Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling
Jason Fontana,
David Sparkman-Yager,
Ian Faulkner,
Ryan Cardiff,
Cholpisit Kiattisewee,
Aria Walls,
Tommy G. Primo,
Patrick C. Kinnunen,
Hector Garcia Martin,
Jesse G. Zalatan () and
James M. Carothers ()
Additional contact information
Jason Fontana: University of Washington
David Sparkman-Yager: University of Washington
Ian Faulkner: University of Washington
Ryan Cardiff: University of Washington
Cholpisit Kiattisewee: University of Washington
Aria Walls: University of Washington
Tommy G. Primo: University of Washington
Patrick C. Kinnunen: Lawrence Berkeley National Laboratory
Hector Garcia Martin: Lawrence Berkeley National Laboratory
Jesse G. Zalatan: University of Washington
James M. Carothers: University of Washington
Nature Communications, 2024, vol. 15, issue 1, 1-16
Abstract:
Abstract Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50528-1
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DOI: 10.1038/s41467-024-50528-1
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