Clostridioides (formerly Clostridium) difficile is a Gram-positive, spore-forming anaerobe and a leading cause of hospital-acquired infection and gastroenteritis-associated death in US hospitals1. The disease state is usually preceded by disruption of the host microbiome in response to antibiotic treatment and is characterized by mild to severe diarrhoea. C. difficile infection is dependent on the secretion of one or more AB-type toxins: toxin A (TcdA), toxin B (TcdB) and the C. difficile transferase toxin (CDT)2. Whereas TcdA and TcdB are considered the primary virulence factors, recent studies suggest that CDT increases the severity of C. difficile infection in some of the most problematic clinical strains3. To better understand how CDT functions, we used cryo-electron microscopy to define the structure of CDTb, the cell-binding component of CDT. We obtained structures of several oligomeric forms that highlight the conformational changes that enable conversion from a prepore to a β-barrel pore. The structural analysis also reveals a glycan-binding domain and residues involved in binding the host-cell receptor, lipolysis-stimulated lipoprotein receptor. Together, these results provide a framework to understand how CDT functions at the host cell interface.
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The data that support the findings of this study are available from the corresponding author on request. Protein Data Bank accession codes for all structures shown here are: 6O2O, 6O2N, 6OKR, 6O2M, 6OKT, 6OKS and 6OKU. Maps are available at the Electron Microscopy Data Bank with accession codes EMD-0608, EMD-0609, EMD-0610, EMD-20102, EMD-20103, EMD-20104 and EMD-20105.
The ImageJ macro scripts used in this work are available in the Supplementary Information.
Lessa, F. C., Winston, L. G. & McDonald, L. C. Burden of Clostridium difficile infection in the United States. N. Engl. J. Med. 372, 825–834 (2015).
Chandrasekaran, R. & Lacy, D. B. The role of toxins in Clostridium difficile infection. FEMS Microbiol. Rev. 41, 723–750 (2017).
Gerding, D. N., Johnson, S., Rupnik, M. & Aktories, K. Clostridium difficile binary toxin CDT: mechanism, epidemiology, and potential clinical importance. Gut Microbes 5, 15–27 (2013).
Aktories, K., Papatheodorou, P. & Schwan, C. Binary Clostridium difficile toxin (CDT)—a virulence factor disturbing the cytoskeleton. Anaerobe 53, 21–29 (2018).
Papatheodorou, P. et al. Lipolysis-stimulated lipoprotein receptor (LSR) is the host receptor for the binary toxin Clostridium difficile transferase (CDT). Proc. Natl Acad. Sci. USA 108, 16422–16427 (2011).
Perelle, S., Gibert, M., Bourlioux, P., Corthier, G. & Popoff, M. R. Production of a complete binary toxin (actin-specific ADP-ribosyltransferase) by Clostridium difficile CD196. Infect. Immun. 65, 1402–1407 (1997).
Schwan, C. et al. Clostridium difficile toxin CDT induces formation of microtubule-based protrusions and increases adherence of bacteria. PLoS Pathog. 5, e1000626 (2009).
Schwan, C. et al. Clostridium difficile toxin CDT hijacks microtubule organization and reroutes vesicle traffic to increase pathogen adherence. Proc. Natl Acad. Sci. USA 111, 2313–2318 (2014).
Friebe, S., van der Goot, F. G. & Bürgi, J. The ins and outs of anthrax toxin. Toxins 8, E69 (2016).
Petosa, C., Collier, R. J., Klimpel, K. R., Leppla, S. H. & Liddington, R. C. Crystal structure of the anthrax toxin protective antigen. Nature 385, 833–838 (1997).
Lacy, D. B., Wigelsworth, D. J., Melnyk, R. A., Harrison, S. C. & Collier, R. J. Structure of heptameric protective antigen bound to an anthrax toxin receptor: A role for receptor in pH-dependent pore formation. Proc. Natl Acad. Sci. USA 101, 13147–13151 (2004).
Kintzer, A. F. et al. The protective antigen component of anthrax toxin forms functional octameric complexes. J. Mol. Biol. 392, 614–629 (2009).
