High-throughput 5′ UTR engineering for enhanced protein production in non-viral gene therapies
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s41467-021-24436-7.pdf
Description
Published version
Size
1.88 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Cao, Jicong
Novoa, Eva Maria
Zhang, Zhizhuo
Chen, William CW
Liu, Dianbo
Choi, Gigi CG
Wong, Alan SL
Wehrspaun, Claudia
Kellis, Manolis
Lu, Timothy K
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Cao, Jicong, Novoa, Eva Maria, Zhang, Zhizhuo, Chen, William CW, Liu, Dianbo et al. 2021. "High-throughput 5′ UTR engineering for enhanced protein production in non-viral gene therapies." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractDespite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major technical challenge. Here, we develop a high-throughput strategy to design, screen, and optimize 5′ UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identify naturally occurring 5′ UTRs with high translation efficiencies and use this information with in silico genetic algorithms to generate synthetic 5′ UTRs. A total of ~12,000 5′ UTRs are then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identify three synthetic 5′ UTRs that outperform commonly used non-viral gene therapy plasmids in expressing protein payloads. In summary, we demonstrate that high-throughput screening of 5′ UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Synthetic Biology Center
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-021-24436-7
