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Nature Biotechnology
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Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

Nature Biotechnologyvolume 30pages271–277 (2012)Cite this article

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Abstract

Learning to read and write the transcriptional regulatory code is of central importance to progress in genetic analysis and engineering. Here we describe a massively parallel reporter assay (MPRA) that facilitates the systematic dissection of transcriptional regulatory elements. In MPRA, microarray-synthesized DNA regulatory elements and unique sequence tags are cloned into plasmids to generate a library of reporter constructs. These constructs are transfected into cells and tag expression is assayed by high-throughput sequencing. We apply MPRA to compare >27,000 variants of two inducible enhancers in human cells: a synthetic cAMP-regulated enhancer and the virus-inducible interferon-β enhancer. We first show that the resulting data define accurate maps of functional transcription factor binding sites in both enhancers at single-nucleotide resolution. We then use the data to train quantitative sequence-activity models (QSAMs) of the two enhancers. We show that QSAMs from two cellular states can be combined to design enhancer variants that optimize potentially conflicting objectives, such as maximizing induced activity while minimizing basal activity.

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Figure 1: Overview of MPRA.
Figure 2: Single-hit scanning mutagenesis of the cAMP-responsive enhancer.
Figure 3: Single-hit scanning mutagenesis of the virus-inducibleIFNB enhancer.
Figure 4: Multi-hit sampling mutagenesis of the cAMP-responsive enhancer.
Figure 5: Multi-hit sampling mutagenesis of the virus-inducibleIFNB enhancer.
Figure 6: Model-based optimization.

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References

  1. Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.Nature447, 799–816 (2007).

    Article CAS  Google Scholar 

  2. Lander, E.S. Initial impact of the sequencing of the human genome.Nature470, 187–197 (2011).

    Article CAS  Google Scholar 

  3. Dorer, D.E. & Nettelbeck, D.M. Targeting cancer by transcriptional control in cancer gene therapy and viral oncolysis.Adv. Drug Deliv. Rev.61, 554–571 (2009).

    Article CAS  Google Scholar 

  4. Fan, F. & Wood, K.V. Bioluminescent assays for high-throughput screening.Assay Drug Dev. Technol.5, 127–136 CrossRef (2007).

    Article CAS  Google Scholar 

  5. Loew, R., Heinz, N., Hampf, M., Bujard, H. & Gossen, M. Improved Tet-responsive promoters with minimized background expression.BMC Biotechnol.10, 81 (2010).

    Article  Google Scholar 

  6. Carey, M., Peterson, C.L. & Smale, S.T.Transcriptional Regulation in Eukaryotes: Concepts, Strategies, and Techniques. Edn. 2 (Cold Spring Harbor Laboratory Press, 2009).

  7. 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).

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

  9. 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. USA107, 9158–9163 (2010).

    Article CAS  Google Scholar 

  10. Panne, D., Maniatis, T. & Harrison, S.C. An atomic model of the interferon-beta enhanceosome.Cell129, 1111–1123 (2007).

    Article CAS  Google Scholar 

  11. Arnosti, D.N. & Kulkarni, M.M. Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards?J. Cell. Biochem.94, 890–898 (2005).

    Article CAS  Google Scholar 

  12. Jonsson, J., Norberg, T., Carlsson, L., Gustafsson, C. & Wold, S. Quantitative sequence-activity models (QSAM)–tools for sequence design.Nucleic Acids Res.21, 733–739 (1993).

    Article CAS  Google Scholar 

  13. Stormo, G.D., Schneider, T.D. & Gold, L. Quantitative analysis of the relationship between nucleotide sequence and functional activity.Nucleic Acids Res.14, 6661–6679 (1986).

    Article CAS  Google Scholar 

  14. Mayr, B. & Montminy, M. Transcriptional regulation by the phosphorylation-dependent factor CREB.Nat. Rev. Mol. Cell Biol.2, 599–609 (2001).

    Article CAS  Google Scholar 

  15. Benbrook, D.M. & Jones, N.C. Different binding specificities and transactivation of variant CRE's by CREB complexes.Nucleic Acids Res.22, 1463–1469 (1994).

    Article CAS  Google Scholar 

  16. Fink, J.S. et al. The CGTCA sequence motif is essential for biological activity of the vasoactive intestinal peptide gene cAMP-regulated enhancer.Proc. Natl. Acad. Sci. USA85, 6662–6666 (1988).

    Article CAS  Google Scholar 

  17. Kunsch, C., Ruben, S.M. & Rosen, C.A. Selection of optimal kappa B/Rel DNA-binding motifs: interaction of both subunits of NF-kappa B with DNA is required for transcriptional activation.Mol. Cell. Biol.12, 4412–4421 (1992).

    Article CAS  Google Scholar 

  18. Falvo, J.V., Parekh, B.S., Lin, C.H., Fraenkel, E. & Maniatis, T. Assembly of a functional beta interferon enhanceosome is dependent on ATF-2-c-jun heterodimer orientation.Mol. Cell. Biol.20, 4814–4825 (2000).

    Article CAS  Google Scholar 

  19. Schneider, T.D. & Stormo, G.D. Excess information at bacteriophage T7 genomic promoters detected by a random cloning technique.Nucleic Acids Res.17, 659–674 (1989).

