Metagenomic mining of regulatory elements enables programmable species-selective gene expression
Name
nihms945382.pdf
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
17aa3b1e432f09a05a3ceb3948267639
Author(s)
Johns, Nathan I
Yim, Sung Sun
Blazejewski, Tomasz
Dias Gomes, Antonio
Yang, Anthony
Smillie, Chris S
Smith, Mark Burnham
Alm, Eric J
Kosuri, Sriram
Wang, Harris H
Date Issued
March 2018
Journal
Nature Methods
Publisher
Springer Nature
Citation
Johns, Nathan I, Antonio L C Gomes, Sung Sun Yim, Anthony Yang, Tomasz Blazejewski, Christopher S Smillie, Mark B Smith, Eric J Alm, Sriram Kosuri, and Harris H Wang. “Metagenomic Mining of Regulatory Elements Enables Programmable Species-Selective Gene Expression.” Nature Methods 15, no. 5 (March 19, 2018): 323–329.
Version
Author's final manuscript
Abstract
Robust and predictably performing synthetic circuits rely on the use of well-characterized regulatory parts across different genetic backgrounds and environmental contexts. Here we report the large-scale metagenomic mining of thousands of natural 5′ regulatory sequences from diverse bacteria, and their multiplexed gene expression characterization in industrially relevant microbes. We identified sequences with broad and host-specific expression properties that are robust in various growth conditions. We also observed substantial differences between species in terms of their capacity to utilize exogenous regulatory sequences. Finally, we demonstrate programmable species-selective gene expression that produces distinct and diverse output patterns in different microbes. Together, these findings provide a rich resource of characterized natural regulatory sequences and a framework that can be used to engineer synthetic gene circuits with unique and tunable cross-species functionality and properties, and also suggest the prospect of ultimately engineering complex behaviors at the community level.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/NMETH.4633
