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Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Author

Listed:
  • Jason Fontana

    (University of Washington
    University of Washington
    University of Washington)

  • David Sparkman-Yager

    (University of Washington
    University of Washington)

  • Ian Faulkner

    (University of Washington
    University of Washington)

  • Ryan Cardiff

    (University of Washington
    University of Washington
    University of Washington)

  • Cholpisit Kiattisewee

    (University of Washington
    University of Washington
    University of Washington)

  • Aria Walls

    (University of Washington
    University of Washington)

  • Tommy G. Primo

    (University of Washington
    University of Washington)

  • Patrick C. Kinnunen

    (Lawrence Berkeley National Laboratory
    DOE Joint BioEnergy Institute
    DOE Agile BioFoundry)

  • Hector Garcia Martin

    (Lawrence Berkeley National Laboratory
    DOE Joint BioEnergy Institute
    DOE Agile BioFoundry)

  • Jesse G. Zalatan

    (University of Washington
    University of Washington)

  • James M. Carothers

    (University of Washington
    University of Washington)

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.

Suggested Citation

  • 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 & James M. Caro, 2024. "Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50528-1
    DOI: 10.1038/s41467-024-50528-1
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    References listed on IDEAS

    as
    1. Giulia I. Corsi & Kunli Qu & Ferhat Alkan & Xiaoguang Pan & Yonglun Luo & Jan Gorodkin, 2022. "CRISPR/Cas9 gRNA activity depends on free energy changes and on the target PAM context," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Jason Fontana & Chen Dong & Cholpisit Kiattisewee & Venkata P. Chavali & Benjamin I. Tickman & James M. Carothers & Jesse G. Zalatan, 2020. "Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Xi Xiang & Giulia I. Corsi & Christian Anthon & Kunli Qu & Xiaoguang Pan & Xue Liang & Peng Han & Zhanying Dong & Lijun Liu & Jiayan Zhong & Tao Ma & Jinbao Wang & Xiuqing Zhang & Hui Jiang & Fengping, 2021. "Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    4. Yichao Han & Wanji Li & Alden Filko & Jingyao Li & Fuzhong Zhang, 2023. "Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    5. Hsin-Ho Huang & Massimo Bellato & Yili Qian & Pablo Cárdenas & Lorenzo Pasotti & Paolo Magni & Domitilla Del Vecchio, 2021. "dCas9 regulator to neutralize competition in CRISPRi circuits," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
    6. Chen Dong & Jason Fontana & Anika Patel & James M. Carothers & Jesse G. Zalatan, 2018. "Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    7. Tijana Radivojević & Zak Costello & Kenneth Workman & Hector Garcia Martin, 2020. "A machine learning Automated Recommendation Tool for synthetic biology," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    8. Chen Dong & Jason Fontana & Anika Patel & James M. Carothers & Jesse G. Zalatan, 2018. "Author Correction: Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
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