High-throughput 5′ UTR engineering for enhanced protein production in non-viral gene therapies

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.


Statistics
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For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
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Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Manolis Kellis, Timothy K Lu

May 24, 2021
No software is used for data collection.
No data were excluded.
Data were analyzed over multiple biological replicates. The results were reproducible over at least two independent experiments.
HEK 293T cells and their derivatives were used. As we performed experiments with a cell line with defined genetic background, there were no need for randomization.
The investigators were not blinded. As we performed experiments with a cell line with defined genetic background, there were no need for blinding.
The cell lines from ATCC were not authenticated because these cell lines were rigorously authenticated by ATCC. All cell lines were obtained from ATCC or collaborators and were expanded and frozen at low passages.
Cell lines are not yet tested for mycoplasma contamination.
No commonly misidentified lines were used in this study.