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Efficient inter-species conjugative transfer of a CRISPR nuclease for targeted bacterial killing

Author

Listed:
  • Thomas A. Hamilton

    (Schulich School of Medicine and Dentistry)

  • Gregory M. Pellegrino

    (Schulich School of Medicine and Dentistry)

  • Jasmine A. Therrien

    (Schulich School of Medicine and Dentistry)

  • Dalton T. Ham

    (Schulich School of Medicine and Dentistry)

  • Peter C. Bartlett

    (Schulich School of Medicine and Dentistry)

  • Bogumil J. Karas

    (Schulich School of Medicine and Dentistry)

  • Gregory B. Gloor

    (Schulich School of Medicine and Dentistry)

  • David R. Edgell

    (Schulich School of Medicine and Dentistry)

Abstract

The selective regulation of bacteria in complex microbial populations is key to controlling pathogenic bacteria. CRISPR nucleases can be programmed to kill bacteria, but require an efficient and broad-host range delivery system to be effective. Here, using an Escherichia coli and Salmonella enterica co-culture system, we show that plasmids based on the IncP RK2 conjugative system can be used as delivery vectors for a TevSpCas9 dual nuclease. Notably, a cis-acting plasmid that encodes the conjugation and CRISPR machinery conjugates from E. coli to S. enterica with high frequency compared to a trans system that separates conjugation and CRISPR machinery. In culture conditions that enhance cell-to-cell contact, conjugation rates approach 100% with the cis-acting plasmid. Targeting of single or multiplexed sgRNAs to non-essential genes results in high S. enterica killing efficiencies. Our data highlight the potential of cis-acting conjugative plasmids as a delivery system for CRISPR nucleases or other microbial-altering agents for targeted bacterial killing.

Suggested Citation

  • Thomas A. Hamilton & Gregory M. Pellegrino & Jasmine A. Therrien & Dalton T. Ham & Peter C. Bartlett & Bogumil J. Karas & Gregory B. Gloor & David R. Edgell, 2019. "Efficient inter-species conjugative transfer of a CRISPR nuclease for targeted bacterial killing," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12448-3
    DOI: 10.1038/s41467-019-12448-3
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    Cited by:

    1. Dalton T. Ham & Tyler S. Browne & Pooja N. Banglorewala & Tyler L. Wilson & Richard K. Michael & Gregory B. Gloor & David R. Edgell, 2023. "A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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