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A predicted CRISPR-mediated symbiosis between uncultivated archaea

Nature Microbiology volume 8, pages 1619–1633 (2023)Cite this article

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Abstract

CRISPR–Cas systems defend prokaryotic cells from invasive DNA of viruses, plasmids and other mobile genetic elements. Here, we show using metagenomics, metatranscriptomics and single-cell genomics that CRISPR systems of widespread, uncultivated archaea can also target chromosomal DNA of archaeal episymbionts of the DPANN superphylum. Using meta-omics datasets from Crystal Geyser and Horonobe Underground Research Laboratory, we find that CRISPR spacers of the hosts Candidatus Altiarchaeum crystalense and Ca. A. horonobense, respectively, match putative essential genes in their episymbionts’ genomes of the genus Ca. Huberiarchaeum and that some of these spacers are expressed in situ. Metabolic interaction modelling also reveals complementation between host–episymbiont systems, on the basis of which we propose that episymbionts are either parasitic or mutualistic depending on the genotype of the host. By expanding our analysis to 7,012 archaeal genomes, we suggest that CRISPR–Cas targeting of genomes associated with symbiotic archaea evolved independently in various archaeal lineages.

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Fig. 1: Phylogenetic positioning of Ca. Altiarchaea and Ca. Huberarchaea, sampling locations, FISH analysis and CRISPR–Cas targets.
Fig. 2: Example of Ca. Altiarchaea CRISPR–Cas type I-B loci, gene targets on host and episymbionts’ genomes and metabolic interactions between Ca. Altiarchaea and Ca. Huberiarchaea as inferred from genome-scaled metabolic modelling.
Fig. 3: Illustration of the newly proposed functionality of CRISPR–Cas systems within Ca. Altiarchaea.
Fig. 4: Directed spacer interaction of DPANN archaea derived from the analysis of 7,012 publicly available archaeal genomes.

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Data availability

Metagenomic datasets generated from the CG20,27 ecosystem in 2009, 2014 and 2015 (n = 66) and the HURL24 environment (n = 2) were downloaded from the NCBI SRA in April 2019 (Supplementary Table 1). SAGs generated in a previous study20 (n = 219) were retrieved from the Joint Genome Institute’s Integrated Microbial Genomes and Microbiomes database111 (Supplementary Table 3). The metagenome-derived genomes of Ca. A. crystalense and Ca. H. crystalense from CG are publicly accessible from NCBI (accession numbers in Supplementary Table 2). The genomes of Ca. A. horonobense and Ca. H. julieae from HURL were newly reconstructed in this investigation (Supplementary Table 2). All previously unpublished genomes used in this study are available in figshare https://doi.org/10.6084/m9.figshare.22339555(ref. 112) and all viral genomes are available at https://doi.org/10.6084/m9.figshare.22738568(ref. 113). All raw FISH images are deposited here: https://doi.org/10.6084/m9.figshare.22739849(ref. 114).

Code availability

The code used in this publication is based on previously published code. Please refer to the Methods for information regarding the software and versions used.

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Acknowledgements

This research was funded by the Ministerium für Kultur und Wissenschaft des Landes Nordrhein- Westfalen (Nachwuchsgruppe Dr. Alexander Probst) and the German Science Foundation under project NOVAC (grant no. DFG PR1603/2-1) and through SPP 2141 (grant no. DFG BE6703/1-1). Genome-scale metabolic modelling was supported by the National Science Foundation under grant no. 1553211. The Ministry of Economy, Trade and Industry of Japan funded a part of the work as ‘The project for validating assessment methodology in geological disposal system’ (2019 FY, grant no. JPJ007597). The work (proposal https://doi.org/10.46936/10.25585/60000800) conducted by the US Department of Energy (DOE) Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the US DOE operated under contract no. DE-AC02-05CH11231. M.P. is supported by the Austrian Science Funds (project MAINTAIN, DOC 69 doc.funds). P.S.A. was supported by a postdoctoral fellowship from the Alexander von Humboldt Foundation. J.P. was supported by Aker BP within the framework of the GeneOil Project given to A.J.P. J.R. was supported by the German Science Foundation (grant no. RA3432/1-1, project no. 446702140). Support by the German Federal Ministry of Education and Research within the project ‘MultiKulti’ (BMBF funding code: 161L0285E) is acknowledged. We thank K. Dreger for exemplary server administration and B. Siebers, I. Berg, J. F. Banfield and B. Meyer for insightful discussions.

