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Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Author

Listed:
  • Kathrin Leppek

    (Stanford University)

  • Gun Woo Byeon

    (Stanford University)

  • Wipapat Kladwang

    (Stanford University)

  • Hannah K. Wayment-Steele

    (Stanford University)

  • Craig H. Kerr

    (Stanford University)

  • Adele F. Xu

    (Stanford University)

  • Do Soon Kim

    (Stanford University)

  • Ved V. Topkar

    (Stanford University)

  • Christian Choe

    (Stanford University)

  • Daphna Rothschild

    (Stanford University)

  • Gerald C. Tiu

    (Stanford University)

  • Roger Wellington-Oguri

    (Stanford University)

  • Kotaro Fujii

    (Stanford University)

  • Eesha Sharma

    (Stanford University)

  • Andrew M. Watkins

    (Stanford University)

  • John J. Nicol

    (Stanford University)

  • Jonathan Romano

    (Stanford University
    State University of New York at Buffalo, Buffalo)

  • Bojan Tunguz

    (Stanford University
    NVIDIA Corporation)

  • Fernando Diaz

    (Pfizer Vaccine Research and Development)

  • Hui Cai

    (Pfizer Vaccine Research and Development)

  • Pengbo Guo

    (Pfizer Vaccine Research and Development)

  • Jiewei Wu

    (Pfizer Vaccine Research and Development)

  • Fanyu Meng

    (Pfizer Vaccine Research and Development)

  • Shuai Shi

    (Pfizer Vaccine Research and Development)

  • Eterna Participants

    (Stanford University)

  • Philip R. Dormitzer

    (Pfizer Vaccine Research and Development
    GlaxoSmithKline)

  • Alicia Solórzano

    (Pfizer Vaccine Research and Development)

  • Maria Barna

    (Stanford University)

  • Rhiju Das

    (Stanford University
    Stanford University
    Stanford University)

Abstract

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured “superfolder” mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Suggested Citation

  • Kathrin Leppek & Gun Woo Byeon & Wipapat Kladwang & Hannah K. Wayment-Steele & Craig H. Kerr & Adele F. Xu & Do Soon Kim & Ved V. Topkar & Christian Choe & Daphna Rothschild & Gerald C. Tiu & Roger We, 2022. "Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28776-w
    DOI: 10.1038/s41467-022-28776-w
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    References listed on IDEAS

    as
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    Cited by:

    1. Tin-Lok Wong & Jia-Jian Loh & Shixun Lu & Helen H. N. Yan & Hoi Cheong Siu & Ren Xi & Dessy Chan & Max J. F. Kam & Lei Zhou & Man Tong & John A. Copland & Leilei Chen & Jing-Ping Yun & Suet Yi Leung &, 2023. "ADAR1-mediated RNA editing of SCD1 drives drug resistance and self-renewal in gastric cancer," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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