Rapid dissection and model-based optimization of inducible enhancers in human cells using a massively parallel reporter assay
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Author(s)
Melnikov, Alexandre
Murugan, Anand
Zhang, Xiaolan
Tesileanu, Tiberiu
Wang, Li
Rogov, Peter
Feizi-Khankandi, Soheil
Gnirke, Andreas
Callan Jr, Curtis G.
Kinney, Justin B.
Date Issued
February 2012
Journal
Nature Biotechnology
Publisher
Nature Publishing Group
Citation
Melnikov, Alexandre et al. “Systematic Dissection and Optimization of Inducible Enhancers in Human Cells Using a Massively Parallel Reporter Assay.” Nature Biotechnology 30.3 (2012): 271–277. Web.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1038/nbt.2137
