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Cited by 239Genomics and Chromatin DynamicsGene Regulatory Network AnalysisChromatin Remodeling and Cancer

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

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Smith, Robin P, Taher, Leila, Patwardhan, Rupali P, Kim, Mee J, Inoue, Fumitaka, Shendure, Jay, Ovcharenko, Ivan, Ahituv, Nadav·2013·Nature Genetics
DOI 10.1038/ng.2713PMC PMC3775494
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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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  • Enhancers consistent with the billboard model are likely also widespread in vertebrate genomes.

    “Enhancers consistent with the billboard model are likely also widespread in vertebrate genomes [55, 93].”

    From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [55]

    The Results section (“Reporter expression is influenced by binding site order”; page unavailable) says the tested configurations are largely consistent with the billboard model. The Results section (“Synthetic elements mimic putative liver enhancers”; page unavailable) also finds similar binding-site configurations in 78% of the putative mouse liver enhancers examined. This supports the model’s relevance to native mammalian enhancers, but does not establish that billboard-consistent enhancers are widespread across vertebrate genomes. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Flexibility of enhancer organisation with respect to binding site order and orientation was observed in a massively parallel synthetic enhancer experiment.

    “Flexibility of enhancer organisation with respect to binding site order and orientation was also observed in a massively parallel synthetic enhancer experiment [55].”

    From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [55]

    The Results and Discussion (page numbers unavailable) report that multiple binding-site orders drove strong expression in the massively parallel synthetic regulatory-element experiment, supporting flexibility with respect to order. The passages do not report a test or finding about binding-site orientation, so that part of the claim is unsupported. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • DeepLiver predictions correlate well with experimental measurements from a previously published MPRA dataset on synthetic sequences in vivo (R = 0.68).

    “To validate DeepLiver predictions, we used a previously published MPRA dataset performed on synthetic sequences in vivo38, finding that DeepLiver predictions correlate well with the experimental measurements (R = 0.68) (Extended Data Fig. 7e).”

    From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [38]

  • DeepLiver predictions were validated by calculating the correlation between DeepLiver predictions and a previously published MPRA dataset performed on synthetic sequences in vivo.

    “To validate DeepLiver predictions, we calculated the correlation between the predictions of DeepLiver and a previously published MPRA dataset performed on synthetic sequences in vivo38.”

    From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [38]

  • DeepLiver predictions were validated by calculating the correlation between DeepLiver predictions and a previously published MPRA data set performed on synthetic sequences in vivo.

    “To validate DeepLiver predictions, we calculated the correlation between the predictions of DeepLiver and a previously published MPRA data set performed on synthetic sequences in vivo57.”

    From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [57]

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Authors
Smith, R. P., Taher, L., Patwardhan, R. P., Kim, M. J., Inoue, F., Shendure, J., Ovcharenko, I., & Ahituv, N.
Affiliations
  • University of California, San Francisco (US)
  • National Institutes of Health (US)
  • University of Washington (US)
Corresponding author
  • Shendure, Jay
  • Ovcharenko, Ivan
  • Ahituv, Nadav
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2013-07-28
Volume
45
Issue
9
Pages
1021-1028
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/ng.2713
PMC
PMC3775494
OpenAlex
W2008047506
Cited by
239
References
4
Citations by year
2026: 13 · 2025: 15 · 2024: 13 · 2023: 16 · 2022: 14 · 2021: 23 · 2020: 22 · 2019: 22 · 2018: 14 · 2017: 21 · 2016: 26 · 2015: 21 · 2014: 18 · 2013: 1
FWCI
9.86
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institutes of Health
  • Canadian Institutes of Health Research
  • National Human Genome Research Institute (R01HG006768, R01HG005058)
  • National Institute of General Medical Sciences (GM61390, U19GM061390, T32GM007175, U01GM061390)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01DK090382, P30DK026743)
  • National Institute of Neurological Disorders and Stroke (R01NS079231)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD059862)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
transcription factor binding sites, enhancer architecture, liver-specific transcription factors, gene expression regulation, massively parallel reporter assay, HepG2 cells, mice
MeSH terms
Animals, Binding Sites, Cell Line, Enhancer Elements, Genetic, Gene Amplification, Gene Expression Regulation, Humans, Liver, Male, Models, Biological, Organ Specificity, Protein Binding, Regulatory Sequences, Nucleic Acid, Transcription Factors, Gene Expression, Cluster Analysis, Genes, Reporter, Gene Dosage, Mice, Nucleotide Motifs

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