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:

Mutual potentiation of plant immunity by cell-surface and intracellular receptors

Abstract

The plant immune system involves cell-surface receptors that detect intercellular pathogen-derived molecules, and intracellular receptors that activate immunity upon detection of pathogen-secreted effector proteins that act inside the plant cell. Immunity mediated by surface receptors has been extensively studied1, but that mediated by intracellular receptors has rarely been investigated in the absence of surface-receptor-mediated immunity. Furthermore, interactions between these two immune pathways are poorly understood. Here, by activating intracellular receptors without inducing surface-receptor-mediated immunity, we analyse interactions between these two distinct immune systems in Arabidopsis. Pathogen recognition by surface receptors activates multiple protein kinases and NADPH oxidases, and we find that intracellular receptors primarily potentiate the activation of these proteins by increasing their abundance through several mechanisms. Likewise, the hypersensitive response that depends on intracellular receptors is strongly enhanced by the activation of surface receptors. Activation of either immune system alone is insufficient to provide effective resistance against the bacterial pathogen Pseudomonas syringae. Thus, immune pathways activated by cell-surface and intracellular receptors in plants mutually potentiate to activate strong defences against pathogens. These findings reshape our understanding of plant immunity and have broad implications for crop improvement.

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

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

Fig. 1: ETI potentiates PTI responses.
Fig. 2: ETI potentiates the activation of PTI signalling components.
Fig. 3: Accumulation of PTI signalling components during ETI.
Fig. 4: PTI and ETI function synergistically to provide robust immunity.

Similar content being viewed by others

Data availability

All data are available within this Article and its Supplementary Information. RNA-seq data are provided in Supplementary Table 4. Statistical analyses are provided in Supplementary Table 5. The original RNA-seq data that support the findings of this study have been deposited and made publicly available in the ENA with accession code PRJEB34955. All original gel blots are shown in Supplementary Fig. 1Source data are provided with this paper.

References

  1. Couto, D. & Zipfel, C. Regulation of pattern recognition receptor signalling in plants. Nat. Rev. Immunol. 16, 537–552 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Jones, J. D. G., Vance, R. E. & Dangl, J. L. Intracellular innate immune surveillance devices in plants and animals. Science 354, aaf6395 (2016).

    Article  PubMed  Google Scholar 

  3. Monteiro, F. & Nishimura, M. T. Structural, functional, and genomic diversity of plant NLR proteins: an evolved resource for rational engineering of plant immunity. Annu. Rev. Phytopathol. 56, 243–267 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Wang, J. et al. Reconstitution and structure of a plant NLR resistosome conferring immunity. Science 364, eaav5870 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Martin, R. et al. Structure of the activated ROQ1 resistosome directly recognizing the pathogen effector XopQ. Science 370, eabd9993 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ma, S. et al. Direct pathogen-induced assembly of an NLR immune receptor complex to form a holoenzyme. Science 370, eabe3069 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Duxbury, Z., Wu, C. & Ding, P. A comparative overview of the intracellular guardians of plants and animals: NLRs in innate immunity and beyond. Annu. Rev. Plant Biol. (in the press).

  8. Lapin, D. et al. A coevolved EDS1-SAG101-NRG1 module mediates cell death signaling by TIR-domain immune receptors. Plant Cell 31, 2430–2455 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  ADS  Google Scholar 

  10. Ngou, B. P. M. et al. Estradiol-inducible AvrRps4 expression reveals distinct properties of TIR-NLR-mediated effector-triggered immunity. J. Exp. Bot. 71, 2186–2197 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Voigt, C. A. Callose-mediated resistance to pathogenic intruders in plant defense-related papillae. Front. Plant Sci. 5, 168 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kadota, Y. et al. Direct regulation of the NADPH oxidase RBOHD by the PRR-associated kinase BIK1 during plant immunity. Mol. Cell 54, 43–55 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Li, L. et al. The FLS2-associated kinase BIK1 directly phosphorylates the NADPH oxidase RbohD to control plant immunity. Cell Host Microbe 15, 329–338 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Meng, X. & Zhang, S. MAPK cascades in plant disease resistance signaling. Annu. Rev. Phytopathol. 51, 245–266 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Kourelis, J. & van der Hoorn, R. A. L. Defended to the nines: 25 years of resistance gene cloning identifies nine mechanisms for R protein function. Plant Cell 30, 285–299 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Meteignier, L.-V. et al. Translatome analysis of an NB-LRR immune response identifies important contributors to plant immunity in Arabidopsis. J. Exp. Bot. 68, 2333–2344 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Ding, P. et al. High-resolution expression profiling of selected gene sets during plant immune activation. Plant Biotechnol. J. 18, 1610–1619 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ding, P. et al. Chromatin accessibility landscapes activated by cell surface and intracellular immune receptors. Preprint at https://doi.org/10.1101/2020.06.17.157040 (2020).

