dc.contributor.author | Cao, Jicong | |
dc.contributor.author | Novoa, Eva Maria | |
dc.contributor.author | Zhang, Zhizhuo | |
dc.contributor.author | Chen, William CW | |
dc.contributor.author | Liu, Dianbo | |
dc.contributor.author | Choi, Gigi CG | |
dc.contributor.author | Wong, Alan SL | |
dc.contributor.author | Wehrspaun, Claudia | |
dc.contributor.author | Kellis, Manolis | |
dc.contributor.author | Lu, Timothy K | |
dc.date.accessioned | 2022-07-13T16:30:12Z | |
dc.date.available | 2022-07-13T16:30:12Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/143714 | |
dc.description.abstract | <jats:title>Abstract</jats:title><jats:p>Despite 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.</jats:p> | en_US |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | 10.1038/S41467-021-24436-7 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Nature | en_US |
dc.title | High-throughput 5′ UTR engineering for enhanced protein production in non-viral gene therapies | en_US |
dc.type | Article | en_US |
dc.identifier.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). | |
dc.contributor.department | Massachusetts Institute of Technology. Research Laboratory of Electronics | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | |
dc.contributor.department | Massachusetts Institute of Technology. Synthetic Biology Center | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.relation.journal | Nature Communications | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2022-07-13T16:27:04Z | |
dspace.orderedauthors | Cao, J; Novoa, EM; Zhang, Z; Chen, WCW; Liu, D; Choi, GCG; Wong, ASL; Wehrspaun, C; Kellis, M; Lu, TK | en_US |
dspace.date.submission | 2022-07-13T16:27:05Z | |
mit.journal.volume | 12 | en_US |
mit.journal.issue | 1 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |