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Phage anti-CRISPR control by an RNA- and DNA-binding helix–turn–helix protein

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Abstract

In all organisms, regulation of gene expression must be adjusted to meet cellular requirements and frequently involves helix–turn–helix (HTH) domain proteins1. For instance, in the arms race between bacteria and bacteriophages, rapid expression of phage anti-CRISPR (acr) genes upon infection enables evasion from CRISPR–Cas defence; transcription is then repressed by an HTH-domain-containing anti-CRISPR-associated (Aca) protein, probably to reduce fitness costs from excessive expression2,3,4,5. However, how a single HTH regulator adjusts anti-CRISPR production to cope with increasing phage genome copies and accumulating acr mRNA is unknown. Here we show that the HTH domain of the regulator Aca2, in addition to repressing Acr synthesis transcriptionally through DNA binding, inhibits translation of mRNAs by binding conserved RNA stem-loops and blocking ribosome access. The cryo-electron microscopy structure of the approximately 40 kDa Aca2–RNA complex demonstrates how the versatile HTH domain specifically discriminates RNA from DNA binding sites. These combined regulatory modes are widespread in the Aca2 family and facilitate CRISPR–Cas inhibition in the face of rapid phage DNA replication without toxic acr overexpression. Given the ubiquity of HTH-domain-containing proteins, it is anticipated that many more of them elicit regulatory control by dual DNA and RNA binding.

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Fig. 1: Aca2 alleviates toxicity of the acrIF8–aca2 operon.
Fig. 2: Aca2 binds RNA structural elements within the acrIF8–aca2 5′ UTR.
Fig. 3: Aca2 represses translation specifically of acrIF8 by blocking ribosome access.
Fig. 4: The mode of Aca2 binding to RNA is distinct from its interactions with DNA.
Fig. 5: Aca2 restrains an initial rapid Acr burst and accumulation as phage DNA replicates.
Fig. 6: DNA and RNA binding sites are conserved in aca2-containing operons and co-vary with the Aca2 protein sequence.

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Data availability

The cryo-EM map of the Aca2–RNA complex has been deposited in the Electron Microscopy Data Bank under accession code EMD-43762. Raw cryo-EM micrographs have been deposited to EMPIAR under accession code EMPIAR-11918. The coordinates of the atomic model have been deposited in the Protein Data Bank under accession code 8W35. Source data are provided with this paper.

Code availability

Code for the mathematical modelling is available at https://github.com/JacksonLab/Modelling_Aca-Acr/ and code for bioinformatic analysis of Aca2 family proteins and their associated motifs is available at https://github.com/MaxFeussner/Aca2_Bioinformatics.

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Acknowledgements

This work was supported by a University of Otago Research Grant, the former Bio-Protection Research Centre (Tertiary Education Commission, New Zealand) and Bioprotection Aotearoa (Tertiary Education Commission, New Zealand). N.B. and K.K. were supported by University of Otago Doctoral Scholarships. P.C.F. was supported by an Experienced Researcher Fellowship from the Alexander von Humboldt Foundation and a James Cook Research Fellowship from the Royal Society Te Apārangi of New Zealand. S.A.J. was supported by the Health Research Council of New Zealand (Sir Charles Hercus Fellowship). M.F. was supported by a fellowship of the German Academic Exchange Service (DAAD) and by German Research Foundation (DFG) grant 468749960 to Z.W. C.L.B. was supported by a European Research Council Consolidator Grant (865973) and the DFG (BE 6703/3-1). M.E.W. was supported by a Helen Hay Whitney Foundation/HHMI postdoctoral fellowship and thanks F. Zhang for funding support. S.C.W. was supported by an Engineering and Physical Sciences Research Council Molecular Sciences for Medicine Centre for Doctoral Training studentship (grant number EP/S022791/1). B.U. was supported by a Springboard Award from the Academy of Medical Sciences (grant number SBF002\1104). T.R.B. was funded by a Lister Institute Prize Fellowship. The authors thank S. Valabhji and T. Arrowsmith for assistance with size-exclusion chromatography; and E. Brignole and C. Borsa for the smooth running the MIT.nano cryo-EM facility, established in part with financial support from the Arnold and Mabel Beckman Foundation. We gratefully acknowledge use of the Otago Micro and Nanoscale Imaging (OMNI) Flow Cytometry unit.

