High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies.
Jicong CaoEva Maria NovoaZhizhuo ZhangWilliam C W ChenDianbo LiuGigi C G ChoiAlan S L WongClaudia WehrspaunManolis KellisTimothy K LuPublished in: Nature communications (2021)
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.
Keyphrases
- high throughput
- copy number
- single cell
- genome wide
- mitochondrial dna
- gene therapy
- dna methylation
- cell therapy
- endothelial cells
- machine learning
- gene expression
- protein protein
- amino acid
- transcription factor
- deep learning
- stem cells
- molecular docking
- klebsiella pneumoniae
- pluripotent stem cells
- binding protein
- bone marrow