Rapid dissection and model-based optimization of inducible enhancers in human cells using a massively parallel reporter assay

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-...

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Bibliographic Details
Main Authors: Melnikov, Alexandre (Author), Murugan, Anand (Author), Zhang, Xiaolan (Author), Tesileanu, Tiberiu (Author), Wang, Li (Author), Rogov, Peter (Author), Feizi-Khankandi, Soheil (Contributor), Gnirke, Andreas (Author), Callan Jr, Curtis G. (Author), Kinney, Justin B. (Author), Kellis, Manolis (Contributor), Lander, Eric S. (Contributor), Mikkelsen, Tarjei Sigurd 1978- (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Biology (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Nature Publishing Group, 2012-10-18T18:32:29Z.
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Summary: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.
National Human Genome Research Institute (U.S.) (grant R01HG004037)
National Science Foundation (U.S.) ((NSF) grant PHY-0957573)
National Science Foundation (U.S.) (NSF grant PHY-1022140)
Broad Institute