  19. Ma, X. et al. Ligand-induced monoubiquitination of BIK1 regulates plant immunity. Nature 581, 199–203 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  20. Lee, D. et al. Regulation of reactive oxygen species during plant immunity through phosphorylation and ubiquitination of RBOHD. Nat. Commun. 11, 1838 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  21. Urquidi Camacho, R. A., Lokdarshi, A. & von Arnim, A. G. Translational gene regulation in plants: a green new deal. Wiley Interdiscip. Rev. RNA 11, e1597 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. 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 

  23. Yuan, M. et al. Pattern-recognition receptors are required for NLR-mediated plant immunity. Nature, https://doi.org/10.1038/s41586-021-03316-6 (2021).

  24. Su, J. et al. Active photosynthetic inhibition mediated by MPK3/MPK6 is critical to effector-triggered immunity. PLoS Biol. 16, e2004122 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Torres, M. A., Dangl, J. L. & Jones, J. D. G. Arabidopsis gp91phox homologues AtrbohD and AtrbohF are required for accumulation of reactive oxygen intermediates in the plant defense response. Proc. Natl Acad. Sci. USA 99, 517–522 (2002).

    Article  CAS  PubMed  ADS  Google Scholar 

  26. Kadota, Y. et al. Quantitative phosphoproteomic analysis reveals common regulatory mechanisms between effector- and PAMP-triggered immunity in plants. New Phytol. 221, 2160–2175 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Xu, J. et al. A chemical genetic approach demonstrates that MPK3/MPK6 activation and NADPH oxidase-mediated oxidative burst are two independent signaling events in plant immunity. Plant J. 77, 222–234 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Yamada, K., Saijo, Y., Nakagami, H. & Takano, Y. Regulation of sugar transporter activity for antibacterial defense in Arabidopsis. Science 354, 1427–1430 (2016).

    Article  CAS  PubMed  ADS  Google Scholar 

  29. Anderson, J. C. et al. Decreased abundance of type III secretion system-inducing signals in Arabidopsis mkp1 enhances resistance against Pseudomonas syringae. Proc. Natl Acad. Sci. USA 111, 6846–6851 (2014).

    Article  CAS  PubMed  ADS  PubMed Central  Google Scholar 

  30. Jones, J. D. Putting knowledge of plant disease resistance genes to work. Curr. Opin. Plant Biol. 4, 281–287 (2001).

    Article  CAS  PubMed  Google Scholar 

  31. Luo, M. et al. A five-transgene cassette confers broad-spectrum resistance to a fungal rust pathogen in wheat. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-00770-x (2021).

  32. Cevik, V. et al. Transgressive segregation reveals mechanisms of Arabidopsis immunity to Brassica-infecting races of white rust (Albugo candida). Proc. Natl Acad. Sci. USA 116, 2767–2773 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kelley, N., Jeltema, D., Duan, Y. & He, Y. The NLRP3 inflammasome: an overview of mechanisms of activation and regulation. Int. J. Mol. Sci. 20, 3328 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  34. Wolf, A. J. & Underhill, D. M. Peptidoglycan recognition by the innate immune system. Nat. Rev. Immunol. 18, 243–254 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Schwessinger, B. et al. Phosphorylation-dependent differential regulation of plant growth, cell death, and innate immunity by the regulatory receptor-like kinase BAK1. PLoS Genet. 7, e1002046 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Asai, S. et al. A downy mildew effector evades recognition by polymorphism of expression and subcellular localization. Nat. Commun. 9, 5192 (2018).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  37. Tornero, P., Chao, R. A., Luthin, W. N., Goff, S. A. & Dangl, J. L. Large-scale structure-function analysis of the Arabidopsis RPM1 disease resistance protein. Plant Cell 14, 435–450 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tsuda, K. et al. Dual regulation of gene expression mediated by extended MAPK activation and salicylic acid contributes to robust innate immunity in Arabidopsis thaliana. PLoS Genet. 9, e1004015 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Qi, D. et al. Recognition of the protein kinase AVRPPHB SUSCEPTIBLE1 by the disease resistance protein RESISTANCE TO PSEUDOMONAS SYRINGAE5 is dependent on S-acylation and an exposed loop in AVRPPHB SUSCEPTIBLE1. Plant Physiol. 164, 340–351 (2014).

