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Systematic discovery of antibacterial and antifungal bacterial toxins

An Author Correction to this article was published on 19 February 2025

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Abstract

Microorganisms use toxins to kill competing microorganisms or eukaryotic cells. Polymorphic toxins are proteins that encode carboxy-terminal toxin domains. Here we developed a computational approach to identify previously undiscovered, conserved toxin domains of polymorphic toxins within 105,438 microbial genomes. We validated nine short toxins, showing that they cause cell death upon heterologous expression in either Escherichia coli or Saccharomyces cerevisiae. Five cognate immunity genes that neutralize the toxins were also discovered. The toxins are encoded by 2.2% of sequenced bacteria. A subset of the toxins exhibited potent antifungal activity against various pathogenic fungi but not against two invertebrate model organisms or macrophages. Experimental validation suggested that these toxins probably target the cell membrane or DNA or inhibit cell division. Further characterization and structural analysis of two toxin–immunity protein complexes confirmed DNase activity. These findings expand our knowledge of microbial toxins involved in inter-microbial competition that may have the potential for clinical and biotechnological applications.

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Fig. 1: Computational prediction of toxin domains (PTs) and cognate immunity genes (PIM genes) in polymorphic toxin proteins.
Fig. 2: PTs efficiently kill bacteria and/or yeast and PIM genes neutralize the toxicity.
Fig. 3: Critical residues and predicted protein structures of the PTs.
Fig. 4: Antibacterial and antifungal activities of PT toxins.
Fig. 5: PT4Ec and PT9Ec of E. coli strain 48 exhibit broad-spectrum antifungal activity.
Fig. 6: Biochemical and structural characterization of PT1Em and PT7Bc mechanisms of action.

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Data availability

All of the raw data were deposited at https://doi.org/10.5281/zenodo.13341769 (ref. 59). Accession codes for the sequences can be found in Supplementary Table 2. The required information for the structural analysis is presented in Supplementary Table 10.

Code availability

The main scripts used in the analysis have been deposited at https://github.com/noamdotan/levylab.

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References

  1. Iyer, L. M., Zhang, D., Rogozin, I. B. & Aravind, L. Evolution of the deaminase fold and multiple origins of eukaryotic editing and mutagenic nucleic acid deaminases from bacterial toxin systems. Nucleic Acids Res. 39, 9473–9497 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Koskiniemi, S. et al. Rhs proteins from diverse bacteria mediate intercellular competition. Proc. Natl Acad. Sci. USA 110, 7032–7037 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Alcoforado Diniz, J. & Coulthurst, S. J. Intraspecies competition in Serratia marcescens is mediated by type VI-secreted Rhs effectors and a conserved effector-associated accessory protein. J. Bacteriol. 197, 2350–2360 (2015).

    PubMed  PubMed Central  Google Scholar 

  4. Morse, R. P. et al. Structural basis of toxicity and immunity in contact-dependent growth inhibition (CDI) systems. Proc. Natl Acad. Sci. USA 109, 21480–21485 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Michalska, K. et al. Structure of a novel antibacterial toxin that exploits elongation factor Tu to cleave specific transfer RNAs. Nucleic Acids Res. 45, 10306–10320 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Russell, A. B. et al. Diverse type VI secretion phospholipases are functionally plastic antibacterial effectors. Nature 496, 508–512 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Ghequire, M. G. K., Buchanan, S. K. & De Mot, R. The ColM family, polymorphic toxins breaching the bacterial cell wall. mBio 9, e02267-17 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. Whitney, J. C. et al. A broadly distributed toxin family mediates contact-dependent antagonism between Gram-positive bacteria. eLife 6, e26938 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Nolan, L. M. et al. Identification of Tse8 as a type VI secretion system toxin from Pseudomonas aeruginosa that targets the bacterial transamidosome to inhibit protein synthesis in prey cells. Nat. Microbiol. 6, 1199–1210 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Ahmad, S. et al. An interbacterial toxin inhibits target cell growth by synthesizing (p)ppApp. Nature 575, 674–678 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ruhe, Z. C., Low, D. A. & Hayes, C. S. Polymorphic toxins and their immunity proteins: diversity, evolution, and mechanisms of delivery. Annu. Rev. Microbiol. 74, 497–520 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang, D., de Souza, R. F., Anantharaman, V., Iyer, L. M. & Aravind, L. Polymorphic toxin systems: comprehensive characterization of trafficking modes, processing, mechanisms of action, immunity and ecology using comparative genomics. Biol. Direct 7, 18 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Aoki, S. K. et al. A widespread family of polymorphic contact-dependent toxin delivery systems in bacteria. Nature 468, 439–442 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Cao, Z., Casabona, M. G., Kneuper, H., Chalmers, J. D. & Palmer, T. The type VII secretion system of Staphylococcus aureus secretes a nuclease toxin that targets competitor bacteria. Nat. Microbiol. 2, 16183 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Garcia, E. C., Perault, A. I., Marlatt, S. A. & Cotter, P. A. Interbacterial signaling via Burkholderia contact-dependent growth inhibition system proteins. Proc. Natl Acad. Sci. USA 113, 8296–8301 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Song, N. et al. Genome-wide dissection reveals diverse pathogenic roles of bacterial Tc toxins. PLoS Pathog. 17, e1009102 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Mok, B. Y. et al. A bacterial cytidine deaminase toxin enables CRISPR-free mitochondrial base editing. Nature 583, 631–637 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Shneider, M. M. et al. PAAR-repeat proteins sharpen and diversify the type VI secretion system spike. Nature 500, 350–353 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Bondage, D. D., Lin, J.-S., Ma, L.-S., Kuo, C.-H. & Lai, E.-M. VgrG C terminus confers the type VI effector transport specificity and is required for binding with PAAR and adaptor–effector complex. Proc. Natl Acad. Sci. USA 113, E3931–E3940 (2016).

  20. Geller, A. M. et al. The extracellular contractile injection system is enriched in environmental microbes and associates with numerous toxins. Nat. Commun. 12, 3743 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Desvaux, M. et al. A conserved extended signal peptide region directs posttranslational protein translocation via a novel mechanism. Microbiology 153, 59–70 (2007).

    CAS  PubMed  Google Scholar 

  22. Salomon, D. et al. Marker for type VI secretion system effectors. Proc. Natl Acad. Sci. USA 111, 9271–9276 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Holm, L. & Laakso, L. M. Dali server update. Nucleic Acids Res. 44, W351–W355 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Van Ness, B. G., Howard, J. B. & Bodley, J. W. ADP-ribosylation of elongation factor 2 by diphtheria toxin. Isolation and properties of the novel ribosyl-amino acid and its hydrolysis products. J. Biol. Chem. 255, 10717–10720 (1980).

    PubMed  Google Scholar 

  26. Capra, J. A. & Singh, M. Predicting functionally important residues from sequence conservation. Bioinformatics 23, 1875–1882 (2007).

    CAS  PubMed  Google Scholar 

  27. Holliday, G. L., Mitchell, J. B. O. & Thornton, J. M. Understanding the functional roles of amino acid residues in enzyme catalysis. J. Mol. Biol. 390, 560–577 (2009).

  28. WHO Fungal Priority Pathogens List to Guide Research, Development and Public Health Action (World Health Organization, 2022); https://www.who.int/publications/i/item/9789240060241

  29. Batista, B. G., de Chaves, M. A., Reginatto, P., Saraiva, O. J. & Fuentefria, A. M. Human fusariosis: an emerging infection that is difficult to treat. Rev. Soc. Bras. Med. Trop. 53, e20200013 (2020).

    PubMed  PubMed Central  Google Scholar 

  30. Nüesch-Inderbinen, M. T. et al. Prevalence of subtilase cytotoxin-encoding subAB variants among Shiga toxin-producing Escherichia coli strains isolated from wild ruminants and sheep differs from that of cattle and pigs and is predominated by the new allelic variant subAB2-2. Int. J. Med. Microbiol. 305, 124–128 (2015).

    PubMed  Google Scholar 

  31. Estes, K. A., Dunbar, T. L., Powell, J. R., Ausubel, F. M. & Troemel, E. R. bZIP transcription factor zip-2 mediates an early response to Pseudomonas aeruginosa infection in Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 107, 2153–2158 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Hersch, S. J. et al. Envelope stress responses defend against type six secretion system attacks independently of immunity proteins. Nat. Microbiol. 5, 706–714 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Meuskens, I., Saragliadis, A., Leo, J. C. & Linke, D. Type V secretion systems: an overview of passenger domain functions. Front. Microbiol. 10, 1163 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. Trunk, K. et al. The type VI secretion system deploys antifungal effectors against microbial competitors. Nat. Microbiol. 3, 920–931 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Si, M. et al. Manganese scavenging and oxidative stress response mediated by type VI secretion system in Burkholderia thailandensis. Proc. Natl Acad. Sci. USA 114, E2233–E2242 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Unterweger, D., Kostiuk, B. & Pukatzki, S. Adaptor proteins of type VI secretion system effectors. Trends Microbiol. 25, 8–10 (2017).

    CAS  PubMed  Google Scholar 

  38. Jurėnas, D., Fraikin, N., Goormaghtigh, F. & Van Melderen, L. Biology and evolution of bacterial toxin–antitoxin systems. Nat. Rev. Microbiol. 20, 335–350 (2022).

    PubMed  Google Scholar 

  39. Ohlendorf, D. H. & Matthew, J. B. Electrostatics and flexibility in protein–DNA interactions. Adv. Biophys. 20, 137–151 (1985).

    CAS  PubMed  Google Scholar 

  40. Pan, C. Q. & Lazarus, R. A. Hyperactivity of human DNase I variants. Dependence on the number of positively charged residues and concentration, length, and environment of DNA. J. Biol. Chem. 273, 11701–11708 (1998).

    CAS  PubMed  Google Scholar 

  41. Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).

    CAS  PubMed  Google Scholar 

  42. Jana, B., Salomon, D. & Bosis, E. A novel class of polymorphic toxins in Bacteroidetes. Life Sci. Alliance 3, e201900631 (2020).

    PubMed  PubMed Central  Google Scholar 

  43. Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2021).

  44. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  46. Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    PubMed  PubMed Central  Google Scholar 

  49. Xu, S. et al. ggtreeExtra: compact visualization of richly annotated phylogenetic data. Mol. Biol. Evol. 38, 4039–4042 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020).

    PubMed  Google Scholar 

  51. Brynildsrud, O., Bohlin, J., Scheffer, L. & Eldholm, V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 17, 238 (2016).

    PubMed  PubMed Central  Google Scholar 

  52. Wheeler, T. J., Clements, J. & Finn, R. D. Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models. BMC Bioinformatics 15, 7 (2014).

    PubMed  PubMed Central  Google Scholar 

  53. Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).

    CAS  PubMed  Google Scholar 

  55. Holm, L. & Rosenström, P. Dali server: conservation mapping in 3D. Nucleic Acids Res. 38, W545–W549 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl Acad. Sci. USA 97, 6640–6645 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Petzoldt, T. growthrates: Estimate growth rates from experimental data. R package version 0.8.2 (2020).

  58. Rocha, M. C. et al. The Aspergillus fumigatus pkcA G579R mutant is defective in the activation of the cell wall integrity pathway but is dispensable for virulence in a neutropenic mouse infection model. PLoS ONE 10, e0135195 (2015).

    PubMed  PubMed Central  Google Scholar 

  59. Oppenheimer shaanan, Y., Nachmias, N., Tzarum, N. & Rocha, M. C. Raw data for Nachmias N.*, Dotan N*, Campos Rocha M* et al. Nature Microbiology 2024 (accepted). Zenodo https://doi.org/10.5281/zenodo.13341769 (2024).

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Acknowledgements

A.L. is generously supported by the Israel Science Foundation (grants 1535/20, 3300/20 and 3062/20), Israeli Ministry of Innovation, Science and Technology (grant 1001695377) and Israel Innovation Authority (grant 81259). N.N. is supported by the Israel Science Foundation (grant 1535/20) and a PhD scholarship from the Faculty of Agriculture, Food and Environment at the Hebrew University of Jerusalem. N.T. is supported by the Israel Science Foundation (grants 1600/21 and 1818/21). N.S. is supported by the Israel Science Foundation (grant 1760/20), Zuckerman STEM Leadership Program, Ministry of Science and Technology of Israel (grant 0001998) and BARD US–Israel Agricultural Research and Development Fund (grant IS-5492-22 R). M.K. and J.K. are supported by the European Research Council (Advanced Grant 743016). We thank R. Stephan at the University of Zurich, Switzerland for providing Shiga toxin-producing E. coli strain 48 and M. Morand at the Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences for help with the liposome-based assays. We thank members of A. Levy’s laboratory for critical reading, advice and helpful discussions throughout, especially A. Geller and A. Bograd. We thank M. Canjalli for helping with some of the experiments with fungi. We thank the ESRF for the provision of synchrotron radiation facilities and ESRF ID30A local contacts for support and help during data collection.

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Authors and Affiliations

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Contributions

A.L. conceived of and designed the study, managed the project and wrote the manuscript. N.N. performed the computational structural analysis, bacteriology and fungal and macrophage assays and wrote the paper. N. Dotan performed the computational analysis. M.C.R. performed the experiments with fungi. R.F. performed the biochemistry and structural analysis. K.D. constructed the knockouts in E. coli strain 48. M.K. performed the LUV experiments. M.S. performed some of the toxicity assays in E. coli. S.C., A.R. and N.S.-H. performed some of the computational analyses. S.P.-K. performed the experiments with nematodes. G.M. performed the experiments with moths. I.C. and N. Deouell helped with the structural and biochemical analyses. J.K., M.O.-S. and H.S. helped to write the manuscript. N.S. and N.T. designed the experiments in mycology and structural biology, respectively, performed data analysis and wrote the manuscript. Y.O.-S. designed and performed the experiments with bacteria and fungi, was responsible for most of the microscopy and wrote the manuscript.

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Correspondence to Asaf Levy.

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Extended data

Extended Data Fig. 1 Cell lysates of PT expressing E. coli inhibit fungal growth.

Antifungal activity of the toxins PT1Em (a), PT2Nm (b), PT3Rs (c), PT7Bc (d), PT8Li (e), and PT9Cn (f) against the fungi (left to right in each panel) A. fumigatus, A. nidulans, C. albicans, C. auris, C. Neoformans, and F. oxysporum. To determine the effect of PTs on fungal growth the indicator Alamar blue Fluorescence intensities (FI) of treated and untreated samples for each strain were obtained after 48 h of incubation. FI were used to calculate the percentage of toxicity. The fungal strains were grown with lysates of PTs expressing E. coli cells. Ctr; control of lysates of E. coli cells with an empty vector. NT, No treatment (on y axis).

Extended Data Fig. 2 PTs lead to cell death in various manners.

E. coli BL21 (DE3) cells harboring toxin genes (PT) or an empty vector control (Ctr) were grown in LB media in conditions that induce expression (0.2% arabinose). After 40 min of incubation, samples were stained with membrane and DNA stains and visualized by fluorescence microscopy. DNA (DAPI, blue), membrane (FM 1-43; green), and overlay images of the bacterial cells with toxins are presented. Arrows point to abnormal cells; PT3Rs: compressed chromosomes, PT4Ka: membrane disintegration and swollen cells, PT7Bc: missing DNA and membrane movement to the poles, PT8Li: abnormal shaped E. coli. Representative images from a single replicate out of three independent replicates are shown. Scale bar corresponds to 2 µm. Number of cells analyzed: PT1Em n = 1469, PT2Nm n = 1680, PT3Rs n = 1276, PT4Ka n = 1245, PT5Rb n = 1515, PT6Mc n = 1285, PT7B n = 1809, PT8Li n = 1564, PT9Cn n = 1381.

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Nachmias, N., Dotan, N., Rocha, M.C. et al. Systematic discovery of antibacterial and antifungal bacterial toxins. Nat Microbiol 9, 3041–3058 (2024). https://doi.org/10.1038/s41564-024-01820-9

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