Author information

Author notes

  1. Marijn Ceelen

    Present address: Institute for Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand

    Nils Birkholz, Kotaro Kamata, Marijn Ceelen, Robert D. Fagerlund, Simon A. Jackson & Peter C. Fineran

  2. Bioprotection Aotearoa, University of Otago, Dunedin, New Zealand

    Nils Birkholz, Kotaro Kamata, Robert D. Fagerlund, Simon A. Jackson & Peter C. Fineran

  3. Genetics Otago, University of Otago, Dunedin, New Zealand

    Nils Birkholz, Robert D. Fagerlund, Simon A. Jackson & Peter C. Fineran

  4. Maurice Wilkins Centre for Molecular Biodiscovery, University of Otago, Dunedin, New Zealand

    Nils Birkholz, Robert D. Fagerlund, Simon A. Jackson & Peter C. Fineran

  5. Bioinformatics Group, Department of Computer Science and Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany

    Maximilian Feussner & Zasha Weinberg

  6. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Max E. Wilkinson

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

    Max E. Wilkinson

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

    Max E. Wilkinson

  9. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Max E. Wilkinson

  10. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Max E. Wilkinson

  11. Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Christian Cuba Samaniego

  12. Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg, Germany

    Angela Migur & Chase L. Beisel

  13. CZ Imaging Institute, Redwood City, CA, USA

    Dari Kimanius

  14. Department of Biosciences, Durham University, Durham, UK

    Sam C. Went, Ben Usher & Tim R. Blower

  15. Department of Biochemistry, University of Otago, Dunedin, New Zealand

    Chris M. Brown

  16. Medical Faculty, University of Würzburg, Würzburg, Germany

    Chase L. Beisel

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  1. Nils Birkholz
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Contributions

N.B., R.D.F., S.A.J. and P.C.F. conceived the study. N.B. and P.C.F. coordinated the study. All authors designed experiments. N.B., K.K., M.F., M.E.W., C.C.S., A.M., D.K., M.C., S.C.W., B.U., R.D.F. and S.A.J. performed experiments, modelling and bioinformatics, with additional analyses provided by T.R.B., C.M.B., C.L.B., Z.W. and P.C.F. N.B. generated figures, except for structure figures which were created by M.E.W. N.B. and P.C.F. wrote the manuscript with input from the other authors. N.B., T.R.B., C.M.B., C.L.B., Z.W., R.D.F., S.A.J. and P.C.F. provided supervision.

Corresponding author

Correspondence to Peter C. Fineran.

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Competing interests

C.L.B. is a co-founder of Leopard Biosciences, co-founder and member of the Scientific Advisory Board of Locus Biosciences and a member of the Scientific Advisory Board of Benson Hill. The other authors declare no competing interests.

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Nature thanks Kevin Corbett and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Phage engineering and phage escape mutants.

a) Illustration of the two-step engineering approach for phage ZF40 using the endogenous type I-E CRISPR–Cas system of its host P. carotovorum RC5297. First, the locus to be altered (purple) is deleted using a “deletion plasmid” in which appropriate homologous regions (grey) flank a placeholder. The plasmid also contains a type I-E spacer (red) targeting the original locus, resulting in the selection for phages that have integrated the placeholder. Next, recombinant phages are used to infect cells containing an “addition plasmid” with any locus of choice (yellow) flanked by homology regions; the plasmid also contains a I-E spacer (pink) targeting the placeholder. This results in the selection for and release of phages containing the locus of choice. b) Sequences of wild-type and escape mutant phages in the region surrounding IR1, −10 and −35 motifs. Mutants 1 to 3, with their genotypes indicated, were isolated from plaques formed in the absence of the Aca2 helper plasmids. Point mutations in the sequence are indicated in red.

Extended Data Fig. 2 Investigation of the acrIF8–aca2 5′ UTR and its interaction with Aca2.

a) Sequencing trace of cDNA generated from the P. carotovorum ZM1 transcriptome using a template-switching reverse transcriptase. The 3′ end of the indicated template-switching oligo sequence borders the 5′ end of the transcript, thus allowing identification of the transcription start site. b) Size-exclusion chromatography traces of Aca2 alone or Aca2 incubated with the 37-nt RNA shown on the right.

Extended Data Fig. 3 Comparison of the acrIF8–eyfp reporter operon with the wild-type acrIF8–aca2 operon.

a) Overview of both operons. In the reporter construct, the red octagon indicates a premature stop codon and the downstream acrIF8 sequence is shown in a lighter shade. Areas in grey boxes are shown in panels b and c. b) Detailed view of the promoter and 5′ UTR region of both operons, with regulatory motifs indicated. c) Detailed view of the intergenic region between acrIF8 and aca2 (wild-type operon) or acrIF8 and eyfp (reporter construct). Note that in the reporter construct, the first three codons of aca2 are fused to eyfp.

