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PSKR1 balances the plant growth–defence trade-off in the rhizosphere microbiome

Nature Plants volume 9, pages 2071–2084 (2023)Cite this article

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Abstract

Microbiota benefit their hosts by improving nutrient uptake and pathogen protection. How host immunity restricts microbiota while avoiding autoimmunity is poorly understood. Here we show that the Arabidopsis phytosulfokine receptor 1 (pskr1) mutant displays autoimmunity (plant stunting, defence-gene expression and reduced rhizosphere bacterial growth) in response to growth-promoting Pseudomonas fluorescens. Microbiome profiling and microbiota colonization showed that PSKR1-mediated reduction in bacterial growth and stunting is largely specific to Pseudomonas. Transcriptional profiling demonstrated that PSKR1 regulates the growth–defence trade-off during Pseudomonas colonization: PSKR1 upregulates plant photosynthesis and root growth but suppresses salicylic-acid-mediated defences. Genetic epistasis experiments showed that pskr1 stunting and restriction of bacterial growth are salicylic acid dependent. Finally, we showed that Pseudomonas, but not other bacteria, induces PSKR1 expression in roots, suggesting that Pseudomonas might manipulate plant signalling to promote its colonization. Our data demonstrate a genetic mechanism to coordinate beneficial functions of the microbiome while preventing autoimmunity.

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Fig. 1: PSKR1 is a negative regulator of rhizosphere immunity against P. fluorescens.
Fig. 2: PSKR1 enriches Pseudomonadaceae in the rhizosphere microbiome.
Fig. 3: Autoimmunity and restriction of bacterial growth in the pskr1 mutants are primarily limited to Pseudomonas strains.
Fig. 4: PSKR1 balances the plant growth–defence trade-off in response to beneficial microorganisms.
Fig. 5: PSKR1 suppresses SA-mediated defences to modulate the rhizosphere microbiome.
Fig. 6: Pseudomonas upregulates root PSKR1 expression.

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Data availability

The raw data for 16S rRNA sequencing are available in the Sequence Read Archive database under BioProject PRJNA896256, accession numbers SRR22133746 to SRR22133785. The raw data for RNA sequencing are available in the Sequence Read Archive database under BioProject PRJNA897874, accession numbers SRR22177437 to SRR22177481.

Code availability

The code related to microbiome sequencing and RNA sequencing is available on the Haney lab GitHub site (https://github.com/haneylab/PSKR1_Song_etal_2023).

References

  1. Tringe, S. G. et al. Comparative metagenomics of microbial communities. Science 308, 554–557 (2005).

    CAS  PubMed  Google Scholar 

  2. Bakker, P. A. H. M. et al. The soil-borne identity and microbiome-assisted agriculture: looking back to the future. Mol. Plant https://doi.org/10.1016/j.molp.2020.09.017 (2020).

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

    CAS  PubMed  PubMed Central  Google Scholar 

  4. van Wersch, R., Li, X. & Zhang, Y. Mighty dwarfs: Arabidopsis autoimmune mutants and their usages in genetic dissection of plant immunity. Front. Plant Sci. 7, 1717 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  6. Lv, S. et al. Dysfunction of histone demethylase IBM1 in Arabidopsis causes autoimmunity and reshapes the root microbiome. ISME J. https://doi.org/10.1038/s41396-022-01297-6 (2022).

  7. Zhang, X., Chen, B. D., Zhao, L. D. & Li, H. The gut microbiota: emerging evidence in autoimmune diseases. Trends Mol. Med. 26, 862–873 (2020).

    CAS  PubMed  Google Scholar 

  8. Zhang, X.-C., Millet, Y. A., Cheng, Z., Bush, J. & Ausubel, F. M. Jasmonate signalling in Arabidopsis involves SGT1b–HSP70–HSP90 chaperone complexes. Nat. Plants 1, 15049 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  10. Ding, Y. et al. Opposite roles of salicylic acid receptors NPR1 and NPR3/NPR4 in transcriptional regulation of plant immunity. Cell 173, 1454–1467.e15 (2018).