    Article  CAS  PubMed  Google Scholar 

  40. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Guo, W. et al. 3D RNA-seq - a powerful and flexible tool for rapid and accurate differential expression and alternative splicing analysis of RNA-seq data for biologists. RNA Biol. https://doi.org/10.1080/15476286.2020.1858253 (2020).

  43. Cho, H. K., Ahn, C. S., Lee, H.-S., Kim, J.-K. & Pai, H.-S. Pescadillo plays an essential role in plant cell growth and survival by modulating ribosome biogenesis. Plant J. 76, 393–405 (2013).

    Article  CAS  PubMed  Google Scholar 

  44. Ingole, K. D., Dahale, S. K. & Bhattacharjee, S. Proteomic analysis of SUMO1-SUMOylome changes during defense elicitation in Arabidopsis. J. Proteomics 232, 105054 (2021).

    Article  Google Scholar 

  45. Sohn, K. H., Zhang, Y. & Jones, J. D. G. The Pseudomonas syringae effector protein, AvrRPS4, requires in planta processing and the KRVY domain to function. Plant J. 57, 1079–1091 (2009).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank J. Dangl, S. Asai, R. Innes, K. Tsuda, S. Zhang, C. Zipfel, J.-M. Zhou, A. von Arnim, B. Staskawicz, C. Cheval, Y. Kadota, R. O’Grady, M. Morris, J. Rhodes and Y. Ding for providing materials, discussion and technical support; C. Zipfel, M. Yuan, X. Xin and S.-Y. He for critical reading of the manuscript; and the Gatsby Foundation for funding to the J.D.G.J. laboratory. B.P.M.N was supported by the Norwich Research Park Biosciences Doctoral Training Partnership from the Biotechnology and Biological Sciences Research Council (BBSRC) (grant agreement BB/M011216/1); H.-K.A. was supported by European Research Council Advanced Grant ‘ImmunitybyPairDesign’ (grant agreement: 669926); and P.D. acknowledges support from the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska-Curie Actions (grant agreement 656243) and a Future Leader Fellowship from BBSRC (grant agreement BB/R012172/1).

Author information

Authors and Affiliations

Authors

Contributions

B.P.M.N., P.D. and J.D.G.J. conceived and conceptualized the study; B.P.M.N. performed the ROS assay, DAB staining, callose quantification, gene expression analysis, immunoblotting, protein serial dilution, RNA-seq, bacterial growth assay, HR assay and electrolyte leakage assay; B.P.M.N performed the extraction of plasma membrane proteins with assistance from H.-K.A.; H.-K.A designed and performed the cycloheximide and MG132 experiment; H.-K.A. and B.P.M.N. performed enrichment of ribosomes; P.D. performed the RNA-seq analyses; H.-K.A. performed the statistical analyses; B.P.M.N. and P.D. wrote the original draft; and B.P.M.N., H.-K.A., P.D. and J.D.G.J. reviewed and edited the manuscript.

Corresponding authors

Correspondence to Pingtao Ding or Jonathan D. G. Jones.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Thorsten Nürnberger and the other, anonymous, reviewer(s) 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 figures and tables

Extended Data Fig. 1 ETIAvrRps4 and ETIAvrRpt2 potentiate PTI responses.

a, Pre-treatment with oestradiol in Est:AvrRps4 leaves leads to a stronger and prolonged ROS burst compared to mock pre-treatment. n = 40 leaf discs. b, ROS accumulation over 55 min in oestradiol-pre-treated leaves is significantly higher than in mock-pre-treated leaves. n = 120 leaves over 3 independent experiments. c, Pre-treatment with oestradiol in Est:AvrRps4 eds1-2 seedlings does not lead to a stronger and prolonged ROS burst compared to mock pre-treatment. n = 40 leaf discs. d, ROS accumulation over 55 min in oestradiol-pre-treated leaves in eds1-2 is comparable to mock-pre-treated leaves. n = 120 leaves over 3 independent experiments. e, ROS accumulation in leaves with activation of PTI, ETIAvrRps4 or both (PTI + ETI) during phase I (0–60 min), phase II (60–300 min) and phase III (300–960 min). n = 120 leaves over 3 independent experiments. f, Summary table of ROS accumulation in different phases. g, Co-activation of PTI and ETIAvrRps4 leads to higher transcript levels of PER4 and WRKY31 compared to activation of PTI or ETIAvrRps4 alone. ICS1 transcript is induced by co-activation of PTI and ETIAvrRps4 as well as being induced by ETIAvrRps4 alone. Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significance differences between PTI + ETIAvrRps4 and PTI or ETIAvrRps4 (*P ≤ 0.05; **P ≤ 0.01; ****P ≤ 0.001). Exact P values are provided in Supplementary Table 5. h, Co-activation of PTI and ETIAvrRpt2 leads to prolonged ROS production during phase II. n = 40 leaf discs. i, ROS accumulation in leaves with activation of PTI, ETIAvrRps4 or both during phase I, phase II, phase III and in total. n = 120 leaves over 3 independent experiments. j, Summary table of ROS accumulation in different phases. In a, c, h, solid line, mean; shaded band, s.e.m. (one biological replicate). In b, d, e, i, centre line, median; bounds of box, 25th and 75th percentiles; whiskers, 1.5 × IQR from 25th and 75th percentiles. Data points from three independent experiments were analysed with a one-sided Kruskal–Wallis test followed by post hoc Dunn’s test. Different letters indicate significant differences of P < 0.05. P values were adjusted using Holm correction, and exact P values are provided in Supplementary Table 5. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 2 ETIAvrRps4 enhances ROS production triggered by different PAMPs and a DAMP.

