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Robustness of plant quantitative disease resistance is provided by a decentralized immune network
Monday, 2020/08/03 | 08:37:03

Florent Delplace, Carine Huard-Chauveau, Ullrich Dubiella, Mehdi Khafif, Eva Alvarez, Gautier Langin, Fabrice Roux, Rémi Peyraud, and Dominique Roby

PNAS July 28, 2020 117 (30) 18099-18109


Molecular studies of plant immune responses have mainly focused on qualitative resistance, a form of immunity determined by a few large effect genes. In contrast, very limited information exists about quantitative disease resistance (QDR), although it is extensively observed in wild and crop species. We used systems biology approaches to describe this form of immunity in Arabidopsis thaliana. On the basis of gene regulation studies and search for protein–protein interactions, we report the reconstruction of a highly interconnected and distributed network, organized in five modules with differential robustness to genetic mutations. These studies revealed key functions of QDR, mainly distinct from those previously identified for plant immunity, and shed some light on the complexity of this plant immune response.


Quantitative disease resistance (QDR) represents the predominant form of resistance in natural populations and crops. Surprisingly, very limited information exists on the biomolecular network of the signaling machineries underlying this form of plant immunity. This lack of information may result from its complex and quantitative nature. Here, we used an integrative approach including genomics, network reconstruction, and mutational analysis to identify and validate molecular networks that control QDR in Arabidopsis thaliana in response to the bacterial pathogen Xanthomonas campestris. To tackle this challenge, we first performed a transcriptomic analysis focused on the early stages of infection and using transgenic lines deregulated for the expression of RKS1, a gene underlying a QTL conferring quantitative and broad-spectrum resistance to XcampestrisRKS1-dependent gene expression was shown to involve multiple cellular activities (signaling, transport, and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) responses already characterized in Athaliana. Protein–protein interaction network reconstitution then revealed a highly interconnected and distributed RKS1-dependent network, organized in five gene modules. Finally, knockout mutants for 41 genes belonging to the different functional modules of the network revealed that 76% of the genes and all gene modules participate partially in RKS1-mediated resistance. However, these functional modules exhibit differential robustness to genetic mutations, indicating that, within the decentralized structure of the QDR network, some modules are more resilient than others. In conclusion, our work sheds light on the complexity of QDR and provides comprehensive understanding of a QDR immune network.


See https://www.pnas.org/content/117/30/18099

Figure 5: Evaluation of robustness of the RKS1-dependent network by phenotypic characterization of mutants corresponding to some network components. (A) The RKS1 protein–protein interaction network plotted with Cytoscape showing the components for which mutants have been phenotyped in response to Xcc. Each circle represents the mutant phenotype corresponding to the protein component of the network: red indicates mutants significantly more susceptible than the wild type accession, green more resistant, and white not affected. No mutant was tested for genes represented in gray. (B) Disease index at 7 dpi after inoculation with a bacterial suspension adjusted to 2.108 cfu/mL of mutants corresponding to genes belonging to the different functional groups. Mutants belonging to “Signaling and regulation of cellular process” (blue), “Vesicle-mediated transport” (green), “Protein metabolism (ubiquitination/proteasome)” (orange), and “Small molecule metabolism” (yellow) are identified by numbers (SI Appendix, Table S3 shows correspondence to gene accession numbers). Mutants corresponding to the same gene have been grouped. Means were calculated from 4 to 24 plants (*Kinetic modeling deference with Col-0 time course, 0 = P value > 0.05 and 1 = P value ≤ 0.05). (CE) A signaling subnetwork was extracted from the RKS1 protein–protein interaction network. (C) Subnetwork with protein functional groups (blue, signaling and regulation; yellow, small molecule metabolism; green, small molecule transport; dark green, vesicle-mediated transport; grey, others) and the protein molecular functions. Large circles indicate proteins encoded by genes from the 268 DEGs, and little circles indicate proteins from Y2H or BioGRID candidates. (D) Subnetwork with the protein subcellular localization (dark blue, plasma membrane; pale blue, cytoplasm; pink, nucleus; grey, others). (E) Subnetwork with the insertional mutant phenotypes (red, mutants more susceptible than the wild-type; green, mutants more resistant than the wild-type; white, mutants as the wild-type; grey, not determined).

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