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Substrate Generation for Endonucleases of CRISPR/Cas Systems
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Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling.

Jason Fontana1,2,3, David Sparkman-Yager1,3, Ian Faulkner1,3

  • 1Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA.

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|July 27, 2024
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Summary
This summary is machine-generated.

This study introduces a new computational method to predict and design RNA structures for CRISPR activation systems. This improves control over multi-gene expression for metabolic engineering in bacteria.

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Area of Science:

  • Synthetic Biology
  • Metabolic Engineering
  • Molecular Biology

Background:

  • Optimizing multi-gene expression is crucial for efficient chemical production via engineered metabolic pathways.
  • CRISPR-Cas transcriptional control offers promise for programming gene expression but is limited by guide RNA folding unpredictability.

Purpose of the Study:

  • To correlate guide RNA folding kinetics with CRISPR activation efficacy in E. coli.
  • To develop a predictive parameter for designing synthetic RNA components for CRISPR activation.
  • To engineer orthogonal synthetic CRISPR activation promoters for precise multi-gene control.

Main Methods:

  • Correlated modified guide RNA (scRNA) efficacy with a computational kinetic folding parameter (rS).
  • Utilized the kinetic parameter for forward design of synthetic CRISPR activation promoters.
  • Implemented a system of three synthetic promoters for orthogonal gene activation.
  • Employed combinatorial tuning to profile a 3D design space for metabolic pathway expression.

Main Results:

  • Identified a strong correlation (rS = 0.8) between scRNA folding rate and CRISPR activation efficacy.
  • Successfully designed three synthetic CRISPR activation promoters with orthogonal control (>35-fold activation).
  • Demonstrated variable production of pteridine and human milk oligosaccharide products through pathway optimization.

Conclusions:

  • The developed RNA design approach enhances predictability and control of CRISPR activation systems.
  • This method facilitates combinatorial optimization of metabolic pathways for chemical production.
  • Accelerates the routine design of effective multi-gene regulation programs in bacterial hosts.