Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Species- and site-specific genome editing in complex bacterial communities

Nature Microbiology volume 7, pages 34–47 (2022)Cite this article

Subjects

Abstract

Understanding microbial gene functions relies on the application of experimental genetics in cultured microorganisms. However, the vast majority of bacteria and archaea remain uncultured, precluding the application of traditional genetic methods to these organisms and their interactions. Here, we characterize and validate a generalizable strategy for editing the genomes of specific organisms in microbial communities. We apply environmental transformation sequencing (ET-seq), in which nontargeted transposon insertions are mapped and quantified following delivery to a microbial community, to identify genetically tractable constituents. Next, DNA-editing all-in-one RNA-guided CRISPR–Cas transposase (DART) systems for targeted DNA insertion into organisms identified as tractable by ET-seq are used to enable organism- and locus-specific genetic manipulation in a community context. Using a combination of ET-seq and DART in soil and infant gut microbiota, we conduct species- and site-specific edits in several bacteria, measure gene fitness in a nonmodel bacterium and enrich targeted species. These tools enable editing of microbial communities for understanding and control.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: ET-seq for quantitative measurement of insertion efficiency in a microbial community.
Fig. 2: Benchmarking all-in-one conjugative targeted editing vectors.
Fig. 3: Selection-free targeted editing and mutant tracking in the synthetic soil consortium.
Fig. 4: Enrichment of targeted strains in microbial communities.
Fig. 5: Strain-resolved targeted editing in the infant gut microbiota.

Data availability

Source data are provided with this paper. Summary data for genomes, plasmids and oligonucleotides used in this study can be found in Table 1 and Supplementary Tables 1 and 3–5. Sequence data for all genomes assembled as part of this study are available at NCBI under bioproject ID PRJNA774280. For accession numbers associated with genomes assembled in previous studies, please see Supplementary Table 1. Genomes and sequences used in the project will also be made available on ggKbase (https://ggkbase.berkeley.edu/). Full plasmid sequences are available in Supplementary Table 3. Raw count data for all experiments, including both metagenome and ET-seq information, are available at https://github.com/SDmetagenomics/ETsuite/tree/master/manuscript_data. VcDART and ShDART plasmids will be made available through Addgene. Plasmids, oligonucleotides and microbial isolates used in this manuscript will also be made available from the authors upon request.

Code availability

Custom R scripts for ET-seq analysis and code used in the construction of figures are available at https://github.com/SDmetagenomics/ETsuite (https://doi.org/10.5281/zenodo.5597397).

References

  1. Steen, A. D. et al. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 13, 3126–3130 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pascual-García, A., Bonhoeffer, S. & Bell, T. Metabolically cohesive microbial consortia and ecosystem functioning. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190245 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Fux, C. A., Shirtliff, M., Stoodley, P. & Costerton, J. W. Can laboratory reference strains mirror ‘real-world’ pathogenesis? Trends Microbiol. 13, 58–63 (2005).

    Article  CAS  PubMed  Google Scholar 

  4. Pukall, R., Tschäpe, H. & Smalla, K. Monitoring the spread of broad host and narrow host range plasmids in soil microcosms. FEMS Microbiol. Ecol. 20, 53–66 (1996).

    Article  CAS  Google Scholar 

  5. De Gelder, L., Vandecasteele, F. P. J., Brown, C. J., Forney, L. J. & Top, E. M. Plasmid donor affects host range of promiscuous IncP-1β Plasmid pB10 in an activated-sludge microbial community. Appl. Environ. Microbiol. 71, 5309–5317 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Musovic, S., Oregaard, G., Kroer, N. & Sørensen, S. J. Cultivation-independent examination of horizontal transfer and host range of an IncP-1 plasmid among Gram-positive and Gram-negative bacteria indigenous to the barley rhizosphere. Appl. Environ. Microbiol. 72, 6687–6692 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Musovic, S., Klümper, U., Dechesne, A., Magid, J. & Smets, B. F. Long-term manure exposure increases soil bacterial community potential for plasmid uptake. Environ. Microbiol. Rep. 6, 125–130 (2014).

    Article  CAS  PubMed  Google Scholar 

  8. Klümper, U. et al. Broad host range plasmids can invade an unexpectedly diverse fraction of a soil bacterial community. ISME J. 9, 934–945 (2015).

    Article  PubMed  Google Scholar 

  9. Ronda, C., Chen, S. P., Cabral, V., Yaung, S. J. & Wang, H. H. Metagenomic engineering of the mammalian gut microbiome in situ. Nat. Methods 16, 167–170 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Brophy, J. A. N. et al. Engineered integrative and conjugative elements for efficient and inducible DNA transfer to undomesticated bacteria. Nat. Microbiol. 3, 1043–1053 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Farzadfard, F., Gharaei, N., Citorik, R. J. & Lu, T. K. Efficient retroelement-mediated DNA writing in bacteria. Cell Syst. 12, 860–872 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Vo, P. L. H. et al. CRISPR RNA-guided integrases for high-efficiency, multiplexed bacterial genome engineering. Nat. Biotechnol. 39, 480–489 (2020).

    Article  PubMed  Google Scholar 

  13. Hsu, B. B., Way, J. C. & Silver, P. A. Stable neutralization of a virulence factor in bacteria using temperate phage in the mammalian gut. mSystems 5, e00013–e00020 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hsu, B. B. et al. In situ reprogramming of gut bacteria by oral delivery. Nat. Commun. 11, 5030 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sheth, R. U., Cabral, V., Chen, S. P. & Wang, H. H. Manipulating bacterial communities by in situ microbiome engineering. Trends Genet. 32, 189–200 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wu, L. R., Chen, S. X., Wu, Y., Patel, A. A. & Zhang, D. Y. Multiplexed enrichment of rare DNA variants via sequence-selective and temperature-robust amplification. Nat. Biomed. Eng. 1, 714–723 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Strecker, J. et al. RNA-guided DNA insertion with CRISPR-associated transposases. Science 365, 48–53 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Klompe, S. E., Vo, P. L. H., Halpin-Healy, T. S. & Sternberg, S. H. Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration. Nature 571, 219–225 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Petassi, M. T., Hsieh, S.-C. & Peters, J. E. Guide RNA categorization enables target site choice in Tn7-CRISPR-Cas transposons. Cell 183, 1757–1771 (2020).

  20. Lou, Y. C. et al. Infant gut strain persistence is associated with maternal origin, phylogeny, and functional potential including surface adhesion and iron acquisition. Cell Rep. Med. 2, 100393 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Picard, B. et al. The link between phylogeny and virulence in Escherichia coli extraintestinal infection. Infect. Immun. 67, 546–553 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Viladomiu, M. et al. Adherent-invasive E. coli metabolism of propanediol in Crohn’s disease regulates phagocytes to drive intestinal inflammation. Cell Host Microbe 29, 607–619 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Dogan, B. et al. Inflammation-associated adherent-invasive Escherichia coli are enriched in pathways for use of propanediol and iron and M-cell translocation. Inflamm. Bowel Dis. 20, 1919–1932 (2014).

    Article  PubMed  Google Scholar 

  24. Leimbach, A., Hacker, J. & Dobrindt, U. E. coli as an all-rounder: the thin line between commensalism and pathogenicity. Curr. Top. Microbiol. Immunol. 358, 3–32 (2013).

    PubMed  Google Scholar 

  25. Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. 39, 727–736 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Diamond, S. et al. Mediterranean grassland soil C-N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nat. Microbiol. 4, 1356–1367 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. He, C. et al. Genome-resolved metagenomics reveals site-specific diversity of episymbiotic CPR bacteria and DPANN archaea in groundwater ecosystems. Nat. Microbiol. 6, 354–365 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Laurenceau, R. et al. Toward a genetic system in the marine cyanobacterium Prochlorococcus. Access Microbiol. 2, acmi000107 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Adler, B. A. et al. Systematic discovery of salmonella phage-host interactions via high-throughput genome-wide screens. Preprint at https://www.researchgate.net/publication/340988219_Systematic_Discovery_of_Salmonella_Phage-Host_Interactions_via_High-Throughput_Genome-Wide_Screens (2020).

  30. Liu, H. et al. Magic Pools: parallel assessment of transposon delivery vectors in bacteria. mSystems 3, e00143–17 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Egbert, R. G. et al. A versatile platform strain for high-fidelity multiplex genome editing. Nucleic Acids Res. 47, 3244–3256 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kalvari, I. et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 49, D192–D200 (2021).

    Article  CAS  PubMed  Google Scholar 

  33. Kalvari, I. et al. Non-coding RNA analysis using the rfam database. Curr. Protoc. Bioinformatics 62, e51 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Price, M. N. et al. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557, 503–509 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Liu, H. et al. Functional genetics of human gut commensal Bacteroides thetaiotaomicron reveals metabolic requirements for growth across environments. Cell Rep. 34, 108789 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Devon, R. S., Porteous, D. J. & Brookes, A. J. Splinkerettes—improved vectorettes for greater efficiency in PCR walking. Nucleic Acids Res. 23, 1644–1645 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Barquist, L. et al. The TraDIS toolkit: sequencing and analysis for dense transposon mutant libraries. Bioinformatics 32, 1109–1111 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).

    Article  CAS  PubMed  Google Scholar 

  39. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinormatics. 11, 119 (2010).

    Article  Google Scholar 

  40. Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chen, L.-X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Res. 30, 315–333 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Beghain, J., Bridier-Nahmias, A., Le Nagard, H., Denamur, E. & Clermont, O. ClermonTyping: an easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb. Genom. 4, e000192 (2018).

    PubMed Central  Google Scholar 

  44. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Article  Google Scholar 

  45. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhao, L., Liu, Z., Levy, S. F. & Wu, S. Bartender: a fast and accurate clustering algorithm to count barcode reads. Bioinformatics 34, 739–747 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Costello, M. et al. Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms. BMC Genomics 19, 332 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); https://www.R-project.org/

  49. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank M. N. Price for data analysis input, P. Pausch for experimental advice, S. L. McDevitt, E. Wagner and H. Asahara for help with sequencing, B. A. Adler for helpful discussions and T. R. Northen for directional advice. Funding was provided by m-CAFEs Microbial Community Analysis & Functional Evaluation in Soils (m-CAFEs@lbl.gov) a Science Focus Area led by Lawrence Berkeley National Laboratory and supported by the US Department of Energy, Office of Science, Office of Biological & Environmental Research under contract no. DE-AC02-05CH11231. This research was developed with funding from the Defense Advanced Research Projects Agency award no. HR0011-17-2-0043. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government. This material is based upon work supported by the National Science Foundation under award no. 1817593. Support was also provided by the Innovative Genomics Institute at UC Berkeley. J.A.D. is an Investigator of the Howard Hughes Medical Institute. B.E.R. and B.F.C. are supported by the National Institute of General Medical Sciences of the National Institute of Health under award nos. F32GM134694 and F32GM131654. Y.C.L. was supported by a National Institute of Health award (no. RAI092531A). A.L.B. was supported by a Miller Basic Science Research Fellowship at University of California, Berkeley. C.H. was supported by a Camille and Henry Dreyfus Foundation postdoctoral fellowship in environmental chemistry. Schematics used in Figs. 1a, 2a, 3b, 4a, 4c, Extended Data Fig. 2, 5a, 5c and 5e were created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Benjamin E. Rubin, Spencer Diamond, Brady F. Cress.

