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Autocatalytic base editing for RNA-responsive translational control

Author

Listed:
  • Raphaël V. Gayet

    (Massachusetts Institute of Technology (MIT)
    MIT
    MIT
    Harvard University)

  • Katherine Ilia

    (Massachusetts Institute of Technology (MIT)
    MIT)

  • Shiva Razavi

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

  • Nathaniel D. Tippens

    (Massachusetts Institute of Technology (MIT)
    MIT
    Harvard University)

  • Makoto A. Lalwani

    (MIT
    Harvard University)

  • Kehan Zhang

    (MIT
    Harvard University)

  • Jack X. Chen

    (Massachusetts Institute of Technology (MIT)
    MIT
    Harvard University)

  • Jonathan C. Chen

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

  • Jose Vargas-Asencio

    (MIT)

  • James J. Collins

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

Abstract

Genetic circuits that control transgene expression in response to pre-defined transcriptional cues would enable the development of smart therapeutics. To this end, here we engineer programmable single-transcript RNA sensors in which adenosine deaminases acting on RNA (ADARs) autocatalytically convert target hybridization into a translational output. Dubbed DART VADAR (Detection and Amplification of RNA Triggers via ADAR), our system amplifies the signal from editing by endogenous ADAR through a positive feedback loop. Amplification is mediated by the expression of a hyperactive, minimal ADAR variant and its recruitment to the edit site via an orthogonal RNA targeting mechanism. This topology confers high dynamic range, low background, minimal off-target effects, and a small genetic footprint. We leverage DART VADAR to detect single nucleotide polymorphisms and modulate translation in response to endogenous transcript levels in mammalian cells.

Suggested Citation

  • Raphaël V. Gayet & Katherine Ilia & Shiva Razavi & Nathaniel D. Tippens & Makoto A. Lalwani & Kehan Zhang & Jack X. Chen & Jonathan C. Chen & Jose Vargas-Asencio & James J. Collins, 2023. "Autocatalytic base editing for RNA-responsive translational control," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36851-z
    DOI: 10.1038/s41467-023-36851-z
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    References listed on IDEAS

    as
    1. Jacqueline A. Valeri & Katherine M. Collins & Pradeep Ramesh & Miguel A. Alcantar & Bianca A. Lepe & Timothy K. Lu & Diogo M. Camacho, 2020. "Sequence-to-function deep learning frameworks for engineered riboregulators," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    2. Yongjun Qian & Jiayun Li & Shengli Zhao & Elizabeth A. Matthews & Michael Adoff & Weixin Zhong & Xu An & Michele Yeo & Christine Park & Xiaolu Yang & Bor-Shuen Wang & Derek G. Southwell & Z. Josh Huan, 2022. "Programmable RNA sensing for cell monitoring and manipulation," Nature, Nature, vol. 610(7933), pages 713-721, October.
    3. Nicolaas M. Angenent-Mari & Alexander S. Garruss & Luis R. Soenksen & George Church & James J. Collins, 2020. "A deep learning approach to programmable RNA switches," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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    Cited by:

    1. Yuanli Gao & Lei Wang & Baojun Wang, 2023. "Customizing cellular signal processing by synthetic multi-level regulatory circuits," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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