IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-24436-7.html
   My bibliography  Save this article

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

Author

Listed:
  • Jicong Cao

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

  • Eva Maria Novoa

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Center for Genomic Regulation (CRG))

  • Zhizhuo Zhang

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • William C. W. Chen

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Dianbo Liu

    (Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology)

  • Gigi C. G. Choi

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    University of Hong Kong)

  • Alan S. L. Wong

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    University of Hong Kong)

  • Claudia Wehrspaun

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Manolis Kellis

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Timothy K. Lu

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

Abstract

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.

Suggested Citation

  • Jicong Cao & Eva Maria Novoa & Zhizhuo Zhang & William C. W. Chen & Dianbo Liu & Gigi C. G. Choi & Alan S. L. Wong & Claudia Wehrspaun & Manolis Kellis & Timothy K. Lu, 2021. "High-throughput 5′ UTR engineering for enhanced protein production in non-viral gene therapies," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24436-7
    DOI: 10.1038/s41467-021-24436-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-24436-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-24436-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pengcheng Zhang & Haochen Wang & Hanwen Xu & Lei Wei & Liyang Liu & Zhirui Hu & Xiaowo Wang, 2023. "Deep flanking sequence engineering for efficient promoter design using DeepSEED," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24436-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.