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:

Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen

Nature Microbiology volume 6, pages 1537–1548 (2021)Cite this article

Subjects

An Author Correction to this article was published on 06 January 2022

This article has been updated

Abstract

The aerial parts of plants are host to taxonomically structured bacterial communities. Members of the core phyllosphere microbiota can protect Arabidopsis thaliana against foliar pathogens. However, whether plant protection is widespread and to what extent the modes of protection differ among phyllosphere microorganisms are not clear. Here, we present a systematic analysis of plant protection capabilities of the At-LSPHERE, which is a collection of >200 bacterial isolates from A. thaliana, against the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. In total, 224 bacterial leaf isolates were individually assessed for plant protection in a gnotobiotic system. Protection against the pathogen varied, with ~10% of leaf microbiota strains providing full protection, ~10% showing intermediate levels of protection and the remaining ~80% not markedly reducing disease phenotypes upon infection. The most protective strains were distributed across different taxonomic groups. Synthetic community experiments revealed additive effects of strains but also that a single strain can confer full protection in a community context. We also identify different mechanisms that contribute to plant protection. Although pattern-triggered immunity coreceptor signalling is involved in protection by a subset of strains, other strains protected in the absence of functional plant immunity receptors BAK1 and BKK1. Using a comparative genomics approach combined with mutagenesis, we reveal that direct bacteria–pathogen interactions contribute to plant protection by Rhizobium Leaf202. This shows that a computational approach based on the data provided can be used to identify genes of the microbiota that are important for plant protection.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Overview of experimental screening system.
Fig. 2: Overview of plant protection in the At-LSPHERE.
Fig. 3: Protective strains of the At-LSPHERE colonize the phyllosphere at high density.
Fig. 4: SynCom experiments reveal community-specific gain of protection.
Fig. 5: Plant protection in the bak1/bkk1 plant background is strongly reduced in a subset of strains based on luminescence analysis.
Fig. 6: The T6SS is associated with plant protection in Rhizobium spp. in the At-LSPHERE.

Similar content being viewed by others

Data availability

Sequencing data for this study have been deposited in the European Nucleotide Archive under accession PRJEB47672. Other genome data are available from NCBI under accessions PRJNA297956, PRJNA471493 and PRJNA84361. The eggNOG database is available from http://eggnog45.embl.de/#/app/home. Rhizobium genomes used for comparative genomics are available from https://img.jgi.doe.gov/m under IMG Genome IDs 2643221743, 2643221780, 2643221832, 2643221860, 2643221889, 2643221891, 2643221896, 2643221905, 2643221915, 2643221931 and 2643221933-36. Source data are provided with this paper.

Code availability

The scripts used for sequencing data processing, genome assembly and data analysis are available at https://gitlab.ethz.ch/chvogel1/vogel_natmicro_2021/.

Change history

References

  1. Müller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The plant microbiota: systems-level insights and perspectives. Annu. Rev. Genet. 50, 211–234 (2016).

    Article  PubMed  Google Scholar 

  2. Udvardi, M. & Poole, P. S. Transport and metabolism in legume–rhizobia symbioses. Annu. Rev. Plant Biol. 64, 781–805 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Parniske, M. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nat. Rev. Microbiol. 6, 763–775 (2008).

    Article  CAS  PubMed  Google Scholar 

  4. Bulgarelli, D., Schlaeppi, K., Spaepen, S., Ver Loren van Themaat, E. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Wagner, M. R. et al. Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative. Ecol. Lett. 17, 717–726 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Glick, B. R. Bacteria with ACC deaminase can promote plant growth and help to feed the world. Microbiol. Res. 169, 30–39 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Ritpitakphong, U. et al. The microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen. New Phytol. 210, 1033–1043 (2016).

    Article  CAS  PubMed  Google Scholar 

  8. Compant, S., Duffy, B., Nowak, J., Clement, C. & Barka, E. A. Use of plant growth-promoting bacteria for biocontrol of plant diseases: principles, mechanisms of action, and future prospects. Appl. Environ. Microbiol. 71, 4951–4959 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Santhanam, R. et al. Native root-associated bacteria rescue a plant from a sudden-wilt disease that emerged during continuous cropping. Proc. Natl Acad. Sci. USA 112, E5013–E5020 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).

    Article  PubMed  Google Scholar 

  11. Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).

    Article  PubMed  Google Scholar 

  12. Vorholt, J. A., Vogel, C., Carlström, C. I. & Müller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Berg, G. Plant–microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl. Microbiol. Biotechnol. 84, 11–18 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Cook, R. J. in Encyclopedia of Agriculture and Food Systems Vol. 4 (ed. Van Alfen, N. K.) 441–455 (Academic Press, 2014).

  16. Weller, D. M., Raaijmakers, J. M., Gardener, B. B. & Thomashow, L. S. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annu. Rev. Phytopathol. 40, 309–348 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. Latz, E., Eisenhauer, N., Rall, B. C., Scheu, S. & Jousset, A. Unravelling linkages between plant community composition and the pathogen-suppressive potential of soils. Sci. Rep. 6, 23584 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Finkel, O. M., Castrillo, G., Herrera Paredes, S., Salas Gonzàlez, I. & Dangl, J. L. Understanding and exploiting plant beneficial microbes. Curr. Opin. Plant Biol. 38, 155–163 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Jacobsen, B. J. in Microbial Ecology of Aerial Plant Surfaces (eds Bailey, M. J. et al.) 133–147 (CAB International, 2006).

  20. Barbey, C. et al. In planta biocontrol of Pectobacterium atrosepticum by Rhodococcus erythropolis involves silencing of pathogen communication by the rhodococcal gamma-lactone catabolic pathway. PLoS ONE 8, e66642 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pieterse, C. M. et al. Induced systemic resistance by beneficial microbes. Annu. Rev. Phytopathol. 52, 347–375 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Dangl, J. L., Horvath, D. M. & Staskawicz, B. J. Pivoting the plant immune system from dissection to deployment. Science 341, 746–751 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10, e1004283 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lebeis, S. L. et al. PLANT MICROBIOME. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Pfeilmeier, S. et al. The plant NADPH oxidase RBOHD is required for microbiota homeostasis in leaves. Nat. Microbiol 6, 852–864 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580, 653–657 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Innerebner, G., Knief, C. & Vorholt, J. A. Protection of Arabidopsis thaliana against leaf-pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system. Appl. Environ. Microbiol. 77, 3202–3210 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vogel, C., Bodenhausen, N., Gruissem, W. & Vorholt, J. A. The Arabidopsis leaf transcriptome reveals distinct but also overlapping responses to colonization by phyllosphere commensals and pathogen infection with impact on plant health. New Phytol. 212, 192–207 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Vogel, C., Innerebner, G., Zingg, J., Guder, J. & Vorholt, J. A. Forward genetic in planta screen for identification of plant-protective traits of Sphingomonas sp. strain Fr1 against Pseudomonas syringae DC3000. Appl. Environ. Microbiol. 78, 5529–DC5535 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Helfrich, E. J. N. et al. Bipartite interactions, antibiotic production and biosynthetic potential of the Arabidopsis leaf microbiome. Nat. Microbiol. 3, 909–919 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ceniceros, A., Dijkhuizen, L., Petrusma, M. & Medema, M. H. Genome-based exploration of the specialized metabolic capacities of the genus Rhodococcus. BMC Genomics 18, 593 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ma, X. Y., Xu, G. Y., He, P. & Shan, L. B. SERKing coreceptors for receptors. Trends Plant Sci. 21, 1017–1033 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Ma, L. S., Hachani, A., Lin, J. S., Filloux, A. & Lai, E. M. Agrobacterium tumefaciens deploys a superfamily of type VI secretion DNase effectors as weapons for interbacterial competition in planta. Cell Host Microbe 16, 94–104 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bernal, P., Allsopp, L. P., Filloux, A. & Llamas, M. A. The Pseudomonas putida T6SS is a plant warden against phytopathogens. ISME J. 11, 972–987 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Decoin, V. et al. A Pseudomonas fluorescens type 6 secretion system is related to mucoidy, motility and bacterial competition. BMC Microbiol 15, 72 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Goberna, M. & Verdu, M. Predicting microbial traits with phylogenies. ISME J. 10, 959–967 (2016).

    Article  PubMed  Google Scholar 

  39. Melnyk, R. A., Hossain, S. S. & Haney, C. H. Convergent gain and loss of genomic islands drive lifestyle changes in plant-associated Pseudomonas. ISME J. 13, 1575–1588 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Savory, E. A. et al. Evolutionary transitions between beneficial and phytopathogenic Rhodococcus challenge disease management. eLife 6, e30925 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Haas, D. & Défago, G. Biological control of soil-borne pathogens by fluorescent pseudomonads. Nat. Rev. Microbiol. 3, 307–319 (2005).

    Article  CAS  PubMed  Google Scholar 

  43. Raaijmakers, J. M. et al. Dose–response relationships in biological-control of Fusarium wilt of radish by Pseudomonas spp. Phytopathology 85, 1075–1081 (1995).

    Article  Google Scholar 

  44. Kwak, M. J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100–1109 (2018).

    Article  CAS  Google Scholar 

  45. Berg, M. & Koskella, B. Nutrient- and dose-dependent microbiome-mediated protection against a plant pathogen. Curr. Biol. 28, 2487–2492 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Berendsen, R. L. et al. Disease-induced assemblage of a plant-beneficial bacterial consortium. ISME J. 12, 1496–1507 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rudrappa, T., Czymmek, K. J., Pare, P. W. & Bais, H. P. Root-secreted malic acid recruits beneficial soil bacteria. Plant Physiol. 148, 1547–1556 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Liu, H., Brettell, L. E., Qiu, Z. & Singh, B. K. Microbiome-mediated stress resistance in plants. Trends Plant Sci. 25, 733–743 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Song, Y. et al. FERONIA restricts Pseudomonas in the rhizosphere microbiome via regulation of reactive oxygen species. Nature Plants 7, 644–654 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Bartoli, C. et al. In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana. ISME J. 12, 2024–2038 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Andrews, J. H. Biological control in the phyllosphere. Annu. Rev. Phytopathol. 30, 603–635 (1992).

    Article  CAS  PubMed  Google Scholar 

  52. Raaijmakers, J. M. & Mazzola, M. Diversity and natural functions of antibiotics produced by beneficial and plant pathogenic bacteria. Annu. Rev. Phytopathol. 50, 403–424 (2012).

    Article  CAS  PubMed  Google Scholar 

  53. Lindow, S. E. & Brandl, M. T. Microbiology of the phyllosphere. Appl. Environ. Microbiol. 69, 1875–1883 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Monier, J. M. & Lindow, S. E. Frequency, size, and localization of bacterial aggregates on bean leaf surfaces. Appl. Environ. Microbiol. 70, 346–355 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Remus-Emsermann, M. N. P. et al. Spatial distribution analyses of natural phyllosphere-colonizing bacteria on Arabidopsis thaliana revealed by fluorescence in situ hybridization. Environ. Microbiol. 16, 2329–2340 (2014).

    Article  CAS  PubMed  Google Scholar 

  56. Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6, 8413 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. An, S. Q. et al. Mechanistic insights into host adaptation, virulence and epidemiology of the phytopathogen Xanthomonas. FEMS Microbiol. Rev. 44, 1–32 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Chaston, J. M., Newell, P. D. & Douglas, A. E. Metagenome-wide association of microbial determinants of host phenotype in Drosophila melanogaster. Mbio 5, e01631–01614 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. MacDonald, N. J. & Beiko, R. G. Efficient learning of microbial genotype–phenotype association rules. Bioinformatics 26, 1834–1840 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Levy, A. et al. Genomic features of bacterial adaptation to plants. Nat. Genet. 50, 138–150 (2018).

    Article  CAS  Google Scholar 

  61. Ryu, C. M. Against friend and foe: type 6 effectors in plant-associated bacteria. J. Microbiol. 53, 201–208 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Bernal, P., Llamas, M. A. & Filloux, A. Type VI secretion systems in plant-associated bacteria. Environ. Microbiol. 20, 1–15 (2018).

    Article  PubMed  Google Scholar 

  63. Decoin, V. et al. A type VI secretion system is involved in Pseudomonas fluorescens bacterial competition. PLoS ONE 9, e89411 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Mosquito, S. et al. In planta colonization and role of T6SS in two rice Kosakonia endophytes. Mol. Plant Microbe Interact. 33, 349–363 (2020).

    Article  CAS  PubMed  Google Scholar 

  65. Vlot, A. C. et al. Systemic propagation of immunity in plants. New Phytol. 229, 1234–1250 (2021).

    Article  CAS  PubMed  Google Scholar 

  66. Roux, M. et al. The Arabidopsis leucine-rich repeat receptor-like kinases BAK1/SERK3 and BKK1/SERK4 are required for innate immunity to hemibiotrophic and biotrophic pathogens. Plant Cell 23, 2440–2455 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Fan, J., Crooks, C. & Lamb, C. High-throughput quantitative luminescence assay of the growth in planta of Pseudomonas syringae chromosomally tagged with Photorhabdus luminescens luxCDABE. Plant J. 53, 393–399 (2008).

    Article  CAS  PubMed  Google Scholar 

  68. Wu, Y. W. ezTree: an automated pipeline for identifying phylogenetic marker genes and inferring evolutionary relationships among uncultivated prokaryotic draft genomes. BMC Genomics 19, 921 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Bushnell, B. BBMap: Short read aligner, and other bioinformatic tools (SourceForge, version 37.56); https://sourceforge.net/projects/bbmap/

  70. Andrews, S. FastQC: A quality control tool for high throughput sequence data (Babraham Institute, 2010); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  71. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Jain, C., Rodriguez, R. L., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  75. Jombart, T., Balloux, F. & Dray, S. adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics 26, 1907–1909 (2010).

    Article  CAS  PubMed  Google Scholar 

  76. Chen, I. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucl. Acids Res 47, D666–D677 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. Mukherjee, S. et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucl. Acids Res. 47, D649–D659 (2019).

    Article  CAS  PubMed  Google Scholar 

  78. Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucl. Acids Res. 44, D286–D293 (2016).

    Article  CAS  PubMed  Google Scholar 

  80. Abby, S. S. et al. Identification of protein secretion systems in bacterial genomes. Sci. Rep. 6, 23080 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Boyer, F., Fichant, G., Berthod, J., Vandenbrouck, Y. & Attree, I. Dissecting the bacterial type VI secretion system by a genome wide in silico analysis: what can be learned from available microbial genomic resources? BMC Genomics 10, 104 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Lin, J. S., Ma, L. S. & Lai, E. M. Systematic dissection of the agrobacterium type VI secretion system reveals machinery and secreted components for subcomplex formation. PLoS ONE 8, e67647 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Ledermann, R., Strebel, S., Kampik, C. & Fischer, H. M. Versatile vectors for efficient mutagenesis of Bradyrhizobium diazoefficiens and other Alphaproteobacteria. Appl. Environ. Microbiol. 82, 2791–2799 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Wu, C. F. et al. Plant-pathogenic Agrobacterium tumefaciens strains have diverse type VI effector-immunity pairs and vary in in-planta competitiveness. Mol. Plant Microbe Interact. 32, 961–971 (2019).

    Article  CAS  PubMed  Google Scholar 

  85. Kassambra, A. rstatix: Pipe-friendly framework for basic statistical tests. R package version 0.6.0 https://CRAN.R-project.org/package=rstatix (2020).

  86. Pinheiro J., DebRoy S., Sarkar D. & Team, R. C. nlme: Linear and nonlinear mixed effects models. R package version 3.1-144 https://CRAN.R-project.org/package=nlme (2020).

  87. Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.6.1 https://CRAN.R-project.org/package=emmeans (2021).

  88. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).

    Article  CAS  PubMed  Google Scholar 

  89. Gu, Z. G., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  90. Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

We thank M. Bortfeld-Miller for technical assistance and A. Levy for helpful discussions. A. thaliana bak1/bkk1 was kindly provided by C. Zipfel (University of Zurich, Switzerland). The study was supported by an ERC Advanced Research Grant (PhyMo - 668991 to J.A.V.), ETH Zurich, a grant from the German Research Foundation (DECRyPT, no. SPP2125 to J.A.V.) and the NCCR Microbiomes (to J.A.V.), funded by the Swiss National Science Foundation.

Author information

Authors and Affiliations

  1. Institute of Microbiology, ETH Zurich, Zurich, Switzerland

    Christine M. Vogel, Daniel B. Potthoff, Martin Schäfer, Niculò Barandun & Julia A. Vorholt

Authors
  1. Christine M. Vogel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel B. Potthoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Schäfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Niculò Barandun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julia A. Vorholt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.V., D.B.P. and J.A.V. conceived and designed the study. C.V., D.B.P. and M.S. performed the protection screen and validation. C.V., D.B.P., M.S. and N.B. performed experiments testing SynComs. C.V. tested in vitro and in planta effect of Leaf202 and its T6SS mutant with help from M.S. C.V. and M.S. extracted DNA for genome sequencing. C.V. analysed sequencing data and assembled genomes. C.V. and D.B.P. analysed screening and validation data. C.V. analysed SynCom data with help from M.S. and analysed T6SS data. C.V. and J.A.V. wrote the manuscript.

Corresponding author

Correspondence to Julia A. Vorholt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Microbiology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Protection potential by protective At-LSPHERE strains scales with infection titre.

A. thaliana were inoculated with fully protective strains Leaf15, Leaf154 or Leaf21 (protection score >90), or with protective strains Leaf205 or Leaf233 (protection score >75) and infected with Pst at the regular infection titre (OD 0.00003), a 100x higher (OD 0.003) or a 10’000x higher (OD 0.3) infection titre. a) Luminescence indicative of pathogen colonization was measured at 6 d post infection (dpi). Shown are boxplots and individual data points. Letters indicate significant differences for each infection titre based on ANOVA followed by Tukey’s post-hoc test (P < 0.05, n = 16–24). Exact P values and number of biological replicates are provided in Supplementary Table 3. Boxplots depict the median and interquartile range with whiskers extending to maximum 1.5x the interquartile range. b) Plants were scored for disease at 21 dpi on a scale of 1 (healthy) to 5 (dead). c) Exemplary images of plants at 21 dpi showing protection of plants by fully protective strains at high infection titre and reduced protection by Leaf205 and Leaf233 at increasing infection titre.

Source data

Extended Data Fig. 2 Protection and luminescence reduction correlate.

Mean protection score and mean luminescence reduction (that is pathogen colonization reduction) correlate well for most strains (Pearson’s R = 0.927, t = 36.8, df = 221, P < 2.2 × 10−16). Colours refer to phylum/class. a.u., arbitrary units.

Source data

Extended Data Fig. 3 Random SynComs of 10 non-protective strains or all Methylobacterium do not protect Arabidopsis against Pst.

Plants were inoculated with random SynComs of 10 strains (M10.2-M10.6) containing only non-protective strains, all 32 non-protective Methylobacterium spp. (M10.1), Fr1 or the control SynCom M10.7, which contains one protective strain before infection with lux-tagged P. syringae DC3000 (Pst). a) Distribution of disease scores on a scale of 1 (healthy) to 5 (dead) at 21 d post infection (dpi). b) Luminescence of the pathogen at 6 dpi. Boxplots depict the median and interquartile range with whiskers extending to maximum 1.5x the interquartile range. P values for the comparison to axenic infected controls are indicated (two-sided Welch’s t-test, corrected for multiple testing using Holm’s method, n = 15–23). Exact number of replicates are provided in Supplementary Table 4.

Source data

Extended Data Fig. 4 Random SynComs of a mixture of 10 non-protective and intermediate protective strains improve plant phenotype.

Plants were inoculated with random SynComs of 10 strains or Fr1 before infection with lux-tagged P. syringae DC3000 (Pst). a) Distribution of disease scores at 13 d post infection (dpi) on a scale of 1 (healthy) to 5 (dead). b) Luminescence as proxy of pathogen colonization at 6 dpi. Boxplots depict the median and interquartile range with whiskers extending to maximum 1.5x the interquartile range. P values for the comparison to axenic infected controls are indicated (two-sided Welch’s t-test, corrected for multiple testing using Holm’s method, n = 14–24). Exact number of replicates are provided in Supplementary Table 5. c) Colonization by individual SynComs on non-infected plants at 12 d post inoculation. Shown are the mean and individual data points of 3 replicates consisting of two plants each. d) Correlation of mean luminescence and mean disease score (R = 0.92, t = 14.76, d.f. = 39, P < 2.2 × 10−16).

Source data

Extended Data Fig. 5 Dropout of one or two strains from a random SynCom of 10 strains can affect plant protection by SynComs.

Plants were inoculated with random SynComs of 10 strains, dropout communities thereof or individual strains and infected with lux-tagged P. syringae DC3000 (Pst). a) Distribution of disease scores at 13 d post infection (dpi) on a scale of 1 (healthy) to 5 (dead). b) Luminescence as proxy of pathogen colonization at 6 dpi. Boxplots depict the median and interquartile range with whiskers extending to maximum 1.5x the interquartile range. P-values for indicated comparisons are shown (two-sided Welch’s t-test, n = 21-24 plants per condition). Exact number of replicates are provided in Supplementary Table 6. c) Colonization by the individual SynComs on non-infected plants at 12 d post inoculation. Shown are the mean and individual data points of 3 replicates consisting of 2 plants each.

Source data

Extended Data Fig. 6 SynComs of three strains can improve protection phenotypes relative to individual strains.

Plants were inoculated with SynComs of three strains (M3.1, M3.2 and M3.3; comprised of non-protective and intermediate protective strains) or the strains individually and infected with lux-tagged P. syringae DC3000 (Pst). a) Distribution of disease scores on a scale of 1 (healthy) to 5 (dead) at 13 d post infection (dpi). b) Pathogen luminescence at 6 dpi. Boxplots depict the median and interquartile range with whiskers extending to maximum 1.5x the interquartile range. Letters indicate statistical significance within each SynCom (one-way ANOVA with Tukey’s post-hoc test, n = 16–24). Exact P values and number of replicates are provided in Supplementary Table 8.

Source data

Extended Data Fig. 7 T6SS gene cluster presence in At-LSPHERE strains and Sphingomonas melonis Fr1.

The outer rings reflect mean protection scores against lux-tagged P. syringae DC3000 on Col-0 plants and the presence of predicted T6SS gene clusters, respectively. a.u., arbitrary units.

Source data

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–7.

Reporting Summary.

Supplementary Tables

Supplementary Tables 1–12, 14.

Supplementary Tables

Supplementary Table 13.

Supplementary Data 1

Results for plant protection by individual strains against lux-tagged P. syringae DC3000 (Pst) by individual batches indicated with Experiment IDs on top. Shown are the distribution of disease scores at 13 d post infection (dpi) on a scale of 1 (healthy) to 5 (dead) (top panel), the luminescence as proxy of pathogen colonization at 6 dpi and the colonization by the individual strains on non-infected plants at 12 d post inoculation. Asterisks in the luminescence panel indicate significantly different luminescence relative to axenic infected controls (two-sided Welch’s t-test, corrected for multiple testing using Holm’s method, n = 11–24). The correlation of mean luminescence (log10(p/s)) at 6 dpi and mean disease score at 13 dpi is shown in the bottom panel. Colours of the three bottom panels correspond to phylum/class. Triangles in panel colonization indicate samples with colonization below detection limit, with a value just below the detection limit shown. For a list of the different strains tested in each batch (that is, Round and Part), exact P values and number of replicates within each experiment, we refer the reader to Supplementary Table 2.

Supplementary Data 2

Plant protection and colonization in the bak1/bkk1 plant background. Col-0 and bak1/bkk1 plants were inoculated with individual bacterial strains and infected with lux-tagged P. syringae DC3000 (Pst). Top panel: distribution of disease scores at 16 d post infection on a scale of 1 (healthy) to 5 (dead). Bottom panel: colonization level (log10(c.f.u. mg–1)) of the tested strains in non-infected plants 12 d after inoculation. Shown are the median and individual data points. P values of significant differences between wild-type and bak1/bkk1 plants are indicated (two-sided Welch’s test, Benjamini–Hochberg adjusted P < 0.05, n = 6). For exact P values and number of replicates we refer the reader to Supplementary Table 10.

Supplementary Data 3

Source data for Supplementary Data 1.

Supplementary Data 4

Source data for Supplementary Data 2.

Peer Review File.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vogel, C.M., Potthoff, D.B., Schäfer, M. et al. Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen. Nat Microbiol 6, 1537–1548 (2021). https://doi.org/10.1038/s41564-021-00997-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-021-00997-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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