    Article CAS  Google Scholar 

  20. Bishop, C.M.Pattern Recognition and Machine Learning (Springer, 2006).

  21. De Mey, M., Maertens, J., Lequeux, G.J., Soetaert, W.K. & Vandamme, E.J. Construction and model-based analysis of a promoter library forE. coli: an indispensable tool for metabolic engineering.BMC Biotechnol.7, 34 (2007).

    Article  Google Scholar 

  22. Quan, J. et al. Parallel on-chip gene synthesis and application to optimization of protein expression.Nat. Biotechnol.29, 449–452 (2011).

    Article CAS  Google Scholar 

  23. Matzas, M. et al. High-fidelity gene synthesis by retrieval of sequence-verified DNA identified using high-throughput pyrosequencing.Nat. Biotechnol.28, 1291–1294 (2010).

    Article CAS  Google Scholar 

  24. Bernstein, B.E. et al. The NIH Roadmap Epigenomics Mapping Consortium.Nat. Biotechnol.28, 1045–1048 (2010).

    Article CAS  Google Scholar 

  25. Edelman, G.M., Meech, R., Owens, G.C. & Jones, F.S. Synthetic promoter elements obtained by nucleotide sequence variation and selection for activity.Proc. Natl. Acad. Sci. USA97, 3038–3043 (2000).

    Article CAS  Google Scholar 

  26. Schlabach, M.R., Hu, J.K., Li, M. & Elledge, S.J. Synthetic design of strong promoters.Proc. Natl. Acad. Sci. USA107, 2538–2543 (2010).

    Article CAS  Google Scholar 

  27. Holland, J.H.Adaptation in Natural and Artificial Systems: AN Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence Edn. 1 (MIT Press, 1992).

  28. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J.R. Stat. Soc. B57, 289–300 (1995).

    Google Scholar 

  29. Treves, A. & Panzeri, S. The upward bias in measures of information derived from limited samples.Neural Comput.7, 399–407 (1995).

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank E.M. LeProust and S. Chen of Agilent for oligonucleotide library synthesis, R.P. Deering for assistance with Sendai virus infections and the staff of the Broad Institute and the Bauer Core facilities for assistance with data generation. This project was supported by funds from the Broad Institute, the Harvard Stem Cell Institute (T.S.M.), National Human Genome Research Institute grant R01HG004037 (M.K.), the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory (J.B.K.), National Science Foundation (NSF) grant PHY-0957573 (C.G.C., T.T.) and NSF grant PHY-1022140 (A. Mur.).

Author information

Author notes
  1. Alexandre Melnikov, Anand Murugan and Xiaolan Zhang: These authors contributed equally to this work.

Authors and Affiliations

  1. Broad Institute, Cambridge, Massachusetts, USA

    Alexandre Melnikov, Xiaolan Zhang, Li Wang, Peter Rogov, Soheil Feizi, Andreas Gnirke, Manolis Kellis, Eric S Lander & Tarjei S Mikkelsen

  2. Department of Physics, Princeton University, Princeton, New Jersey, USA

    Anand Murugan, Tiberiu Tesileanu & Curtis G Callan Jr

  3. Simons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, USA

    Tiberiu Tesileanu & Curtis G Callan Jr

  4. MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA

    Soheil Feizi & Manolis Kellis

  5. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA

    Justin B Kinney

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

    Eric S Lander

  7. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Eric S Lander

  8. Harvard Stem Cell Institute and Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    Tarjei S Mikkelsen

Authors
  1. Alexandre Melnikov

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  3. Xiaolan Zhang

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  4. Tiberiu Tesileanu

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  5. Li Wang

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  6. Peter Rogov

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  7. Soheil Feizi

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  8. Andreas Gnirke

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  9. Curtis G Callan Jr

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  10. Justin B Kinney

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  11. Manolis Kellis

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  12. Eric S Lander

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  13. Tarjei S Mikkelsen

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Contributions

A. Mel., X.Z., P.R., A.G. and T.S.M. developed MPRA and performed the molecular biology experiments. L.W. cultured the cells, and performed the plasmid transfections and luciferase assays. A.Mur., T.T., S.F., C.G.C., J.B.K., M.K., E.S.L. and T.S.M. analyzed the data. T.S.M. wrote the main text with substantial input from all authors. C.G.C. and J.B.K. wrote theSupplementary Notes with substantial input from A. Mur. and T.S.M.

Corresponding author

Correspondence toTarjei S Mikkelsen.

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Competing interests

A patent application describing ideas presented in this article has been filed by the Broad Institute.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 5,6, Supplementary Notes and Supplementary Figs. 1–10 (PDF 5994 kb)

Supplementary Table 1

CRE variants (XLSX 5145 kb)

Supplementary Table 2

IFNB variants (XLSX 3389 kb)

Supplementary Table 3

CRE mutagenesis/models (XLSX 39 kb)

Supplementary Table 4

IFNB mutagenesis/models (XLSX 36 kb)

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Melnikov, A., Murugan, A., Zhang, X.et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay.Nat Biotechnol30, 271–277 (2012). https://doi.org/10.1038/nbt.2137

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