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:

Cooperative virulence via the collective action of secreted pathogen effectors

Abstract

Although virulence is typically attributed to single pathogenic strains, here we investigated whether effectors secreted by a population of non-virulent strains could function as public goods to enable the emergence of collective virulence. We disaggregated the 36 type III effectors of the phytopathogenic bacterium Pseudomonas syringae strain PtoDC3000 into a ‘metaclone’ of 36 coisogenic strains, each carrying a single effector in an effectorless background. Each coisogenic strain was individually unfit, but the metaclone was collectively as virulent as the wild-type strain on Arabidopsis thaliana, suggesting that effectors can drive the emergence of cooperation-based virulence through their public action. We show that independently evolved effector suits can equally drive this cooperative behaviour by transferring the effector alleles native to the strain PmaES4326 into the conspecific but divergent strain PtoDC3000. Finally, we transferred the disaggregated PtoDC3000 effector arsenal into Pseudomonas fluorescens and show that their cooperative action was sufficient to convert this rhizosphere-inhabiting beneficial bacterium into a phyllosphere pathogen. These results emphasize the importance of microbial community interactions and expand the ecological scale at which disease may be attributed.

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

Access options

Buy this article

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

Fig. 1: Metaclones of P.syringae facilitate the emergence of population virulence.
Fig. 2: Collective action of the PtoDC3000 metaclone supports growth of laboratory and natural freeloader strains.
Fig. 3: Portability of pathogenic effector repertoires.
Fig. 4: Effectors are sufficient for converting a beneficial strain into a pathogen.

Similar content being viewed by others

Data availability

Metaclone data are available in Supplementary Data 1. Barcode sequence data are available at https://doi.org/10.5281/zenodo.7254975.

Code availability

The script used for reading metaclone barcodes is available at https://doi.org/10.5281/zenodo.7249118 (https://zenodo.org/record/7249118).

References

  1. Mayr, E. The Growth of Biological Thought: Diversity, Evolution, and Inheritance (Belknap Press, 1982).

  2. Afkhami, M. E. & Stinchcombe, J. R. Multiple mutualist effects on genomewide expression in the tripartite association between Medicago truncatula, nitrogen-fixing bacteria and mycorrhizal fungi. Mol. Ecol. 25, 4946–4962 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Shalev, O. et al. Commensal Pseudomonas strains facilitate protective response against pathogens in the host plant. Nat. Ecol. Evol. 6, 383–396 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Flemming, H. C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Casadevall, A., Fang, F. C. & Pirofski, L. A. Microbial virulence as an emergent property: consequences and opportunities. PLoS Pathog. 7, e1002136 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hanson, R. P. Koch is dead. J. Wildl. Dis. 24, 193–200 (1988).

    Article  CAS  PubMed  Google Scholar 

  7. Falkow, S. Molecular Koch’s postulates applied to bacterial pathogenicity—a personal recollection 15 years later. Nat. Rev. Microbiol. 2, 67–72 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Vonaesch, P., Anderson, M. & Sansonetti, P. J. Pathogens, microbiome and the host: emergence of the ecological Koch’s postulates. FEMS Microbiol. Rev. 42, 273–292 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Friesen, M. L. Social evolution and cheating in plant pathogens. Annu. Rev. Phytopathol. 58, 55–75 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Smith, J. The social evolution of bacterial pathogenesis. Proc. Biol. Sci. 268, 61–69 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Smith, P. & Schuster, M. Public goods and cheating in microbes. Curr. Biol. 29, R442–R447 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. West, S. A., Diggle, S. P., Buckling, A., Gardner, A. & Griffin, A. S. The social lives of microbes. Annu. Rev. Ecol. Evol. Syst. 38, 53–77 (2007).

    Article  Google Scholar 

  13. West, S. A., Griffin, A. S., Gardner, A. & Diggle, S. P. Social evolution theory for microorganisms. Nat. Rev. Microbiol. 4, 597–607 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Riehl, C. & Frederickson, M. E. Cheating and punishment in cooperative animal societies. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150090 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. West, S. A. & Buckling, A. Cooperation, virulence and siderophore production in bacterial parasites. Proc. Biol. Sci. 270, 37–44 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Barrett, L. G., Bell, T., Dwyer, G. & Bergelson, J. Cheating, trade-offs and the evolution of aggressiveness in a natural pathogen population. Ecol. Lett. 14, 1149–1157 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Buttner, D. Behind the lines-actions of bacterial type III effector proteins in plant cells. FEMS Microbiol. Rev. 40, 894–937 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Khan, M., Seto, D., Subramaniam, R. & Desveaux, D. Oh, the places they’ll go! A survey of phytopathogen effectors and their host targets. Plant J. 93, 651–663 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Jones, J. D. & Dangl, J. L. The plant immune system. Nature 444, 323–329 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Dillon, M. M. et al. Recombination of ecologically and evolutionarily significant loci maintains genetic cohesion in the Pseudomonas syringae species complex. Genome Biol. 20, 3 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Morris, C. E., Monteil, C. L. & Berge, O. The life history of Pseudomonas syringae: linking agriculture to earth system processes. Annu. Rev. Phytopathol. 51, 85–104 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Xin, X. F., Kvitko, B. & He, S. Y. Pseudomonas syringae: what it takes to be a pathogen. Nat. Rev. Microbiol. 16, 316–328 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dillon, M. M. et al. Molecular evolution of Pseudomonas syringae type III secreted effector proteins. Front. Plant Sci. 10, 418 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Cunnac, S. et al. Genetic disassembly and combinatorial reassembly identify a minimal functional repertoire of type III effectors in Pseudomonas syringae. Proc. Natl Acad. Sci. USA 108, 2975–2980 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rundell, E. A., McKeithen-Mead, S. A. & Kazmierczak, B. I. Rampant cheating by pathogens? PLoS Pathog. 12, e1005792 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Laflamme, B. et al. The pan-genome effector-triggered immunity landscape of a host–pathogen interaction. Science 367, 763–768 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Yucel, I. et al. Avirulence gene avrPphC from Pseudomonas syringae pv. phaseolicola 3121: a plasmid-borne homolog of avrC closely linked to an avrD allele. Mol. Plant Microbe Interact. 7, 677–679 (1994).

    Article  CAS  PubMed  Google Scholar 

  28. Jackson, R. W. et al. Identification of a pathogenicity island, which contains genes for virulence and avirulence, on a large native plasmid in the bean pathogen Pseudomonas syringae pathovar phaseolicola. Proc. Natl Acad. Sci. USA 96, 10875–10880 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tsiamis, G. et al. Cultivar-specific avirulence and virulence functions assigned to avrPphF in Pseudomonas syringae pv. phaseolicola, the cause of bean halo-blight disease. EMBO J. 19, 3204–3214 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jackson, R. W. et al. Location and activity of members of a family of virPphA homologues in pathovars of Pseudomonas syringae and P. savastanoi. Mol. Plant Pathol. 3, 205–216 (2002).

    Article  CAS  PubMed  Google Scholar 

  31. Buell, C. R. et al. The complete genome sequence of the Arabidopsis and tomato pathogen Pseudomonas syringae pv. tomato DC3000. Proc. Natl Acad. Sci. USA 100, 10181–10186 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rohmer, L., Kjemtrup, S., Marchesini, P. & Dangl, J. L. Nucleotide sequence, functional characterization and evolution of pFKN, a virulence plasmid in Pseudomonas syringae pathovar maculicola. Mol. Microbiol. 47, 1545–1562 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Stavrinides, J. & Guttman, D. S. Nucleotide sequence and evolution of the five-plasmid complement of the phytopathogen Pseudomonas syringae pv. maculicola ES4326. J. Bacteriol. 186, 5101–5115 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sundin, G. W. et al. Complete nucleotide sequence and analysis of pPSR1 (72,601 bp), a pPT23A-family plasmid from Pseudomonas syringae pv. syringae A2. Mol. Genet. Genomics 270, 462–476 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Joardar, V. et al. Whole-genome sequence analysis of Pseudomonas syringae pv. phaseolicola 1448A reveals divergence among pathovars in genes involved in virulence and transposition. J. Bacteriol. 187, 6488–6498 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhao, Y., Ma, Z. & Sundin, G. W. Comparative genomic analysis of the pPT23A plasmid family of Pseudomonas syringae. J. Bacteriol. 187, 2113–2126 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Perez-Martinez, I., Zhao, Y., Murillo, J., Sundin, G. W. & Ramos, C. Global genomic analysis of Pseudomonas savastanoi pv. savastanoi plasmids. J. Bacteriol. 190, 625–635 (2008).

    Article  CAS  PubMed  Google Scholar 

  38. Baltrus, D. A. et al. Dynamic evolution of pathogenicity revealed by sequencing and comparative genomics of 19 Pseudomonas syringae isolates. PLoS Pathog. 7, e1002132 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gutierrez-Barranquero, J. A., Cazorla, F. M., de Vicente, A. & Sundin, G. W. Complete sequence and comparative genomic analysis of eight native Pseudomonas syringae plasmids belonging to the pPT23A family. BMC Genomics 18, 365 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wei, H. L. et al. Pseudomonas syringae pv. tomato DC3000 type III secretion effector polymutants reveal an interplay between HopAD1 and AvrPtoB. Cell Host Microbe 17, 752–762 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Chakravarthy, S., Worley, J. N., Montes-Rodriguez, A. & Collmer, A. Pseudomonas syringae pv. tomato DC3000 polymutants deploying coronatine and two type III effectors produce quantifiable chlorotic spots from individual bacterial colonies in Nicotiana benthamiana leaves. Mol. Plant Pathol. 19, 935–947 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Wei, H. L., Zhang, W. & Collmer, A. Modular study of the type III effector repertoire in Pseudomonas syringae pv. tomato DC3000 reveals a matrix of effector interplay in pathogenesis. Cell Rep. 23, 1630–1638 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Wei, H. L. & Collmer, A. Defining essential processes in plant pathogenesis with Pseudomonas syringae pv. tomato DC3000 disarmed polymutants and a subset of key type III effectors. Mol. Plant Pathol. 19, 1779–1794 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Engel, C. Scientific disintegrity as a public bad. Perspect. Psychol. Sci. 10, 361–379 (2015).

    Article  PubMed  Google Scholar 

  45. Silby, M. W. et al. Genomic and genetic analyses of diversity and plant interactions of Pseudomonas fluorescens. Genome Biol. 10, R51 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Haney, C. H., Samuel, B. S., Bush, J. & Ausubel, F. M. Associations with rhizosphere bacteria can confer an adaptive advantage to plants. Nat. Plants 1, 15051 (2015).

  47. Goris, J. et al. DNA–DNA hybridization values and their relationship to whole-genome sequence similarities. Int. J. Syst. Evol. Microbiol 57, 81–91 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Martel, A. et al. Metaeffector interactions modulate the type III effector-triggered immunity load of Pseudomonas syringae. PLoS Pathog. 18, e1010541 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Thomas, W. J., Thireault, C. A., Kimbrel, J. A. & Chang, J. H. Recombineering and stable integration of the Pseudomonas syringae pv. syringae 61 hrp/hrc cluster into the genome of the soil bacterium Pseudomonas fluorescens Pf0-1. Plant J. 60, 919–928 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Stavrinides, J., Ma, W. & Guttman, D. S. Terminal reassortment drives the quantum evolution of type III effectors in bacterial pathogens. PLoS Pathog. 2, e104 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kim, H. S. et al. The Pseudomonas syringae effector AvrRpt2 cleaves its C-terminally acylated target, RIN4, from Arabidopsis membranes to block RPM1 activation. Proc. Natl Acad. Sci. USA 102, 6496–6501 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ritter, C. & Dangl, J. L. Interference between two specific pathogen recognition events mediated by distinct plant disease resistance genes. Plant Cell 8, 251–257 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Jamir, Y. et al. Identification of Pseudomonas syringae type III effectors that can suppress programmed cell death in plants and yeast. Plant J. 37, 554–565 (2004).

    Article  CAS  PubMed  Google Scholar 

  54. Guo, M., Tian, F., Wamboldt, Y. & Alfano, J. R. The majority of the type III effector inventory of Pseudomonas syringae pv. tomato DC3000 can suppress plant immunity. Mol. Plant Microbe Interact. 22, 1069–1080 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gimenez-Ibanez, S. et al. Differential suppression of Nicotiana benthamiana innate immune responses by transiently expressed Pseudomonas syringae type III effectors. Front. Plant Sci. 9, 688 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wilton, M. et al. The type III effector HopF2Pto targets Arabidopsis RIN4 protein to promote Pseudomonas syringae virulence. Proc. Natl Acad. Sci. USA 107, 2349–2354 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Cressler, C. E., Mc, L. D., Rozins, C., J, V. D. H. & Day, T. The adaptive evolution of virulence: a review of theoretical predictions and empirical tests. Parasitology 143, 915–930 (2016).

    Article  PubMed  Google Scholar 

  58. Ona, L. et al. Obligate cross-feeding expands the metabolic niche of bacteria. Nat. Ecol. Evol. 5, 1224–1232 (2021).

    Article  PubMed  Google Scholar 

  59. Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio https://doi.org/10.1128/mBio.00036-12 (2012).

  61. Lamichhane, J. R. & Venturi, V. Synergisms between microbial pathogens in plant disease complexes: a growing trend. Front. Plant Sci. 6, 385 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Rodrigues, L. M. R., Sera, G. H., Filho, O. G., Beriam, L. O. S. & de Almeida, I. M. G. First report of mixed infection by Pseudomonas syringae pathovars garcae and tabaci on coffee plantations. Bragantia 74, 543–549 (2017).

    Article  Google Scholar 

  63. Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Kapp, D., Niehaus, K., Quandt, J., Muller, P. & Puhler, A. Cooperative action of Rhizobium meliloti nodulation and infection mutants during the process of forming mixed infected alfalfa nodules. Plant Cell 2, 139–151 (1990).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Morris, C. E. et al. Pseudomonas syringae on plants in Iceland has likely evolved for several million years outside the reach of processes that mix this bacterial complex across Earth’s temperate zones. Pathogens 11, 357 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Arnold, D. L. & Preston, G. M. Pseudomonas syringae: enterprising epiphyte and stealthy parasite. Microbiology (Reading) 165, 251–253 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Casadevall, A. & Pirofski, L. A. What is a pathogen? Ann. Med. 34, 2–4 (2002).

    Article  PubMed  Google Scholar 

  68. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.6-4 http://cran.r-project.org/web/packages/vegan/index.html (2011).

Download references

Acknowledgements

We thank the members of the Desveaux and Guttman laboratories for their advice and feedback throughout this project, with particular thanks to A. Martel for his assistance with strain transformations and to M. Dillon for his insights into effector extraction and construct synthesis. We would also like to thank and acknowledge A. Collmer and J. Chang for providing us with the strains PtoDC3000D36E (that is, D36E, CUCPB6119) and PfEtHAn, which were invaluable to this study. This project is supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants (to D.S.G. and D.D.).

Author information

Authors and Affiliations

Authors

Contributions

T.R.-B., D.D. and D.S.G. designed the project. T.R.-B. performed transformations, tri-parental matings, metaclone constructions, spray infections, in planta and in vitro growth assays, primer design, barcode quantification and analysed the data. P.W.W. generated sequence data and assisted with many aspects of the project. T.R.-B., D.D. and D.S.G. wrote the paper. All authors reviewed and agreed on the manuscript.

Corresponding authors

Correspondence to Darrell Desveaux or David S. Guttman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Maren Friesen, John Mansfield, Hai-Lei Wei and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 In-planta fitness of individual effector clones.

Bacterial growth was quantified on day 6 post spray-infection for at least 4 independent plant replicates infected with each of the 36 effector clones that comprise the well-expressed effector arsenal of the pathogen PtoDC3000. All infections were performed at the same time and controls were placed in each flat. D36E and PtoDC3000, each harboring an empty vector (EV), were used as negative and positive virulence controls, respectively. No individual effector clone deviated significantly from the mean of the effector-less control strain. Elements in boxplots represent the median, 25th and 75th percentiles, and 1.5 * inter-quantile ranges. All comparisons were performed via one-way ANOVA with post-hoc Tukey-Kramer HSD using a 95% confidence interval (Source data and p-values provided in Supplementary Data 1).

Extended Data Fig. 2 In-vitro fitness of individual effector clones.

Growth curves of individual effector clones composing the PtoDC3000 metaclone D36E::McDC[36], and control strains harboring empty vectors (EV). Harboring any of the individual effector plasmids did not substantially burden or benefit the growth of D36E in vitro. Data points represent the average optical density (OD) measured at 600 nm across 3 technical replicates grown in rich media (KB) with kanamycin. Time measures with SE > 0.07 were excluded as the high variability was likely due to condensation, which was noticeable during lag phase, predominantly over the first 10 hours. This, however, did not impact the overall trajectory of the growth curves and there were no overall substantial differences between effector clones. NC: negative buffer control.

Extended Data Fig. 3 Collective virulence emerges when using syringe pressure infiltration assays on A. thaliana.

a. Endophytic bacterial growth at 0-, 3-, 5- and 7-days post infection using syringe pressure infiltration. Diagrams on the right represent the corresponding population and genetic makeup of each infection. Error bars correspond to standard deviation from the mean of at least 4 biological replicates per treatment at each timepoint. b-d. Host symptoms shown in photos taken of 4 representative leaves per bacterial treatment, and 2 plants for MgSO4 controls at b, 3-, c, 5- and d, 7- days post infection.

Extended Data Fig. 4 Mean Shannon’s diversity index for metaclones does not deviate significantly throughout disease progression.

Diversity calculations were based on the relative abundance of each D36E::effector barcode composing the PtoDC3000 metaclones D36E::McDC[36] (ETI-eliciting) and D36E::McDC[35] (not ETI-eliciting). Barcode sequence diversity is reported as the mean Shannon index across four biological replicates + /- SE for each metaclone. No significant differences were found between any stage of infection and the mean barcode diversity of the original inoculum (Bonferroni-corrected two-sided Student’s t-tests. p-values: D36E::McDC[35]: D0pi-D3pi = 0.66, D0pi-D5pi = 0.41, D0pi-D7pi = 0.89; D36E::McDC[36]: D0pi-D3pi = 0.56, D0pi-D5pi = 0.57, D0pi-D7pi = 0.35)). Error bars represent standard error across 4 biological replicates. Higher variance across replicates is observed in the ETI-eliciting metaclone D36E::McDC[36], particularly on the latter stages of the infection process, which can be attributed to smaller population sizes and increased drift in the metaclone composition.

Extended Data Fig. 5 Freeloading strains benefit from collective virulence and coexist with cooperating portion of the metaclone.

The virulent PtoDC3000 metaclone D36E::McDC[35] or mock population (D36E::EV) were mixed with strains unfit for multiplication in the plant apoplastic space (that is. freeloaders). Bacterial endophytic growth is reported at 7 dpi for cooperator and freeloader, separately and together, following standard spray infection of mixed populations and controls (Fig2). Differential antibiotic selection shown was used to measure fitness of the two genotypes present in each population, together (Total Growth –Rifampicin only -grey) and separately (Cooperator -rifampicin and tetracycline -blue; Freeloader- rifampicin and kanamycin -orange) a. The effectorless polymutant strain D36E, and b. the natural soil-associated sister species Pf0-1 are rescued to significantly higher load when in the presence of public goods provided cooperatively by the metaclone. Elements in boxplots represent the median of 6 biological replicates per treatment per selection category, the 25th and 75th percentiles, and 1.5 * inter-quantile ranges. Letters represent significant groups estimated via one-way ANOVA with post-hoc Tukey-Kramer HSD using a 95% confidence interval (Source data and p-values in in Supplementary Data 1).

Extended Data Fig. 6 Metaclone representing the effector allele overlap between PtoDC3000 and PmaES4326, McDC&ES[7], fails to match fitness of PtoDC3000 and the non-ETI-eliciting metaclone D36E::McDC[35].

D36E::McDC&ES[7] shows a fitness significantly closer to that of the effectorless polymutant D36E::EV and does not restore the growth levels typical of their wildtype parental strain PtoDC3000 or its non-ETI eliciting metaclone D36E::McDC[35]. In-planta fitness was quantified 6 dpi for 5 biological replicates per treatment and 4 independent infection replicates. Elements in boxplots represent the median, 25th and 75th percentiles, and 1.5 * inter-quantile ranges. Comparisons were performed via one-way ANOVA and post-hoc Tukey-Kramer HSD using a 95% confidence interval was performed separately for 4 independent experiments (Source data and p-values in Supplementary Data 1).

Extended Data Fig. 7 Representative plant photos show symptom development for the metaclones D36E::McDC[35] and PfEtHAn::McDC[35] compared to their effector-less background and the pathogen PtoDC3000.

For each treatment, a total of 8 leaves from 2 plants show host symptoms at a. 3, b. 5, and c. 7 days post bacterial pressure infiltration. The progression of symptom development is visible in photos taken with a no-flash standard settings (left panels) and with a camera green-pass effect filter setting which facilitates the inspection of lesions (grey) and viable (green) tissue (right panels). The MgS04 control, D36E::EV and PfEtHAn::EV treatments are presented on the left (top to bottom), and PtoDC3000::EV, D36E::McDC[35] and PfEtHAn::McDC[35] are presented on the right (top to bottom).

Extended Data Fig. 8 Dose response of the PtoDC3000 metaclone D36E::McDC[35].

A range of inoculum concentrations (OD600 0.5, 1, 2 and 4) was tested on 8 plants and visual symptoms were followed at 3, 5, 6 and 12 dpi. Photos were taken with a green-only pass filter, showing in grey any chlorotic yellow or brown tissue. Plant fitness (ie. green) detectably declines more when infected with the pathogen PtoDC3000 and the D36E::McDC[35] than it does for the effectorless control D36E::EV. No visual differences are observed across the range of concentrations tested for the metaclone D36E::McDC[35] and plant fitness is visually reduced at an optical density as low as 0.5.

Supplementary information

Reporting Summary

Supplementary Tables 1–3.

Supplementary Data 1

Source data and statistical analyses for Figs. 1–4 and Extended Data Figs. 1, 5 and 6.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruiz-Bedoya, T., Wang, P.W., Desveaux, D. et al. Cooperative virulence via the collective action of secreted pathogen effectors. Nat Microbiol 8, 640–650 (2023). https://doi.org/10.1038/s41564-023-01328-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-023-01328-8

This article is cited by

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