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Enabling genetic analysis of diverse bacteria with Mobile-CRISPRi

Nature Microbiology volume 4, pages 244–250 (2019)Cite this article

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Abstract

The vast majority of bacteria, including human pathogens and microbiome species, lack genetic tools needed to systematically associate genes with phenotypes. This is the major impediment to understanding the fundamental contributions of genes and gene networks to bacterial physiology and human health. Clustered regularly interspaced short palindromic repeats interference (CRISPRi), a versatile method of blocking gene expression using a catalytically inactive Cas9 protein (dCas9) and programmable single guide RNAs, has emerged as a powerful genetic tool to dissect the functions of essential and non-essential genes in species ranging from bacteria to humans1,2,3,4,5,6. However, the difficulty of establishing effective CRISPRi systems across bacteria is a major barrier to its widespread use to dissect bacterial gene function. Here, we establish ‘Mobile-CRISPRi’, a suite of CRISPRi systems that combines modularity, stable genomic integration and ease of transfer to diverse bacteria by conjugation. Focusing predominantly on human pathogens associated with antibiotic resistance, we demonstrate the efficacy of Mobile-CRISPRi in gammaproteobacteria and Bacillales Firmicutes at the individual gene scale, by examining drug–gene synergies, and at the library scale, by systematically phenotyping conditionally essential genes involved in amino acid biosynthesis. Mobile-CRISPRi enables genetic dissection of non-model bacteria, facilitating analyses of microbiome function, antibiotic resistances and sensitivities, and comprehensive screens for host–microorganism interactions.

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Fig. 1: Mobile-CRISPRi overview.
Fig. 2: Mobile-CRISPRi stability, transfer and knockdown efficiency.
Fig. 3: CRISPRi knockdown of folA increases sensitivity to trimethoprim in multiple species.
Fig. 4: A Mobile-CRISPRi library targeting auxotrophic genes in E. cloacae.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon request.

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Acknowledgements

We thank J. Goldberg (Emory University) and H. Schweizer (University of Florida) for Tn7 plasmids, L. (Stanley) Qi (Stanford University) for a plasmid encoding human codon-optimized dCas9, the American Type Culture Collection, H. Mobley (University of Michigan), B. DeGrado (University of California, San Francisco), K. C. Huang (Stanford University), A. Banta (Stanford University) and P. Welander (Stanford University) for strains, J. Garbarino (University of California, San Francisco) and M. Jost (University of California, San Francisco) for help with flow cytometry, and the C.A.G. and O.S.R. labs for helpful comments. This work was supported by the NIH F32 GM108222 (to J.M.P.), the US Department of Agriculture National Institute of Food and Agriculture Hatch Project NYC-189438 (to J.E.P.), NIH R35 GM118061 and Innovative Genomics Institute, UC Berkeley (to C.A.G.), and NIAID R01 AI128214, Chan-Zuckerberg Biohub, CF Foundation Research Development Program, and Gilead Sciences Research Scholars Program in Cystic Fibrosis (to O.S.R).

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  1. Jason M. Peters

    Present address: Pharmaceutical Sciences Division, and Departments of Bacteriology, and of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI, USA

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA

    Jason M. Peters, Byoung-Mo Koo, Ramiro Patino, Cameron C. Hearne, Jiuxin Qu, Yuki F. Inclan, John S. Hawkins, Candy H. S. Lu, Melanie R. Silvis, Hendrik Osadnik, Joanne N. Engel, Carol A. Gross & Oren S. Rosenberg

  2. Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Ramiro Patino, Jiuxin Qu, Yuki F. Inclan, Joanne N. Engel & Oren S. Rosenberg

  3. Division of Biological Sciences, University of California, San Diego, San Diego, CA, USA

    Gary E. Heussler & Rachel J. Dutton

  4. Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, San Diego, CA, USA

    Gary E. Heussler & Rachel J. Dutton

  5. Department of Clinical Laboratory, The Third People’s Hospital of Shenzhen, Shenzhen, China

    Jiuxin Qu

  6. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    M. Michael Harden & Alan D. Grossman

  7. Department of Microbiology, Cornell University, Ithaca, NY, USA

    Joseph E. Peters

  8. Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, USA

    Carol A. Gross

  9. California Institute of Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA

    Carol A. Gross

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Contributions

J.M.P., B.-M.K., M.M.H., A.D.G., J.E.P., J.N.E., R.J.D., C.A.G. and O.S.R. designed the study. J.M.P., B.-M.K., R.P., G.E.H., C.C.H., Y.F.I., C.H.S.L., J.Q. and M.R.S. performed the experiments. J.M.P., B.-M.K., R.P., G.E.H., Y.F.I. and J.S.H. analysed the data. J.M.P., B.-M.K., H.O., C.A.G. and O.S.R. wrote the manuscript.

Corresponding authors

Correspondence to Jason M. Peters, Carol A. Gross or Oren S. Rosenberg.

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Supplementary information

Supplementary Information

Supplementary Figures 1–11.

Reporting Summary

Supplementary Table 1

Primers and oligos used in this study.

Supplementary Table 2

Growth phenotypes for E. cloacae CRISPRi strains in minimal media (pooled screen).

Supplementary Table 3

Growth phenotypes for E. cloacae CRISPRi strains in minimal media (arrayed screen).

Supplementary Table 4

Plasmids used in this study.

Supplementary Table 5

Strains used in this study.

Supplementary Table 6

Next generation sequencing oligos used in this study.

Supplementary Table 7

MIC values for folA knockdown strains.

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Peters, J.M., Koo, BM., Patino, R. et al. Enabling genetic analysis of diverse bacteria with Mobile-CRISPRi. Nat Microbiol 4, 244–250 (2019). https://doi.org/10.1038/s41564-018-0327-z

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