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.

Multidimensional gene regulatory landscape of a bacterial pathogen in plants

Nature Plants volume 6, pages 883–896 (2020)Cite this article

Subjects

An Author Correction to this article was published on 21 July 2020

This article has been updated

Abstract

Understanding the gene regulation of plant pathogens is crucial for pest control and thus global food security. An integrated understanding of bacterial gene regulation in the host is dependent on multi-omic datasets, but these are largely lacking. Here, we simultaneously characterized the transcriptome and proteome of a bacterial pathogen in plants. We found a number of bacterial processes affected by plant immunity at the transcriptome and proteome levels. For instance, salicylic acid-mediated plant immunity suppressed the accumulation of proteins comprising the tip component of the bacterial type III secretion system. Interestingly, there were instances of concordant and discordant regulation of bacterial messenger RNAs and proteins. Gene co-expression analysis uncovered previously unknown gene regulatory modules underlying virulence. This study provides molecular insights into the multiple layers of gene regulation that contribute to bacterial growth in planta, and elucidates the role of plant immunity in affecting pathogen responses.

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

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Fig. 1: In planta transcriptomics and proteomics of Pto.
Fig. 2: Distinct patterns of Pto transcriptomes and proteomes under various conditions.
Fig. 3: Dynamic regulation of bacterial function across different conditions.
Fig. 4: Conserved and divergent regulation of mRNA and protein expression in Pto.
Fig. 5: Bacterial functions showing concordant and discordant regulation at the mRNA and protein levels.
Fig. 6: Component-specific suppression of the type III secretion system by plant SA pathways.
Fig. 7: mRNA co-expression analysis of Pto reveals functional mRNA modules.
Fig. 8: The roles of transcriptional regulators found in bacterial gene regulatory modules.

Data availability

Source Data for Fig. 6b are provided with the paper. The RNA-seq data used in this study are deposited in the National Center for Biotechnology Information Gene Expression Omnibus database (accession no. GSE138901). The mass spectrometry proteomics data are available at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015839.

Change history

References

  1. Nobori, T., Mine, A. & Tsuda, K. Molecular networks in plant–pathogen holobiont. FEBS Lett. 592, 1937–1953 (2018).

  2. Nobori, T. & Tsuda, K. The plant immune system in heterogeneous environments. Curr. Opin. Plant Biol. 50, 58–66 (2019).

    CAS  PubMed  Google Scholar 

  3. Nobori, T. et al. Transcriptome landscape of a bacterial pathogen under plant immunity. Proc. Natl Acad. Sci. USA 115, E3055–E3064 (2018).

  4. Kunkel, B. N., Bent, A. F., Dahlbeck, D., Innes, R. W. & Staskawicz, B. J. RPS2, an Arabidopsis disease resistance locus specifying recognition of Pseudomonas syringae strains expressing the avirulence gene avrRpt2. Plant Cell 5, 865–875 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Yu, G.-L., Katagiri, F. & Ausubel, F. M. Arabidopsis mutations at the RPS2 locus result in loss of resistance to Pseudomonas syringae strains expressing the avirulence gene avrRpt. Mol. Plant Microbe Interact. 6, 434–443 (1993).

    CAS  PubMed  Google Scholar 

  6. Tsuda, K., Sato, M., Stoddard, T., Glazebrook, J. & Katagiri, F. Network properties of robust immunity in plants. PLOS Genet. 5, e1000772 (2009).

    PubMed  PubMed Central  Google Scholar 

  7. Zhang, W. et al. Plant–necrotroph co-transcriptome networks illuminate a metabolic battlefield. eLife 8, 1–32 (2019).

    Google Scholar 

  8. O’Connell, R. J. et al. Lifestyle transitions in plant pathogenic Colletotrichum fungi deciphered by genome and transcriptome analyses. Nat. Genet. 44, 1060–1065 (2012).

    PubMed  Google Scholar 

  9. Abreu, R., de, S., Penalva, L. O., Marcotte, E. M. & Vogel, C. Global signatures of protein and mRNA expression levels. Mol. Biosyst. 5, 1512–1526 (2009).

    CAS  PubMed Central  Google Scholar 

  10. Maier, T., Güell, M. & Serrano, L. Correlation of mRNA and protein in complex biological samples. FEBS Lett. 583, 3966–3973 (2009).

    CAS  PubMed  Google Scholar 

  11. Müller, D. B., Schubert, O. T., Röst, H., Aebersold, R. & Vorholt, J. A. Systems-level proteomics of two ubiquitous leaf commensals reveals complementary adaptive traits for phyllosphere colonization. Mol. Cell. Proteomics 15, 3256–3269 (2016).

    PubMed  PubMed Central  Google Scholar 

  12. Levy, A., Conway, J. M., Dangl, J. L. & Woyke, T. Elucidating bacterial gene functions in the plant microbiome. Cell Host Microbe 24, 475–485 (2018).

    CAS  PubMed  Google Scholar 

  13. Llamas, A., Imperi, F., Visca, P. & Lamont, I. L. Cell-surface signaling in Pseudomonas: stress responses, iron transport, and pathogenicity. FEMS Microbiol. Rev. 38, 569–597 (2014).

    CAS  PubMed  Google Scholar 

  14. Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124 (2007).

    CAS  PubMed  Google Scholar 

  15. Ishihama, Y. et al. Index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell. Proteomics 4, 1265–1272 (2005).

    CAS  PubMed  Google Scholar 

  16. Corbin, R. W. et al. Toward a protein profile of Escherichia coli: comparison to its transcription profile. Proc. Natl Acad. Sci. USA 100, 9232–9237 (2003).

  17. Toruño, T. Y., Stergiopoulos, I. & Coaker, G. Plant-pathogen effectors: cellular probes interfering with plant defenses in spatial and temporal manners. Annu. Rev. Phytopathol. 54, 419–441 (2016).

    PubMed  PubMed Central  Google Scholar 

  18. Sreedharan, A., Penaloza-Vazquez, A., Kunkel, B. N. & Bender, C. L. CorR regulates multiple components of virulence in Pseudomonas syringae pv. tomato DC3000. Mol. Plant. Microbe. Interact. 19, 768–779 (2006).

    CAS  PubMed  Google Scholar 

  19. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Ghazalpour, A. et al. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 7, e1001393 (2011).

  21. Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nat. Genet. 39, 1369–1375 (2007).

    CAS  PubMed  Google Scholar 

  22. Lan, P., Li, W. & Schmidt, W. Complementary proteome and transcriptome profiling in phosphate-deficient Arabidopsis roots reveals multiple levels of gene egulation. Mol. Cell. Proteomics 11, 1156–1166 (2012).

    PubMed  PubMed Central  Google Scholar 

  23. Marguerat, S. et al. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell 151, 671–683 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Yao, J. & Allen, C. Chemotaxis is required for virulence and competitive fitness of the bacterial wilt pathogen Ralstonia solanacearum. J. Bacteriol. 188, 3697–3708 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Papenfort, K. & Bassler, B. L. Quorum sensing signal–response systems in Gram-negative bacteria. Nat. Rev. Microbiol. 14, 576–588 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Kvitko, B. H., Ramos, A. R., Morello, J. E., Oh, H. S. & Collmer, A. Identification of harpins in Pseudomonas syringae pv. tomato DC3000, which are functionally similar to HrpK1 in promoting translocation of type III secretion system effectors. J. Bacteriol. 189, 8059–8072 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Petnicki-Ocwieja, T., Van Dijk, K. & Alfano, J. R. The hrpK operon of Pseudomonas syringae pv. tomato DC3000 encodes two proteins secreted by the type III (Hrp) protein secretion system: HopB1 and HrpK, a putative type III translocator. Society 187, 649–663 (2005).

    CAS  Google Scholar 

  28. Wang, Y. et al. Site-specific cleavage of bacterial MucD by secreted proteases mediates antibacterial resistance in Arabidopsis. Nat. Commun. 10, 2853 (2019).

  29. Mackey, D., Belkhadir, Y., Alonso, J. M., Ecker, J. R. & Dangl, J. L. Arabidopsis RIN4 is a target of the type III virulence effector AvrRpt2 and modulates RPS2-mediated resistance. Cell 112, 379–389 (2003).

    CAS  PubMed  Google Scholar 

  30. Jirage, D. et al. Arabidopsis thaliana PAD4 encodes a lipase-like gene that is important for salicylic acid signaling. Proc. Natl Acad. Sci. USA 96, 13583–13588 (1999).

    CAS  PubMed  PubMed Central  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. 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).

    PubMed  PubMed Central  Google Scholar 

  33. Kvitko, B. H. & Collmer, A. Construction of Pseudomonas syringae pv. tomato DC3000 mutant and polymutant strains. Methods Mol. Biol. 712, 109–128 (2011).

    CAS  PubMed  Google Scholar 

  34. Huynh, T. V., Dahlbeck, D. & Staskawicz, B. J. Bacterial blight of soybean: regulation of a pathogen gene determining host cultivar specificity. Science 245, 1374–1377 (1989).

    CAS  PubMed  Google Scholar 

  35. Tsuda, K., Sato, M., Glazebrook, J., Cohen, J. D. & Katagiri, F. Interplay between MAMP-triggered and SA-mediated defense responses. Plant J. 53, 763–775 (2008).

    CAS  PubMed  Google Scholar 

  36. Nobori, T. & Tsuda, K. In planta transcriptome analysis of Pseudomonas syringae. Bio Protoc. 8, 1–8 (2018).

    Google Scholar 

  37. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–360 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Anders, S., Pyl, P. T. & Huber, W. HTSeq A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Hoon, M. J. L. De, Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004).

    PubMed  Google Scholar 

  41. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 12930–12933 (1998).

    Google Scholar 

  42. Maere, S., Heymans, K. & Kuiper, M. Systems biology BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics 21, 3448–3449 (2005).

    CAS  PubMed  Google Scholar 

  43. Bronstein, P. A. et al. Global transcriptional responses of Pseudomonas syringae DC3000 to changes in iron bioavailability in vitro. BMC Microbiol. 8, 209 (2008).

    PubMed  PubMed Central  Google Scholar 

  44. Borner, G. H. H. & Fielding, A. B. Using in-solution digestion, peptide fractionation, and a q exactive mass spectrometer to analyze the proteome of clathrin-coated vesicles. Cold Spring Harb. Protoc. 2014, 1192–1195 (2014).

    PubMed  Google Scholar 

  45. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  PubMed  Google Scholar 

  46. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    CAS  PubMed  Google Scholar 

  47. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  48. Lin, Y., Hsu, S. & Huang, H. Evidence for the interaction between HrpT and HrcC of Pseudomonas syringae pv. syringae 61. Plant Pathol. Bull. 15, 171–185 (2006).

    CAS  Google Scholar 

  49. Alfano, J. R., Bauer, D. W., Milos, T. M. & Collmer, A. Analysis of the role of the Pseudomonas syringae pv. syringae HrpZ harpin in elicitation of the hypersensitive response in tobacco using functionally non-polar hrpZ deletion mutations, truncated HrpZ fragments, and hrmA mutations. Mol. Microbiol. 19, 715–728 (1996).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank S. Yang He for providing the HrpZ antibody, H.-C. Huang for providing the HrcC antibody and A. Harzen and K. Kramer for support in sample preparation and mass spectrometry analysis. We thank A. Collmer for the pCPP5040 plasmid, the Max Planck Genome Centre for sequencing support and N. Donnelly for critical comments on the manuscript. This work was supported by the Huazhong Agricultural University Scientific & Technological Self-innovation Foundation, the Max Planck Society, a German Research Foundation grant (no. SPP2125 to K.T.), a predoctoral fellowship from the Nakajima Foundation (to T.N.) and a postdoctoral fellowship from the Alexander von Humboldt Foundation (to Y.W.).

Author information

Author notes

  1. Tatsuya Nobori

    Present address: Salk Institute for Biological Studies, La Jolla, CA, USA

  2. Yiming Wang

    Present address: College of Plant Protection, Nanjing Agriculture University, Nanjing, China

  3. Jingni Wu

    Present address: National Key Laboratory of Plant Molecular Genetics, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, China

  4. Iris Finkemeier

    Present address: Institute of Plant Biology and Biotechnology, University of Muenster, Münster, Germany

  5. These authors contributed equally: Tatsuya Nobori, Yiming Wang.

Authors and Affiliations

  1. Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Tatsuya Nobori, Yiming Wang, Jingni Wu, Yayoi Tsuda & Kenichi Tsuda

  2. Protein Mass Spectrometry Group, Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Sara Christina Stolze, Iris Finkemeier & Hirofumi Nakagami

  3. State Key Laboratory of Agricultural Microbiology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China

    Yayoi Tsuda & Kenichi Tsuda

Authors
  1. Tatsuya Nobori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingni Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sara Christina Stolze
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yayoi Tsuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Iris Finkemeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hirofumi Nakagami
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kenichi Tsuda
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.N. and K.T. designed the research. T.N., Y.W., J.W., S.C.S., Y.T., I.F. and H.N. performed experiments. T.N. and K.T. wrote the paper with input from all authors.

