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.

Mapping phyllosphere microbiota interactions in planta to establish genotype–phenotype relationships

Nature Microbiology volume 7, pages 856–867 (2022)Cite this article

Subjects

Abstract

Host-associated microbiomes harbour hundreds of bacterial species that co-occur, creating the opportunity for manifold bacteria–bacteria interactions, which in turn contribute to the overall community structure. The mechanisms that underlie this self-organization among bacteria remain largely elusive. Here, we studied bacterial interactions in the phyllosphere microbiota. We screened for microbial interactions in planta by adding 200 endogenous strains individually to a 15-member synthetic community and tracking changes in community composition upon colonization of the model plant Arabidopsis. Ninety percent of the identified interactions in planta were negative, and phylogenetically closely related strains elicited consistent effects on the synthetic community, providing support for trait conservation. Community changes could be largely explained by binary interactions; however, we also identified a higher-order interaction of more than two interacting strains. We further focused on a prominent interaction between two members of the Actinobacteria. In the presence of Aeromicrobium Leaf245, the population of Nocardioides Leaf374 was reduced by almost two orders of magnitude. We identified a potent antimicrobial peptidase in Aeromicrobium Leaf245, which resulted in Nocardioides Leaf374 lysis. A respective Leaf245 mutant strain was necessary and sufficient to restore Nocardioides colonization in planta, demonstrating that direct bacteria–bacteria interactions were responsible for the population shift originally observed. Our study highlights the power of synthetic community screening and uncovers a strong microbial interaction that occurs despite a spatially heterogeneous environment.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: A 15-strain focal SynCom to map bacteria–bacteria interactions.
Fig. 2: Interactions of the At-LSPHERE collection with the focal SynCom in planta.
Fig. 3: Biochemical characterization of the interaction between Nocardioides Leaf374 and Aeromicrobium Leaf245.
Fig. 4: Leaf374 phyllosphere colonization is less affected when co-inoculated with Leaf245 EMS mutants that no longer secrete ASF05_00205 compared with the wild type.
Fig. 5: Producer and target range of the NlaP.

Similar content being viewed by others

Data availability

The 16S rRNA gene amplicon sequencing reads and the reads obtained from Leaf245 EMS mutant genome sequencing are available online at the European nucleotide archive (https://www.ebi.ac.uk/ena/) under the accession numbers PRJEB50894 and PRJEB47688, respectively. The SILVA SSU Ref NR database (release 132) can be found in www.arb-silva.de/download/archive. Source data are provided with this paper.

Code availability

The code used for analysis of 16S rRNA gene amplicon data can be found in the package ‘phylloR’ available on GitHub (https://github.com/MicrobiologyETHZ/phylloR/). No unpublished algorithms were used.

References

  1. Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. 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 

  3. Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. 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 

  7. Coyte, K. Z. & Rakoff-Nahoum, S. Understanding competition and cooperation within the mammalian gut microbiome. Curr. Biol. 29, 538–544 (2019).

    Article  CAS  Google Scholar 

  8. Turner, T. R. et al. Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J. 7, 2248–2258 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. 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  CAS  Google Scholar 

  11. Lugtenberg, B. & Kamilova, F. Plant-growth-promoting Rhizobacteria. Annu. Rev. Microbiol. 63, 541–556 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. 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 

  14. Shekhawat, K. et al. Root endophyte induced plant thermotolerance by constitutive chromatin modification at heat stress memory gene loci. EMBO Rep. 22, e51049 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. 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  CAS  Google Scholar 

  17. Reisberg, E. E., Hildebrandt, U., Riederer, M. & Hentschel, U. Distinct phyllosphere bacterial communities on Arabidopsis wax mutant leaves. PLoS ONE 8, e78613 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kniskern, J. M., Traw, M. B. & Bergelson, J. Salicylic acid and jasmonic acid signaling defense pathways reduce natural bacterial diversity on Arabidopsis thaliana. Mol. Plant Microbe Interact. 20, 1512–1522 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. 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  PubMed Central  Google Scholar 

  20. 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 

  21. Hassani, M. A., Duran, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 58 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lidicker, W. Z. Clarification of interactions in ecological systems. Bioscience 29, 475–477 (1979).

    Article  Google Scholar 

  23. Schlechter, R. O., Miebach, M. & Remus-Emsermann, M. N. P. Driving factors of epiphytic bacterial communities: a review. J. Adv. Res. 19, 57–65 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Grosskopf, T. & Soyer, O. S. Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Blair, P. M. et al. Exploration of the biosynthetic potential of the Populus microbiome. mSystems 3, e00045-00018 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Suda, W., Nagasaki, A. & Shishido, M. Powdery mildew-infection changes bacterial community composition in the phyllosphere. Microbes Environ. 24, 217–223 (2009).

    Article  PubMed  Google Scholar 

  28. Manching, H. C., Balint-Kurti, P. J. & Stapleton, A. E. Southern leaf blight disease severity is correlated with decreased maize leaf epiphytic bacterial species richness and the phyllosphere bacterial diversity decline is enhanced by nitrogen fertilization. Front. Plant Sci. 5, 403 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, 100235 (2016).

    Article  CAS  Google Scholar 

  30. Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Carr, A., Diener, C., Baliga, N. S. & Gibbons, S. M. Use and abuse of correlation analyses in microbial ecology. ISME J. 13, 2647–2655 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 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 

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

    Article  CAS  PubMed  Google Scholar 

  35. Knief, C., Frances, L. & Vorholt, J. A. Competitiveness of diverse Methylobacterium strains in the phyllosphere of Arabidopsis thaliana and identification of representative models, including M. extorquens PA1. Microb. Ecol. 60, 440–452 (2010).

    Article  PubMed  Google Scholar 

  36. 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 

  37. Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Vogel, C. M., Potthoff, D. M., Schäfer, M., Barandun, N. & Vorholt, J. A. Protective role of the Arabidopsis leaf microbiota against a bacterial pathogen. Nat. Microbiol. 6, 1537–1548 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 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, 751–763 (2020).

    Article  CAS  Google Scholar 

  40. Ortiz, A., Vega, N. M., Ratzke, C. & Gore, J. Interspecies bacterial competition regulates community assembly in the C. elegans intestine. ISME J. 15, 2131–2145 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  42. Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).

    Article  Google Scholar 

  43. Cahill, J. F., Kembel, S. W., Lamb, E. G. & Keddy, P. A. Does phylogenetic relatedness influence the strength of competition among vascular plants? Perspect. Plant Ecol. 10, 41–50 (2008).

    Article  Google Scholar 

  44. Maherali, H. & Klironomos, J. N. Influence of phylogeny on fungal community assembly and ecosystem functioning. Science 316, 1746–1748 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Duncan, R. P. & Williams, P. A. Ecology - Darwin’s naturalization hypothesis challenged. Nature 417, 608–609 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. Slingsby, J. A. & Verboom, G. A. Phylogenetic relatedness limits co-occurrence at fine spatial scales: evidence from the schoenoid sedges (Cyperaceae: Schoeneae) of the Cape Floristic Region, South Africa. Am. Nat. 168, 14–27 (2006).

    Article  PubMed  Google Scholar 

  47. Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).

    Article  PubMed  Google Scholar 

  48. Teixeira, P. J. P. L., Colaianni, N. R., Fitzpatrick, C. R. & Dangl, J. L. Beyond pathogens: microbiota interactions with the plant immune system. Curr. Opin. Microbiol. 49, 7–17 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Maier, B. A. et al. A general non-self response as part of plant immunity. Nat. Plants 7, 696–705 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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 

  54. Billick, I. & Case, T. J. Higher-order interactions in ecological communities – what are they and how can they be detected. Ecology 75, 1529–1543 (1994).

    Article  Google Scholar 

  55. Grilli, J., Barabas, G., Michalska-Smith, M. J. & Allesina, S. Higher-order interactions stabilize dynamics in competitive network models. Nature 548, 210–213 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Sundarraman, D. et al. Higher-order interactions dampen pairwise competition in the zebrafish gut microbiome. mBio 11, e01667-20 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Morris, C. in Encyclopedia for Life Sciences (National Publishing Group, 2002).

  59. 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 

  60. Iversen, O. J. & Grov, A. Studies on lysostaphin – separation and characterization of 3 enzymes. Eur. J. Biochem. 38, 293–300 (1973).

    Article  CAS  PubMed  Google Scholar 

  61. Recsei, P. A., Gruss, A. D. & Novick, R. P. Cloning, sequence, and expression of the lysostaphin gene from Staphylococcus simulans. Proc. Natl Acad. Sci. USA 84, 1127–1131 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kessler, E., Safrin, M., Abrams, W. R., Rosenbloom, J. & Ohman, D. E. Inhibitors and specificity of Pseudomonas aeruginosa LasA. J. Biol. Chem. 272, 9884–9889 (1997).

    Article  CAS  PubMed  Google Scholar 

  63. Trayer, H. R. & Buckley, C. E. Molecular properties of lysostaphin, a bacteriolytic agent specific for Staphylococcus aureus. J. Biol. Chem. 245, 4842–4846 (1970).

    Article  CAS  PubMed  Google Scholar 

  64. Heymer, B. & Schmidt, W. C. Purification and characterization of a Streptomyces albus endo-N-acetylmuramidase lytic for group A and other beta hemolytic streptococci. Microbios 12, 51–66 (1975).

    CAS  PubMed  Google Scholar 

  65. Vollmer, W., Joris, B., Charlier, P. & Foster, S. Bacterial peptidoglycan (murein) hydrolases. FEMS Microbiol. Rev. 32, 259–286 (2008).

    Article  CAS  PubMed  Google Scholar 

  66. Peyraud, R. et al. Demonstration of the ethylmalonyl-CoA pathway by using C-13 metabolomics. Proc. Natl Acad. Sci. USA 106, 4846–4851 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Schlesier, B., Breton, F. & Mock, H. P. A hydroponic culture system for growing Arabidopsis thaliana plantlets under sterile conditions. Plant Mol. Biol. Rep. 21, 449–456 (2003).

    Article  CAS  Google Scholar 

  68. 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 

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

    Article  Google Scholar 

  70. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  PubMed  Google Scholar 

  71. Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Integrated Development Environment for R (R Studio, 2020).

  73. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

  74. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. Oksanen, J. et al. vegan: Community Ecology Package. R package v. 2.5-7 (2020).

  76. Armenteros, J. J. A. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).

    Article  CAS  Google Scholar 

  77. Gasteiger, E. et al. in The Proteomics Protocols Handbook 571–607 (ed Walker, J. M.) (Humana Press, 2005).

  78. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  79. Bushnell, B. BBMap short read aligner, and other bioinformatic tools (SourceForge, version 38.87); https://sourceforge.net/projects/bbmap

  80. Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).

    Article  CAS  PubMed  Google Scholar 

  82. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank A. Minder and S. Kobel at the Genetic Diversity Center Zurich (GDC, ETH Zurich) for MiSeq amplicon sequencing service, C. Field (Institute of Microbiology, ETH Zurich) for amplicon raw read processing, the Functional Genomics Center Zurich (FGCZ) for protein identification service and P. Schulze-Lefert (Max Planck Institute for Plant Breeding Research, Cologne, Germany) for sharing At-RSPHERE isolates. This work was supported by the Swiss National Science Foundation through NRP72 (no. 407240_167051) and as part of NCCR Microbiomes, a National Centre of Competence in Research (no. 51NF40_180575) and a European Research Council Advanced Grant (PhyMo; no. 668991).

Author information

Authors and Affiliations

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

    Martin Schäfer, Christine M. Vogel, Miriam Bortfeld-Miller, Maximilian Mittelviefhaus & Julia A. Vorholt

Authors
  1. Martin Schäfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christine M. Vogel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miriam Bortfeld-Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maximilian Mittelviefhaus
    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

M.S. and J.A.V. designed the research. M.S., M.B.-M. and C.M.V. conducted the in planta interaction screen and binary competition experiments. M.S. prepared the amplicon sequencing libraries and analysed amplicon sequencing data, performed biochemical analyses of Leaf245 supernatants and heterologous expression of proteins. M.M. conducted microscopy time course experiment to monitor Leaf374 lysis. M.S. prepared and M.S. and C.M.V screened the Leaf245 EMS library. C.M.V. closed the Leaf245 genome and mapped point mutations of genome re-sequenced EMS clones. M.S. screened the inhibition spectrum of NlaP producer strains, performed statistical analyses and visualized the data. M.S. and J.A.V. wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence to Julia A. Vorholt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Gwyn Beattie, Steven Lindow, Linda Thomashow and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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 Changes in focal community strain abundances upon strain drop-in.

Heatmap showing log2-transformed fold changes (treatment vs control) for each focal community strain (top) upon strain drop-in (left). Significant changes (DESeq2-normalized counts, Wald test, Benjamini-Hochberg corrected, padj ≤ 0.01, nfocal community = 16, ndrop-in = 3) are indicated with a black frame. Drop-in strains are colored by phylum or Proteobacteria class. Exact p-values are provided in Supplementary Table 2.

Source data

Extended Data Fig. 2 Phylogenetic distance of interacting strains.

Focal strains that showed ≥ 1 interaction are shown. The phylogenetic distance of the focal strain to all added strains is indicated on the x-axis. Log2-fold changes (log2FC) are shown on the y-axis. Significant fold-changes are highlighted with blue (negative interactions) or red (positive interactions) fill color. Strains that belong to the same ASV as a focal strain have a light grey fill color.

Source data

Extended Data Fig. 3 Phyllosphere colonization of Leaf220 and Leaf408.

Phyllosphere colonization of a) Leaf220 or b) Leaf408 in mono-association or in combination with other strains (indicated below). Shown are the median and individual data points of log10-transformed CFU per gram fresh weight across two independent experiments. Colors/shapes refer to experiment. Exact p-values (Krusksal-Wallis test with post-hoc Dunn test, Bonferroni adjusted p) and log2 fold-changes (log2FC) for comparisons to the mono-association condition are indicated above or below the graph, respectively. For corresponding Leaf88 colonization levels see Fig. 2c.

Source data

Extended Data Fig. 4 Validation of Leaf374-Leaf245 interaction by sequential inoculation of A. thaliana.

Phyllosphere colonization of a) Nocardioides Leaf374 and b) Aeromicrobium Leaf245 in mono-association or in combination with the other strain. Shown are the median and individual data points of log10-transformed CFU per gram fresh weight recovered after plant colonization (n = 12). CFU were enumerated on MM maltose agar plates and R2A + M for Nocardioides Leaf374 and Aeromicrobium Leaf245, respectively. The timepoint of inoculation with Leaf245 or Leaf374 is indicated below the graph as days (d) after planting. For mono-association controls half of the replicates were inoculated on day7 (black) or day8 (grey) and mock-treated with 10 mM MgCl2 on the other day. Exact p-values (two-sided Wilcoxon rank sum test) and log2 fold changes (log2FC) compared to the mono-association control are indicated above or below the graph, respectively.

Source data

Extended Data Fig. 5 Characterization of selected proteins identified in the supernatant of Leaf245.

a) List of proteins identified in the supernatant of Leaf245 and selected for heterologous expression in E. coli. For each gel band in Fig. 3d as well as the most active fractions from two independent experiments the rank abundance of the proteins based on total ion count as identified by mass spectrometry are shown. b, c, d) Leaf374 inhibition and lysis by heterologously produced candidate proteins. b) Activity of cleared cell lysate after expression of proteins (top label) in E. coli BL21 DE3 gold. Lysate was applied on Leaf374 overlay plates directly after preparation (left panel) or on Leaf374 overlay plates that were pre-incubated for 24 h to assess lysis (right panel). Lysate concentration was normalized to the final OD600 that each expression culture reached. Up to 50-fold dilutions in the buffer used for lysis (see methods) were prepared as indicated on the left. c) Activity of purified proteins against 24 h pre-grown Leaf374. d) Activity of ASF05_00205 native and mutant (S70L) protein against Leaf374. Concentrations of both purified proteins were normalized (0.1 mg mL−1) and a 10- and 100- fold dilution was prepared and assayed on a Leaf374 overlay plate directly after preparation (left panel) or after the overlay was pre-incubated for 24 h (right panel) to test for growth inhibition and cell lysis, respectively. Data shown for ASF05_00205 native protein is the same as shown in Fig. 3e and panel c) of this figure.

Extended Data Fig. 6 Aeromicrobium Leaf245 wild type and EMS mutant colonization level.

Phyllosphere colonization of Leaf245 in mono-association or in combination with Leaf374. Shown are the median and individual data points of log10-transformed CFU per g fresh weight across 2–3 independent experiments. Log2 fold changes and exact p-values (two-sided Wilcoxon rank sum test) between Leaf245 mono-association and co-colonization treatments with Leaf374 are shown above the graph. For the corresponding Leaf374 colonization see Fig. 4d.

Source data

Extended Data Fig. 7 In vitro inhibition assay with different producer and target cell densities.

The concentrations of the NlaP producer strains (Leaf245 and Root85) spotted on top (2 µL) and target strains (Leaf374 and Root122) within the agar are indicated on top and on the left of the graph, respectively. Pictures were taken 36 hours after overlay preparation and spotting. The standard concentrations used for all other assays performed in this study are highlighted in red.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–13.

Reporting Summary

Peer Review File

Supplementary Video

Time-lapse video of Leaf374 cell lysis upon addition of Leaf245 supernatant. Imaging was started 1 min after supernatant addition and a picture was recorded every 20 s over the course of 20 min.

Supplementary Table 1

Supplementary Tables 1–7.

Supplementary Data 1

Demultiplexed 16S RNA gene amplicon sequencing read counts and corresponding metadata.

Supplementary Data 2

Source data for Supplementary Figs. 3, 4, 6 and 9–13.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

SDS–PAGE gel.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

SDS–PAGE gel.

Source Data Fig. 5

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. 6

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schäfer, M., Vogel, C.M., Bortfeld-Miller, M. et al. Mapping phyllosphere microbiota interactions in planta to establish genotype–phenotype relationships. Nat Microbiol 7, 856–867 (2022). https://doi.org/10.1038/s41564-022-01132-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-022-01132-w

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