ac, elf18-triggered ROS production in the presence of ETIAvrRps4 is stronger than that triggered by elf18 treatment alone. df, pep1-triggered ROS production in the presence of ETIAvrRps4 is stronger than that triggered by pep1 treatment alone. gi, C10:0-triggered ROS production in the presence of ETIAvrRps4 is stronger than that triggered by C10:0 treatment alone. jl, nlp20-triggered ROS production in the presence of ETIAvrRps4 is stronger than that triggered by nlp20 treatment alone. mo, Chitin-triggered ROS production in the presence of ETIAvrRps4 is stronger than that triggered by chitin treatment alone. In a, d, g, j, m, solid line, mean; shaded band, s.e.m. n = 40 leaf discs. ROS production in phase I, phase II, phase III and in total are shown as box plots in b, e, h, k, n; centre line, median; bounds of box, 25th and 75th percentiles; whiskers, 1.5 × IQR from 25th and 75th percentiles. Data points from three independent experiments were analysed with a one-sided Kruskal–Wallis test followed by post hoc Dunn’s test. Different letters indicate significant differences of P < 0.05. n = 120 leaves over 3 independent experiments. P values were adjusted using Holm correction, and exact P values are provided in Supplementary Table 5. c, f, i, l, o, Tabular summary of total ROS production in different phases after different PAMP or DAMP treatments with co-activation of PTI and ETIAvrRps4. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 3 Protein accumulation of PTI signalling components during ETI.

a, PTI signalling pathway. b, c, Schematic of ‘natural infection mimicking’ and ‘ETI pre-activation’ experimental designs. ETIAvrRps4 was activated by oestradiol treatment. Star symbol indicates activated immune system (red, PTI activation; yellow, ETI activation; blue, PTI and ETI co-activation). d, Pre-activation of ETIAvrRps4 leads to accumulation and prolonged phosphorylation of MPK3 compared to mock pre-treatment. e, Pre-activation of ETIAvrRps4 leads to accumulation and prolonged phosphorylation of BIK1 and RBOHD (Ser39 and Ser343; detected by RBOHD(pS39) and RBOHD(pS343) antibodies, respectively) compared to mock pre-treatment. BIK1 indicates phosphorylated BIK1 (top band) and non-phosphorylated BIK1 (bottom band). Microsomal fractions from each sample were isolated for immunoblotting. Molecular weight markers (in kDa) are indicated on the left. Ponceau staining was used as loading control. f, Transcript induction of corresponding effectors and ICS1 after dexamethasone-induced expression of AvrRpm1 (Dex:AvrRpm1) and oestradiol-induced expression of AvrRpt2 (Est:AvrRpt2), AvrPphB (Est:AvrPphB), AvrRps4 (Est:AvrRps4) and AvrRpp4 (Est:AvrRpp4). Extracted RNA was analysed by qPCR and expression level is presented relative to EF1A. Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significant differences of 4-h and 8-h data points from 0 h (*P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.005; ****P ≤ 0.001). Exact P values are provided in Supplementary Table 5. g, Protein accumulation of BAK1, SOBIR1, BIK1, RBOHD, MPK3, MPK6, FLS2, CERK1 and MPK4 after activation of ETI for 4 h and 8 h in several effector-inducible lines. Five-week-old leaves of inducible AvrRpm1, AvrRpt2, AvrPphB, AvrRps4 and AvrRpp4 lines were infiltrated with 50 μM dexamethasone (for Dex:AvrRpm1) or 50 μM oestradiol. Samples were collected at 0 h, 4 h and 8 h after infiltration for protein extraction. Molecular weight markers (in kDa) are indicated on the left. Ponceau staining (PS) was used as loading control. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 4 mRNA transcript accumulation of PTI signalling components during ETI.

