Article | Published:

Genome-wide assessment of sequence-intrinsic enhancer responsiveness at single-base-pair resolution

Nature Biotechnology volume 35, pages 136144 (2017) | Download Citation

Abstract

Gene expression is controlled by enhancers that activate transcription from the core promoters of their target genes. Although a key function of core promoters is to convert enhancer activities into gene transcription, whether and how strongly they activate transcription in response to enhancers has not been systematically assessed on a genome-wide level. Here we describe self-transcribing active core promoter sequencing (STAP-seq), a method to determine the responsiveness of genomic sequences to enhancers, and apply it to the Drosophila melanogaster genome. We cloned candidate fragments at the position of the core promoter (also called minimal promoter) in reporter plasmids with or without a strong enhancer, transfected the resulting library into cells, and quantified the transcripts that initiated from each candidate for each setup by deep sequencing. In the presence of a single strong enhancer, the enhancer responsiveness of different sequences differs by several orders of magnitude, and different levels of responsiveness are associated with genes of different functions. We also identify sequence features that predict enhancer responsiveness and discuss how different core promoters are employed for the regulation of gene expression.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

References

  1. 1.

    , & Expression of a β-globin gene is enhanced by remote SV40 DNA sequences. Cell 27, 299–308 (1981).

  2. 2.

    , & Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).

  3. 3.

    The role of general initiation factors in transcription by RNA polymerase II. Trends Biochem. Sci. 21, 327–335 (1996).

  4. 4.

    Perspectives on the RNA polymerase II core promoter. Wiley Interdiscip. Rev. Dev. Biol. 1, 40–51 (2012).

  5. 5.

    et al. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet. 46, 1311–1320 (2014).

  6. 6.

    & Architectural and functional commonalities between enhancers and promoters. Cell 162, 948–959 (2015).

  7. 7.

    & Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

  8. 8.

    et al. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature 512, 91–95 (2014).

  9. 9.

    , & Rational design of a super core promoter that enhances gene expression. Nat. Methods 3, 917–922 (2006).

  10. 10.

    et al. Enhancer-core-promoter specificity separates developmental and housekeeping gene regulation. Nature 518, 556–559 (2015).

  11. 11.

    , , & Quantitative analyses of core promoters enable precise engineering of regulated gene expression in mammalian cells. ACS Synth. Biol. 5, 395–404 (2016).

  12. 12.

    et al. Core promoter sequence in yeast is a major determinant of expression level. Genome Res. 25, 1008–1017 (2015).

  13. 13.

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

  14. 14.

    et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 1074–1077 (2013).

  15. 15.

    et al. Perspectives on unidirectional versus divergent transcription. Mol. Cell 60, 348–349 (2015).

  16. 16.

    et al. Human gene promoters are intrinsically bidirectional. Mol. Cell 60, 346–347 (2015).

  17. 17.

    et al. CapSeq and CIP-TAP identify Pol II start sites and reveal capped small RNAs as C. elegans piRNA precursors. Cell 151, 1488–1500 (2012).

  18. 18.

    et al. Global analysis of short RNAs reveals widespread promoter-proximal stalling and arrest of Pol II in Drosophila. Science 327, 335–338 (2010).

  19. 19.

    et al. A paired-end sequencing strategy to map the complex landscape of transcription initiation. Nat. Methods 7, 521–527 (2010).

  20. 20.

    , , & Computational analysis of core promoters in the Drosophila genome. Genome Biol. 3, RESEARCH0087 (2002).

  21. 21.

    et al. A regulatory circuit for piwi by the large Maf gene traffic jam in Drosophila. Nature 461, 1296–1299 (2009).

  22. 22.

    , & Transcriptional silencing of transposons by Piwi and maelstrom and its impact on chromatin state and gene expression. Cell 151, 964–980 (2012).

  23. 23.

    et al. Defining the status of RNA polymerase at promoters. Cell Rep. 2, 1025–1035 (2012).

  24. 24.

    et al. Tools for neuroanatomy and neurogenetics in Drosophila. Proc. Natl. Acad. Sci. USA 105, 9715–9720 (2008).

  25. 25.

    & Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13, 720–731 (2012).

  26. 26.

    modENCODE Consortium. et al. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010).

  27. 27.

    & Drosophila TFIID binds to a conserved downstream basal promoter element that is present in many TATA-box-deficient promoters. Genes Dev. 10, 711–724 (1996).

  28. 28.

    et al. The MTE, a new core promoter element for transcription by RNA polymerase II. Genes Dev. 18, 1606–1617 (2004).

  29. 29.

    et al. RNA polymerase stalling at developmental control genes in the Drosophila melanogaster embryo. Nat. Genet. 39, 1512–1516 (2007).

  30. 30.

    , , , & Genomic regulatory blocks underlie extensive microsynteny conservation in insects. Genome Res. 17, 1898–1908 (2007).

