Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

An improved understanding of enhancers in mammalian genomes could facilitate the design of new regulatory elements. Melnikov et al . synthesize thousands of ~90 nt enhancer variants, assay their activity in human cells and use the data to rationally optimize synthetic enhancers. Learning to read and...

Full description

Saved in:
Bibliographic Details
Published inNature biotechnology Vol. 30; no. 3; pp. 271 - 277
Main Authors Melnikov, Alexandre, Murugan, Anand, Zhang, Xiaolan, Tesileanu, Tiberiu, Wang, Li, Rogov, Peter, Feizi, Soheil, Gnirke, Andreas, Callan, Curtis G, Kinney, Justin B, Kellis, Manolis, Lander, Eric S, Mikkelsen, Tarjei S
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.03.2012
Nature Publishing Group
Subjects
Online AccessGet full text
ISSN1087-0156
1546-1696
1546-1696
DOI10.1038/nbt.2137

Cover

More Information
Summary:An improved understanding of enhancers in mammalian genomes could facilitate the design of new regulatory elements. Melnikov et al . synthesize thousands of ~90 nt enhancer variants, assay their activity in human cells and use the data to rationally optimize synthetic enhancers. 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/nbt.2137