatory regions.
Figure 3: An illustrative example of interpretation of DNase I cleavage to determine TF occupancy.
Figure 4: De novo versus TF recognition site–directed analysis of TF occupancy.
Figure 5: Modeling variation in DNase I cleavages rates due to primary DNA structure.

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References

  1. Ptashne, M. & Gann, A. Genes and Signals (Cold Spring Harbor Laboratory Press, 2002).

  2. Galas, D.J. & Schmitz, A. DNAse footprinting: a simple method for the detection of protein-DNA binding specificity. Nucleic Acids Res. 5, 3157–3170 (1978).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Church, G.M., Ephrussi, A., Gilbert, W. & Tonegawa, S. Cell-type-specific contacts to immunoglobulin enhancers in nuclei. Nature 313, 798–801 (1985).

    Article  CAS  PubMed  Google Scholar 

  4. Jackson, P.D. & Felsenfeld, G. A method for mapping intranuclear protein-DNA interactions and its application to a nuclease hypersensitive site. Proc. Natl. Acad. Sci. USA 82, 2296–2300 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zinn, K. & Maniatis, T. Detection of factors that interact with the human beta-interferon regulatory region in vivo by DNAase I footprinting. Cell 45, 611–618 (1986).

    Article  CAS  PubMed  Google Scholar 

  6. Ephrussi, A., Church, G.M., Tonegawa, S. & Gilbert, W. B lineage–specific interactions of an immunoglobulin enhancer with cellular factors in vivo. Science 227, 134–140 (1985).

    Article  CAS  PubMed  Google Scholar 

  7. Becker, P.B., Ruppert, S. & Schütz, G. Genomic footprinting reveals cell type-specific DNA binding of ubiquitous factors. Cell 51, 435–443 (1987).

    Article  CAS  PubMed  Google Scholar 

  8. Hesselberth, J.R. et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Boyle, A.P. et al. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res. 21, 456–464 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sullivan, A.M. et al. Mapping and dynamics of regulatory DNA and transcription factor networks in A. thaliana. Cell Rep. 8, 2015–2030 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Stergachis, A.B. et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature 515, 365–370 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Galas, D.J. The invention of footprinting. Trends Biochem. Sci. 26, 690–693 (2001).

    Article  CAS  PubMed  Google Scholar 

  15. Gross, D.S. & Garrard, W.T. Nuclease hypersensitive sites in chromatin. Annu. Rev. Biochem. 57, 159–197 (1988).

    Article  CAS  PubMed  Google Scholar 

  16. Tullius, T.D. Physical studies of protein-DNA complexes by footprinting. Annu. Rev. Biophys. Biophys. Chem. 18, 213–237 (1989).

    Article  CAS  PubMed  Google Scholar 

  17. Hampshire, A.J., Rusling, D.A., Broughton-Head, V.J. & Fox, K.R. Footprinting: a method for determining the sequence selectivity, affinity and kinetics of DNA-binding ligands. Methods 42, 128–140 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Weiss, B., Live, T.R. & Richardson, C.C. Enzymatic breakage and joining of deoxyribonucleic acid. V. End group labeling and analysis of deoxyribonucleic acid containing single stranded breaks. J. Biol. Chem. 243, 4530–4542 (1968).

    CAS  PubMed  Google Scholar 

  19. Ehrlich, S.D., Bertazzoni, U. & Bernardi, G. The specificity of pancreatic deoxyribonuclease. Eur. J. Biochem. 40, 143–147 (1973).

    Article  CAS  PubMed  Google Scholar 

  20. Dingwall, C., Lomonossoff, G.P. & Laskey, R.A. High sequence specificity of micrococcal nuclease. Nucleic Acids Res. 9, 2659–2673 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lazarovici, A. et al. Probing DNA shape and methylation state on a genomic scale with DNase I. Proc. Natl. Acad. Sci. USA 110, 6376–6381 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Johnson, A.D., Meyer, B.J. & Ptashne, M. Interactions between DNA-bound repressors govern regulation by the lambda phage repressor. Proc. Natl. Acad. Sci. USA 76, 5061–5065 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Payvar, F. et al. Sequence-specific binding of glucocorticoid receptor to MTV DNA at sites within and upstream of the transcribed region. Cell 35, 381–392 (1983).

    Article  CAS  PubMed  Google Scholar 

  26. Dynan, W.S. & Tjian, R. The promoter-specific transcription factor Sp1 binds to upstream sequences in the SV40 early promoter. Cell 35, 79–87 (1983).

