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CRISPR-Cas and restriction–modification systems are compatible and increase phage resistance

Author

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  • Marie-Ève Dupuis

    (de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval
    Groupe de Recherche en Écologie Buccale, Faculté de médecine dentaire, Université Laval)

  • Manuela Villion

    (de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval
    Groupe de Recherche en Écologie Buccale, Faculté de médecine dentaire, Université Laval)

  • Alfonso H. Magadán

    (de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval
    Groupe de Recherche en Écologie Buccale, Faculté de médecine dentaire, Université Laval)

  • Sylvain Moineau

    (de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval
    Groupe de Recherche en Écologie Buccale, Faculté de médecine dentaire, Université Laval
    Félix d’Hérelle Reference Center for Bacterial Viruses)

Abstract

Bacteria have developed a set of barriers to protect themselves against invaders such as phage and plasmid nucleic acids. Different prokaryotic defence systems exist and at least two of them directly target the incoming DNA: restriction–modification (R-M) and CRISPR-Cas systems. On their own, they are imperfect barriers to invasion by foreign DNA. Here, we show that R-M and CRISPR-Cas systems are compatible and act together to increase the overall phage resistance of a bacterial cell by cleaving their respective target sites. Furthermore, we show that the specific methylation of phage DNA does not impair CRISPR-Cas acquisition or interference activities. Taken altogether, both mechanisms can be leveraged to decrease phage contaminations in processes relying on bacterial growth and/or fermentation.

Suggested Citation

  • Marie-Ève Dupuis & Manuela Villion & Alfonso H. Magadán & Sylvain Moineau, 2013. "CRISPR-Cas and restriction–modification systems are compatible and increase phage resistance," Nature Communications, Nature, vol. 4(1), pages 1-7, October.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms3087
    DOI: 10.1038/ncomms3087
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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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