Jiang, J., Pentelute, B. L., Collier, R. J. & Hong Zhou, Z. Atomic structure of anthrax protective antigen pore elucidates toxin translocation. Nature 521, 545–549 (2015).
Krantz, B. A. et al. Microbiology: a phenylalanine clamp catalyzes protein translocation through the anthrax toxin pore. Science 309, 777–781 (2005).
Wynia-Smith, S. L., Brown, M. J., Chirichella, G., Kemalyan, G. & Krantz, B. A. Electrostatic ratchet in the protective antigen channel promotes anthrax toxin translocation. J. Biol. Chem. 287, 43753–43764 (2012).
Eckhardt, M., Barth, H., Blöcker, D. & Aktories, K. Binding of Clostridium botulinum C2 toxin to asparagine-linked complex and hybrid carbohydrates. J. Biol. Chem. 275, 2328–2334 (2000).
Hemmasi, S. et al. Interaction of the Clostridium difficile binary toxin CDT and its host cell receptor, lipolysis-stimulated lipoprotein receptor (LSR). J. Biol. Chem. 290, 14031–14044 (2015).
Papatheodorou, P. et al. Identification of the cellular receptor of Clostridium spiroforme toxin. Infect. Immun. 80, 1418–1423 (2012).
Pfeifer, G. et al. Cellular uptake of Clostridium difficile toxin B. Translocation of the N-terminal catalytic domain into the cytosol of eukaryotic cells. J. Biol. Chem. 278, 44535–44541 (2003).
Kaiser, E. et al. Membrane translocation of binary actin-ADP-ribosylating toxins from Clostridium difficile and Clostridium perfringens Is facilitated by cyclophilin A and Hsp90. Infect. Immun. 79, 3913–3921 (2011).
Madeira, F. et al. The EMBL–EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 47, W636–W641 (2019).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Mindell, J. A. & Grigorieff, N. Accurate determination of local defocus and specimen tilt in electron microscopy. J. Struct. Biol. 142, 334–347 (2003).
Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).
Kimanius, D., Forsberg, B. O., Scheres, S. H. W. & Lindahl, E. Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. eLife 5, e18722 (2016).
Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).
Morin, A. et al. Collaboration gets the most out of software. eLife 2013, e01456 (2013).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Peng, T. et al. A BaSiC tool for background and shading correction of optical microscopy images. Nat. Commun. 8, 14836 (2017).
Arganda-Carreras, I. et al. Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).
The authors thank J. Fitzpatrick and M. Rau (both Washington University), and S. Collier and E. Binshtein (both Vanderbilt University) for their assistance with cryo-EM data collection, and B. Spiller and members of the Lacy laboratory for critical feedback. Flow Cytometry experiments were performed in the Vanderbilt University Medical Center Flow Cytometry Shared Resource, which is supported by the Vanderbilt Ingram Cancer Center (P30 CA68485) and the Vanderbilt Digestive Disease Research Center (DK058404). This work was supported by United States Department of Veterans Affairs Award BX002943, Public Health Service grant AI095755 from the National Institutes of Health, and Vanderbilt University. M.J.S. and J.L.J. are supported by the Training Grant in Gastroenterology (DK007673). A portion of the molecular graphics and analyses was performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at University of California, San Francisco, with support from NIH P41-GM103311.
The authors declare no competing interests.
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Once CDTb is secreted from the bacterium it targets the cell surface by binding the lipolysis stimulated lipoprotein receptor (LSR). CDTb then undergoes proteolytic cleavage allowing for oligomerization. It is thought that CDTa then binds the prepore, and this entire complex enters the cell through endocytosis. Upon acidification in the endosome, CDTb transitions from the prepore to the lipid-inserted pore state. CDTa unfolds, is translocated into the cytosol, and then refolds with the help of cytosolic chaperones such as HSP90 and cyclophilin A. Upon refolding, CDTa ADP-ribosylates actin, leading to actin depolymerization and the formation of microtubule protrusions. These protrusions promote increased adherence of the bacterium. Similar Iota toxin family members such as CSTb (C. spiroforme) and Ib (C. perfringens) are thought to utilize a similar process based on a high degree of sequence identity (identity shown in insert). PA (B. anthracis) and C2 (C. botulinum) possess a much lower level of identity and are not considered part of the Iota toxin family.
