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A general non-self response as part of plant immunity

Nature Plants volume 7, pages 696–705 (2021)Cite this article

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Abstract

Plants, like other multicellular lifeforms, are colonized by microorganisms. How plants respond to their microbiota is currently not well understood. We used a phylogenetically diverse set of 39 endogenous bacterial strains from Arabidopsis thaliana leaves to assess host transcriptional and metabolic adaptations to bacterial encounters. We identified a molecular response, which we termed the general non-self response (GNSR) that involves the expression of a core set of 24 genes. The GNSR genes are not only consistently induced by the presence of most strains, they also comprise the most differentially regulated genes across treatments and are predictive of a hierarchical transcriptional reprogramming beyond the GNSR. Using a complementary untargeted metabolomics approach we link the GNSR to the tryptophan-derived secondary metabolism, highlighting the importance of small molecules in plant–microbe interactions. We demonstrate that several of the GNSR genes are required for resistance against the bacterial pathogen Pseudomonas syringae. Our results suggest that the GNSR constitutes a defence adaptation strategy that is consistently elicited by diverse strains from various phyla, contributes to host protection and involves secondary metabolism.

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Fig. 1: Magnitude of the Arabidopsis response to bacterial colonization at the transcriptional and metabolic levels.
Fig. 2: GNSR genes.
Fig. 3: Correlation analysis of GNSR and metabolome.
Fig. 4: Disease-susceptibility phenotypes of GNSR mutants.

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Data availability

Transcriptomics raw data are available at the European Bioinformatics Institute (EBI) under the following identifier: PRJEB40488. Metabolomics raw data, together with relevant metadata, are available at Metabolights79 under the following identifier: MTBLS2522. Source data are provided with this paper.

Code availability

The code used in the analysis of the data can be found in the following repositories. The scripts for RNA-seq raw data analysis according to the standard operating procedure of the FGCZ is available at their GitHub repository: https://github.com/uzh/ezRun. The code used for metabolomics raw data analysis based on the in-house pipeline can be found at: https://gitlab.ethz.ch/emzed2-plant-metabolomics-workflows. The scripts for all major downstream data processing/figure generation are available at the project’s GitLab repository: https://gitlab.ethz.ch/emzed2-plant-metabolomics-workflows.

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Acknowledgements

We thank C. Zipfel, W. Gruissem and C. Sánchez-Rodríguez for helpful discussions and A. Imboden for reproduction of plant lines. We thank E. Sattely for providing the cyp71a12 and fox1 mutant lines. We also thank L. Opitz and C. Aquino Fournier from the Functional Genomics Center Zurich (FGCZ) for their work on the RNA sequencing part of the experiment. This work was funded through a European Research Council advanced grant (PhyMo; no. 668991), ETH Zurich, a grant from the German Research Foundation (DECRyPT, no. SPP2125) and the NCCR Microbiomes, funded by the Swiss National Science Foundation.

Author information

Authors and Affiliations

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

    Benjamin A. Maier, Patrick Kiefer, Christopher M. Field, Lucas Hemmerle, Miriam Bortfeld-Miller, Barbara Emmenegger, Martin Schäfer, Sebastian Pfeilmeier, Shinichi Sunagawa, Christine M. Vogel & Julia A. Vorholt

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  1. Benjamin A. Maier
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Contributions

B.A.M., C.M.V. and J.A.V. designed the study. B.A.M. led and conducted the experimental work. P.K. designed the metabolomics pipeline. P.K. and C.M.F carried out omics raw data analysis. B.A.M., C.M.F. and S.S. performed the data analysis. L.H., M.B.-M., B.E. and M.S. contributed to the generation of the plant material for the transcriptomics and metabolomics experiments. S.P. helped with the design of the pathogen assays. B.A.M. and J.A.V. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Julia A. Vorholt.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Alisdair Fernie and Susannah Tringe for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Phenotype of Arabidopsis inoculated with the 39 bacterial strains.

Representative phenotypes of 3-week-old Arabidopsis wild-type plants (Col-0), 9 days after inoculation with individual bacterial strains.

Extended Data Fig. 2 Cluster analysis of DEGs and DAMs of Arabidopsis in response to bacterial treatments.

a-b, Heat-maps of total DEGs (|log2FC|> 1. FDR < 0.01) (a) and DAMs (|log2FC|> 1, FDR < 0.05) (b). Strains and DEGs/ DAMs are clustered using Ward’s method. Top colour bars indicate phylogeny. c-d, Principle component analysis (PCA) plot depicting distances based on gene expression counts (for DEGs) or metabolite areas under the curve (for DAMs). Colours represent bacterial phyla/classes. Selected treatments are annotated with strain name and replicate number. Data from five independent biological replicates (R1-R5), each representing 18–24 plants.

Source data

Extended Data Fig. 3 Correlation between phyllosphere colonization and host response intensity.

a-c, Linear regression of cfu*gfw-1 against nDEGs or nDAMs and nDEGs against nDAMs in the respective conditions. Axis scaled to log10 for improved readability. Dots are annotated by the strain used in the treatment. Colours represent bacterial phyla/class analogous to Supplementary Fig. 2. Coefficients of variation from the linear regression (R²) value and Spearman’s ranked correlation values (ρ) are provided. Data from five independent biological replicates, each representing 18–24 plants.

Source data

Extended Data Fig. 4 Hierarchy heatmap of DAMs.

Sorted heat-maps of total DAMs (log2FC > 1, FDR < 0.05). Conditions were sorted by strains causing the weakest to strongest host response based on the number of DAMs (x axis, left to right) and amount of times a metabolite feature was differentially regulated from most frequent to least frequent (y axis, top to bottom). Top colour bars indicate bacterial phyla/classes analogous to Extended Data Fig. 2. Data from five independent biological replicates, each representing 18–24 plants.

Source data

Extended Data Fig. 5 Genevestigator analysis of GNSR genes.

Selected query results for GNSR genes in Genevestigator category ‘Perturbations’. Names adjusted to match names used in this study a, Results for mRNA seq datasets from various experiments (|FC|> 2, p-value < 0.01)) for biotic, chemical, hormonal, nutritional, photoperiod, temperature or other abiotic perturbations. b, Results for micro-array datasets from various experiments (|FC|> 1.5, p-value < 0.001) for biotic, chemical, elicitor, light intensity, nutritional and other abiotic stress perturbations. All p-values were computed by two-sided t-test implemented in limma (for micro-array data) and by Voom’s algorithm (two-sided) for RNAseq data and are adjusted by Benjamini–Hochberg to compute the threshold under which the p-values are considered sufficiently small22 (https://genevestigator.com/userdocs/manual/GENEVESTIGATOR_UserManual.pdf).

Extended Data Fig. 6 Functional enrichment and subcellular location analysis.

Analysis of subcellular locations using SUBA4 prediction scores (left, coloured) and summarized associated GO analysis of GNSR genes based on AgriGO2 functional enrichment (right, grey).

Source data

Extended Data Fig. 7 Tryptophan-derived secondary metabolism in Arabidopsis.

Simplified version of the TDSM with the three main branches, important intermediates and enzymes. GNSR genes are highlighted in green, genes homologous to GNSR genes are highlighted in yellow.

Extended Data Fig. 8 Targeted metabolomics on the cyp71a12 mutant.

Heatmap of log2-transformed metabolite fold changes in the cyp71a12 mutant against the respective wild-type conditions. Only changes in 349, 365 and 383 are significant (two-sided t-test, Benjamini–Hochberg adjusted p-value < 0.05). Data from ten technical replicates, each representing 1 plant.

Source data

Extended Data Fig. 9 Phyllosphere colonization by bacteria native to the potting soil used in this experiment.

a, Median cfu*gfw-1 with 95% confidence interval of 30 different plants (n = 30) in one experiment. b, Representative picture of bacterial colonies extracted from leaves, grown on R2A agar with 0.5% (v/v) methanol and supplemented with cycloheximide to prevent fungal growth.

Source data

Extended Data Fig. 10 Gene expression correlation of GNSR and transcription factor encoding genes.

Network graph showing significant, positive correlations (Spearman correlation, ρ ≥ 0.85, best two correlations) between GNSR genes and transcription factors of the defence-associated MYB and WRKY families. GNSR genes are displayed in red, transcription factor genes in blue. WRKY30 in yellow as it is both a GNSR and a transcription factor gene.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–10.

Reporting Summary

Supplementary Table 1

Genes differentially expressed (|log2FC|> 1, FDR < 0.01) in plants colonized by At-LSPHERE strains

Supplementary Table 2

Metabolite features differentially abundant (|log2FC|> 1, FDR < 0.05) in plants colonized by At-LSPHERE strains

Supplementary Table 3

GO-terms associated with GNSR genes

Supplementary Table 4

Results of linear regression analysis between transcriptional response intensity [nDEGs] and individual gene fold-changes. All genes with adj. R²-values > 0.75

Supplementary Table 5

rho-values of Spearmans ranked correlation between GNSR genes and metabolite features

Supplementary Table 6–16

m/z values of fragment ions obtained during MS2 measurements of GNSR associated compounds

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Maier, B.A., Kiefer, P., Field, C.M. et al. A general non-self response as part of plant immunity. Nat. Plants 7, 696–705 (2021). https://doi.org/10.1038/s41477-021-00913-1

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