  2. These authors jointly supervised this work: Jillian F. Banfield, Jennifer A. Doudna.

Authors and Affiliations

  1. Innovative Genomics Institute, University of California, Berkeley, CA, USA

    Benjamin E. Rubin, Spencer Diamond, Brady F. Cress, Yue Clare Lou, Adair L. Borges, Haridha Shivram, Christine He, Michael Xu, Zeyi Zhou, Sara J. Smith, Rachel Rovinsky, Dylan C. J. Smock, Kimberly Tang, Netravathi Krishnappa, Rohan Sachdeva, Jillian F. Banfield & Jennifer A. Doudna

  2. Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA

    Benjamin E. Rubin, Brady F. Cress, Haridha Shivram, Christine He, Michael Xu, Zeyi Zhou, Sara J. Smith, Rachel Rovinsky, Dylan C. J. Smock, Kimberly Tang & Jennifer A. Doudna

  3. Department of Earth and Planetary Science, University of California, Berkeley, CA, USA

    Spencer Diamond, Christine He, Rohan Sachdeva & Jillian F. Banfield

  4. Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA

    Alexander Crits-Christoph, Yue Clare Lou & Adam M. Deutschbauer

  5. Environmental Science, Policy and Management, University of California, Berkeley, CA, USA

    Adair L. Borges & Jillian F. Banfield

  6. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Trenton K. Owens & Adam M. Deutschbauer

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

    Rodolphe Barrangou

  8. School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia

    Jillian F. Banfield

  9. California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA

    Jennifer A. Doudna

  10. Department of Chemistry, University of California, Berkeley, CA, USA

    Jennifer A. Doudna

  11. Howard Hughes Medical Institute, University of California, Berkeley, CA, USA

    Jennifer A. Doudna

  12. Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jennifer A. Doudna

  13. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

    Jennifer A. Doudna

Authors
  1. Benjamin E. Rubin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Spencer Diamond
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brady F. Cress
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexander Crits-Christoph
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yue Clare Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adair L. Borges
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haridha Shivram
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christine He
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Michael Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zeyi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sara J. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Rachel Rovinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Dylan C. J. Smock
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kimberly Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Trenton K. Owens
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Netravathi Krishnappa
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Rohan Sachdeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Rodolphe Barrangou
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Adam M. Deutschbauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jillian F. Banfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jennifer A. Doudna
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.E.R., S.D., B.F.C., R.B., A.M.D., J.F.B. and J.A.D. conceived the work and designed the experiments. B.E.R. led the establishment of microbial communities and development of ET-seq. S.D. led bioinformatics and development of ETSuite. B.F.C. led genetic design and development of DART systems. B.E.R., B.F.C., A.L.B., C.H., M.X., Z.Z., D.C.J.S., K.T., T.K.O., N.K. and R.R. conducted the molecular biology included. S.D., A.C.-C., Y.C.L., H.S., C.H., R.S. and S.J.S. developed and conducted bioinformatic analysis. B.E.R., S.D., B.F.C., Y.C.L., R.B., A.M.D., J.F.B. and J.A.D. analyzed and interpreted data.

Corresponding authors

Correspondence to Jillian F. Banfield or Jennifer A. Doudna.

Ethics declarations

Competing interests

The Regents of the University of California have patents pending related to this work on which B.E.R., S.D., B.F.C., A.M.D., J.F.B. and J.A.D. are inventors. J.A.D. is a cofounder of Caribou Biosciences, Editas Medicine, Scribe Therapeutics, Intellia Therapeutics and Mammoth Biosciences. J.A.D. is a scientific advisory board member of Vertex, Caribou Biosciences, Intellia Therapeutics, eFFECTOR Therapeutics, Scribe Therapeutics, Mammoth Biosciences, Synthego, Algen Biotechnologies, Felix Biosciences, The Column Group and Inari. J.A.D. is a Director at Johnson & Johnson and Tempus and has research projects sponsored by Biogen, Pfizer, AppleTree Partners and Roche. J.F.B. is a founder of Metagenomi. R.B. is a shareholder of Caribou Biosciences, Intellia Therapeutics, Locus Biosciences, Inari, TreeCo and Ancilia Biosciences. All other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Library preparation and data normalization for ET-Seq.