Author information

Author notes

  1. Janina Rahlff

    Present address: Centre for Ecology and Evolution in Microbial Model Systems (EEMiS), Department of Biology and Environmental Science, Linnaeus University, Kalmar, Sweden

  2. Weishu Zhao

    Present address: Shanghai Jiao Tong University, School of Life Sciences and Biotechnology, International Center for Deep Life Investigation (IC-DLI), Shanghai Jiao Tong University, Shanghai, China

  3. Katrin Schwank

    Present address: University of Regensburg, Biochemistry III, Regensburg, Germany

  4. These authors contributed equally: Sarah P. Esser, Janina Rahlff.

Authors and Affiliations

  1. Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany

    Sarah P. Esser, Julia Plewka, Katharina Sures, Victoria Turzynski, Indra Banas, Till L. V. Bornemann, Perla Abigail Figueroa-Gonzalez & Alexander J. Probst

  2. Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, University of Duisburg-Essen, Essen, Germany

    Sarah P. Esser, Janina Rahlff, Julia Plewka, Katharina Sures, Panagiotis S. Adam, Victoria Turzynski, Indra Banas, Katrin Schwank, Till L. V. Bornemann, Perla Abigail Figueroa-Gonzalez & Alexander J. Probst

  3. Department of Cell and Molecular Biology, College of the Environment and Life Sciences, University of Rhode Island, Kingston, RI, USA

    Weishu Zhao & Ying Zhang

  4. Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria

    Michael Predl & Thomas Rattei

  5. Doctoral School in Microbiology and Environmental Science, University of Vienna, Vienna, Austria

    Michael Predl & Thomas Rattei

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

    Franziska Wimmer & Chase L. Beisel

  7. DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Janey Lee, Jessica Jarett, Ian K. Blaby, Jan-Fang Cheng & Tanja Woyke

  8. School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

    Julia McGonigle & William J. Brazelton

  9. Institut Pasteur, Université Paris Cité, CNRS UMR6047, Archaeal Virology Unit, Paris, France

    Mart Krupovic

  10. Nuclear Fuel Cycle Engineering Laboratories, Japan Atomic Energy Agency, Tokai, Japan

    Yuki Amano

  11. Medical faculty, University of Würzburg, Würzburg, Germany

    Chase L. Beisel

  12. Centre of Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany

    Alexander J. Probst

  13. Centre of Medical Biotechnology (ZMB), University of Duisburg-Essen, Essen, Germany

    Alexander J. Probst

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  1. Sarah P. Esser
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Contributions

S.P.E. and A.J.P. performed genome-resolved metagenomics, while S.P.E. and J.R. performed viromics. J.R. analysed viral genomes with input from M.K. CRISPR–Cas analyses were done by S.P.E., J.R. and A.J.P. SNP analysis was performed by M.P. and T.R. Genome-scale modelling was conducted by W.Z. and Y.Z. with input from S.P.E., P.A.F.G. and A.J.P. J.P. conducted the sliding window analysis with the input from S.P.E., J.R., and A.J.P. Phylogenomic analyses were carried out by P.S.A. T.L.V.B. provided bioinformatic assistance and K. Schwank, I.B. and V.T. performed microscopy and initial metabolic analyses. J.M. and W.B. resampled CG and J.L., T.W. and A.J.P. conducted RNA extraction and sequencing and S.P.E. analysed transcriptomes. J-F.C. synthesized the Cas genes with input from I.K.B., F.W. and C.B. performed binding, cleavage and PAM assays and J.L., J.J., Y.A., T.W. and A.J.P. generated/provided raw data. K. Sures and S.P.E. analysed the archaeal CRISPR–Cas interactions from published NCBI archaeal genomes. A.J.P. conceptualized the work. S.P.E., J.R., W.Z., Y.Z. and A.J.P. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Alexander J. Probst.