CDTb oligomers were first screened using negative stain EM as shown in panel (a). Representative 2D classes from this analysis revealed double heptamer particles in both the long and short form as denoted by the placement of the D4 ring (yellow arrows). CDTb particles were first picked manually to generate templates (shown at the top) to be used to autopick each dataset. The images corresponding to the larger molecular weight oligomers were analyzed twice, once for the smaller particles (b) and once for the larger particles (c). Micrographs collected on the smaller molecular weight oligomer were similarly picked (d). All data were classified and aligned in 2D as an initial means of purification (bottom). Analysis of the larger particles revealed two separate forms referred to as the short and long forms (c, middle) which differ in the location of a central ring as indicated by the yellow arrow. Though some views allowed for easy differentiation between the two forms, these features could not be observed in all classes. Classes representing different views observed during 2D classification are shown with percentages reflecting the number of particles per class divided by the total number of particles in the analysis. (d) Classification of the smaller molecular weight oligomeric sample of CDTb in complex with the cell surface receptor LSR. The classification revealed particles in only the pre-insertion state with the LSR, D3’ and D4 domains not visualized, likely due to flexibility.
Cryo-EM maps were reconstructed in Relion from three distinct datasets. Datasets 1 and 2 were collected on the larger molecular weight oligomeric sample (shown in blue) and dataset 3 on the pre-insertion state (shown in green). Particles were autopicked from all three datasets in Relion and the purified particles from 2D classification used to generate initial 3D models. The first dataset was subjected to one round of 3D classification without symmetry. Particles were then selected from the best 3D class and joined with particles from the second dataset for further processing. These particles were then subjected to two more rounds of 3D classification without applying symmetry. Two forms of particles were obtained corresponding to the short form and long form. The short form particles were first refined using C1 symmetry to a resolution of 4.5 Å. After applying C7 symmetry, the resolution of the reconstruction reached 3.7 Å. An initial map of the long form was obtained at 6.3 Å. These data were further refined using multi-body refinement to yield a reconstruction at resolution of 3.9 Å. The pre-insertion state was obtained from particles picked from dataset 3. These particles were classified over two rounds of 3D classification with C7 symmetry applied. The set of particles was refined to a resolution of 4.2 Å with applied C7 symmetry.
The local resolutions of the short form refined with C1 symmetry (a), the short form refined with C7 symmetry (b), the long form of CDTb refined with C7 (c) and the pre- insertion state (d), as determined by Relion LocalRes and displayed in UCSF Chimera. The angular distribution of particles used in the reconstruction of these maps is also shown. The corresponding Pearson’s correlation coefficient (CC) for each residue is plotted on each model described in this manuscript: the CDTb oligomer (e), the pore (f), the partial β- barrel states (g,h), and the pre-insertion state (i).
Surface rendering of the four CDTb cryo-EM structures colored by the five CDTb domains (a). Analogous domains for the prepore (PDB: 1TZO) and pore state (PDB: 3J9C) of PA (b). Vacuum electrostatic calculations of the surface of the CDTb (c, left) and PA (d, left) prepore show a distinct difference in electrostatic character between the two toxins. This surface may allow for the recognition of different cargo as suggested by the change in electrostatics observed in CDTa (c, right) when compared to LF (d, right). A band of hydrophobicity is observed at the bottom of the pore form of both CDTb (e, left) and PA (e, right). This is presumed to be the surface inserted into the lipid bilayer. Of note, the interior of the prepore is not hydrophobic and there are minimal contacts with the portion of the CDTb pore that spans the membrane, suggesting that the in vitro formation of double heptamers is driven by the trans interactions in the D4 ring. The pores of CDTb and PA differ by ~12 Å in length as shown in the inset. The difference could be important for accommodating the size of different receptors or it could be important for the translocation of different lengths of cargo. Two acidic bands line the interior of the β-barrel pore of CDTb (left, inset) as opposed to the five observed in PA (right, inset). Proteins with mutations at residues corresponding to the phenylalanine gate (F455) located at the top of the β-barrel (top panel) and a residue at the bottom of the β-barrel (F349) hypothesized to be inserted into the lipid bilayer were generated (e, insets). The ability of these mutants to disrupt monolayers (right panel) was significantly lower than that observed with wild type toxin (one-way ANOVA **** = P ≤ 0.0001) (f). Each data point represents the average of four technical replicates. The bar height represents the average of the 3 or 4 independent assays, and the error bars indicate the standard deviation.