a, ET-Seq requires low-coverage metagenomic sequencing and customized insertion sequencing. Insertion sequencing relies on custom splinkerette adaptors, which minimize non-specific amplification, a digestion step for degradation of delivery vector containing fragments, and nested PCR to enrich for fragments containing insertions with high specificity. The second round of nested PCR adds unique dual index adaptors for Illumina sequencing. b, This insertion sequencing data is first normalized by the reads to internal standard DNA which is added equally to all samples and serves to correct for variation in reads produced per sample. Secondly, it is normalized by the relative metagenomic abundances of the community members.

Extended Data Fig. 2 Measurement and correction of chimeric reads.

a, The response of chimeric reads, measured as total normalized read counts to insertions into wildtype S. meliloti DNA spiked-in before library preparation, to increasing quantities of donor vector. Plot is log10 scaled on the x and y-axis for readability. Dashed lines indicate log-log linear fit to data (R2No Correction = 0.86, n = 7 biological replicates; R2Correction = 0.92, n = 7 biological replicates) b, Frequency of read properties (imperfect insert sequence = single difference in last 5 bp of transposon right end from expected sequence; imperfect host sequence = mismatch in first 3 bp of genomic sequence at transposon genome junction when aligned to host genome) identified as strongly associated with S. meliloti insertions, in which all reads are expected to be chimeric, used as markers for filtering chimeric reads. Box plots indicate median and bound 1st and 3rd quartile, whiskers indicate max/min values (n = 7 biological replicates). Plot is log10 scaled on the y-axis for readability. c, Fraction of insertion mapping reads filtered out of each dataset, for each organism/vector (n = 7 biological replicates) following chimera filtering. Box plots indicate median and bound 1st and 3rd quartile, whiskers indicate max/min values. Plot is log10 scaled on the y-axis for readability.

Source data

Extended Data Fig. 3 ET-Seq determined insertion efficiencies for all nine consortium members as a fraction of the entire community.

ET-Seq determined insertion efficiencies for conjugation, electroporation, and natural transformation on the synthetic soil community (n = 3 biological replicates). The values shown are the estimated fraction a constituent species’s transformed cells make of the total community population. Control samples received no exogenous DNA. Average relative abundance across all samples is indicated in parentheses (n = 18 independent samples).

Source data

Extended Data Fig. 4 Benchmarking DART vectors.

a, E. coli WM3064 to E. coli BL21(DE3) conjugation, transposition, and selection schematic (top) and guide RNAs targeting the lacZ α-fragment of recipient BL21(DE3), which is absent from donor WM3064 (bottom). b,d,f, Percent selectable transposed colonies is calculated as the number of colonies obtained with gentamicin selection divided by total viable colonies in absence of selection. b, Insertion-receiving colonies divided into on- and off-targeted. This was calculated by multiplying % selectable colonies for representative guides in d and f (highlighted by grey bars) by the on- or off-target rates (shown in Fig. 2b). c, Transposition with VcDART was tested using three promoters. The variant using the Plac promoter, harvested from pHelper_ShCAST_sgRNA16, was also used for Figs. 2–5 and Extended Data Figs. 4b, 5, 6, and 8. d, Efficiencies of VcDART using various promoters. e, Transposition with ShDART was tested with three transcriptional configurations, all using Plac16. The configuration used for characterization of ShCasTn originally16 was also used for Fig. 2 and Extended Data Fig. 4b. f, Efficiencies of ShDART using various promoters. b, d, f, Crossbar indicates mean and error bars indicate one standard deviation from the mean (n = 3 biological replicates). Guide RNAs ending in ‘NT’ are non-targeting negative control samples.

Source data

Extended Data Fig. 5 Sanger sequencing of VcDART mutants from the synthetic soil microbial community.

a, Representative Sanger sequencing chromatogram of PCR product spanning transposon insertion site at targeted pyrF locus in K. michiganensis and b, in P. simiae mutant colonies following VcDART-mediated transposon integration and selection. Target-site duplications (TSD) are indicated with dashed boxes.

Extended Data Fig. 6 Insertion counts in Ralstonia sp. after metabolic enrichment for P. simiae.

a, Raw number of paired end reads in shotgun sequencing analysis detected as spanning a transposon-genome junction for the P. simiae and Ralstonia sp. genomes in each of three replicate enrichment samples. b, Number of paired end reads detected normalized to the coverage of each genome within each respective sample. The mean number of inserts normalized to coverage were compared between P. simiae and Ralstonia sp. (MeanPsim = 0.1250; MeanRal = 0.0042) and were significantly different (P-value = 0.00058; two-sample t-test).

Source data

Extended Data Fig. 7 Relative abundance of stool sample inoculum and infant gut community used for VcDART editing.

The gut microbiome compositions were obtained by read mapping to 1005 reference genomes from Lou et al. 2021. Bar height represents normalized subspecies relative abundance, and bars are colored by strain.

Source data

Extended Data Fig. 8 ET-Seq determined insertion efficiency for the infant gut community.

Insertion efficiency as quantified by ET-Seq for nine microbial species determined to be present by metagenomic sequencing. Experimental samples were conjugated with a donor containing the unguided mariner transposon (pHLL250; n = 3 biological replicates). Control samples did not receive the donor (n = 3 biological replicates). Percentages next to species names indicate their mean relative fraction in the infant gut community, averaged across the 6 biological replicate experiments performed.

Source data

Extended Data Fig. 9 Target site locus and strain comparisons for selective enrichment from the infant gut community.

a, Clinically relevant gene clusters targeted by VcDART for selective enrichment included a locus associated with fimbriae biosynthesis (top) and a propanediol utilization gene cluster (bottom). Insets show mapped reads to these loci in E. coli subsp. 2 and subsp. 3, which were assembled from enrichment culture shotgun sequencing data. The right end of the VcDART transposon cargo was assembled (green), is bridged to the genome, and is supported by paired end read mapping. VcDART target sites (protospacer) are indicated in dark red. b, Dendrogram displaying average nucleotide identity differences between all E. coli genomes analyzed as part of the infant gut community. Strains in black were genomes originally recovered from metagenomic assembly in Lou, et al. 2021. Strains in red were assembled out of enrichment cultures in this study.

Extended Data Fig. 10 Location of VcDART transposon insertions in isolated E. coli mutant colonies following infant gut community editing.

a, Insertion orientations and locations relative to target site were determined by locus-specific PCR and Sanger sequencing on colonies picked from selective solid medium after editing the infant gut community with VcDART guided by the fimbriae associated locus-targeting guide RNA and b, the propanediol metabolism locus-targeting guide RNA (n = 3 biological replicates).

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–5.

Source data

Source Data Fig. 1

Numerical Source data for plots.

Source Data Fig. 2

Numerical Source data for plots.

Source Data Fig. 3

Numerical Source data for plots.

Source Data Fig. 4

Numerical Source data for plots.

Source Data Fig. 5

Numerical Source data for plots.

Source Data Extended Data Fig. 2

Numerical Source data for plots.

Source Data Extended Data Fig. 3

Numerical Source data for plots.

Source Data Extended Data Fig. 4

Numerical Source data for plots.

Source Data Extended Data Fig. 6

Numerical Source data for plots.

Source Data Extended Data Fig. 7

Numerical Source data for plots.

Source Data Extended Data Fig. 8

Numerical Source data for plots.

Source Data Extended Data Fig. 10

Numerical Source data for plots.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rubin, B.E., Diamond, S., Cress, B.F. et al. Species- and site-specific genome editing in complex bacterial communities. Nat Microbiol 7, 34–47 (2022). https://doi.org/10.1038/s41564-021-01014-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-021-01014-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research