Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

Abstract

Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited. We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters and applied it to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences. Our analyses reveal a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts which are transcription-factor specific, including a striking 10-bp periodic relationship between gene expression and binding-site location. We show how this approach can measure transcription-factor sequence specificities and the sensitivity of transcription-factor sites to the surrounding sequence context, and compare the activity of 75 yeast transcription factors. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Obtaining accurate expression measurements for thousands of designed promoter sequences.
Figure 2: Profiling the activity of 75 yeast transcription factors.
Figure 3: The effect of binding-site location on expression.
Figure 4: The effect of nucleosome disfavoring-sequences on expression.
Figure 5: Effect of binding-site number on expression.
Figure 6: Comparing the effects of different types of sequence changes.

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. 1

    Chiang, D.Y., Nix, D.A., Shultzaberger, R.K., Gasch, A.P. & Eisen, M.B. Flexible promoter architecture requirements for coactivator recruitment. BMC Mol. Biol. 7, 16 (2006).

    Article  Google Scholar 

  2. 2

    Ligr, M., Siddharthan, R., Cross, F.R. & Siggia, E.D. Gene expression from random libraries of yeast promoters. Genetics 172, 2113–2122 (2006).

    CAS  Article  Google Scholar 

  3. 3

    Kinkhabwala, A. & Guet, C.C. Uncovering cis regulatory codes using synthetic promoter shuffling. PLoS ONE 3, e2030 (2008).

    Article  Google Scholar 

  4. 4

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

    CAS  Article  Google Scholar 

  5. 5

    Cox, R.S. III., Surette, M.G. & Elowitz, M.B. Programming gene expression with combinatorial promoters. Mol. Syst. Biol. 3, 145 (2007).

    PubMed  PubMed Central  Google Scholar 

  6. 6

    Kinney, J.B., Murugan, A., Callan, C.G. Jr. & Cox, E.C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl. Acad. Sci. USA 107, 9158–9163 (2010).

    CAS  Article  Google Scholar 

  7. 7

    Giniger, E. & Ptashne, M. Cooperative DNA binding of the yeast transcriptional activator GAL4. Proc. Natl. Acad. Sci. USA 85, 382–386 (1988).

    CAS  Article  Google Scholar 

  8. 8

    Iyer, V. & Struhl, K. Poly(dA:dT), a ubiquitous promoter element that stimulates transcription via its intrinsic DNA structure. EMBO J. 14, 2570–2579 (1995).

    CAS  Article  Google Scholar 

  9. 9

    Lam, F.H., Steger, D.J. & O'Shea, E.K. Chromatin decouples promoter threshold from dynamic range. Nature 453, 246–250 (2008).

    CAS  Article  Google Scholar 

  10. 10

    Murphy, K.F., Balazsi, G. & Collins, J.J. Combinatorial promoter design for engineering noisy gene expression. Proc. Natl. Acad. Sci. USA 104, 12726–12731 (2007).

    CAS  Article  Google Scholar 

  11. 11

    Patwardhan, R.P. et al. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat. Biotechnol. 27, 1173–1175 (2009).

    CAS  Article  Google Scholar 

  12. 12

    Patwardhan, R.P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265–270 (2012).

    CAS  Article  Google Scholar 

  13. 13

    Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

    CAS  Article  Google Scholar 

  14. 14

    LeProust, E.M. et al. Synthesis of high-quality libraries of long (150mer) oligonucleotides by a novel depurination controlled process. Nucleic Acids Res. 38, 2522–2540 (2010).

    CAS  Article  Google Scholar 

  15. 15

    Kaplan, N. et al. The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 458, 362–366 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Baliga, N.S. Promoter analysis by saturation mutagenesis. Biol. Proced. Online 3, 64–69 (2001).

    CAS  Article  Google Scholar 

  17. 17

    Anderson, J.D. & Widom, J. Poly(dA-dT) promoter elements increase the equilibrium accessibility of nucleosomal DNA target sites. Mol. Cell. Biol. 21, 3830–3839 (2001).

    CAS  Article  Google Scholar 

  18. 18

    Segal, E. & Widom, J. Poly(dA:dT) tracts: major determinants of nucleosome organization. Curr. Opin. Struct. Biol. 19, 65–71 (2009).

    CAS  Article  Google Scholar 

  19. 19

    Zeevi, D. et al. Compensation for differences in gene copy number among yeast ribosomal proteins is encoded within their promoters. Genome Res. 21, 2114–2128 (2011).

    CAS  Article  Google Scholar 

  20. 20

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

    CAS  Article  Google Scholar 

  21. 21

    Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).

    CAS  Article  Google Scholar 

  22. 22

    Huh, W.K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).

    CAS  Article  Google Scholar 

  23. 23

    Zhao, Y. et al. Fine-structure analysis of ribosomal protein gene transcription. Mol. Cell. Biol. 26, 4853–4862 (2006).

    CAS  Article  Google Scholar 

  24. 24

    Blaiseau, P.L., Lesuisse, E. & Camadro, J.M. Aft2p, a novel iron-regulated transcription activator that modulates, with Aft1p, intracellular iron use and resistance to oxidative stress in yeast. J. Biol. Chem. 276, 34221–34226 (2001).

    CAS  Article  Google Scholar 

  25. 25

    Lamb, T.M. & Mitchell, A.P. The transcription factor Rim101p governs ion tolerance and cell differentiation by direct repression of the regulatory genes NRG1 and SMP1 in Saccharomyces cerevisiae. Mol. Cell. Biol. 23, 677–686 (2003).

    CAS  Article  Google Scholar 

  26. 26

    Hanlon, S.E., Rizzo, J.M., Tatomer, D.C., Lieb, J.D. & Buck, M.J. The stress response factors Yap6, Cin5, Phd1, and Skn7 direct targeting of the conserved co-repressor Tup1-Ssn6 in S. cerevisiae. PLoS ONE 6, e19060 (2011).

    CAS  Article  Google Scholar 

  27. 27

    Canizares, J.V., Pallotti, C., Sainz-Pardo, I., Iranzo, M. & Mormeneo, S. The SRD2 gene is involved in Saccharomyces cerevisiae morphogenesis. Arch. Microbiol. 177, 352–357 (2002).

    CAS  Article  Google Scholar 

  28. 28

    Akache, B., Wu, K. & Turcotte, B. Phenotypic analysis of genes encoding yeast zinc cluster proteins. Nucleic Acids Res. 29, 2181–2190 (2001).

    CAS  Article  Google Scholar 

  29. 29

    Woudt, L.P., Smit, A.B., Mager, W.H. & Planta, R.J. Conserved sequence elements upstream of the gene encoding yeast ribosomal protein L25 are involved in transcription activation. EMBO J. 5, 1037–1040 (1986).

    CAS  Article  Google Scholar 

  30. 30

    Lieb, J.D., Liu, X., Botstein, D. & Brown, P.O. Promoter-specific binding of Rap1 revealed by genome-wide maps of protein-DNA association. Nat. Genet. 28, 327–334 (2001).

    CAS  Article  Google Scholar 

  31. 31

    Nutiu, R. et al. Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat. Biotechnol. 29, 659–664 (2011).

    CAS  Article  Google Scholar 

  32. 32

    Maerkl, S.J. & Quake, S.R. A systems approach to measuring the binding energy landscapes of transcription factors. Science 315, 233–237 (2007).

    CAS  Article  Google Scholar 

  33. 33

    Bulyk, M.L., Gentalen, E., Lockhart, D.J. & Church, G.M. Quantifying DNA-protein interactions by double-stranded DNA arrays. Nat. Biotechnol. 17, 573–577 (1999).

    CAS  Article  Google Scholar 

  34. 34

    Raveh-Sadka, T. et al. Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast. Nat. Genet. (in the press).

  35. 35

    Kim, J.H., Polish, J. & Johnston, M. Specificity and regulation of DNA binding by the yeast glucose transporter gene repressor Rgt1. Mol. Cell. Biol. 23, 5208–5216 (2003).

    CAS  Article  Google Scholar 

  36. 36

    Karolchik, D. et al. The UCSC Genome Browser Database. Nucleic Acids Res. 31, 51–54 (2003).

    CAS  Article  Google Scholar 

  37. 37

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

    CAS  Article  Google Scholar 

  38. 38

    Cleary, M.A. et al. Production of complex nucleic acid libraries using highly parallel in situ oligonucleotide synthesis. Nat. Methods 1, 241–248 (2004).

    CAS  Article  Google Scholar 

  39. 39

    Fazekas, A., Steeves, R. & Newmaster, S. Improving sequencing quality from PCR products containing long mononucleotide repeats. Biotechniques 48, 277–285 (2010).

    CAS  Article  Google Scholar 

  40. 40

    Sheff, M.A. & Thorn, K.S. Optimized cassettes for fluorescent protein tagging in Saccharomyces cerevisiae. Yeast 21, 661–670 (2004).

    CAS  Article  Google Scholar 

  41. 41

    Breslow, D.K. et al. A comprehensive strategy enabling high-resolution functional analysis of the yeast genome. Nat. Methods 5, 711–718 (2008).

    CAS  Article  Google Scholar 

  42. 42

    Otsuka, C. et al. Use of yeast transformation by oligonucleotides to study DNA lesion bypass in vivo. Mutat. Res. 502, 53–60 (2002).

    CAS  Article  Google Scholar 

  43. 43

    Hoaglin, D.C., Mosteller, F. & Tukey, J.W. Understanding Robust and Exploratory Data Anlysis (Wiley, 1983).

Download references

Acknowledgements

We thank J. Widom for assistance and inspiration throughout this project. This work was supported by grants from the European Research Council and the US National Institutes of Health to E. Segal. E. Segal is the incumbent of the Soretta and Henry Shapiro career development chair. We thank S. Lubliner for help with computational analyses. We thank C. Boone (University of Toronto) for kindly giving us the Y8205 strain.

Author information

Affiliations

Authors

Contributions

E. Sharon and E. Segal conceived the project. E. Sharon., Y.K., A.W. and E. Segal planned the experiments. E. Sharon and Y.K. performed the experiments. E. Sharon and E. Segal analyzed the results. T.R.-S., M.L. and Z.Y. contributed to the design of the promoters. A.S., D.Z. and L.K. contributed to experimental work. Z.Y. also provided technical guidance.

Corresponding authors

Correspondence to Adina Weinberger or Eran Segal.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1, 2, Supplementary Figures 1–21 and Supplementary Tables 1, 2 (PDF 2070 kb)

Supplementary Table 3

Library description and measured expression values (XLSX 630 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Sharon, E., Kalma, Y., Sharp, A. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat Biotechnol 30, 521–530 (2012). https://doi.org/10.1038/nbt.2205

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing