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.

  • Article
  • Published:

Tc toxin activation requires unfolding and refolding of a β-propeller

Nature volume 563, pages 209–213 (2018)Cite this article

Subjects

Abstract

Tc toxins secrete toxic enzymes into host cells using a unique syringe-like injection mechanism. They are composed of three subunits, TcA, TcB and TcC. TcA forms the translocation channel and the TcB–TcC heterodimer functions as a cocoon that shields the toxic enzyme. Binding of the cocoon to the channel triggers opening of the cocoon and translocation of the toxic enzyme into the channel. Here we show in atomic detail how the assembly of the three components activates the toxin. We find that part of the cocoon completely unfolds and refolds into an alternative conformation upon binding. The presence of the toxic enzyme inside the cocoon is essential for its subnanomolar binding affinity for the TcA subunit. The enzyme passes through a narrow negatively charged constriction site inside the cocoon, probably acting as an extruder that releases the unfolded protein with its C terminus first into the translocation channel.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Cryo-EM structure of the ABC holotoxin.
Fig. 2: Transition between two states of the TcB β-propeller.
Fig. 3: ADP-ribosyltransferase in ABC(WT) and ABC(D651A).
Fig. 4: Analysis of the gatekeeper hairpin.
Fig. 5: Model of Tc toxin activation.

Similar content being viewed by others

Data availability

The densities and atomic coordinates of ABC(WT) and ABC(D651A) have been deposited in the Electron Microscopy Data Bank under the accession numbers EMD-0149 and EMD-0150, respectively, and in the Protein Data Bank under accession numbers 6H6E and 6H6F, respectively. The atomic coordinates of the crystal structure of TcdB2–TccC3 without HVR have been deposited in the Protein Data Bank under the accession number 6H6G. The HDX data are available in the Source Data for Fig. 4. The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.

References

  1. Waterfield, N. R., Bowen, D. J., Fetherston, J. D., Perry, R. D. & ffrench-Constant, R. H. The tc genes of Photorhabdus: a growing family. Trends Microbiol. 9, 185–191 (2001).

    Article  CAS  Google Scholar 

  2. ffrench-Constant, R. H. & Bowen, D. J. Novel insecticidal toxins from nematode-symbiotic bacteria. Cell. Mol. Life Sci. 57, 828–833 (2000).

    Article  CAS  Google Scholar 

  3. ffrench-Constant, R. H., Dowling, A. & Waterfield, N. R. Insecticidal toxins from Photorhabdus bacteria and their potential use in agriculture. Toxicon 49, 436–451 (2007).

    Article  CAS  Google Scholar 

  4. Pinheiro, V. B. & Ellar, D. J. Expression and insecticidal activity of Yersinia pseudotuberculosis and Photorhabdus luminescens toxin complex proteins. Cell. Microbiol. 9, 2372–2380 (2007).

    Article  CAS  Google Scholar 

  5. Zahaf, N.-I. et al. Targeted delivery of an ADP-ribosylating bacterial toxin into cancer cells. Sci. Rep. 7, 41252 (2017).

    Article  ADS  CAS  Google Scholar 

  6. Meusch, D. et al. Mechanism of Tc toxin action revealed in molecular detail. Nature 508, 61–65 (2014).

    Article  ADS  CAS  Google Scholar 

  7. Busby, J. N., Panjikar, S., Landsberg, M. J., Hurst, M. R. H. & Lott, J. S. The BC component of ABC toxins is an RHS-repeat-containing protein encapsulation device. Nature 501, 547–550 (2013).

    Article  ADS  CAS  Google Scholar 

  8. Lang, A. E. et al. Photorhabdus luminescens toxins ADP-ribosylate actin and RhoA to force actin clustering. Science 327, 1139–1142 (2010).

    Article  ADS  CAS  Google Scholar 

  9. Gatsogiannis, C. et al. A syringe-like injection mechanism in Photorhabdus luminescens toxins. Nature 495, 520–523 (2013).

    Article  ADS  CAS  Google Scholar 

  10. Gatsogiannis, C. et al. Membrane insertion of a Tc toxin in near-atomic detail. Nat. Struct. Mol. Biol. 23, 884–890 (2016).

    Article  CAS  Google Scholar 

  11. Moriya, T. et al. High-resolution single particle analysis from electron cryo-microscopy images using SPHIRE. J. Vis. Exp. 123, (2017).

  12. Bewley, M. C., Graziano, V., Griffin, K. & Flanagan, J. M. The asymmetry in the mature amino-terminus of ClpP facilitates a local symmetry match in ClpAP and ClpXP complexes. J. Struct. Biol. 153, 113–128 (2006).

    Article  CAS  Google Scholar 

  13. Baker, T. A. & Sauer, R. T. ClpXP, an ATP-powered unfolding and protein-degradation machine. Biochim. Biophys. Acta 1823, 15–28 (2012).

    Article  CAS  Google Scholar 

  14. Schweitzer, A. et al. Structure of the human 26S proteasome at a resolution of 3.9 Å. Proc. Natl Acad. Sci. USA 113, 7816–7821 (2016).

    Article  CAS  Google Scholar 

  15. Bolten, M. et al. Structural analysis of the bacterial proteasome activator Bpa in complex with the 20S proteasome. Structure 24, 2138–2151 (2016).

    Article  CAS  Google Scholar 

  16. Morais, M. C. et al. Cryoelectron-microscopy image reconstruction of symmetry mismatches in bacteriophage φ29. J. Struct. Biol. 135, 38–46 (2001).

    Article  CAS  Google Scholar 

  17. Lawrence, M. C., Lawrence, M. C., Colman, P. M. & Colman, P. M. Shape complementarity at protein/protein interfaces. J. Mol. Biol. 234, 946–950 (1993).

    Article  CAS  Google Scholar 

  18. Lang, A. E., Konukiewitz, J., Aktories, K. & Benz, R. TcdA1 of Photorhabdus luminescens: electrophysiological analysis of pore formation and effector binding. Biophys. J. 105, 376–384 (2013).

    Article  ADS  CAS  Google Scholar 

  19. Burmann, B. M. et al. An α helix to β barrel domain switch transforms the transcription factor RfaH into a translation factor. Cell 150, 291–303 (2012).

    Article  CAS  Google Scholar 

  20. Ramírez-Sarmiento, C. A., Noel, J. K., Valenzuela, S. L. & Artsimovitch, I. Interdomain contacts control native state switching of RfaH on a dual-funneled landscape. PLoS Comput. Biol. 11, e1004379 (2015).

    Article  ADS  Google Scholar 

  21. Lin, X. et al. Order and disorder control the functional rearrangement of influenza hemagglutinin. Proc. Natl Acad. Sci. USA 111, 12049–12054 (2014).

    Article  ADS  CAS  Google Scholar 

  22. Murphy, J. R. Mechanism of diphtheria toxin catalytic domain delivery to the eukaryotic cell cytosol and the cellular factors that directly participate in the process. Toxins (Basel) 3, 294–308 (2011).

    Article  CAS  Google Scholar 

  23. Zhang, S., Finkelstein, A. & Collier, R. J. Evidence that translocation of anthrax toxin’s lethal factor is initiated by entry of its N terminus into the protective antigen channel. Proc. Natl Acad. Sci. USA 101, 16756–16761 (2004).

    Article  ADS  CAS  Google Scholar 

  24. Fölsch, H., Gaume, B., Brunner, M., Neupert, W. & Stuart, R. A. C- to N-terminal translocation of preproteins into mitochondria. EMBO J. 17, 6508–6515 (1998).

    Article  Google Scholar 

  25. Tang, G. et al. EMAN2: an extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007).

    Article  CAS  Google Scholar 

  26. Penczek, P. A. et al. CTER—rapid estimation of CTF parameters with error assessment. Ultramicroscopy 140, 9–19 (2014).

    Article  CAS  Google Scholar 

  27. Yang, Z., Fang, J., Chittuluru, J., Asturias, F. J. & Penczek, P. A. Iterative stable alignment and clustering of 2D transmission electron microscope images. Structure 20, 237–247 (2012).

    Article  CAS  Google Scholar 

  28. Gatsogiannis, C., Hofnagel, O., Markl, J. & Raunser, S. Structure of mega-hemocyanin reveals protein origami in snails. Structure 23, 93–103 (2014).

    Article  Google Scholar 

  29. Li, X. et al. Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM. Nat. Methods 10, 584–590 (2013).

    Article  CAS  Google Scholar 

  30. Penczek, P. A. Resolution measures in molecular electron microscopy. Methods Enzymol. 482, 73–100 (2010).

    Article  CAS  Google Scholar 

  31. Grant, T. & Grigorieff, N. Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus VP6. eLife 4, e06980 (2015).

    Article  Google Scholar 

  32. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D 66, 12–21 (2010).

    Article  CAS  Google Scholar 

  33. Winn, M. D. et al. Overview of the CCP4 suite and current developments. Acta Crystallogr. D 67, 235–242 (2011).

    Article  CAS  Google Scholar 

  34. Masood, T. B., Sandhya, S., Chandra, N. & Natarajan, V. CHEXVIS: a tool for molecular channel extraction and visualization. BMC Bioinformatics 16, 179 (2015).

    Article  Google Scholar 

  35. Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).

    Article  CAS  Google Scholar 

  36. Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  Google Scholar 

  37. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).

    Article  Google Scholar 

  38. Ashkenazy, H., Erez, E., Martz, E., Pupko, T. & Ben-Tal, N. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res. 38, W529–W533 (2010).

    Article  Google Scholar 

  39. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  Google Scholar 

  40. Webb, B. & Sali, A. in Functional Genomics (eds Kaufmann, M., Klinger, C. & Savelsbergh, A.) 39–54 (Springer, New York, 2017).

  41. Richter, S., Wenzel, A., Stein, M., Gabdoulline, R. R. & Wade, R. C. webPIPSA: a web server for the comparison of protein interaction properties. Nucleic Acids Res. 36, W276–W280 (2008).

    Article  CAS  Google Scholar 

  42. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (2008).

    Article  Google Scholar 

  43. Iacob, R. E., Murphy, J. P. III & Engen, J. R. Ion mobility adds an additional dimension to mass spectrometric analysis of solution-phase hydrogen/deuterium exchange. Rapid Commun. Mass Spectrom. 22, 2898–2904 (2008).

    Article  ADS  CAS  Google Scholar 

  44. Guttman, M. et al. Tuning a high transmission ion guide to prevent gas-phase proton exchange during H/D exchange MS analysis. J. Am. Soc. Mass Spectrom. 27, 662–668 (2016).

    Article  ADS  CAS  Google Scholar 

  45. Wales, T. E. & Engen, J. R. Hydrogen exchange mass spectrometry for the analysis of protein dynamics. Mass Spectrom. Rev. 25, 158–170 (2005).

    Article  ADS  Google Scholar 

  46. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Cryst. 40, 658–674 (2007).

    Article  CAS  Google Scholar 

  47. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article  CAS  Google Scholar 

  48. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  49. Wang, R. Y.-R. et al. De novo protein structure determination from near-atomic-resolution cryo-EM maps. Nat. Methods 12, 335–338 (2015).

    Article  CAS  Google Scholar 

  50. DiMaio, F. et al. Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement. Nat. Methods 12, 361–365 (2015).

    Article  CAS  Google Scholar 

  51. Trabuco, L. G., Villa, E., Mitra, K., Frank, J. & Schulten, K. Flexible fitting of atomic structures into electron microscopy maps using molecular dynamics. Structure 16, 673–683 (2008).

    Article  CAS  Google Scholar 

  52. Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).

    Article  CAS  Google Scholar 

  53. Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).

    Article  CAS  Google Scholar 

  54. Song, Y. et al. High-resolution comparative modeling with RosettaCM. Structure 21, 1735–1742 (2013).

    Article  CAS  Google Scholar 

  55. Barad, B. A. et al. EMRinger: side chain-directed model and map validation for 3D cryo-electron microscopy. Nat. Methods 12, 943–946 (2015).

    Article  CAS  Google Scholar 

  56. Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    Article  CAS  Google Scholar 

  57. Busby, J. N. et al. Structural analysis of Chi1 chitinase from Yen-Tc: the multisubunit insecticidal ABC toxin complex of Yersinia entomophaga. J. Mol. Biol. 415, 359–371 (2012).

    Article  CAS  Google Scholar 

  58. Kilambi, K. P. & Gray, J. J. Rapid calculation of protein pKa values using Rosetta. Biophys. J. 103, 587–595 (2012).

    Article  ADS  CAS  Google Scholar 

  59. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Article  ADS  Google Scholar 

  60. Combet, C., Blanchet, C., Geourjon, C. & Deléage, G. NPS@: network protein sequence analysis. Trends Biochem. Sci. 25, 147–150 (2000).

    Article  CAS  Google Scholar 

  61. Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C. & Bussi, G. PLUMED 2: New feathers for an old bird. Comput. Phys. Commun. 185, 604–613 (2014).

    Article  ADS  CAS  Google Scholar 

  62. Lammert, H., Schug, A. & Onuchic, J. N. Robustness and generalization of structure-based models for protein folding and function. Proteins 77, 881–891 (2009).

    Article  CAS  Google Scholar 

  63. Whitford, P. C. et al. An all-atom structure-based potential for proteins: bridging minimal models with all-atom empirical forcefields. Proteins 75, 430–441 (2009).

    Article  CAS  Google Scholar 

  64. Noel, J. K. et al. SMOG 2: a versatile software package for generating structure-based models. PLoS Comput. Biol. 12, e1004794 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

We thank O. Hofnagel for assistance in electron microscopy; A. Elsner and K. Vogel-Bachmayr for technical support; the SPHIRE developer team, in particular P. A. Penczek, for the software used in this study; and R. Matadeen and S. de Carlo (FEI) for acquisition of the ABC(D651A) dataset at the National Center for Electron Nanoscopy in Leiden. This work was supported by the Max Planck Society (to S.R.) and the European Research Council (FP7/2007-2013) (grant no. 615984) (to S.R.).

Reviewer information

Nature thanks R. ffrench-Constant and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Christos Gatsogiannis, Felipe Merino, Daniel Roderer

Authors and Affiliations

  1. Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Dortmund, Germany

    Christos Gatsogiannis, Felipe Merino, Daniel Roderer, Evelyn Schubert, Anne Kuhlee & Stefan Raunser

  2. Department of Cellular Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany

    David Balchin & Manajit Hayer-Hartl

Authors
  1. Christos Gatsogiannis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felipe Merino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Roderer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Balchin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Evelyn Schubert
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anne Kuhlee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Manajit Hayer-Hartl
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stefan Raunser
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.R. designed and managed the project. D.R. designed proteins and performed mutational and functional studies. C.G. and A.K. processed, and C.G. analysed, cryo-EM data. C.G. and F.M. built atomic models. F.M. calculated and analysed molecular dynamics simulations. E.S. determined the TcB–TcC structure. D.B. performed HDX–MS. D.B. and M.H.-H. analysed HDX–MS data. C.G., F.M., D.R. and E.S. prepared figures. All authors discussed the results and contributed to the writing of the manuscript.

Corresponding author

Correspondence to Stefan Raunser.

Ethics declarations

Competing interests

The 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 figures and tables

Extended Data Fig. 1 Cryo-EM of the wild-type ABC and ABC(D651A) holotoxin complex.

a, Typical digital micrograph area of vitrified wild-type holotoxin complexes at a defocus of 2 μm and a total dose of 60 e− Å−2 acquired with a Falcon II direct electron detector. Scale bar, 60 nm. b, Representative reference-free 2D class averages obtained by ISAC and subsequently resampled to the original pixel size, refined and sharpened, using the Beautifier tool implemented in the SPHIRE software package. Scale bar, 20 nm. c, Fourier shell correlation (FSC). The 0.143 FSC cut-off criterion indicates that the cryo-EM map has an average resolution of 3.94 Å. The inset shows a representative area of the density map superimposed with the atomic model. d, Angular distribution for the final round of the refinement. Each stick represents a projection view. Size and colour of the stick is proportional to the number of particles. e, Surface and cross-section of the cryo-EM density map coloured according to the local resolution. f, Molecular model of the wild-type holotoxin complex coloured by B-factor. g, Typical digital micrograph area of vitrified ABC(D651A) complexes at a defocus of 2 μm and a total dose of 35 e− Å−2. Scale bar, 60 nm. h, Characteristic reference-free 2D class averages of ABC(D651A) complexes. Scale bar, 20 nm. i, The 0.143 FSC cut-off criterion indicates that the cryo-EM map of ABC(D651A) has an average resolution of 3.72 Å. The inset shows a representative area of the density map superimposed with the molecular model. j, Angular distribution for the final round of the refinement. k, Surface and cross section of the cryo-EM density map coloured according to the local resolution. l, Molecular model of the ABC(D651A) holotoxin complex coloured by B-factor. Note that the TcC density in both volumes shows comparatively low local resolution and the molecular models of these specific areas were obtained by flexible fitting of the available crystal structure using MDFF and subsequent local refinement with Rosetta.

Extended Data Fig. 2 Details of the TcA–TcB interface.

a, Interactions at the TcA–TcB interface. Binding of TcA to TcB–TcC is stabilized by interactions between the pseudo-six-fold symmetrical β-propeller of TcB and the five-fold symmetrical TcB-binding domain of TcA. The gatekeeper domain, shown in blue, undergoes the largest conformational changes during gate opening. In the open state, residues R485 (i) and K534 (iv) of blades 3 and 4, respectively, are positioned within negatively charged grooves of the TcB-binding domain of TcA. In addition, two copies of residue L2422 of two adjacent TcA subunits are positioned within a prominent hydrophobic groove of blade 3 (ii) and 4 (iii), respectively. Interacting residues of TcB are shown as sticks. Surfaces of TcA involved in the interfaces are coloured from high (orange) to low (white) hydrophobicity (ii, iii, vi) or according the Coulomb potential (ranging from −10 kcal mol−1 (red) to 10 kcal mol−1 (blue) at pH 7.5 (i, iv, v)). Similar to the interfaces between blade 3 and 4 and TcA, blade 5 forms strong hydrophobic interactions with residue L2422 of TcA (vi) and electrostatic interactions with negatively charged patches of the opposing TcA domain (v). In contrast to the other blades, no prominent electrostatic or hydrophobic interactions can be observed with high certainty at this interface. Several candidates for amino acid residues were identified as putative hydrogen bond donors or acceptors (vii, viii). Colours correspond to those in Fig. 1. b, Conservation of residues at the TcA–TcB interface. Positions of residues of the β-propeller domain of TcB interacting with TcA are shown as in a. The model of the β-propeller domain is coloured according to sequence conservation, with cyan representing non-conserved residues and magenta representing highly conserved residues. The TcB-binding domain of TcA is shown in light green. c, Sequence alignment of TcB sequences. Asterisks indicate the positions of the residues highlighted in b. The sequence of P. luminescens TcdB2 is outlined. The alignment is coloured according to b.

Extended Data Fig. 3 Binding affinities of TcB–TcC, TcB–TcC(D651A) and empty TcB–TcC for TcA and of TcB–TcC for TcA(L2422E).

a, Interaction of TcA with TcB–TcC(WT), empty TcB–TcC, or TcB–TcCD651A, and interaction of TcA(L2422) with TcB–TcC were measured by BLI. KD, kon and koff obtained from global fits are shown. Data are mean ± error of fit; 5–7 individual curves were included in the global fits. b–e, BLI sensorgrams of TcA(WT) interacting with immobilized TcB–TcC(WT) (b), TcB–TcC(D651A) (c), empty TcB–TcC (d) and TcA(L2422E) interacting with immobilized TcB–TcC(WT) (e). TcA pentamer concentrations were 1.25 nM–80 nM in b and c, 16–1,000 nM in d and 2.8–180 nM in e. A global fit according to a 1:1 binding model was applied (black dashed curves). Resulting KD, kon and koff values are shown. Association and dissociation phases are separated by a grey dotted line. f, g, Negative-stain electron micrographs of TcA(WT) (f) and TcA(L2422E) (g) incubated with wild-type TcB–TcC. TcA (200 nM) was incubated with TcB–TcC (300 nM), and the excess of free TcB–TcC was removed by size-exclusion chromatography before imaging. Red circles in g highlight side views of TcA without TcB–TcC. Experiments were performed three times with comparable results. Scale bar, 200 nm.

Extended Data Fig. 4 Opening of the β-propeller gate of TcB.

a, Opening of the β-propeller gate is triggered by sensor loops. Side views of the closed, (left; before binding to TcA) (PDB 4O9X) and open (right; after binding to TcA) state of the TcA-binding six-bladed β-propeller domain of TcB–TcC. The structure of the closed state was structurally aligned with the structure of the open state and is shown together with the TcB-binding domain of TcA (middle). Note the clashing loops (sensor loops) between TcA (residues 2418–2430, green) and TcB (residues 527–536, orange). Colours correspond to those in Fig. 1. b, c, Effect of the ADP-ribosyltransferase on the structure of TcB–TcC. Crystal structures of unbound wild-type and empty TcB–TcC are shown in b. TcB–TcC structures obtained from the cryo-EM structures of ABC(WT) and ABC(D651A) are shown in c. For the structures of TcB–TcC(WT), the channel radius profile is shown as a function of distance along the channel axis before and after TcA binding in b and c, respectively (left). The narrowest constriction of the channel lumen towards the TcA-binding domain (constriction site) has a diameter of 10.5 Å in the open state. For all structures, the mesh surface of the computed channel along the cocoon interior is shown in yellow. Note that the presence or absence of the ADP-ribosyltransferase does not affect the channel profile in the unbound state of the cocoon b. Note also, that cocoons with cleaved (TcB–TcC(WT)) or uncleaved (TcB–TcC(D651A)) ADP-ribosyltransferase show an almost identical channel profile following binding to TcA (c). d, e, Comparison of crystal contacts in the structures of empty TcB–TcC and TcB–TcC(WT). Top and side views of the crystal structures of empty TcB–TcC (d) and TcB–TcC(WT) (PDB 4O9X) (e) including crystal contacts. The TcA-binding domain of TcB is indicated by a dashed box and coloured as in Fig. 1b, c. The gatekeeper hairpin (residues 514–524) is highlighted in red.

Extended Data Fig. 5 Analysis of TcA and TcB sensor-loop mutants.

a, Analysis of the first purification step (Ni2+-affinity chromatography) of TcA(WT) and sensor-loop deletion variants. P, insoluble fraction; S, soluble fraction; F, flow-through; M, protein marker. The gradient corresponds to 20–300 mM imidazole. Marker proteins: 250, 180, 130, 100, 70, 50 and 40 kDa. TcA is marked by an asterisk. Purification was performed once for each TcA variant. Purification of TcA(WT) resulted in comparable results for more than 5 experiments. b, KD, kon and koff values of the global fits obtained from the BLI measurements (c–f), analogous to Extended Data Fig. 3a. Data are mean ± error of fit; 6–7 individual curves were included in the global fits. c–f, BLI sensorgrams and binding affinities of TcB–TcC sensor-loop mutants with TcA. BLI sensorgrams of TcA(WT) interacting with immobilized TcB(F532A)–TcC (c), TcB(D530A/F532A)–TcC (d), TcB(Δ528–534-1Gly)–TcC (e) and TcB(Δ528–534-2Gly)–TcC (f) (-Gly indicates glycines replacing the TcB sensor loop). A global fit according to a 1:1 binding model was applied (black dashed lines). TcA pentamer concentrations were 3.75–240 nM in c–e and 2.5–80 nM in (f). Association and dissociation phases are separated by a grey dotted line. g, Intoxication of HEK293T cells with holotoxin formed by TcA(WT) and the indicated TcB–TcC variants. Scale bars, 200 µm. Cells (2 × 104) in DMEM/F12 medium were incubated with 0.5 or 5 nM of holotoxin for 16 h at 37 °C before imaging. Experiments were performed in triplicate with qualitatively identical results. The wild-type holotoxin and the complexes with the two sensor-loop point mutations show strong cytotoxic effects. Both loop deletion variants, however, are not toxic to cells, even at 10 times higher concentration. h, i, Holotoxin complex formation between TcA(WT) and L2422E and TcB–TcC sensor-loop variants. h, Chromatograms of TcB–TcC(WT) and selected variants with TcA(WT) (solid lines) and TcA(L2422E) (dashed lines). TcA pentamer (200 nM) and TcB–TcC (400 nM) were incubated for 1 h at 22 °C before loading on a Superose 6 5/150 column (GE Life Science). Experiments were performed twice with identical results. i, Electron micrographs of different combinations of TcA and TcB–TcC. The main peaks of the chromatography runs (h) were negatively stained and imaged. Scale bar, 200 nm. Experiments were performed twice with qualitatively identical results. All sensor-loop mutants show almost exclusively holotoxins when mixed with TcA(WT), but practically no holotoxins with TcA(L2422E). TcB–TcC(WT), however, can form a holotoxin with TcA(L2422E).

Extended Data Fig. 6 Structural stability of the β-propeller of TcB during molecular dynamics simulations.

Principal component analysis of the trajectories started from the closed (a–c) or open (d–f) states of the TcB–TcC complex. The plots show the root mean square fluctuation associated with the first (a, d), second (d, e) and third (c, f) principal components. The percentages in the legends indicate the fractional contribution that each component makes to the total variance. The structures show the range of conformations observed along their corresponding component. The colour scale in the structures represents the position along each principal component going between the extremes, from red to green to blue. For guidance, the blades of the β-propeller are labelled in structures and plots, with blades 3 and 4 highlighted in blue. g, h, Graphical representation of the hybrid potentials used for the dual conformation structure-based SMOG force field. A Gaussian potential (g) can be used to create a contact term that includes exactly two minima (states a and b) with equal well depth, corresponding to the observed distance in two independent structures. In addition, by using a Gaussian potential the excluded volume of the contact can be independently controlled. A representation of the hybrid dihedral angle potential used in this study is shown in h. Starting from the dihedral functions for states a and b, the functions are combined as described in Methods. The regions between the averages (av1 and av2) and the closest extrema are connected using a polynomial function, to guarantee continuity and differentiability.

Extended Data Fig. 7 Conformational distribution observed in the structure-based molecular dynamics simulations.

a, Distribution of states of blades 3 and 4 during the simulations of free TcB–TcC. b, Histogram of the conformations sampled by the gatekeeper domain during the simulations of free TcB–TcC complex. c, Distribution of states of blades 3 and 4 during the simulations of the holotoxin. d, Histogram of the conformations sampled by the gatekeeper domain during the simulations of the holotoxin. In the simulations started from the closed state, the protein explores two minor conformations, which are highlighted in the plot and shown in g. e, Distribution of states of blades 3 and 4 during the simulations of the holotoxin with destabilized β-propeller. f, Histogram of the conformations sampled by the gatekeeper domain during the simulations of the holotoxin with destabilized β-propeller. g, Representative structures for the two states highlighted in d. Colours correspond to those in Fig. 2. The histograms in d and f were calculated using running averages of the trajectories, using a window size of 150,000 time steps.

Extended Data Fig. 8 Conservation of negatively charged residues that form a constriction between the barrel and the β-propeller domain of TcB.

a, Positions of the residues D34, N60, D73, E100 and E120 in the constriction site of TcB. The model is coloured according to sequence conservation, with cyan representing non-conserved residues and magenta representing highly conserved residues. b, Sequence alignment of TcB sequences. Asterisks indicate the positions of the residues highlighted in a. The sequence of P. luminescens TcdB2 is outlined. The alignment is coloured according to a. c, Dendrogram of TcdB2 homology models (from b), created by PIPSA. Models are clustered according to their surface electrostatic potential in the clamp region. d–f, Electrostatic surface potential representation of one representative model per cluster: P. luminescens TcdB2 (d), Photorhabdus temperata TcaC (e) and Burkholderia pseudomallei TcB (f). The position of D34 is indicated.

Extended Data Fig. 9 Modelling of a helical peptide at the constriction site of TcB–TcC.

We placed two different peptides with predicted α-helical secondary structure from the HVR of TcC and performed simulations with the peptides free (unrestrained) or restrained to their helical conformation. a, Central section through TcB–TcC showing the average water occupancy during the 300-ns-long simulations. Bulk water is shown in white (~0.5 occupancy), whereas bound water molecules appear in red. Blue represents the regions that are never hydrated, which corresponds to the space occupied by the protein. The occupancy shows that there is still a small channel of water next to the helical peptide. b, c, Representative snapshot of the trajectories. TcB (cyan) and TcC (violet) are shown as cut surfaces to illustrate the interior of the barrel. The bound helix is shown as a blue ribbon. Side chains of the bound helix and water molecules at most 8 Å away from it are shown in ball and stick representation. For each simulation, we included a close-up of the bound helix (c). d, Stability of the helical ligand bound to the constriction site. The variable αr.m.s.d. represents the number of 6-residue-long segments with helical conformation within the peptide.

Extended Data Fig. 10 HDX–MS of TcB–TcC and holotoxin complexes.

a, Relative deuteration of TcB–TcC and holotoxin complexes at 50-min deuteration time. Relative deuteration values normalized to the maximum deuteration value of all peptides are shown in a gradient from 0.0 (blue) to 0.64 (red) relative deuteration level. Peptides without deuteration data are coloured grey. The position of the gatekeeper hairpin (residues 514–524, Fig. 4b) is indicated by a box. b, c, HDX–MS of residues 463–471 as part of the TcA interface in bound and unbound TcB–TcC. The position of residues 463–471 of the TcA-binding domain in unbound TcB–TcC (b, left, red) and TcA-bound TcB–TcC (b, right, red) is indicated. The TcB-binding domain of TcA is shown in green (b, right). Equilibrium hydrogen–deuterium exchange at residues 463–471 in empty TcB–TcC (black circles), TcB–TcC(WT) (purple circles), TcB–TcC(D651A) (blue triangles), ABC(WT) (green triangles) and ABC(D651A) (light green squares) is displayed in c. Relative deuterium uptake of the residues as a function of incubation time in D2O is shown. Data points show two independent replicates of each measurement. The solid lines represent the arithmetic mean of both replicates.

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1-2.

Reporting Summary

Video 1

Cryo-EM density map of ABCwt. The molecular model is superimposed onto a representative area of the cryo-EM density.

Video 2

Representative trajectory of the transition between open and closed β-propeller of the free TcB-TcC complex. Simulation of the free TcB-TcC complex started from the open state, where the β-propeller undergoes the full transition to the closed state. For guidance, the video starts with an overview of the simulated model and a definition of the protein architecture. The colours in the structure as well as the plot correspond to those of Figure 2. For each trajectory frame, the plot shows the instantaneous (gray circle) and running average (white circle) values.

Video 3

Representative trajectory of the simulations started from the complex between TcB-TcC in the closed conformation and TcA. The video starts with an overview of the simulated systems. The simulation of the holotoxin with destabilized β-propeller (first trajectory) shows a full transition from closed to open state. The simulation with the original potential (second trajectory) shows that the clash of the sensor loops induces the local unfolding of blade 4. The colours in the structure as well as the plot correspond to those of Figure 2 and Extended Data Figure 9. For each trajectory frame, the plot shows instantaneous (gray circle) and running average (white circle) values.

Video 4

Cryo-EM density of ABCD651A. A representative region of the molecular model is shown superimposed with the respective cryo-EM density.

Video 5

Cryo-EM density of the ADP-ribosyltransferase. The video shows a morph between the structures of the uncleaved ADP-ribosyltransferase extracted from the cryo-EM density of ABCD651A and the cleaved ADP-ribosyltransferase extracted from the cryo-EM density of ABCwt (dim gray). The cryo-EM densities of the TcA channel and TcB-TcC cocoon ABCwt are shown. The colours correspond to those of Figure 3.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gatsogiannis, C., Merino, F., Roderer, D. et al. Tc toxin activation requires unfolding and refolding of a β-propeller. Nature 563, 209–213 (2018). https://doi.org/10.1038/s41586-018-0556-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-018-0556-6

Keywords

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology