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Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants

Nature Reviews Microbiology volume 18, pages 67–83 (2020)Cite this article

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Abstract

The number and diversity of known CRISPR–Cas systems have substantially increased in recent years. Here, we provide an updated evolutionary classification of CRISPR–Cas systems and cas genes, with an emphasis on the major developments that have occurred since the publication of the latest classification, in 2015. The new classification includes 2 classes, 6 types and 33 subtypes, compared with 5 types and 16 subtypes in 2015. A key development is the ongoing discovery of multiple, novel class 2 CRISPR–Cas systems, which now include 3 types and 17 subtypes. A second major novelty is the discovery of numerous derived CRISPR–Cas variants, often associated with mobile genetic elements that lack the nucleases required for interference. Some of these variants are involved in RNA-guided transposition, whereas others are predicted to perform functions distinct from adaptive immunity that remain to be characterized experimentally. The third highlight is the discovery of numerous families of ancillary CRISPR-linked genes, often implicated in signal transduction. Together, these findings substantially clarify the functional diversity and evolutionary history of CRISPR–Cas.

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Fig. 1: Updated classification of class 1 CRISPR–Cas systems.
Fig. 2: Updated classification of class 2 CRISPR–Cas systems.
Fig. 3: Distribution of the six types of CRISPR–Cas system in the major archaeal and bacterial phyla.
Fig. 4: Ancillary genes in CRISPR–Cas systems.
Fig. 5

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Acknowledgements

K.S.M., Y.I.W., J.I., S.A.S. and E.V.K. are supported through the Intramural Research Program of the US National Institutes of Health; F.J.M.M. was supported by grants BIO2014-53029-P (Ministerio de Ciencia, Innovación y Universidades, Spain), and 291815 Era-Net ANIHWA (7th Framework Programme, European Commission) and PROMETEO/2017/129 (Conselleria d'Educació, Investigació, Cultura i Esport, Generalitat Valenciana, Spain); S.A.S. was supported by RFBR (research project 18-34-00012) and a Systems Biology Fellowship from Philip Morris Sales and Marketing; S.M. was funded by funding from the Natural Sciences and Engineering Research Council of Canada (Discovery program) and holds a Tier 1 Canada Research Chair in Bacteriophages.

Author information

Authors and Affiliations

  1. National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD, USA

    Kira S. Makarova, Yuri I. Wolf, Jaime Iranzo, Sergey A. Shmakov, Daniel H. Haft & Eugene V. Koonin

  2. Bioinformatics group, Department of Computer Science, University of Freiberg, Freiberg, Germany

    Omer S. Alkhnbashi & Rolf Backofen

  3. Kavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, The Netherlands

    Stan J. J. Brouns

  4. Max Planck Unit for the Science of Pathogens, Humboldt University, Berlin, Germany

    Emmanuelle Charpentier

  5. Arbor Biotechnologies, Cambridge, MA, USA

    David Cheng, David Scott & Winston Yan

  6. DuPont Nutrition and Health, Dangé–Saint–Romain, France

    Philippe Horvath

  7. Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Groupe de recherche en écologie buccale, Félix d’Hérelle Reference Center for Bacterial Viruses, Faculté de médecine dentaire, Université Laval, Québec City, Québec, Canada

    Sylvain Moineau

  8. Departamento de Fisiología, Genética y Microbiología, Universidad de Alicante, Alicante, Spain

    Francisco J. M. Mojica

  9. COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Gentofte, Denmark

    Shiraz A. Shah

  10. Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania

    Virginijus Siksnys & Česlovas Venclovas

  11. Biochemistry and Molecular Biology, Genetics and Microbiology, University of Georgia, Athens, GA, USA

    Michael P. Terns

  12. Biomedical Sciences Research Complex, University of St. Andrews, St. Andrews, UK

    Malcolm F. White

  13. Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada

    Alexander F. Yakunin

  14. Centre for Environmental Biotechnology, School of Natural Sciences, Bangor University, Bangor, Gwynedd, UK

    Alexander F. Yakunin

  15. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Feng Zhang

  16. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Feng Zhang

  17. Howard Hughes Medical Institute, Cambridge, MA, USA

    Feng Zhang

  18. Department of Brain and Cognitive Sciences and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Feng Zhang

  19. Archaea Centre, Department of Biology, Copenhagen University, Copenhagen, Denmark

    Roger A. Garrett

  20. BIOSS Centre for Biological Signaling Studies, Cluster of Excellence, University of Freiburg, Freiburg, Germany

    Rolf Backofen

  21. Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands

    John van der Oost

  22. Department of Food, Bioprocessing, and Nutrition Sciences, North Carolina State University, Raleigh, NC, USA

    Rodolphe Barrangou

Authors
  1. Kira S. Makarova
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  2. Yuri I. Wolf
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  3. Jaime Iranzo
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  4. Sergey A. Shmakov
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  5. Omer S. Alkhnbashi
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  6. Stan J. J. Brouns
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  7. Emmanuelle Charpentier
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  8. David Cheng
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  9. Daniel H. Haft
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  10. Philippe Horvath
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  11. Sylvain Moineau
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  12. Francisco J. M. Mojica
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  13. David Scott
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  14. Shiraz A. Shah
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  15. Virginijus Siksnys
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  16. Michael P. Terns
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  17. Česlovas Venclovas
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  18. Malcolm F. White
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  19. Alexander F. Yakunin
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  20. Winston Yan
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  21. Feng Zhang
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  22. Roger A. Garrett
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  23. Rolf Backofen
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  24. John van der Oost
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  25. Rodolphe Barrangou
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  26. Eugene V. Koonin
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Contributions

K.S.M., Y.I.W., J.I., S.A.S., D.C., Č.V. and E.V.K. researched data for the article. K.S.M., Y.I.W., S.J.J.B., O.S.A., E.C., D.C., D.H.H., P.H., S.M., F.J.M.M., D.S., S.A.A., V.S., M.P.T., Č.V., M.F.W., A.F.Y., W.Y., F.Z., R.A.G., R.B., J.v.d.O., R.B. and E.V.K. substantially contributed to discussion of the content. K.S.M., J.I., Y.I.W. and E.V.K. wrote the article. K.S.M., Y.I.W, S.M., R.A.G, J.v.d.O., R.B. and E.V.K. reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Eugene V. Koonin.

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Supplementary information

Glossary

CRISPR

Clustered regularly interspaced short palindromic repeats, present in most archaeal and many bacterial genomes.

Cas

CRISPR-associated (proteins).

Adaptation

First stage of the CRISPR–Cas response that involves spacer acquisition.

Interference

Final stage of the CRISPR–Cas response, which involves recognition and cleavage of the target DNA or RNA.

Protospacer-adjacent motif

(PAM). A short nucleotide sequence next to the protospacer that is required for target recognition by the crRNA effector.

Protospacer

Segment of DNA (typically, from a virus or plasmid) that is acquired by CRISPR–Cas systems via the activity of the adaptation complex.

CRISPR array

Genomic locus containing multiple, tandem CRISPR.

Spacer

Unique segment of DNA inserted between CRISPR units.

CRISPR–cas

Archaeal and bacterial system of adaptive immunity that consists of a CRISPR array and cas genes.

pre-crRNA

Long transcript of a CRISPR locus that is processed to yield the crRNA CRISPR–Cas system, where it is incorporated as a spacer.

crRNAs

Short RNA molecules containing the spacer sequence and parts of the CRISPR, used as the guide to target and cleave cognate foreign DNA or RNA.

Transposon

A mobile genetic element, typically flanked by inverted terminal repeats, that changes its location in the host genome by inserting into new sites with the help of a transposon-encoded enzyme known as transposase, integrase or recombinase.

Casposon

A member of a distinct class of transposons that employ a Cas1 homologue as the transposases and are thought to be the ancestors of CRISPR–Cas adaptation modules.

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Makarova, K.S., Wolf, Y.I., Iranzo, J. et al. Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol 18, 67–83 (2020). https://doi.org/10.1038/s41579-019-0299-x

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