Corresponding author

Correspondence to Kenichi Tsuda.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Asaf Levy and the other, anonymous, reviewer(s) 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 Overview of RNA-seq and proteome datasets.

Hierarchical clustering of relative expression (RE) of (a) RNA-seq data and (b) proteome data that were normalized separately. a, b, ps, pad4 sid2; rr, rpm1 rps2.

Extended Data Fig. 2 Normalization of RNA-seq and proteome data.

a, Heatmap of RNA-seq count data displaying 387 genes that are omitted from further analyses due to low average count (<5). Genes with average counts of zero were not displayed. b, Scatter plot comparing TMM-normalized iBAQ values and LFQ values of proteome data. The Pearson’s correlation coefficient was shown. (C) Q-Q plot of TMM-normalized iBAQ values. b, c, In vitro (KB) samples were used (n = 3).

Extended Data Fig. 3 Regulation of selected mRNAs and proteins.

a, b, Expression (z-score) of mRNAs (left) and proteins (right) related to “coronatine biosynthesis” and “catalase” under various conditions. Light and dark gray sidebars represent Pto and Pto AvrRpt2, respectively. Black, orange, and brown sidebars represent in vitro (KB), in planta 6 hpi, and in planta 48 hpi, respectively. MM, minimal medium; ps, pad4 sid2; rr, rpm1 rps2. See Supplementary Data 13 for the sample size. Different letters indicate statistically significant differences (adjusted p-value < 0.01; two-tailed Student’s t test followed by Benjamini–Hochberg method). c, Growth of Pto (infiltrated at OD600 = 0.005) in Col-0 and pad4 sid2 at 24 and 48 hpi. n = 12 biological replicates from three independent experiments. Different letters indicate statistically significant differences (adjusted p-value < 0.01; two-tailed Student’s t test followed by Benjamini–Hochberg method). a–c, Results are shown as box plots with boxes displaying the 25th–75th percentiles, the centerline indicating the median, whiskers extending to the minimum and maximum values no further than 1.5 inter-quartile range.

Extended Data Fig. 4 Integration of bacterial transcriptome and proteome data.

Comparisons between transcriptome and proteome data in each condition. The Pearson’s correlation coefficients were shown. mRNAs/proteins detected in both the transcriptome and proteome in each condition were used for this analysis. See Supplementary Data 13 for the sample size.

Extended Data Fig. 5 mRNA expression of genes comprising T3SS based on RNA-seq.

Expression of hrcC and hrpZ after TMM normalization was shown. Asterisks indicate statistically significant differences in mutants relative to Col-0 after fitting linear model (adjusted P < 0.01; two-tailed Student’s t test followed by Benjamini–Hochberg method). ps, pad4 sid2. See Supplementary Data 13 for the sample size. Results are shown as box plots with boxes displaying the 25th–75th percentiles, the centerline indicating the median, whiskers extending to the minimum and maximum values no further than 1.5 inter-quartile range.

Extended Data Fig. 6 Co-expression analysis of Pto mRNAs.

a, Correlation matrix of 4,765 Pto mRNAs based on 125 transcriptome datasets in 38 conditions. b, Correlation matrix of Pto mRNAs related to coronatine, alginate, and the type III secretion system (T3SS). a, b, The Pearson’s correlation coefficients were used.

Supplementary information

Reporting Summary

Supplementary Data 1

Full RNA-seq and proteome datasets.

Supplementary Data 2

RNA-seq and proteome data for Fig. 2.

Supplementary Data 3

Full GO list for Fig. 2a.

Supplementary Data 4

Full GO list for Fig. 2b.

Supplementary Data 5

Full GO list for Fig. 2c.

Supplementary Data 6

Full GO list for Fig. 2d.

Supplementary Data 7

Data for Fig. 3.

Supplementary Data 8

Full GO list for Fig. 5.

Supplementary Data 9

Gene expression data used in Fig. 7.

Supplementary Data 10

Genes in clusters used in Fig. 8.

Supplementary Data 11

Correlation matrix for Fig. 8.

Supplementary Data 12

List of primers.

Supplementary Data 13

List of samples.

Supplementary Data 14

Sample cluster list for Fig. 1d–f.

Source data

Source Data Fig. 6

Unprocessed immunoblots.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nobori, T., Wang, Y., Wu, J. et al. Multidimensional gene regulatory landscape of a bacterial pathogen in plants. Nat. Plants 6, 883–896 (2020). https://doi.org/10.1038/s41477-020-0690-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-020-0690-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

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

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