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Selective TnsC recruitment enhances the fidelity of RNA-guided transposition

Nature volume 609, pages 384–393 (2022)Cite this article

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Abstract

Bacterial transposons are pervasive mobile genetic elements that use distinct DNA-binding proteins for horizontal transmission. For example, Escherichia coli Tn7 homes to a specific attachment site using TnsD1, whereas CRISPR-associated transposons use type I or type V Cas effectors to insert downstream of target sites specified by guide RNAs2,3. Despite this targeting diversity, transposition invariably requires TnsB, a DDE-family transposase that catalyses DNA excision and insertion, and TnsC, a AAA+ ATPase that is thought to communicate between transposase and targeting proteins4. How TnsC mediates this communication and thereby regulates transposition fidelity has remained unclear. Here we use chromatin immunoprecipitation with sequencing to monitor in vivo formation of the type I-F RNA-guided transpososome, enabling us to resolve distinct protein recruitment events before integration. DNA targeting by the TniQ–Cascade complex is surprisingly promiscuous—hundreds of genomic off-target sites are sampled, but only a subset of those sites is licensed for TnsC and TnsB recruitment, revealing a crucial proofreading checkpoint. To advance the mechanistic understanding of interactions responsible for transpososome assembly, we determined structures of TnsC using cryogenic electron microscopy and found that ATP binding drives the formation of heptameric rings that thread DNA through the central pore, thereby positioning the substrate for downstream integration. Collectively, our results highlight the molecular specificity imparted by consecutive factor binding to genomic target sites during RNA-guided transposition, and provide a structural roadmap to guide future engineering efforts.

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Fig. 1: In vivo assembly of the RNA-guided transpososome, monitored using ChIP–seq.
Fig. 2: Analysis of off-target binding events by Cascade and downstream transposase factors.
Fig. 3: PAM-distal RNA–DNA mismatches affect TnsC recruitment and RNA-guided transposition.
Fig. 4: Biophysical characterization and overall architecture of the TnsC•ATP heptamer.
Fig. 5: The architecture of the DNA-bound TnsC•ATP heptamer and an updated model for transposition mechanism.

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Data availability

Next-generation sequencing data are available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (BioProject accession: PRJNA759215) and the Gene Expression Omnibus (GSE183114). The published genome used for analyses was obtained from the NCBI (GenBank: CP001509.3). Maps and models for the apo-TnsC heptamer, DNA-bound single-ring complex and DNA-bound double-ring complex have been deposited at the Electron Microscopy Data Bank under accession codes EMDB-24783, EMDB-26476 and EMDB-26477, and the PDB under accession codes 7RZY, 7UFI and 7UFM, respectively. Other atomic models used in this study are available at the PDB under accession codes 7N6I (TniQ-bound ShoTnsC), 7M99 (ShoTnsC), 6VBW (VchTniQ–Cascade), 6V9P (VchTniQ) and 7MCS (EcoTnsC). Datasets generated and analysed in the current study are available from the corresponding authors on reasonable request.

Code availability

Custom scripts used for the described ChIP–seq data analyses are available at GitHub (https://github.com/sternberglab/Hoffmann_Kim_Beh_etal_2022).

References

  1. Bainton, R. J., Kubo, K. M., Feng, J. & Craig, N. L. Tn7 transposition: target DNA recognition is mediated by multiple Tn7-encoded proteins in a purified in vitro system. Cell 72, 931–943 (1993).

    Article  CAS  PubMed  Google Scholar 

  2. 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 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Peters, J. E. Tn7. Microbiol. Spectr. 2, MDNA3-0010-2014 (2014).

  5. Koonin, E. V. Viruses and mobile elements as drivers of evolutionary transitions. Philos. Trans. R. Soc. B 371, 20150442 (2016).

    Article  Google Scholar 

  6. Hickman, A. B. & Dyda, F. Mechanisms of DNA Transposition. Microbiol. Spectr. 3, MDNA3-0034-2014 (2015).

    Article  PubMed  Google Scholar 

  7. Peters, J. E. Targeted transposition with Tn7 elements: safe sites, mobile plasmids, CRISPR/Cas and beyond. Mol. Microbiol. 112, 1635–1644 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sarnovsky, R. J., May, E. W. & Craig, N. L. The Tn7 transposase is a heteromeric complex in which DNA breakage and joining activities are distributed between different gene products. EMBO J. 15, 6348–6361 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Choi, K. Y., Spencer, J. M. & Craig, N. L. The Tn7 transposition regulator TnsC interacts with the transposase subunit TnsB and target selector TnsD. Proc. Natl Acad. Sci. USA 111, E2858–E2865 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Peters, J. E. & Craig, N. L. Tn7 recognizes transposition target structures associated with DNA replication using the DNA-binding protein TnsE. Gene Dev. 15, 737–747 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Stellwagen, A. E. & Craig, N. L. Gain-of-function mutations in TnsC, an ATP-dependent transposition protein that activates the bacterial transposon Tn7. Genetics 145, 573–585 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kuduvalli, P. N., Rao, J. E. & Craig, N. L. Target DNA structure plays a critical role in Tn7 transposition. EMBO J. 20, 924–932 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Stellwagen, A. E. & Craig, N. L. Analysis of gain-of-function mutants of an ATP-dependent regulator of Tn7 transposition. J. Mol. Biol. 305, 633–642 (2001).

    Article  CAS  PubMed  Google Scholar 

  14. Peters, J. E., Makarova, K. S., Shmakov, S. & Koonin, E. V. Recruitment of CRISPR-Cas systems by Tn7-like transposons. Proc. Natl Acad. Sci. USA 114, E7358–E7366 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Faure, G. et al. CRISPR–Cas in mobile genetic elements: counter-defence and beyond. Nat. Rev. Microbiol. 17, 513–525 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Saito, M. et al. Dual modes of CRISPR-associated transposon homing. Cell 184, 2441–2453 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vo, P. L. H. et al. CRISPR RNA-guided integrases for high-efficiency, multiplexed bacterial genome engineering. Nat. Biotechnol. 39, 480–489 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Halpin-Healy, T. S., Klompe, S. E., Sternberg, S. H. & Fernández, I. S. Structural basis of DNA targeting by a transposon-encoded CRISPR–Cas system. Nature 577, 271–274 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Snider, J., Thibault, G. & Houry, W. A. The AAA+ superfamily of functionally diverse proteins. Genome Biol. 9, 216 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Puchades, C., Sandate, C. R. & Lander, G. C. The molecular principles governing the activity and functional diversity of AAA+ proteins. Nat. Rev. Mol. Cell Biol. 21, 43–58 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Holder, J. W. & Craig, N. L. Architecture of the Tn7 posttransposition complex: an elaborate nucleoprotein structure. J. Mol. Biol. 401, 167–181 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Shen, Y. et al. Structural basis for DNA targeting by the Tn7 transposon. Nat. Struct. Mol. Biol. 29, 143–151 (2022).

    Article  CAS  PubMed  Google Scholar 

  23. Park, J.-U. et al. Structural basis for target site selection in RNA-guided DNA transposition systems. Science 373, 768–774 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Querques, I., Schmitz, M., Oberli, S., Chanez, C. & Jinek, M. Target site selection and remodelling by type V CRISPR-transposon systems. Nature 599, 497–502 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vo, P. L. H., Acree, C., Smith, M. L. & Sternberg, S. H. Unbiased profiling of CRISPR RNA-guided transposition products by long-read sequencing. Mob. DNA 12, 13 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ronning, D. R. et al. The carboxy‐terminal portion of TnsC activates the Tn7 transposase through a specific interaction with TnsA. EMBO J. 23, 2972–2981 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Leenay, R. T. & Beisel, C. L. Deciphering, communicating, and engineering the CRISPR PAM. J. Mol. Biol. 429, 177–191 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Kuscu, C., Arslan, S., Singh, R., Thorpe, J. & Adli, M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 32, 677–683 (2014).

    Article  CAS  PubMed  Google Scholar 

  29. Wu, X. et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nat. Biotechnol. 32, 670–676 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cooper, L. A., Stringer, A. M. & Wade, J. T. Determining the specificity of cascade binding, interference, and primed adaptation in vivo in the Escherichia coli type I-E CRISPR-Cas system. mBio 9, e02100-17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Thakore, P. I. et al. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 1143–1149 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. O’Geen, H., Henry, I. M., Bhakta, M. S., Meckler, J. F. & Segal, D. J. A genome-wide analysis of Cas9 binding specificity using ChIP-seq and targeted sequence capture. Nucleic Acids Res. 43, 3389–3404 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Bailey, T. L. et al. MEME suite: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Klompe, S. E. et al. Evolutionary and mechanistic diversity of type I-F CRISPR-associated transposons. Mol. Cell 82, 616–628 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Xiao, Y. et al. Structure basis for directional R-loop formation and substrate handover mechanisms in type I CRISPR-Cas system. Cell 170, 48–60 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Semenova, E. et al. The Cas6e ribonuclease is not required for interference and adaptation by the E. coli type I-E CRISPR-Cas system. Nucleic Acids Res. 43, 6049–6061 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jung, C. et al. Massively parallel biophysical analysis of CRISPR-Cas complexes on next generation sequencing chips. Cell 170, 35–47 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chen, C.-H. et al. Improved design and analysis of CRISPR knockout screens. Bioinformatics 34, 4095–4101 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rutkauskas, M. et al. Directional R-loop formation by the CRISPR-Cas surveillance complex cascade provides efficient off-target site rejection. Cell Rep. 10, 1534–1543 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Young, G. et al. Quantitative mass imaging of single biological macromolecules. Science 360, 423–427 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Mizuno, N. et al. MuB is an AAA+ ATPase that forms helical filaments to control target selection for DNA transposition. Proc. Natl Acad. Sci. USA 110, E2441–E2450 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Davey, M. J. & O’Donnell, M. Replicative helicase loaders: ring breakers and ring makers. Curr. Biol. 13, R594–R596 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Jia, N., Xie, W., de la Cruz, M. J., Eng, E. T. & Patel, D. J. Structure–function insights into the initial step of DNA integration by a CRISPR–Cas–transposon complex. Cell Res. 30, 182–184 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Arinkin, V., Smyshlyaev, G. & Barabas, O. Jump ahead with a twist: DNA acrobatics drive transposition forward. Curr. Opin. Struct. Biol. 59, 168–177 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Walker, D. M., Freddolino, P. L. & Harshey, R. M. A well-mixed E. coli Genome: widespread contacts revealed by tracking Mu transposition. Cell 180, 703–716 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jackson, R. N., van Erp, P. B., Sternberg, S. H. & Wiedenheft, B. Conformational regulation of CRISPR-associated nucleases. Curr. Opin. Microbiol. 37, 110–119 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Schmiedeberg, L., Skene, P., Deaton, A. & Bird, A. A temporal threshold for formaldehyde crosslinking and fixation. PLoS ONE 4, e4636 (2009).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  51. Anzalone, A. V., Koblan, L. W. & Liu, D. R. Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38, 824–844 (2020).

    Article  CAS  PubMed  Google Scholar 

  52. Bonocora, R. P. & Wade, J. T. Bacterial transcriptional control, methods and protocols. Methods Mol. Biol. 1276, 327–340 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 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 

  55. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 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 

  59. Russo, C. J. & Passmore, L. A. Ultrastable gold substrates for electron cryomicroscopy. Science 346, 1377–1380 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. Suloway, C. et al. Automated molecular microscopy: the new Leginon system. J. Struct. Biol. 151, 41–60 (2005).

    Article  CAS  PubMed  Google Scholar 

  61. Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Zivanov, J., Nakane, T. & Scheres, S. H. W. Estimation of high-order aberrations and anisotropic magnification from cryo-EM data sets in RELION-3.1. IUCrJ 7, 253–267 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zivanov, J., Nakane, T. & Scheres, S. H. W. A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis. IUCrJ 6, 5–17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Abrishami, V. et al. Localized reconstruction in Scipion expedites the analysis of symmetry mismatches in cryo-EM data. Prog. Biophys. Mol. Biol. 160, 43–52 (2021).

    Article  CAS  PubMed  Google Scholar 

  65. Casañal, A., Lohkamp, B. & Emsley, P. Current developments in Coot for macromolecular model building of electron Cryo‐microscopy and crystallographic data. Protein Sci. 29, 1055–1064 (2020).

    Article  Google Scholar 

  66. Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).

    Article  ADS  CAS  Google Scholar 

  67. Murshudov, G. N., Vagin, A. A. & Dodson, E. J. Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr. D 53, 240–255 (1997).

    Article  ADS  CAS  PubMed  Google Scholar 

  68. Brown, A. et al. Tools for macromolecular model building and refinement into electron cryo-microscopy reconstructions. Acta Crystallogr. D 71, 136–153 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. Prisant, M. G., Williams, C. J., Chen, V. B., Richardson, J. S. & Richardson, D. C. New tools in MolProbity validation: CaBLAM for CryoEM backbone, UnDowser to rethink “waters,” and NGL Viewer to recapture online 3D graphics. Protein Sci. 29, 315–329 (2020).

    Article  CAS  PubMed  Google Scholar 

  70. Baker, N. A., Sept, D., Joseph, S., Holst, M. J. & McCammon, J. A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl Acad. Sci. USA 98, 10037–10041 (2001).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank N. Jaber and S. R. Pesari for laboratory support; D. J. Villano, X. Chen and C. Lu for discussions about ChIP–seq data analysis; D. Chen for statistics consultation; C. Lu for Covaris sonicator access; N. E. Sanjana for helpful discussions about crRNA library experiments; F. Vallese and O. B. Clarke for assistance with iSCAMS experiments and Refeyn instrument access; L. F. Landweber for qPCR instrument access; and the staff at the JP Sulzberger Columbia Genome Center for NGS support. Cryo-grids were generated and screening data were collected at the Columbia University Cryo-Electron Microscopy Center. Some cryo-EM data collection was performed at the Simons Electron Microscopy Center and the National Resource for Automated Molecular Microscopy, located at the New York Structural Biology Center, supported by grants from the Simons Foundation (SF349247) and NIH National Institute of General Medical Sciences (GM103310), with additional support from NYSTAR and the New York State Assembly Majority. J.T.G. was supported by the International Human Frontier Science Program Organization Postdoctoral Fellowship LT001117/2021-C. This research was supported by NIH grant DP2HG011650-01, NIH grant R01EB031935-01, a Pew Biomedical Scholarship and Sloan Research Fellowship, and a start-up package from the Columbia University Irving Medical Center Dean’s Office and the Vagelos Precision Medicine Fund (to S.H.S.).

Author information

Author notes

  1. Minjoo Kim

    Present address: Department of Biomedical Engineering, New York University Tandon School of Engineering, New York, NY, USA

  2. Leslie Y. Beh

    Present address: Illumina Singapore Pte, Ltd., Singapore, Singapore

  3. Jing Wang

    Present address: The National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA

  4. Phuc Leo H. Vo

    Present address: Vertex Pharmaceuticals, Boston, MA, USA

  5. Jason T. Mohabir

    Present address: Genomic Center for Infectious Diseases, Broad Institute, Cambridge, MA, USA

  6. Israel S. Fernández

    Present address: Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

  7. These authors contributed equally: Florian T. Hoffmann, Minjoo Kim, Leslie Y. Beh

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA

    Florian T. Hoffmann, Minjoo Kim, Leslie Y. Beh, Jing Wang, Diego R. Gelsinger, Jerrin Thomas George, Christopher Acree, Israel S. Fernández & Samuel H. Sternberg

  2. Department of Molecular Pharmacology and Therapeutics, Columbia University, New York, NY, USA

    Phuc Leo H. Vo

  3. Department of Computer Science, Columbia University, New York, NY, USA

    Jason T. Mohabir

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Contributions

L.Y.B. and S.H.S. conceived and designed the initial project, with input from the other authors. F.T.H. performed transposition and ChIP–seq experiments, with guidance from L.Y.B., and performed ChIP–seq data analysis. D.R.G. and C.A. performed additional ChIP–seq data analyses, with input from J.T.M.; P.L.H.V. performed crRNA library and transcriptional repression experiments, with input from L.Y.B., and helped with molecular cloning. P.L.H.V. and C.A. performed crRNA library data analysis. F.T.H., M.K., L.Y.B. and J.T.G. performed TnsC biochemical experiments. I.S.F., M.K. and J.W. collected negative-stain and cryo-EM data. I.S.F. analysed cryo-EM data and determined VchTnsC structures. All of the authors discussed the data and wrote the manuscript.

Corresponding authors

Correspondence to Israel S. Fernández or Samuel H. Sternberg.

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Competing interests

Columbia University has filed patent applications related to CRISPR-transposon systems, for which S.H.S. and P.L.H.V. are listed as inventors. S.H.S. is a co-founder and scientific advisor to Dahlia Biosciences, a scientific advisor to CrisprBits and Prime Medicine, and an equity holder in Dahlia Biosciences and CrisprBits.

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Extended data figures and tables

Extended Data Fig. 1 Design of Flag-tagged transpososome components and analysis of ChIP data.

a, Schematic highlighting the 3×Flag-tag positions in each protein component that was used for ChIP–seq experiments. Molecular weights are indicated in parentheses. b, Simplified schematic of the in vivo transposition assay used to quantify RNA-guided DNA integration in E. coli. Mini-transposon (Mini-Tn) DNA substrates are mobilized from pDonor to genomic DNA, downstream of the target site complementary to crRNA, and integration is quantified by junction PCR. c, RNA-guided transposition assays for 3×Flag-tagged constructs demonstrate that near-wild-type activity is maintained. A single point mutation in the TnsB active site (D223N) completely ablates DNA integration. TnsA-Flag was not pursued further after weak enrichment at the target site was observed (see panel d), and hence was only tested once. NT, non-targeting crRNA control. d, ChIP–seq data for 3×Flag-tagged TnsA with crRNA-4, shown as in Fig. 1c, d, reveal inefficient DNA enrichment at the genomic target site (left). However, the prominent peaks at both transposon ends on pDonor (right) indicate that the protein retains in vivo function and likely exhibits inefficient crosslinking to the target DNA within the pre-integration transpososome complex. Read coverage (RPKM) is scaled to the highest peak (E. coli genome mapping reads) or TnsB ChIP–seq data (pDonor mapping reads). e, Representative ChIP–seq data for experiments with crRNA-287, shown as in Fig. 1c, d, reveal accurate recruitment of multiple distinct factors to the corresponding genomic target site. f, ChIP–seq data reveal robust self-targeting of the CRISPR array across multiple crRNA datasets. Reads mapping to pQCascade are shown for two distinct crRNAs for the indicated Flag-tagged components, and were normalized to the highest peak, except for input samples, which were normalized to the highest peak in their corresponding Cas8 sample. The vector map of pQCascade is shown (top), and the position of the CRISPR spacer is denoted with maroon triangles (bottom). Non-unique reads mapping to lacI were not analysed (dotted lines). g, CRISPR interference (CRISPRi)-based repression of genomically-encoded mRFP using Cascade in the presence (light blue) or absence (ΔTniQ, dark blue) of TniQ. Transcriptional repression is measured as OD-normalized mRFP fluorescence relative to the perfectly matching (PM) crRNA. NT, non-targeting; 1–4, crRNA mismatches at position 1–4; 25–28, crRNA mismatches at position 25–28. h, Target DNA binding by Cascade (crRNA-4) with and without TniQ, as measured by fluorescence polarization. Colouring is as shown in g. i, TnsC may be stabilized in the pre-integration transpososome complex by the presence of TnsAB. ChIP–seq reads mapping to a 3-kb window centred at the genomic target site are shown for 3×Flag-tagged TnsC in experiments that differed only in the presence or absence of TnsA and TnsB (top), with read coverage (RPKM) scaled the same in both graphs. Note that the same data are presented in Fig. 1f. Quantitative PCR was performed on the same ChIP samples alongside a non-targeting control, confirming the diminished target DNA enrichment levels when TnsA-TnsB were omitted (bottom). Relative enrichment is calculated as ΔΔCq between the target locus and reference gene. j, TnsB can be recruited to the RNA-guided target site in the absence of TnsA and donor DNA, albeit with reduced efficiency. ChIP–seq reads mapping to a 3-kb window centred at the genomic target site are shown for 3×Flag-tagged TnsB in experiments that differed only in the presence or absence of TnsA and pDonor, with read coverage (RPKM) scaled the same in all three graphs. Data in c, g, h and i are shown as mean ± s.d. of n = 3 independent biological replicates, except for TnsA-Flag (c, n = 1) and ΔTnsAB (i, n = 1).

Extended Data Fig. 2 Additional analyses of off-target DNA binding from ChIP–seq data.

a, Scatter plot showing correlation between two biological replicates of ChIP–seq experiments with 3×Flag-tagged Cas8 and crRNA-4. ChIP–seq enrichment values are plotted for n = 424 peaks called by MACS3 that were present in both datasets, as identified by overlapping peak start and end coordinates. A linear regression fit and Pearson linear correlation coefficient (r) are shown; the on-target site is indicated. b, Scatter plot showing correlation between off-target peaks observed for Cas8 and TnsC ChIP–seq experiments with crRNA-287. ChIP–seq enrichment values are plotted for n = 60 peaks called by MACS3 that were present in both datasets, as identified by overlapping peak start and end coordinates. A linear regression fit and Pearson linear correlation coefficient (r) are shown; the on-target site is off-scale for TnsC and was not included. c, Histogram of the distance between the genomic coordinates of Cas8 and TnsC peak summits for the overlapping peaks in b. Most peaks are within 20 bp of each other. d, Global visualization of ChIP–seq peaks with crRNA-291 for off-target sites using heat maps, plotted side-by-side for Cascade (left), TnsC (middle), and TnsB (right); data are shown as in Fig. 2b, c. A 2-kb window for 487 genomic loci (y axis) is plotted in order of decreasing peak enrichment for ChIP–seq peaks called for the Cascade dataset, with the RPKM scale bar shown. The inset for TnsB highlights the strong enrichment of the on-target site (row 1) and immediate drop-off for other Cascade off-targets (rows 2–5). e, Global visualization of ChIP–seq peaks with crRNA-295, plotted as in d. f, Venn diagrams showing the overlap in off-target peaks called for Cascade, TnsC, and TnsB. Peaks were called individually for each ChIP–seq dataset using MACS3, and overlaps were analysed based on the coordinates for each peak. Data are shown for the same two crRNAs as in d and e. g, Multiple Cascade ChIP–seq datasets from experiments with distinct crRNAs exhibit common off-target peaks that share a common motif, as assessed by MEME analysis. Shown for each dataset (top to bottom) are the motif visualized above the corresponding nucleotides at the 5’ end of pseudo-crRNA spacer (left), and the motif probability graph showing the probability of a motif match occurring at a given position in the input sequence (right). The PAM and seed regions are indicated at the top, as well as the lack of discrimination at position 6 (red asterisk/arrow). n, the number of peaks contributing to the motif (and their percentage of total peaks called); E, the E-value significance of the motif resulting from MEME analysis. h, The motifs for common off-target peaks in g can be ascribed to pseudo-crRNAs that use the spacer-like sequence downstream of the terminal repeat within the CRISPR array. The schematic shows the architecture of the CRISPR array encoded by pQCascade and the presumed mechanism of crRNA biogenesis. A pseudo-crRNA relies on a 5′-handle derived from the second repeat and a pseudo-spacer derived from the downstream sequence, which is common to all pQCascade vectors used in this study. Notably, the pseudo-crRNA will lack the repeat-derived 3′-handle (stem-loop bound by Cas6), is unlikely to have a single defined length, and is predicted to form a minimal Cascade complex lacking both Cas6 and TniQ.

Extended Data Fig. 3 Additional analyses of crRNA library and CRISPRi data.

a, Two-plasmid system used for in vivo transposition assays with crRNA libraries. We relocated the CRISPR array from pEffector to inside the mini-transposon within pDonor itself (bottom), relative to pEffector and pDonor plasmids we described previously17 (top), thereby generating pEffector(–CRISPR) and pDonor(+CRISPR). b, RNA-guided transposition assays for the two sets of plasmid constructs shown in a, as measured by qPCR. Transposon-encoded crRNAs direct indistinguishable integration efficiencies. c, Enrichment scores for crRNA library members associated with their corresponding target sites, plotted as in Fig. 3d. d, Position-specific enrichment scores are plotted as the change in log2(fold change) relative to the perfectly matching crRNA, for crRNAs containing quadruple mismatches across all 10 target sites. Data are shown as box plots with single-bp resolution along the crRNA; boxes show the median and interquartile range, whiskers indicate the range or extend 1.5× the interquartile range from the 25th and 75th percentile in the presence of outliers (dots). The data reveal off-target discrimination in both the PAM-proximal seed sequence and a PAM-distal region (indicated at the top). e, Schematic of mismatched crRNA library members focused on positions 22–32 in the PAM-distal region. Members were designed to test the effects of non-adjacent mismatches on transposition. f, Position-specific enrichment scores were averaged across all 10 target sites and plotted for each mismatch type (SM, DM, QM) as the change in log2(fold change), similarly to Fig. 3e. Data from focused mismatches within positions 22–32 (schematised in e) are shown at the top, revealing a lack of discrimination at positions 24 and 30 (red arrows), where crRNA nucleotides are flipped out. g, Schematic showing the location of crRNAs targeting genomically encoded mRFP and sfGFP genes. Guides were designed to target both the template and non-template strands, and were positioned ~35 bp and ~65 bp downstream of the gene start codon, respectively. h, Transcriptional repression was measured for each of the targeting crRNAs alongside a non-targeting (NT) control; the crRNA targeting the template strand shows strongest repression. Mismatches at positions 1–4 and 25–28 for an sfGFP-targeting crRNA show similar effects as with the mRFP data shown in Fig. 3g. Data in b and h are shown as mean ± s.d. of n = 3 independent biological replicates. The absolute sample numbers shown in d are n = 10, 20, 30, 40, 30, 20, and 10 for mismatch position 1, 2, 3, 4–29, 30, 31, and 32, respectively.

Extended Data Fig. 4 Raw electron micrographs and image processing workflow for TnsC•ATP heptamer.

a, Representative cryo-micrographs for the TnsC•ATP heptamer at various defoci; the corresponding CTF is shown (top right). b, Representative reference-free 2D class averages with four types of views identified: top and bottom views, a lateral side view and intermediate views. The size of circular masks used during the classification run is indicated in the first class average for each type. c, Image processing workflow employed for the identification and high-resolution refinement of a homogenous class of particles for the TnsC•ATP heptamer. One of the seven TnsC•ATP monomers exhibited weaker density (orange asterisk/arrow).

Extended Data Fig. 5 Fourier Shell Correlation curves, local resolution, representative densities, and secondary structure diagram for TnsC.

a, Fourier Shell Correlation (FSC) curves computed for two independently refined half-maps of the final sub-class of particles before masking (blue), after masking (black), and after phase randomization (red). Curves are shown for the refinement with a C7 symmetry condition imposed (top) and for a masked refinement of the four best TnsC monomers without the imposition of any symmetrical constraint (bottom). The final resolution by the FSC-0.143 criteria is 3.5 Å for the symmetrical reconstruction and 3.6 Å for the masked refinement without symmetry impositions. b, Unsharpened maps coloured according to local resolution calculations, showing the symmetrical map in orthogonal orientations (top) and the masked map without symmetrical impositions (bottom). A Mollweide diagram with the Euler angle distribution for the masked map refined in C1 is shown (bottom right), coloured according to the density distribution. c, Model-map FSC curve (black line) and model overfitting test performed by randomly displacing 0.5 Å of the atomic coordinates of the final model and re-refining the displaced coordinates against half-map 1. The coincidence of the blue curve (FSC against half-map 1) with the red curve (FSC against half-map 2, not included in the refinement) guarantees the absence of overfitting in the original model. d, Representative final cryo-EM densities from the post-processed symmetrical map, with the refined model represented as sticks. The putative density for a Mg2+ ion is indicated by a black arrow (right). e, Secondary structure diagram of VchTnsC derived from the structural data. α-helices are depicted as blue cylinders, β-strands as green arrows and loops as grey traces. ATP binding regions, pore facing regions and hallmark sequences from the AAA+ ATPase family are indicated.

Extended Data Fig. 6 Inter-monomer interactions, biochemical characterization, and TnsC heptamer formation.

a, Purification and heptamer assembly experiments with ATPase mutants of TnsC. SDS–PAGE analysis (left) and SEC chromatograms (right) are shown for the indicated mutants, with TnsC monomer and heptamer peaks indicated. Mutations in the Walker A (K55A), Walker B (E135A), and arginine finger (R187A) motifs prevent formation of stable heptamers. b, Adjacent TnsC monomers interact via two proximal loops, in which residues 275–280 from one monomer engage residues 188–191 from the neighbouring monomer (inset on the right) c, Purification and heptamer assembly experiments with interface and pore loop mutants of TnsC, shown as in a. Mutations at the monomer-monomer interface are unable to form stable heptamers in the presence of ATP. For gel source data, see Supplementary Fig. 1. d, CRISPR RNA-guided transposition assays for the indicated loop mutants, as measured by qPCR. Data are normalized relative to WT. Data in d are shown as mean ± s.d. for n = 3 independent biological replicates.

Extended Data Fig. 7 Characterization of flexible loops in TnsC.

a, Multiple flexible loops within the NTD protrude from the top of the heptameric TnsC disc (top left), which are potential sites of engagement with the TniQ–Cascade complex. Apical loops E69-D75 and A106–A113 are highlighted by red boxes, as are putative DNA-interacting residues including R143, S144 and R146 (top right). A long flexible tail at the CTD folds back towards the centre of the pore, and is likely to engage TnsAB, based on functional similarity to EcoTnsC. Magnified views of A106–A113 and the putative DNA binding region are shown in ribbon representation (centre right). b, CRISPR RNA-guided transposition assays for the indicated TnsC loop mutants, CTD deletions and pore-facing residues (left; coloured in orange), and TniQ ZnF mutations (right; coloured in green), as measured by qPCR. Data are normalized relative to WT. Mutation of R143 and R146 together abrogates transposition. Data are shown as mean ± s.d. of n = 3 independent biological replicates, with the exception of R146A (n = 2) and K89A, Y108A and S144A (n = 1). c, Purification and heptamer assembly experiments with the TnsC A106–A113 loop deletion mutant. SDS–PAGE analysis (left), and SEC chromatograms (right) are shown, with TnsC monomer and TnsC•ATP heptamer peaks indicated. Despite abrogating transposition, this loop deletion mutant is still able to form ATP-dependent heptamers. For TnsC(ΔA106–A113), the baseline was set to 0 at the 5 ml retention volume mark, for clarity. For gel source data, see Supplementary Fig. 1.

Extended Data Fig. 8 DNA binding, representative micrographs, Fourier Shell Correlation curves, local resolution, and representative densities for DNA-bound TnsC.

a, DNA binding by TnsC as measured by fluorescence polarization, tested with ssDNA and dsDNA substrates +/– ATP. Data are shown as mean ± s.d. of n = 2 experimental replicates. b, ATPase activity of TnsC in the absence or presence of ssDNA and dsDNA; ND, not detected. Data are shown as mean ± s.d. of n = 2 technical replicates. c, iSCAMS analysis of a TnsC sample after incubation with ATP and dsDNA reveals three distinct high-molecular-weight species, in good agreement with the DNA-bound single, double, and triple heptameric rings observed by cryo-EM. d, Representative cryo-micrographs for the DNA-bound TnsC sample at various defoci; the corresponding CTF is shown at the top right for each panel. e–h, DNA-bound single heptamer complex. i–l, DNA-bound double heptamer complex. e,i, Fourier Shell Correlation (FSC) curves (top) computed for the final independently refined half-maps before masking (blue), after masking (black), and after phase randomization (red). The final resolution by the FSC-0.143 criteria is 3.46 Å (single heptamer) and 3.9 Å (double heptamer) for the refinement without symmetry impositions. The bottom graph shows a model-map FSC curve (black line) and model overfitting test performed by randomly displacing 0.5 Å of the atomic coordinates of the final model and re-refining the displaced coordinates against half-map 1. The coincidence of the blue curve (FSC against half-map 1) with the red curve (FSC against half-map 2, not included in the refinement) guarantees the absence of overfitting in the original model. f,j, Unsharpened maps coloured according to local resolution calculations shown in orthogonal orientations. g,k, Representative final cryoEM densities from the post-processed maps, with the refined model represented as sticks. h,l, Mollweide diagrams showing the Euler angle distribution for the final refined maps, coloured according to the density distribution.

Extended Data Fig. 9 Structural diversity of DNA-bound TnsC heptamers.

a, Final cryo-EM density of a single DNA-bound TnsC heptamer showing unsliced (left) and sliced (right) side views to highlight DNA that is threaded through the central pore. Individual subunits are labelled. b, Ribbon representation of the views shown in a. c, Representative 2D class averages for the DNA-bound TnsC double heptamer. d, Two orthogonal views of the final cryo-EM map for the DNA-bound TnsC double heptamer. Each of the seven protomers is labelled, and DNA density is shown in grey. Both heptameric rings bind DNA in the same orientation. e, Ribbon representation of the DNA-bound TnsC double heptamer shown as in b, with ATP molecules present in all protomers shown as spheres. f, Three representative 2D class averages of the DNA-bound TnsC triple heptamer. Dynamics of individual heptamers are highlighted by dotted arrows, and TnsC heptamers located at the end of the bound DNA exhibit flexibility relative to the adjacent heptamers (see Supplementary Video 2).

Extended Data Fig. 10 Comparison of VchTnsC and TnsC homologues from evolutionarily diverse transposons.

a, The structure of TnsC is highly similar between apo and DNA-bound states. Structural superposition of a monomer from the apo-TnsC and DNA-bound TnsC single heptamer structure yields a global RMSD of 0.53 Å (left); a superposition with the double heptamer yields a global RMSD of 0.64 Å (right). ATP molecules are shown as sticks, and the NTD and CTD are labelled. b, Multiple sequence alignment of EcoTnsC22 from Tn7 (non-CRISPR associated), VchTnsC from this study (type I-F CRISPR-associated), and ShoTnsC from ShCAST3,23,24 (type V-K CRISPR-associated). Residues critical for ATPase activity are indicated. Sequence identities are as follows: VchTnsC and EcoTnsC, 18.84%; VchTnsC and ShoTnsC, 22.18%; EcoTnsC and ShoTnsC, 20.83%. c, Comparison of domain organization between three TnsC homologues, with EcoTnsC regions that interact with TnsA, TnsB, and TnsD indicated4. Note the significantly shorter N- and C-termini for VchTnsC and ShoTnsC. d, Comparison of domain organization between three TniQ-family protein homologues; note that the E. coli Tn7 protein is referred to as TnsD. Zinc-finger (ZnF) motifs are highlighted. e, Comparison of overall oligomeric architecture (top) and APBS electrostatic potential70 (bottom) for DNA-bound VchTnsC•ATP (left; this study), DNA-bound EcoTnsC(A225V)•AMPPNP (centre; PDB ID: 7MCS)22 and DNA-bound ShoTnsC•ATPɣS (right; PDB ID: 7M99)23, with the C-terminal face of the pore pointing up in each oligomer. DNA was omitted for clarity and for all electrostatics calculations. Heptamer formation of VchTnsC and EcoTnsC may be favoured by more efficient protomer-protomer packing, which appears less apparent in the hexameric ShoTnsC complex. f, Structural superposition of DNA-bound VchTnsC•ATP (this study) and EcoTnsC (PDB ID: 7MCS)22 monomers, by alignment of NTD residues 16–196 (VchTnsC) and residues 70–90 and 128–295 (EcoTnsC), yields an RMSD of 2.42 Å. The conformation of α1 (labelled) may create a more open protomer architecture enabling efficient packing of adjacent subunits, and thus, heptamer formation. ATP and AMPPNP are shown as sticks. g, Structural superposition of DNA-bound VchTnsC•ATP (this study) and ShoTnsC (PDB ID: 7M99)23 monomers, by alignment of NTD residues 16–196 (VchTnsC) and 34–198 (ShoTnsC), yields an RMSD of 1.27 Å. In ShoTnsC, the conformation of α1 may cause a more closed protomer architecture that obstructs efficient packing of adjacent subunits and promotes hexamer formation. ATP and ATPɣS are shown as sticks. h, Structural superposition of VchTniQ (PDB ID: 6V9P)46 and ShoTniQ monomers (PDB ID: 7N6I)23, which align with a global RMSD of 1.01 Å. ZnF motifs are indicated. i, Model of VchTniQ•TnsC interaction and its positioning relative to dsDNA, based on structural superpositions with the TniQ-bound TnsC complex from S. hofmannii (PDB ID: 7N6I; Methods)23, as shown in Fig. 5f. Protomers 3 and 4 of VchTnsC are shown in yellow and orange, respectively. Loops protruding from the N-terminal face of VchTnsC, which we found were essential for RNA-guided transposition (Extended Data Fig. 7b), are near the putative VchTniQ contact point. E69-D75 and A106–A113 are coloured dark orange and pink, respectively. j, An electrostatic rendering of VchTniQ-2 from the model shown in i suggests that DNA exiting the R-loop (dark grey) is well positioned to interact with a positively charged (blue) surface (left); TniQ-1 is shown in ribbon representation. DNA strand re-annealing may occur on this TniQ surface that is rich in lysine and arginine residues (right), guiding DNA from Cascade into the central pore of the VchTnsC heptamer. The 5′-CC-3′ PAM nucleotides are indicated by yellow dots.

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

Supplementary information

Supplementary Figure 1

Uncropped SDS–PAGE images. The red dashed boxes indicate the cropped area used in the figures.

Reporting Summary

Supplementary Table 1

Description and sequence of the plasmids used in this study.

Supplementary Table 2

Description and sequence of the purified protein constructs used in this study.

Supplementary Table 3

crRNAs and genomic target sites used in this study. PAM sequences (asterisk) denote the two nucleotides immediately 5′ of the target protospacer.

Supplementary Table 4

A list of the primers and oligonucleotides used in this study.

Supplementary Table 5

ChIP–seq read and meta information for this study.

Supplementary Table 6

The crRNA library spacers used in this study.

Supplementary Video 1

Final cryo-EM map of DNA-bound TnsC•ATP double-heptamer. The cryo-EM map is shown in different orientations to highlight the overall oligomeric architecture and the bound dsDNA. Monomers in each heptamer are labelled and coloured as in Extended Data Fig. 9d. Density for dsDNA, which protrudes through the central pore of each heptamer, is shown in grey. The two heptamers bind to DNA in the same orientation.

Supplementary Video 2

2D class averages of DNA-bound TnsC•ATP triple heptamer. The sequential playback of triple heptamer 2D class averages highlights the flexibility of a third TnsC heptamer (top left) relative to the other two, less dynamic DNA-bound heptamers (middle and bottom right).

Supplementary Video 3

Conformational changes of the TnsC•ATP heptamer after DNA binding. A morph between the cryo-EM models of the TnsC•ATP single heptamer in apo and DNA-bound states, viewed from different orientations, highlights the subtle conformational changes associated with DNA binding. In particular, a cleft formed between two subunits suggests a potential ring-breaking mechanism that may enable DNA loading.

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Hoffmann, F.T., Kim, M., Beh, L.Y. et al. Selective TnsC recruitment enhances the fidelity of RNA-guided transposition. Nature 609, 384–393 (2022). https://doi.org/10.1038/s41586-022-05059-4

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