a, Relative gene expression of BAK1, SOBIR1, BIK1, RBOHD, MPK3, MPK6, FLS2, CERK1, MPK4 and RBOHF relative to EF1A in several effector-inducible lines. Five-week-old leaves of inducible AvrRpm1, AvrRpt2, AvrPphB, AvrRps4 and AvrRpp4 lines were infiltrated with 50 μM dexamethasone (for Dex:AvrRpm1) or 50 μM oestradiol. Samples were collected at 0 h, 4 h and 8 h after infiltration for RNA extraction. b, Heat map representing log2-transformed fold changes in gene expression of BAK1, SOBIR1, BIK1, RBOHD, MPK3, MPK6, FLS2, CERK1, MPK4 and RBOHF from a (normalized against expression of the corresponding genes in sample at 0 h). Gene expression at 4 h and 8 h was normalized to expression level at 0 h. Red, upregulation; blue, downregulation. c, Protein accumulation of BIK1, RBOHD and MPK3 during ETIAvrRps4 is abrogated in eds1-2. Proteins were extracted from Est:AvrRps4 and Est:AvrRps4 eds1-2 after treatment with oestradiol for 0 h, 4 h and 8 h. Molecular weight markers (in kDa) are indicated on the left. Ponceau staining was used as loading control. d, Transcript induction of BIK1, RBOHD and MPK3 during ETIAvrRps4 is abrogated in eds1-2. For a, d, extracted RNA was analysed by qPCR and expression level is presented relative to EF1A. Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significant differences of 4-h and 8-h data points from 0 h (*P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.005; ****P ≤ 0.001). Exact P values are provided in Supplementary Table 5. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 5 Genome-wide gene expression profiling of ETIAvrRps4.

a, Schematic design of RNA-seq analysis. Five-week-old inducible lines of wild-type AvrRps4 (Est:AvrRps4) and mutant AvrRps4 (oestradiol-inducible AvrRps4KRVY135–138AAAA-expressing line; Est:AvrRps4mut) were infiltrated with mock or 50 μM oestradiol and samples were collected at 0 and 4 h. Samples from three biological replicates were collected for RNA-seq analysis. b, A total of 2,573 differentially expressed (DE) genes were identified as significant in a comparison between Est:AvrRps4 treated with oestradiol for 0 h (Est:AvrRps4 (Est-0h)) and Est:AvrRps4 treated with oestradiol for 4 h (Est:AvrRps4 (Est-4h)). P values for differentially expressed genes were generated with Fisher Z-transformation after Student’s t-test. Differentially expressed genes with a two-sided adjusted P value < 0.01 (adjusted by Benjamini and Hochberg’s false discovery rate (FDR) method) are categorized as significant. Heat map represents the 2,573 differentially expressed genes during five treatments: Est:AvrRps4 (untreated), Est:AvrRps4 treated with oestradiol for 0 h (Est-0h), Est:AvrRps4 treated with oestradiol for 4 h (Est-4h), Est:AvrRps4mut treated with oestradiol for 0 h (Est-0h) and Est:AvrRps4mut treated with oestradiol for 4 h (Est-4h). Genes that are specifically upregulated during ETIAvrRps4 are in clusters 7 and 8. ce, Gene ontology (GO) enrichment analysis of genes from clusters 7 and 8. c, Top three significantly enriched biological process GO terms in clusters 7 and 8. d, Top four significantly enriched molecular function GO terms in clusters 7 and 8. e, Top four significantly enriched cellular component GO terms in clusters 7 and 8. For details of GO enrichment analysis refer to the relevant Source Data. f, Red (positive log2-transformed fold change) represents genes that are significantly induced and blue (negative log2-transformed fold change) represents genes that are significantly repressed. Data represent log2-transformed fold changes in gene expression (normalized against expression of the corresponding genes before ETIAvrRps4 activation). Benjamini–Hochberg FDR two-sided adjusted P value (adj.pval) < 0.05 is considered as significant. Gradient of green colour indicates significance of the adjusted P value. For a full list of differentially expressed genes refer to Supplementary Table 4.