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Extended data

Extended Data Fig. 1 Correlation of repeat abundance and abundance of Ca. Altiarchaea genomes.

Spearman rank correlation (two-tailed) of logarithmic abundances of Ca. A. crystalense and logarithmic abundances of repeat sequences of the unassigned CRISPR array (p-value < 3.4 e−16) and the CRISPR system type I-B (p < 2.2 e−16) in metagenomes from CG (n = 66). The grey area depicts the confidence interval of 0.95. The line indicates that the correlation of the genome abundance and repeat abundance is linear. Visualization was performed with R87,117 (version 3.6.1).

Extended Data Fig. 2 Viral clusters predicted by VIRIDIC79.

Heatmap showing intergenomic similarity for viral scaffolds of viral clusters (VC_XY) and some singletons (black). Colouring of viral OTUs (vOTUs) according to Supplementary Table 6. VC_09, _12, _13 determined by the other tools were not found by VIRIDIC. Only scaffolds with intergenomic similarity of >10 between two viral scaffolds are shown.

Extended Data Fig. 3 Coverage analyses of scaffolds targeted by spacers from Ca. Altiarchaea.

Coverage changes within targeted regions by CRISPR system type I-B of Ca. Altiarchaeum and Ca. Huberiarchaeum based on metagenomic read mapping. The vertically grey marked regions are spacer targeted regions of either Ca. Altiarchaeum or Ca. Huberiarchaeum, whereby the horizontally dark grey lines are showing the average coverage of the scaffold. The coloured graphs show the coverage across the spacer targeted region of three samples from the minor eruption phase, where Ca. Altiarchaeum is the most abundant organism (Supplementary Fig. 1).

Extended Data Fig. 4 Spacer targeting analyses of publicly available archaeal genomes.

Directed spacer analysis of 7,012 publicly available archaeal genomes (Supplementary Table 4) shows large clusters of spacers targeting at species level. The targeting spacers (edges) of the genomes Sulfolobus, Methanomicrobia and Halobacterium (nodes) form large clusters performing self-targeting or targeting other genomes of the same family. Edges are colored according to their relationship at least familiy level or lower. The clustering was illustrated with Cytoscape83 (version 3.9.1). Please note that targeting within the same genus might limit the interspecies recombination, as demonstrated in haloarchaea37, or reflect the presence of multiple conserved genomic regions between the genomes.

Supplementary information

Supplementary Information

Supplementary Results, Table 7 and Figs. 1–9.

Reporting Summary

Supplementary Data

Collection of all phylogenetic trees calculated for this study. (1) Phylogenetic tree of Ca. Altiarchaeum crystalense/horonobense and Ca. Huberiarchaeum crystalense/julieae located within the archaeal branch. (2) Phylogenetic analysis of the phenylalanine-tRNA synthetase of Ca. Huberiarchaeum crystalense located within the archaeal branch. Calculated to determine the closest relative within the archaeal branch. (3) Phylogenetic analysis of the phenylalanine-tRNA synthetase of Ca. Altiarchaeum crystalense located within the archaeal branch. Calculated to determine the closest relative within the archaeal branch. (4) Phylogenetic analysis of the lysine-tRNA synthetase of Ca. Huberiarchaeum crystalense located within the archaeal branch. Calculated to determine the closest relative within the archaeal branch. (5) Phylogenetic analysis of the lysine-tRNA synthetase of Ca. Altiarchaeum crystalense located within the archaeal branch. Calculated to determine the closest relative within the archaeal branch.

Supplementary Tables

Supplementary Tables 1–6 and 8–13.

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Esser, S.P., Rahlff, J., Zhao, W. et al. A predicted CRISPR-mediated symbiosis between uncultivated archaea. Nat Microbiol 8, 1619–1633 (2023). https://doi.org/10.1038/s41564-023-01439-2

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