    Article  CAS  PubMed  Google Scholar 

  27. Church, G.M. & Gilbert, W. Genomic sequencing. Proc. Natl. Acad. Sci. USA 81, 1991–1995 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mueller, P.R. & Wold, B. In vivo footprinting of a muscle specific enhancer by ligation mediated PCR. Science 246, 780–786 (1989).

    Article  CAS  PubMed  Google Scholar 

  29. Warshawsky, D. & Miller, L. Mapping protein-DNA interactions using in vivo footprinting. Methods Mol. Biol. 127, 199–212 (1999).

    Article  CAS  PubMed  Google Scholar 

  30. Weston, S.A., Lahm, A. & Suck, D. X-ray structure of the DNase I-d(GGTATACC)2 complex at 2.3 A resolution. J. Mol. Biol. 226, 1237–1256 (1992).

    Article  CAS  PubMed  Google Scholar 

  31. Drew, H.R. & Travers, A.A. DNA structural variations in the E. coli tyrT promoter. Cell 37, 491–502 (1984).

    Article  CAS  PubMed  Google Scholar 

  32. Melgar, E. & Goldthwait, D.A. Deoxyribonucleic acid nucleases. II. The effects of metals on the mechanism of action of deoxyribonuclease I. J. Biol. Chem. 243, 4409–4416 (1968).

    CAS  PubMed  Google Scholar 

  33. Campbell, V.W. & Jackson, D.A. The effect of divalent cations on the mode of action of DNase I. The initial reaction products produced from covalently closed circular DNA. J. Biol. Chem. 255, 3726–3735 (1980).

    CAS  PubMed  Google Scholar 

  34. Lutter, L.C. Precise location of DNase I cutting sites in the nucleosome core determined by high resolution gel electrophoresis. Nucleic Acids Res. 6, 41–56 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rhodes, D. & Klug, A. Helical periodicity of DNA determined by enzyme digestion. Nature 286, 573–578 (1980).

    Article  CAS  PubMed  Google Scholar 

  36. Stamatoyannopoulos, J.A., Goodwin, A., Joyce, T. & Lowrey, C.H. NF-E2 and GATA binding motifs are required for the formation of DNase I hypersensitive site 4 of the human beta-globin locus control region. EMBO J. 14, 106–116 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ptashne, M. A Genetic Switch (Cold Spring Harbor Laboratory Press, 2004).

  38. Dabrowiak, J.C., Goodisman, J. & Ward, B. Quantitative DNA footprinting. Methods Mol. Biol. 90, 23–42 (1997).

    CAS  PubMed  Google Scholar 

  39. Pellerin, I., Schnabel, C., Catron, K.M. & Abate, C. Hox proteins have different affinities for a consensus DNA site that correlate with the positions of their genes on the hox cluster. Mol. Cell. Biol. 14, 4532–4545 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Renda, M. et al. Critical DNA binding interactions of the insulator protein CTCF: a small number of zinc fingers mediate strong binding, and a single finger-DNA interaction controls binding at imprinted loci. J. Biol. Chem. 282, 33336–33345 (2007).

    Article  CAS  PubMed  Google Scholar 

  41. N'soukpoé-Kossi, C.N., Diamantoglou, S. & Tajmir-Riahi, H.A. DNase I-DNA interaction alters DNA and protein conformations. Biochem. Cell Biol. 86, 244–250 (2008).

    Article  CAS  PubMed  Google Scholar 

  42. Coulon, A., Chow, C.C., Singer, R.H. & Larson, D.R. Eukaryotic transcriptional dynamics: from single molecules to cell populations. Nat. Rev. Genet. 14, 572–584 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. John, S. et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat. Genet. 43, 264–268 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pique-Regi, R. et al. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447–455 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sherwood, R.I. et al. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol. 32, 171–178 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Siersbæk, R. et al. Molecular architecture of transcription factor hotspots in early adipogenesis. Cell Rep. 7, 1434–1442 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. He, H.H. et al. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat. Methods 11, 73–78 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Sung, M.-H., Guertin, M.J., Baek, S. & Hager, G.L. DNase footprint signatures are dictated by factor dynamics and DNA sequence. Mol. Cell 56, 275–285 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sabo, P.J. et al. Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays. Nat. Methods 3, 511–518 (2006).

