Autocatalytic base editing for RNA-responsive translational control

Raphaël V. Gayet, Katherine Ilia, Shiva Razavi, Nathaniel D. Tippens, Makoto A. Lalwani, Kehan Zhang, Jack X. Chen, Jonathan C. Chen, Jose Vargas-Asencio and James J. Collins ()
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Raphaël V. Gayet: Massachusetts Institute of Technology (MIT)
Katherine Ilia: Massachusetts Institute of Technology (MIT)
Shiva Razavi: Massachusetts Institute of Technology (MIT)
Nathaniel D. Tippens: Massachusetts Institute of Technology (MIT)
Makoto A. Lalwani: MIT
Kehan Zhang: MIT
Jack X. Chen: Massachusetts Institute of Technology (MIT)
Jonathan C. Chen: Massachusetts Institute of Technology (MIT)
Jose Vargas-Asencio: MIT
James J. Collins: Massachusetts Institute of Technology (MIT)

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36851-z

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