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

Robin P Smith, Leila Taher, Rupali P Patwardhan, Mee J Kim, Fumitaka Inoue, Jay Shendure*, Ivan Ovcharenko*, Nadav Ahituv*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1021-1028
Number of pages8
JournalNature Genetics
Issue number9
Publication statusPublished - Sep 2013


  • Animals
  • Binding Sites
  • Cell Line
  • Cluster Analysis
  • Enhancer Elements, Genetic
  • Gene Amplification
  • Gene Dosage
  • Gene Expression
  • Gene Expression Regulation
  • Genes, Reporter
  • Humans
  • Liver/metabolism
  • Male
  • Mice
  • Models, Biological
  • Nucleotide Motifs
  • Organ Specificity/genetics
  • Protein Binding
  • Regulatory Sequences, Nucleic Acid
  • Transcription Factors/metabolism


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