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dc.contributor.authorCao, Jicong
dc.contributor.authorNovoa, Eva Maria
dc.contributor.authorZhang, Zhizhuo
dc.contributor.authorChen, William CW
dc.contributor.authorLiu, Dianbo
dc.contributor.authorChoi, Gigi CG
dc.contributor.authorWong, Alan SL
dc.contributor.authorWehrspaun, Claudia
dc.contributor.authorKellis, Manolis
dc.contributor.authorLu, Timothy K
dc.date.accessioned2022-07-13T16:30:12Z
dc.date.available2022-07-13T16:30:12Z
dc.date.issued2021
dc.identifier.urihttps://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.isoen
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionof10.1038/S41467-021-24436-7en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNatureen_US
dc.titleHigh-throughput 5′ UTR engineering for enhanced protein production in non-viral gene therapiesen_US
dc.typeArticleen_US
dc.identifier.citationCao, 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.departmentMassachusetts Institute of Technology. Research Laboratory of Electronics
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineering
dc.contributor.departmentMassachusetts Institute of Technology. Synthetic Biology Center
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.relation.journalNature Communicationsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-13T16:27:04Z
dspace.orderedauthorsCao, J; Novoa, EM; Zhang, Z; Chen, WCW; Liu, D; Choi, GCG; Wong, ASL; Wehrspaun, C; Kellis, M; Lu, TKen_US
dspace.date.submission2022-07-13T16:27:05Z
mit.journal.volume12en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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