Source data

Extended Data Fig. 6 Expression dynamics of PTI signalling components during ETIAvrRps4.

a, Transcript induction of SOBIR1, BAK1, BIK1, RBOHD, MPK3, CERK1, MPK4, MPK6, ICS1 and PR1 during ETIAvrRps4 over 24 h. Transcript levels were normalized to EF1A. Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significant differences of data points from ETIAvrRps4-activated samples compared to untreated samples (*P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.005; ****P ≤ 0.001; otherwise, not significant). Exact P values are provided in Supplementary Table 5. b, Relative mRNA expression changes of ICS1 (green) and PR1 (black) during ETIAvrRps4. Expression changes of the corresponding genes relative to untreated samples (dotted line shows a log2-transformed fold change of 0) are shown. Solid line, mean; shaded band, s.e.m. c, Heat map representing log2-transformed fold changes in gene expression of transcripts from a (normalized against expression of the corresponding genes in the untreated sample). Gene expression values at the indicated time points are relative to untreated samples. Red, upregulation; blue, downregulation. d, Protein accumulation of PR1 at different time points. Ponceau staining of western blots from Fig. 3b is also shown. e, Serial dilution to estimate protein accumulation of BIK1, RBOHD and MPK3 at 8 h after ETIAvrRps4 activation compared to 0 h. Red asterisk indicates approximate fold differences between 0 h and 8 h. f, Five-week old Arabidopsis Est:AvrRps4 rosette leaves were treated with hrcC, oestradiol or both (hrcC + est) for the indicated times and both RNA and proteins were extracted. Extracted RNA was analysed by qPCR and expression level is presented relative to EF1A. Data are mean ±s.e.m. (from three independent experiments; PTI, red; ETIAvrRps4, yellow; PTI + ETIAvrRps4, blue). A two-sided Welch’s t-test was used to analyse significant differences of 4-h and 8-h data points from 0 h (*P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.005; ****P ≤ 0.001). Exact P values are provided in Supplementary Table 5. For df, Ponceau staining was used as loading control. Molecular weight markers (in kDa) are indicated on the left. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 7 Several mechanisms are involved in the upregulation of PTI signalling components during ETIAvrRps4.

a, Relative gene expression of ICS1, BIK1, RBOHD, MPK3 and MPK6 in seedlings with pre-activation of ETIAvrRps4 for 3 h before treatment with cycloheximide and MG132. Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significant differences of 3-h data points from 0 h (*P ≤ 0.05; ****, P ≤ 0.001). Exact P values are provided in Supplementary Table 5. b, c, Protein accumulation of MPK3, RBOHD, BIK1, MPK6 and actin in seedlings pre-treated with mock (DMSO) for 3 h (b), and RPS4–HA, FLS2 and BAK1 in seedlings pre-treated with mock or oestradiol and subsequently treated with cycloheximide (50 μM), MG132 (10 μM) or both for indicated times (2 h, 4 h and 8 h) (c). Actin was used as the loading control. Ponceau staining of corresponding blots is shown below. For FLS2 and actin, as well as BAK1 and BIK1, immunoblotting was performed with membranes cut in half (above 70 kDa for FLS2 and BAK1; below 70 kDa for actin and BIK1 immunoblot). Therefore, the Ponceau staining for FLS2 and actin, BAK1 and BIK1, respectively, is identical. d, Schematic of ribosome enrichment. e, f, Ribosomes were enriched, and total extract (T), supernatant (S) and ribosomal pellet (P) samples were blotted with RPS6 and RPL10 antibody (e). For b, c, e, Ponceau staining was used as loading control. Molecular weight markers (in kDa) are indicated on the left. f, RNA extracted from total extract (total RNA), and ribosomal pellet (ribosome RNA) from mock and oestradiol-treated Est:AvrRps4 samples were loaded on an agarose gel; 28S and 18S ribosomal RNA (rRNA) are indicated. g, Expression of ICS1, SOBIR1, BAK1, BIK1, RBOHD and MPK3 to relative to EF1A from total RNA (total) and ribosomal pellet (ribosomal). Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significant differences of 6-h data points from 0 h (*P ≤ 0.05; ***, P ≤ 0.005; ****, P ≤ 0.001). Exact P values are provided in Supplementary Table 5. h, Ratio of ribosomal RNA to total RNA (relative to EF1A) of ICS1, SOBIR1, BAK1, BIK1, RBOHD and MPK3 in mock and ETI-activated samples. Values are calculated from the transcripts retained in the ribosomal samples over total samples. Data are mean ±s.e.m. (from three independent experiments). A two-sided Welch’s t-test was used to analyse significant differences in the translation efficiency (T.E.) between mock and ETI-activated samples. Exact P values are provided in Supplementary Table 5. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 8 ETI functions through PTI.