(a) Representative bright-field images captured at the time-point indicated on a Cytation5 (BioTek) imaging plate reader (top) with the corresponding classified image showing background pixels in black (bottom). Data shown depicts cell rounding in the presence of 10 nM CDTa and 10 nM CDTb as highlighted by the area circled. (b) Classified images were quantified to produce a readout of monolayer integrity over the time course of the assay. The points represent the average of 3 or 4 independent biological replicates, and the error bars reflect the standard error of the mean. The region highlighted in grey was used for linear fitting to quantify the slope of pixel loss (cell coverage) over time. (c) Bio-layer interferometry (BLI) sensorgram comparing wild-type and F774D CDTb binding to NHS-biotinylated LSR immobilized on the sensor tip.
Extended Data Fig. 7 Representative flow cytometry scatter plots for the wild-type CDTb- Alexa-647 titration.
The far-left column represents the toxin-free control, while columns ascending to the right are plots of a two-fold increasing amount of toxin starting at 1.5 nM and ending at 50 nM toxin. Candidate cells were initially gated via side scatter area versus forward scatter area, followed by multiplet elimination via forward scatter height versus forward scatter area and side and forward scatter width versus height gating. Further intact, live cells were isolated next by a side scatter area versus 7AAD area gate. Lastly, the fraction of bound cells was calculated by the number of cells present in a side scatter area by Alexa-647 area gate. While this figure represents one experiment, the assay was independently performed twice for each protein and the data points are shown in Fig. 3b.
Conversion from the prepore state to the β-barrel pore state is shown in panel (a). The transition begins with the prepore form of CDTb in which the pore forming loop is docked into a cleft between the D3 and D3’ domains (i). Next, a ~15 Å shift of the D2 domain (ii) to a position similar to that observed in the pore configuration. This results in displacement of the pore forming loop to a flexible conformation (iii). Upon formation of the β- barrel pore, the pore forming loop rotates ~80° relative to the eventual barrel (iv). We predict the remainder of the barrel then forms through a “zipper” like mechanism (v) which buries the hydrophobic tip of the barrel in the membrane (vi). We also observe conformational changes in the D4 domains as illustrated in panel (b). Though we observe a static arrangement of the D4 ring in the prepore and pre-insertion states, the flexibility of this ring is evident as it was rotated ~70° about the center of the ring, in the pore state. As a result, the extended linker adopts a horizontal conformation as shown in panel (c). Though the orientations differ, the length of the linker remains relatively constant between all forms described here.
The D4 receptor binding domain interacts with other D4 domains within the same oligomer (referred to as the ‘cis’ interaction) as well as the adjacent oligomer (referred to as the ‘trans’ interaction) in both the short and long forms (a). These interactions appear to be relatively weak, though, nearly identical in both forms. The cis interactions are comprised of electrostatic interactions between D831 and R843 as well as Van der Waals interactions between P835 and F846 in all observed orientations (b). Similarly, all trans interactions appear to exploit similar interactions consisting of Van der Waals interactions occurring between L772, F774, and P776.
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Anderson, D.M., Sheedlo, M.J., Jensen, J.L. et al. Structural insights into the transition of Clostridioides difficile binary toxin from prepore to pore. Nat Microbiol 5, 102–107 (2020). https://doi.org/10.1038/s41564-019-0601-8
Nature Reviews Microbiology (2022)
Nature Structural & Molecular Biology (2020)