Extended Data Fig. 4 Aca2 residues involved in DNA and RNA binding.

a) Cryo-EM density of the ~40-kDa Aca2–RNA complex at 2.6 Å resolution. b) Close-up view of residues Arg30 (R30), Tyr34 (Y34) and Arg39 (R39) in the Aca2–RNA complex (interacting with IR2 or IR-RBS) and the Aca2–DNA complex. c) Clustal Omega Alignment of Aca2 homologs from the indicated species (with accession numbers indicated on the right) to the first 60 amino acids of Aca2. Residues involved in DNA and RNA binding are highlighted in purple, the D45 residue only involved in RNA binding in turquoise. Asterisks indicate conserved residues whereas colons and periods indicate conservation between groups of strongly and weakly similar properties, respectively.

Extended Data Fig. 5 Impact of different regulation modes on AcrIF8 levels and production rate.

a) Modelled mean acrIF8–aca2 mRNA levels (solid green line) over a period of 60 min, with the standard deviation indicated by lighter shading. b-d) Acr production rates as in Fig. 5b but for different modes of regulation: only DNA-based transcriptional regulation (b), only RNA-based translational regulation (c), or no regulation (d). The green line in panels b-d represents the fully regulated state for comparison (same data as displayed in Fig. 5b). e) Time courses corresponding to the end point data in Fig. 5e for the indicated modes of regulation and final phage genome copy numbers. All modelling data shown is the mean (solid line) and standard deviation (shading) from 200 simulations. f) Experimental data for plasmids of different origins and unregulated eyfp–aca2 operons, with eYFP fluorescence detected by flow cytometry (mean and standard deviation of four biologically independent replicates). The determined copy numbers for each plasmid are indicated with a circle.

Source Data

Extended Data Fig. 6 Aca2-encoding operons are associated with predicted 5′ UTR RNA motifs.

a) Phylogenetic tree of a set of 145 Aca2 homologs. Coloured boxes at the ends of the branches indicate the bacterial phylum of origin for the corresponding operon, and coloured dots indicate the presence or absence of proteo- and actino-motifs associated with the operons. b) Position plot displaying, relative to the transcription start site, the locations of the DNA binding motif (corresponding to IR1 in the ZF40 acrIF8–aca2 promoter), the actino-motif for RNA-based regulation and the start codon. Approximate locations of promoter motifs (−10, −35) are indicated on the x axis. c) Structure and sequence conservation within the actino-motif and its upstream DNA motif. Coloured boxes represent the presence of any nucleotide at this position, with the frequency indicated by the shade as described on the right; coloured letters indicate the likelihood of the nucleotide identity at this position. d) Co-variation analysis of a subset of aca2-containing operons for the Aca2 HTH domain (helix α3 shown) and the IR1 and IR2 sequences. Co-varying residues are indicated by vertical purple and yellow bars. Note that the nucleotide complementary to the co-varying nucleotide in IR2 (grey bar) does not significantly co-vary, likely because a T at this position can, as a U at the RNA level, base-pair with either A or G, thus requiring fewer evolutionary changes.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics
Full size table

Supplementary information

Supplementary Information

Supplementary Tables 1–7 and references.

Reporting Summary

Supplementary Fig. 1

Uncropped gels. Boxed areas are shown in the indicated figures in the main text.

Supplementary Fig. 2

Modelling parameter sweeps and their impact on Acr levels and production rates. For each parameter, five different values were tested and are indicated in different colours; the intermediate values (black) were used in the base model described in the main text, unless otherwise indicated.

Supplementary Fig. 3

Flow cytometry gating. Cells were gated based on forward and side scatter and eYFP fluorescence of individual cells was measured within the gated cell population.

Supplementary Fig. 4

Cryo-EM data processing. a, Example cryo-EM micrograph of the Aca2–RNA complex. b, 2D class averages of the Aca2–RNA complex. c, Flowchart outlining the cryo-EM data processing scheme.

Supplementary Fig. 5

Cryo-EM map statistics and example densities. a, Gold-standard Fourier shell correlation curve for the final reconstruction. b, Orientation distribution plot for the final reconstruction. c, Map-to-model Fourier shell correlation (calculated in PHENIX), softly masking the map around the fitted model. d, Unsharpened cryo-EM map coloured by local resolution with RELION. e, Example cryo-EM densities.

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Birkholz, N., Kamata, K., Feussner, M. et al. Phage anti-CRISPR control by an RNA- and DNA-binding helix–turn–helix protein. Nature (2024). https://doi.org/10.1038/s41586-024-07644-1

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