  31. 31.

    et al. Transcriptional and structural impact of TATA-initiation site spacing in mammalian core promoters. Genome Biol. 7, R78 (2006).

  32. 32.

    Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).

  33. 33.

    et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182–187 (2010).

  34. 34.

    et al. A large fraction of extragenic RNA pol II transcription sites overlap enhancers. PLoS Biol. 8, e1000384 (2010).

  35. 35.

    , , & Enhancer RNAs and regulated transcriptional programs. Trends Biochem. Sci. 39, 170–182 (2014).

  36. 36.

    et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

  37. 37.

    et al. Bidirectional transcription arises from two distinct hubs of transcription factor binding and active chromatin. Mol. Cell 58, 1101–1112 (2015).

  38. 38.

    et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145, 622–634 (2011).

  39. 39.

    et al. Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 3, RESEARCHH0088 (2002).

  40. 40.

    , , & Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res. 39, e141 (2011).

  41. 41.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  42. 42.

    , , & WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

  43. 43.

    et al. CAGE: cap analysis of gene expression. Nat. Methods 3, 211–222 (2006).

  44. 44.

    , , , & High-fidelity promoter profiling reveals widespread alternative promoter usage and transposon-driven developmental gene expression. Genome Res. 23, 169–180 (2013).

  45. 45.

    et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

  46. 46.

    et al. Transcriptional regulators form diverse groups with context-dependent regulatory functions. Nature 528, 147–151 (2015).

  47. 47.

    & FlyTF: a systematic review of site-specific transcription factors in the fruit fly Drosophila melanogaster. Bioinformatics 22, 1532–1533 (2006).

  48. 48.

    & Combining evidence using p-values: application to sequence homology searches. Bioinformatics 14, 48–54 (1998).

  49. 49.

    et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

  50. 50.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  51. 51.

    R Development Core Team. R: a Language and Environment for Statistical Computing (Vienna, Austria, 2012).

Download references

Acknowledgements

We thank L. Cochella and members of the Stark group for comments on the manuscript and Life Science Editors (http://lifescienceeditors.com) for editorial support. We are grateful to P. Heine and E. Jans (MaxCyte) for help setting up efficient plasmid transfection. Deep sequencing was performed at the Vienna Biocenter Core Facilities GmbH (VBCF) Next-Generation Sequencing Unit (http://vbcf.ac.at). The Stark group is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 647320) and by the Austrian Science Fund (FWF, F4303-B09). Basic research at the IMP is supported by Boehringer Ingelheim GmbH and the Austrian Research Promotion Agency (FFG).

Author information

Author notes

    • Cosmas D Arnold
    •  & Muhammad A Zabidi

    These authors contributed equally to this work.

Affiliations

  1. Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria.

    • Cosmas D Arnold
    • , Muhammad A Zabidi
    • , Michaela Pagani
    • , Martina Rath
    • , Katharina Schernhuber
    • , Tomáš Kazmar
    •  & Alexander Stark

Authors

  1. Search for Cosmas D Arnold in:

  2. Search for Muhammad A Zabidi in:

  3. Search for Michaela Pagani in:

  4. Search for Martina Rath in:

  5. Search for Katharina Schernhuber in:

  6. Search for Tomáš Kazmar in:

  7. Search for Alexander Stark in:

Contributions

C.D.A., M.A.Z., and A.S. conceived the project. C.D.A., M.P., and M.R. performed the experiments with the help of K.S., and M.A.Z. the computational analyses. T.K. performed the k-mer based predictions. C.D.A., M.A.Z., and A.S. wrote the manuscript. A.S. supervised the project.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alexander Stark.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9

Excel files

  1. 1.

    Supplementary Table 1

    Enhancers used for STAP-seq screens. Genomic coordinates (dm3) and cloned sequences of S2 cell developmental (zfh1, sgl, ham), OSC developmental (tj) and housekeeping (ncm, ssp3) enhancers used for the respective STAP-seq screens.

  2. 2.

    Supplementary Table 2

    BACs that are contained in the focused libraries. Indicated are the coordinates and IDs of the BACs that were used to generate the focused STAP-seq libraries.

  3. 3.

    Supplementary Table 3

    Primers used for amplification of the D. pseudoobscura sequences (spike-in controls). Indicated are the primers used to amplify the sequences from the D. pseudoobscura genome, which were used to generate the STAP-seq spike-in control plasmids.

  4. 4.

    Supplementary Table 4

    Details on individual candidates for luciferase validations. Primer pairs used to amplify candidates for luciferase validation as well as their genomic coordinates, the luciferase fold change, standard deviations and enhancer-responsiveness are indicated.

  5. 5.

    Supplementary Table 5

    Number of mapped reads and eTSSs for STAP-seq screens. Reported are total mapped reads and collapsed fragments (see Methods) for all STAP-seq screens (dm3) and the respective D. pseudoobscura spike-in controls (dp3).

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nbt.3739

Further reading