    Article  CAS  PubMed  Google Scholar 

  51. Vierstra, J., Wang, H., John, S., Sandstrom, R. & Stamatoyannopoulos, J.A. Coupling transcription factor occupancy to nucleosome architecture with DNase-FLASH. Nat. Methods 11, 66–72 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. Roadmap Epigenomics Consortium. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  53. Kähärä, J. & Lähdesmäki, H. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data. Bioinformatics 31, 2852–2859 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012).

    Article  CAS  Google Scholar 

  55. Piper, J. et al. Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data. Nucleic Acids Res. 41, e201 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gusmao, E.G., Dieterich, C., Zenke, M. & Costa, I.G. Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications. Bioinformatics 30, 3143–3151 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Chen, X., Hoffman, M.M., Bilmes, J.A., Hesselberth, J.R. & Noble, W.S. A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data. Bioinformatics 26, i334–i342 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Frank, C.L., Crawford, G.E. & Ohler, U. Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection. Nucleic Acids Res. 42, 11865–11878 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Teytelman, L., Thurtle, D.M., Rine, J. & van Oudenaarden, A. Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins. Proc. Natl. Acad. Sci. USA 110, 18602–18607 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lickwar, C.R., Mueller, F., Hanlon, S.E., McNally, J.G. & Lieb, J.D. Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function. Nature 484, 251–255 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Koohy, H., Down, T.A. & Hubbard, T.J. Chromatin accessibility data sets show bias due to sequence specificity of the DNase I enzyme. PLoS One 8, e69853 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hashimoto, T.B., Edwards, M.D. & Gifford, D.K. Universal count correction for high-throughput sequencing. PLoS Comput. Biol. 10, e1003494 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    Article  CAS  PubMed  Google Scholar 

  65. Jolma, A. et al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527, 384–388 (2015).

    Article  CAS  PubMed  Google Scholar 

  66. Luo, K. & Hartemink, A.J. Using DNase digestion data to accurately identify transcription factor binding sites. Pac. Symp. Biocomput. 2013, 80–91 (2013).

    Google Scholar 

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Acknowledgements

This work was supported by the US National Human Genome Research Institute (grant U54HG007010 to J.A.S.).

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Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    Jeff Vierstra & John A Stamatoyannopoulos

  2. Altius Institute for Biomedical Sciences, Seattle, Washington, USA

    Jeff Vierstra & John A Stamatoyannopoulos

  3. Division of Oncology, Department of Medicine, University of Washington, Seattle, Washington, USA

    John A Stamatoyannopoulos

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Correspondence to Jeff Vierstra or John A Stamatoyannopoulos.

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Integrated supplementary information

Supplementary Figure 1 Aggregated DNase I cleavage patterns for TF recognition sequences reflecting diverse DNA-binding domains.

(a) Heatmaps of per-nucleotide DNase I cleavages and discovered footprints surrounding NRF1 recognition sequences. Left, observed cleavages. Right, the ratio of the observed cleavages to expected cleavages computed by reassigning tags to a hexamer model DNase I cleavage bias. Blue ticks indicate that the recognition sequence has an associated DNase I footprint. Line plots show the aggregate profile of mean per-nucleotide DNase I cleavages at the 20% most (left column) and 20% least (right column) accessible NRF1 recognition sequences. Top row, observed cleavages. Middle, expected cleavages computed using the hexamer model. Bottom, the log2 ratio of observed to expected. (b-g) The same as (a) for the recognition sequences for (b) SP1, (c) ELK1, (d) USF1, (e) RFX3, (f) NFIB, and (g) CTCF within accessible chromatin. In each case the cleavage patterns at occupied templates (coinciding with de novo TF footprint calls) parallel known structural features of the respective DNA binding domains.

Supplementary Figure 2 General features of DNase I sequence preference.

(a) Relative cleavage preference of all 4,096 hexamers with respect to the median hexamer as determined by deep sequencing (~100 million tags) of a DNase I digestion of deproteinized DNA from human IMR90 cells (data from ref.). (b) Biased hexamers contribute disproportionately to total DNase I cleavages for both naked DNA and chromatin (regulatory T cells cleavages mapping within DHS) when compared to the 36 bp mappable genome. Shown is the cumulative fraction all mappable positions or sequencing tags within respect to their hexamer context. Hexamers are ranked by decreasing cleavage preference as in a.

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Vierstra, J., Stamatoyannopoulos, J. Genomic footprinting. Nat Methods 13, 213–221 (2016). https://doi.org/10.1038/nmeth.3768

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