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Developing Nucleic Acid-Based Sensors and Actuators

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Author(s)
Gayet, Raphaël Vincent
Advisor(s)
Collins, James J.
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
Abstract
As the field of synthetic biology matures, engineers are tackling increasingly ambitious problems that require the integration of regulatory logic in complex environments. Nucleic acids are attractive molecules for designing sense-and-respond modules: they are ubiquitous, information-rich and interact with each other through simple rules. Here, through two examples, I show that nucleic acids are particularly suited to create programmable molecular tools, in which inputs and outputs are defined independently from each other. In the first half of this thesis, I describe the development of a strategy to design nucleic acid-responsive materials using the CRISPR-associated nuclease Cas12a as a user-programmable sensor and material actuator. I exploit the programmability of Cas12a to actuate hydrogels containing DNA as an anchor for pendant groups or as a structural element. This versatile approach improves on the sensitivity of current DNA-responsive materials while enabling their rapid repurposing toward new sequence targets. In the second half of this thesis, I describe how to engineer programmable single-transcript RNA sensors in vivo, in which adenosine deaminases acting on RNA (ADARs) autocatalytically convert target hybridization into a translational output. This system amplifies the signal from editing by endogenous ADAR through a positive feedback loop. This topology confers high dynamic range, low background, minimal off-target effects, and a small genetic footprint. I envision that the approaches described here have broad applications from basic science to advanced diagnostics and therapeutics, illustrating the great potential of programmable nucleic acid-based controllers.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Microbiology Graduate Program
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https://hdl.handle.net/1721.1/148610
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