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Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model

Nature Genetics volume 45, pages 1021–1028 (2013)Cite this article

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Abstract

Despite continual progress in the cataloging of vertebrate regulatory elements, little is known about their organization and regulatory architecture. Here we describe a massively parallel experiment to systematically test the impact of copy number, spacing, combination and order of transcription factor binding sites on gene expression. A complex library of ∼5,000 synthetic regulatory elements containing patterns from 12 liver-specific transcription factor binding sites was assayed in mice and in HepG2 cells. We find that certain transcription factors act as direct drivers of gene expression in homotypic clusters of binding sites, independent of spacing between sites, whereas others function only synergistically. Heterotypic enhancers are stronger than their homotypic analogs and favor specific transcription factor binding site combinations, mimicking putative native enhancers. Exhaustive testing of binding site permutations suggests that there is flexibility in binding site order. Our findings provide quantitative support for a flexible model of regulatory element activity and suggest a framework for the design of synthetic tissue-specific enhancers.

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Figure 1: Synthetic enhancer sequence design and controls.
Figure 2: Homotypic amplification of expression is compatible with a subset of transcription factor binding sites, independent of their spacing.
Figure 3: Heterotypic elements drive stronger expression than homotypic ones.
Figure 4: Combinatorial effects in heterotypic clusters of two different transcription factor binding sites.
Figure 5: Synthetic enhancers mimic mouse liver enhancers.
Figure 6: Effects of binding site order in heterotypic enhancers.

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Acknowledgements

This work was supported by National Human Genome Research Institute (NHGRI) grant 1R01HG006768 (N.A. and J.S.) and the Pilot/Feasibility grant from the University of California, San Francisco (UCSF) Liver Center (P30 DK026743). N.A. is also supported by National Institute of Child and Human Development grant R01HD059862, NHGRI grant R01HG005058, National Institute of General Medical Sciences grant GM61390, National Institute of Neurological Disorders and Stroke grant 1R01NS079231, National Institute of Diabetes and Digestive and Kidney Diseases grant 1R01DK090382 and the Simons Foundation (SFARI 256769). R.P.S. was supported in part by a Canadian Institutes of Health Research (CIHR) fellowship in hepatology. M.J.K. was supported in part by US National Institutes of Health training grant T32 GM007175, the UCSF Quantitative Biosciences Consortium fellowship for Interdisciplinary Research and the Amgen Research Excellence in Bioengineering and Therapeutic Sciences fellowship. This work was funded in part by the Intramural Research Program of the US National Institutes of Health, National Library of Medicine (I.O.).

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Author notes

  1. Robin P Smith and Leila Taher: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA

    Robin P Smith, Mee J Kim, Fumitaka Inoue & Nadav Ahituv

  2. Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA

    Robin P Smith, Mee J Kim, Fumitaka Inoue & Nadav Ahituv

  3. Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, US National Institutes of Health, Bethesda, Maryland, USA

    Leila Taher & Ivan Ovcharenko

  4. Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany

    Leila Taher

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

    Rupali P Patwardhan & Jay Shendure

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Contributions

R.P.S., L.T., R.P.P., J.S., I.O. and N.A. conceived key aspects of the project and planned experiments. R.P.S., R.P.P., M.J.K. and F.I. performed experiments. L.T., R.P.S. and R.P.P. analyzed data. R.P.S., L.T., R.P.P., I.O., J.S. and N.A. wrote the manuscript. All authors commented on and revised the manuscript.

Corresponding authors

Correspondence to Jay Shendure, Ivan Ovcharenko or Nadav Ahituv.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10, Supplementary Tables 1–3 and 6, and Supplementary Note (PDF 2631 kb)

Supplementary Table 4

Complete listing of 4,966 SRESs (no controls) tested in the study (XLSX 506 kb)

Supplementary Table 5

Summary of expression for the best and worst permutations from 211 sets of SRESs containing the same complement of transcription factor binding sites in different orders (XLSX 96 kb)

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Smith, R., Taher, L., Patwardhan, R. et al. Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nat Genet 45, 1021–1028 (2013). https://doi.org/10.1038/ng.2713

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