    CAS  PubMed  Google Scholar 

  11. Song, Y. et al. A screen for mutants deficient in coronatine-mediated suppression of root immunity identifies Arabidopsis SDA1 as a novel integrator of immunity and phytohormone signaling. Preprint at bioRxiv https://doi.org/10.1101/2021.09.12.459990 (2021).

  12. Shen, Y. & Diener, A. C. Arabidopsis thaliana RESISTANCE TO FUSARIUM OXYSPORUM 2 implicates tyrosine-sulfated peptide signaling in susceptibility and resistance to root infection. PLoS Genet. 9, e1003525 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Matsubayashi, Y., Shinohara, H. & Ogawa, M. Identification and functional characterization of phytosulfokine receptor using a ligand-based approach. Chem. Rec. 6, 356–364 (2006).

    CAS  PubMed  Google Scholar 

  14. Matsubayashi, Y., Ogawa, M., Kihara, H., Niwa, M. & Sakagami, Y. Disruption and overexpression of Arabidopsis phytosulfokine receptor gene affects cellular longevity and potential for growth. Plant Physiol. 142, 45–53 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Hartmann, J., Stührwohldt, N., Dahlke, R. I. & Sauter, M. Phytosulfokine control of growth occurs in the epidermis, is likely to be non-cell autonomous and is dependent on brassinosteroids. Plant J. 73, 579–590 (2013).

    CAS  PubMed  Google Scholar 

  16. Simons, M. et al. Gnotobiotic system for studying rhizosphere colonization by plant growth-promoting Pseudomonas bacteria. Mol. Plant Microbe Interact. 9, 600–607 (1996).

    CAS  PubMed  Google Scholar 

  17. Mosher, S. et al. The tyrosine-sulfated peptide receptors PSKR1 and PSY1R modify the immunity of Arabidopsis to biotrophic and necrotrophic pathogens in an antagonistic manner. Plant J. 73, 469–482 (2013).

    CAS  PubMed  Google Scholar 

  18. Geider, R. J. & La Roche, J. The role of iron in phytoplankton photosynthesis, and the potential for iron-limitation of primary productivity in the sea. Photosynth. Res. https://doi.org/10.1007/BF00014588 (1994).

  19. Lai, A. G. et al. Circadian Clock-Associated 1 regulates ROS homeostasis and oxidative stress responses. Proc. Natl Acad. Sci. USA 109, 17129–17134 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Demmig-Adams, B. et al. Intersections: photosynthesis, abiotic stress, and the plant microbiome. Photosynthetica 60, 59–69 (2022).

    CAS  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Sauter, M. Phytosulfokine peptide signalling. J. Exp. Bot. https://doi.org/10.1093/jxb/erv071 (2015).

  23. Ogawa-Ohnishi, M. et al. Peptide ligand-mediated trade-off between plant growth and stress response. Science 378, 175–180 (2022).

    CAS  PubMed  Google Scholar 

  24. Pruitt, R. N. et al. A microbially derived tyrosine-sulfated peptide mimics a plant peptide hormone. N. Phytol. 215, 725–736 (2017).

    CAS  Google Scholar 

  25. Expósito, R. G., de Bruijn, I., Postma, J. & Raaijmakers, J. M. Current insights into the role of rhizosphere bacteria in disease suppressive soils. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.02529 (2017).

  26. Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. https://doi.org/10.1146/annurev.micro.62.081307.162918 (2009).

  27. Stührwohldt, N., Dahlke, R. I., Steffens, B., Johnson, A. & Sauter, M. Phytosulfokine-α controls hypocotyl length and cell expansion in Arabidopsis thaliana through phytosulfokine receptor 1. PLoS ONE 6, e21054 (2011).

    PubMed  PubMed Central  Google Scholar 

  28. Hartmann, J., Fischer, C., Dietrich, P. & Sauter, M. Kinase activity and calmodulin binding are essential for growth signaling by the phytosulfokine receptor PSKR1. Plant J. 78, 192–202 (2014).

    CAS  PubMed  Google Scholar 

  29. Cao, H., Bowling, S. A., Gordon, A. S. & Dong, X. Characterization of an Arabidopsis mutant that is nonresponsive to inducers of systemic acquired resistance. Plant Cell 6, 1583–1592 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhang, Y. et al. Negative regulation of defense responses in Arabidopsis by two NPR1 paralogs. Plant J. 48, 647–656 (2006).

    CAS  PubMed  Google Scholar 

  31. Wildermuth, M. C., Dewdney, J., Wu, G. & Ausubel, F. M. Isochorismate synthase is required to synthesize salicylic acid for plant defence. Nature 414, 562–565 (2001).

    CAS  PubMed  Google Scholar 

  32. Lawton, K. et al. Systemic acquired resistance in Arabidopsis requires salicylic acid but not ethylene. Mol. Plant Microbe Interact. 8, 863–870 (1995).

    CAS  PubMed  Google Scholar 

  33. Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophotonics Int. 11, 36–42 (2004).

    Google Scholar 

  34. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhang, X. C., Millet, Y., Ausubel, F. M. & Borowsky, M. Next-gen sequencing-based mapping and identification of ethyl methanesulfonate-induced mutations in Arabidopsis thaliana. Curr. Protoc. Mol. Biol. 108, 7.18.1–7.18.16 (2014).

    PubMed  Google Scholar 

  36. Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).

    CAS  PubMed  Google Scholar 

  37. Tamura, K., Nei, M. & Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc. Natl Acad. Sci. USA 101, 11030–11035 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Stecher, G., Tamura, K. & Kumar, S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular Evolutionary Genetics Analysis Version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Bushnell, B. BBMap. SourceForge https://sourceforge.net/projects/bbmap/ (2015).

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

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Lamesch, P. et al. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 40, D1202–D1210 (2012).

    CAS  PubMed  Google Scholar 

  43. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  44. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

  45. 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 

  46. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    CAS  PubMed  Google Scholar 

  47. Chin, C. H. et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8, S11 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Kucera, M., Isserlin, R., Arkhangorodsky, A. & Bader, G. D. AutoAnnotate: a Cytoscape app for summarizing networks with semantic annotations [version 1; referees: 2 approved]. F1000Res. 5, 1717 (2016).

    PubMed  PubMed Central  Google Scholar 

  51. Ge, S. X., Son, E. W. & Yao, R. iDEP: an integrated web application for differential expression and pathway analysis of RNA-seq data. BMC Bioinform. 19, 534 (2018).

    CAS  Google Scholar 

  52. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    PubMed  PubMed Central  Google Scholar 

  53. Zhou, G. et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 47, W234–W241 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Millet, Y. A. et al. Innate immune responses activated in Arabidopsis roots by microbe-associated molecular patterns. Plant Cell 22, 973–990 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 1138–1143 (2011).

    Google Scholar 

  56. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0209-9 (2019).

  57. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    CAS  PubMed  Google Scholar 

  58. Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2, e00191-16 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).

    PubMed  Google Scholar 

  61. Zar, J. Biostatistical Analysis 4th edn, Vol. 12 (Prentice Hall, 1999).

  62. Hochberg, Y. & Benjamini, Y. More powerful procedures for multiple significance testing. Stat. Med. 9, 811–818 (1990).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Y. Zhang and M. Sauter for sharing the seed stocks, and F. Menke and M. Draeger for providing the PSK peptide and constructive suggestions. This work was supported by an NSERC Discovery Grant and Accelerator Award (no. NSERC-RGPIN-2021-03587) and a Canada Research Chair salary award to C.H.H. Additional trainee support was provided by Chinese Scholarship Council Awards to S.S. Early stages of this work were supported by NIH grant no. R37 GM48707 and NSF grants no. MCB-0519898 and no. IOS-0929226 awarded to F. M. Ausubel.

