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Multidimensional gene regulatory landscape of a bacterial pathogen in plants

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

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

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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.

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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.

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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.

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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

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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.

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Correspondence to Kenichi Tsuda.

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Peer review information Nature Plants thanks Asaf Levy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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.

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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

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