a, Five-week-old leaves of Est:AvrRps4 plants were infiltrated with Pst DC3000 hrcC (Pst hrcC; triggers PTI), Pst DC3000 (Pst; triggers PTI and effector-triggered susceptibility (PTI + ETS)), or 50 μM oestradiol and Pst hrcC (Pst + est; triggers PTI – ETS + ETI), and samples were collected at the indicated time points for protein extraction and immunoblotting. PTI leads to activation of MAPKs and accumulation of BIK1 and RBOHD (red). Pst secretes effectors to block PTI (green). Co-activation of PTI and ETIAvrRps4 leads to stronger accumulation of MPK3, BIK1 and RBOHD compared to activation of PTI alone (blue). MAPK activation is also prolonged during co-activation of PTI and ETIAvrRps4. b, Updated version of the ‘zig-zag-zig’ model9. c, Col-0, rps4-2 rps4b-2 and bak1-5 bkk1-1 leaves were infected with Pst DC3000 containing AvrRps4 (red) or empty vector (grey). Bacterial growth at 0 days post-infection (dpi) was measured. n = 12 leaves. d, Col-0, rps4-2 rps4b-2 and fls2 efr seedlings were infected with Pst DC3000 containing AvrRps4 (red) or empty vector (grey). Both rps4-2 rps4b-2 (no ETI) and fls2 efr (PTI-reduced) are insufficient to provide resistance against Pst DC3000:AvrRps4 compared to Col-0 (PTI + ETI). Day 0, n = 12 leaves; day 3, n = 18 leaves. In c, d, data points were analysed by one-way ANOVA followed by post hoc Tukey’s HSD test. Data points with different letters indicate significant differences of P < 0.01. e, flg22-induced ROS burst is not affected in rps4-2 rps4b-2 plants. Solid line, mean; shaded band, s.e.m. (average value from 24 leaves in each treatment). f, flg22-induced ROS production over 55 min in Col-0 and rps4-2 rps4b-2. Data points from three biological replicates were analysed with a one-sided Kruskal–Wallis test followed by post hoc Dunn’s test. Data points with different letters indicate significant differences of P < 0.05. n = 72 leaves over 3 independent experiments. g, flg22-induced MPK phosphorylation is not affected in rps4-2 rps4b-2. After treatment with flg22, samples were taken at the indicated time points for immunoblotting. For a, g, Ponceau staining was used as loading control. Molecular weight markers (in kDa) are indicated on the left. All experiments were repeated at least three times with similar results. In c, d, f centre line, median; bounds of box, 25th and 75th percentiles; whiskers, 1.5 × IQR from 25th and 75th percentiles. Exact P values are provided in Supplementary Table 5.

Source data

Extended Data Fig. 9 Potentiation of ETI-induced HR by PTI.

a, Pf0-1:AvrRps4 leads to macroscopic HR in Est:AvrRps4 leaves. Both PTI (Pf0-1:AvrRps4mut) and ETIAvrRps4 (Est) do not lead to macroscopic HR. Co-activation of PTI and ETIAvrRps4 (Est + Pf0-1:AvrRps4mut) leads to macroscopic HR. The numbers indicate the number of leaves that display HR of the total number of leaves infiltrated. n = 18 leaves. b, Est:AvrRps4 leaves were hand-infiltrated with indicated solutions and electrolyte leakage was measured over 48 h after infiltration. The combination of PTI + ETIAvrRps4 activation (blue dots, Est + Pf0-1:AvrRps4mut) leads to stronger electrolyte leakage compared to activation of ETIAvrRps4 (Est) or PTI (Pf0-1:AvrRps4mut) alone. Pf0-1:AvrRps4 (green) acts as a positive control. Data points from three biological replicates were analysed with a one-way ANOVA followed by post hoc Tukey’s HSD test. Data points from each biological replicate are indicated with different shapes. Data points with different letters indicate P < 0.01. n = 9 data points; each represents data from 15 leaf discs. Exact P values are provided in Supplementary Table 5. c, PTI induced by flg22, elf18, pep1, C10:0, nlp20 or chitin does not lead to macroscopic HR. Co-activation of PTI (triggered by these PAMPs or DAMP) with ETIAvrRps4 leads to macroscopic HR. The numbers indicate the number of leaves that display HR of the total number of leaves infiltrated. n = 18 leaves. d, Five-week-old inducible AvrRpm1 (Dex:AvrRpm1), AvrRpt2 (Est:AvrRpt2), AvrPphB (Est:AvrPphB), AvrRps4 (Est:AvrRps4) and AvrRpp4 (Est:AvrRpp4) Arabidopsis leaves were infiltrated with either dexamethasone (for Dex:AvrRpm1 only) or oestradiol. All pictures were taken at 3 dpi. The numbers indicate the number of leaves that display HR of the total number of leaves infiltrated. n = 18 leaves. e, The combination of PTI and ETI leads to stronger macroscopic HR in inducible AvrRpm1, AvrRpt2, AvrPphB and AvrRpp4 Arabidopsis lines. All pictures were taken at 3 dpi. The numbers indicate the number of leaves that display HR of the total number of leaves infiltrated. n = 18 leaves. All experiments were repeated at least three times with similar results.

Source data

Extended Data Fig. 10 MAPKs and NADPH oxidases are involved in HR induced by PTI and ETI.

a, MPK phosphorylation during ETI triggered by multiple effectors. Seedlings of Dex:AvrRpm1, Est:AvrRpt2, Est:AvrPphB and Est:AvrRpp4 lines were soaked in dexamethasone or oestradiol solution, respectively, for the indicated times (dark yellow). Untreated seedlings were used as a negative control; seedlings treated with 100 nM flg22 for 15 min (red, flg22) were used as a positive control. b, RBOHD phosphorylation during ETI triggered by multiple effectors. Seedlings of Dex:AvrRpm1, Est:AvrRpt2, Est:AvrPphB and Est:AvrRpp4 were soaked in mock (black), dexamethasone or oestradiol solution (dark yellow) for 6 h. Microsomal fractions from seedlings were isolated for immunoblotting with RBOHD(pS39) antibody. For a, b, Ponceau staining was used as the loading control. Molecular weight markers (in kDa) are indicated on the left. c, MPK6SR#58 (mpk3 mpk6 PMPK6:MPK6YG) is a conditional mpk3 mpk6 double mutant. MPK6YG has a larger ATP-binding pocket than wild-type MPK6 and is sensitive to the inhibitor 1-naphthyl-PP1 (NA-PP1, ATP analogue). Pre-treatment with NA-PP1 inhibits MPK6YG and temporarily generates a mpk3 mpk6 double mutant. Both Col-0 and MPK6SR#58 leaves were pre-infiltrated with either 1% DMSO (mock) or 10 μM NA-PP1. After 3 h, these leaves were infiltrated with either Pf0-1:empty vector (triggers PTI) or Pf0-1:AvrRps4 (triggers PTI + ETIAvrRps4). With mock pre-treatment, Pf0-1:AvrRps4 infiltration leads to macroscopic HR in both Col-0 and MPKS6R#58. NA-PP1 pre-treatment attenuates HR caused by Pf0-1:AvrRps4 only in the MPK6SR#58 line. All pictures were taken at 1 dpi. The numbers indicate the number of leaves that display HR of the total number of leaves infiltrated. n = 18 leaves. d, Col-0 and rbohd rbohf leaves were infiltrated with either Pf0-1:empty vector (triggers PTI) or Pf0-1:AvrRps4 (triggers PTI + ETIAvrRps4) at a varying OD600. At OD600 = 0.025, Pf0-1:AvrRps4 infiltration leads to less macroscopic HR in rbohd rbohf leaves. All pictures were taken at 1 dpi. The numbers indicate the number of leaves that display HR of the total number of leaves infiltrated. n = 18 leaves. All experiments were repeated at least three times with similar results. e, Model. Upon ligand detection by PRRs, PTI leads to activation of BIK1, RBOHD and MAPKs. Activation of an NLR (ETI without PTI) increases the accumulation of PTI signalling components. Co-activation of both PTI and ETI increases this accumulation and enhances the activation of multiple PTI signalling components, enabling a stronger immune response.

Supplementary information

Supplementary Figure 1

Uncropped blots. Molecular weight in kDa (kilodalton) is indicated on the left.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-3.

Supplementary Table 4

Differentially expressed gene list during ETI. List of differentially expressed (DE) genes identified in the comparison between Est:AvrRps4 treated with estradiol for 0h (seti_e2_0h) and 4h (seti_e2_4h). Positive log2FC values (red) indicates upregulation and negative log2FC values (blue) indicates downregulation. P-values for differentially expressed (DE) genes were generated with Fisher Z-transformation after Student’s t-test. DE genes with “Benjamini and Hochberg’s (BH) method” false discovery rate (FDR) two-sided adjusted P-value (adj.pval) < 0.05 (green) is categorized as significant.

Supplementary Table 5

Statistical summary. Summary of all statistical analysis are listed. The specification of samples and the number of samples used for each dataset, and the statistical tests and the P-values are summarized. The methods used for adjustment of P-values are also shown. The source data of all the statistical analysis can be found in the Source File.

Peer Review File

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ngou, B.P.M., Ahn, HK., Ding, P. et al. Mutual potentiation of plant immunity by cell-surface and intracellular receptors. Nature 592, 110–115 (2021). https://doi.org/10.1038/s41586-021-03315-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-03315-7

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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