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Author notes

  1. Cara H. Haney

    Present address: Department of Biological Sciences, The University of Pittsburgh, Pittsburgh, PA, USA

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada

    Siyu Song, Zayda Morales Moreira, Annika L. Briggs & Cara H. Haney

  2. Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA

    Xue-Cheng Zhang & Andrew C. Diener

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Contributions

C.H.H. and S.S. conceptualized and designed the experiments. S.S. conducted the majority of the experiments and data analysis. Z.M.M. conducted the 16S rRNA sequencing data analysis and differential gene expression analysis for the RNA-seq data. A.L.B. helped with the SynCom-related experiments. X.-C.Z. performed the hsm screen and the mapping of HSM7. A.C.D. helped with the characterization of the hsm7 mutant.

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Correspondence to Cara H. Haney.

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Extended data

Extended Data Fig. 1 PSKR1 enriches rhizosphere P. fluorescens abundance independent of changes in root architecture.

(a) Root phenotype of 8-day-old Col-0, hsm7, 35S::PSKR1–GFP and pskr1-3 seedlings planted on 1⁄2 MS media plate with 2% sucrose. The red lines highlight the ending point of the primary roots. (b) Primary root length of 8-day-old Col-0, hsm7, pskr1-3 and 35S::PSKR1–GFP seedlings. Each dot indicates the primary root length of one plant. Letters denote significant difference by ANOVA and Tukey’s HSD (P < 0.05). Bars indicate means ± s.e.m. Data from two independent experiments are shown and colored by experiment. n = 5-10 independent plants per experiment. (c) A diagram showing the natural soil colonization system. Natural soil containing natural microbiota was collected from the UBC farm, and 6-day-old plant seedlings from 1/2 MS plates were transplanted. After 17 days growing under 12 h light/12 h dark in growth chambers, root rhizosphere soil was collected, weighed and buffer was added to 0.05 g/mL (7.5 mM MgSO4 and 20% glycerol). The homogenized rhizosphere soil in buffer was plated on King’s B media after dilution, after which P. fluorescens colonies were identified under an UV light source and counted.

Extended Data Fig. 2 pskr1-3 shows an autoimmune growth deficiency phenotype in response to commensal P. fluorescens treatment.

(a) Root phenotype of 18-day-old Col-0 and pskr1-3 seedlings with MgSO4 buffer or P. fluorescens WCS365 treatment. (b) Primary root length of 18-day-old Col-0 and pskr1-3 seedlings with MgSO4 buffer or P. fluorescens WCS365 treatment. Each dot indicates the primary root length of 1 plant. Data from each temporal replate is labelled with a different color. n = 5 plants per experiment. Data shown are pooled from two temporal replicates. Letters indicate statistical differences (P < 0.05) by ANOVA and Tukey’s HSD. Bars indicate means ± s.e.m.

Extended Data Fig. 3 Gene Ontology (GO) analysis of P. fluorescens WCS365 induced differentially expressed genes in Col-0 and pskr1-3.

Bar length and fill color represent p values for individual GO term. The hypergeometric test and Benjamini-Hochberg p-value correction algorithm were used to identify all ontology terms that contain a statistically greater number of genes in common with an input list than expected by chance through Metascape.

Extended Data Fig. 4 Gene set enrichment analysis (GESA) revealed plant defense or growth-related pathways dependent on PSKR1.

(a) GESA map of enriched GO terms for DEGs in 35S::PSKR1–GFP versus pskr1-3 48 hours after P. fluorescens inoculation (35S::PSKR1–GFP_WCS365 versus pskr1-3_365, Padj < 0.05, |log2FC| ≥ 2). Individual GO terms are plotted as one node; node size represents the number of genes belonging to each GO term; node border color indicates the GO term significance; node filling color suggests PSKR1 positively (red) or negatively (blue) regulated GO terms. Similar GO terms are clustered and circled; similar clusters form functional groups labelled defense, growth, photosynthesis, and secondary metabolism. The significance of enriched GO terms was determined through g:Profiler (Methods) by the cumulative hypergeometric test and adjusted by multiple testing correction g:SCS. (b), (c) Genes belonging to GO term defense response (GO:0006952) and photosynthesis (GO:0015979) specifically for Col-0, pskr1-3, 35S::PSKR1–GFP samples with or without P. fluorescens treatment are displayed in heatmaps.

Extended Data Fig. 5 PSKR1 upregulates growth and suppresses defense responses.

(a) KEGG search and color pathway analysis of PSKR1-regulated DEGs [35S::PSKR1–GFP_WCS365 versus pskr1-3_WCS365, adjusted p value (Padj < 0.05)] in the plant-pathogen interaction pathway by comparing P. fluorescens WCS365 treated 35S::PSKR1–GFP versus pskr1-3 samples. Red and green color indicate PSKR1 up- and down-regulated DEGs respectively. (b) The ridgeline plot visualizing expression distributions of PSKR1 regulated DEGs with the presence of commensals [35S::PSKR1–GFP_WCS365 versus pskr1-3_WCS365, adjusted p value (Padj < 0.05)] by comparing P. fluorescens WCS365 treated 35S::PSKR1–GFP versus pskr1-3 samples. The black circles indicate the Log10 fold change of individual DEG in each KEGG pathway. The p values of indicated KEGG pathways were determined by hypergeometric tests with NetworkAnalyst (Methods).

Extended Data Fig. 6 PSKR1 regulates multiple processes in addition to SA-mediated defense responses.

(a) Heatmap displaying the top 2000 variable genes from the RNAseq dataset of Col-0, pskr1-3, npr1 npr4-4D, and pskr1-3 npr1 npr4-4D plants treated with MgSO4 buffer or P. fluorescens for 48 hours, and Col-0 plants treated with 40 µM SA for 6 hours. Cluster A indicates SA-upregulated genes (Col-0_SA versus Col-0_buffer) that show epistasis by npr1 npr4-4D in the pskr1-3 npr1 npr4-4D triple mutant. Cluster B and Cluster C indicate PSKR1-regulated genes without a full epistasis effect by npr1 npr4-4D. (b) The Gene Ontology (GO) processes enriched in Cluster A, B and C genes by Metascape. The hypergeometric test and Benjamini-Hochberg p-value correction algorithm were used to identify all ontology terms that contain a statistically greater number of genes in common with an input list than expected by chance.

Extended Data Fig. 7 The SA inducible gene expression is inhibited in pskr1-3 npr1 npr4-4D.

(a), (b) Expression levels of WRKY51 (a) and WRKY70 (b) in Col-0, npr1, npr4-4D, npr1 npr4-4D and pskr1-3 npr1 npr4-4D as measured by qRT-PCR. Expression values were normalized to the expression of house-keeping gene EF1. n = 3 replicates with 10 plants per replicate. Letters indicate significance as determined by ANOVA and Tukey’s HSD (P < 0.05). Error bars indicate means ± s.e.m.

Extended Data Fig. 8 Induction of root PSKR1 expression is largely specific to Pseudomonas.

(a) PSKR1pro::GUS plants inoculated with 10 mM MgSO4 buffer, P. fluorescens WCS365 (OD600 = 0.002) for 24 hours. (b) Representative images of PSKR1pro::GUS plants inoculated with 10 mM MgSO4 buffer or indicated bacteria treatments. The experiment was repeated 3 times independently with similar results.

Extended Data Fig. 9 A working model for the role of PSKR1 in balancing plant growth-defense trade-off in association with the microbiome.

Pseudomonas upregulates root PSKR1 expression to hijack plant signaling. The activation of PSKR1 in turn enriches rhizosphere Pseudomonas abundance by upregulating photosynthesis and other growth-related events while suppressing SA-mediated defense responses.

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Song, S., Morales Moreira, Z., Briggs, A.L. et al. PSKR1 balances the plant growth–defence trade-off in the rhizosphere microbiome. Nat. Plants 9, 2071–2084 (2023). https://doi.org/10.1038/s41477-023-01539-1

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