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Robustness of Plant Quantitative Disease Resistance Is Provided by a Decentralized Immune Network

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Robustness of Plant Quantitative Disease Resistance Is Provided by a Decentralized Immune Network Robustness of plant quantitative disease resistance is provided by a decentralized immune network Florent Delplacea,1, Carine Huard-Chauveaua,1, Ullrich Dubiellaa,b, Mehdi Khafifa, Eva Alvareza, Gautier Langina, Fabrice Rouxa, Rémi Peyrauda,c, and Dominique Robya,2 aLaboratoire des Interactions Plantes-Microorganismes, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France; bKWS SAAT SE & Co, 37574 Einbeck, Germany; and ciMean, 31520 Toulouse, France Edited by Paul Schulze-Lefert, Max Planck Institute for Plant Breeding Research, Cologne, Germany, and approved June 15, 2020 (received for review January 3, 2020) Quantitative disease resistance (QDR) represents the predominant maize wall-associated kinase, ZmWAK1, conferring QDR to form of resistance in natural populations and crops. Surprisingly, northern corn leaf blight, and for which the molecular function very limited information exists on the biomolecular network of the has been associated with the biosynthesis of secondary metabolites signaling machineries underlying this form of plant immunity. This (9, 10). In some cases, NLR genes can also confer QDR (1, 11). lack of information may result from its complex and quantitative Thus, the molecular functions underlying QDR seem to be more nature. Here, we used an integrative approach including geno- diverse than those responsible for qualitative resistance. However, mics, network reconstruction, and mutational analysis to identify for these few identified genes, the identity of the downstream Arabidopsis and validate molecular networks that control QDR in components of QDR and the corresponding gene networks re- thaliana Xanthomonas cam- in response to the bacterial pathogen main largely unknown. pestris . To tackle this challenge, we first performed a transcrip- QDR is determined by multiple QTLs of minor to major ef- tomic analysis focused on the early stages of infection and using fects and can be hypothetically defined as a complex network RKS1 transgenic lines deregulated for the expression of , a gene integrating diverse pathways in response to multiple pathogenic underlying a QTL conferring quantitative and broad-spectrum re- determinants (2). As other responses to pathogen attack, QDR sistance to X. campestris. RKS1-dependent gene expression was may require the coordinated and early expression of plant defense shown to involve multiple cellular activities (signaling, transport, mechanisms including several defense-associated metabolites, PLANT BIOLOGY and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern proteins, or genes potentially involved in other plant immunity (PAMP)-triggered immunity (PTI) responses already characterized pathways. While many transcriptomic approaches were used to in A. thaliana. Protein–protein interaction network reconstitution then characterize other forms of immunity (12), to date, comparative – revealed a highly interconnected and distributed RKS1-dependent transcriptome analyses of plant pathogen interactions showing network, organized in five gene modules. Finally, knockout mutants QDR remain scarce. In some cases, differential gene expression for 41 genes belonging to the different functional modules of the was studied in near-isogenic lines (NILs), or susceptible versus network revealed that 76% of the genes and all gene modules par- tolerant lines (13, 14). Transcriptomic analysis of NILs differing ticipate partially in RKS1-mediated resistance. However, these func- tional modules exhibit differential robustness to genetic mutations, Significance indicating that, within the decentralized structure of the QDR net- work, some modules are more resilient than others. In conclusion, Molecular studies of plant immune responses have mainly fo- our work sheds light on the complexity of QDR and provides com- cused on qualitative resistance, a form of immunity determined prehensive understanding of a QDR immune network. by a few large effect genes. In contrast, very limited informa- tion exists about quantitative disease resistance (QDR), al- plant pathogen interactions | immunity | quantitative disease resistance | though it is extensively observed in wild and crop species. We systems biology | regulatory networks used systems biology approaches to describe this form of im- munity in Arabidopsis thaliana. On the basis of gene regulation n nature, plants are continuously exposed to various patho- studies and search for protein–protein interactions, we report Igenic microbes and have evolved multiple layers of immune the reconstruction of a highly interconnected and distributed responses in order to protect themselves. A large body of data network, organized in five modules with differential robust- has been produced to unravel the molecular mechanisms under- ness to genetic mutations. These studies revealed key func- lying qualitative resistance that is determined by a few genes tions of QDR, mainly distinct from those previously identified conferring an almost complete resistance such as nucleotide- for plant immunity, and shed some light on the complexity of binding domain leucine-rich repeat (NLR) receptors. In contrast, this plant immune response. quantitative disease resistance (QDR) remains poorly understood (1–4) despite the fact that it represents the predominant form of Author contributions: R.P. and D.R. designed research; F.D., C.H.-C., U.D., M.K., E.A., and G.L. performed research; F.D., C.H.-C., U.D., M.K., E.A., G.L., F.R., R.P., and D.R. analyzed resistance in natural populations and crops (5). The complexity of data; and F.D., C.H.-C., F.R., R.P., and D.R. wrote the paper. the genetic architecture underlying natural variation of QDR, The authors declare no competing interest. which involves many QTLs with minor to moderate effects, has This article is a PNAS Direct Submission. limited the analysis of the underlying molecular mechanisms. Published under the PNAS license. However, several genes with various functions have been cloned in Data deposition: Raw data of RNA sequencing experiments have been deposited in the recent years (6). For example, an ABC (adenosine triphosphate National Center for Biotechnology Information Sequence Read Archive database [ATP]-binding cassette) transporter encoded by the gene Lr34 was (accession number SRP233656). identified and confers resistance against multiple fungi in wheat 1F.D. and C.H.-C. contributed equally to this work. (7). A caffeoyl-CoA O-methyltransferase associated with lignin 2To whom correspondence may be addressed. Email: [email protected]. production is shown to confer QDR to diverse maize necrotrophic This article contains supporting information online at https://www.pnas.org/lookup/suppl/ diseases (8). Interestingly, kinases have been shown in several doi:10.1073/pnas.2000078117/-/DCSupplemental. cases to play key roles in QDR. One of the best studied cases is the First published July 15, 2020. www.pnas.org/cgi/doi/10.1073/pnas.2000078117 PNAS | July 28, 2020 | vol. 117 | no. 30 | 18099–18109 Downloaded by guest on September 25, 2021 for ZmWAK-RLK1 (10) showed a close association of WAK- and amiRNA lines for RKS1 (RKS1-si/Col-0) were previously mediated QDR with changes in expression of genes involved in characterized and showed a susceptible phenotype (25). Trans- the biosynthesis of benzoxazinoids, thereby revealing a new role genic lines overexpressing RKS1 were generated (RKS1-OE/Col- for this metabolism in immunity to fungal pathogens. This dem- 0; SI Appendix, Fig. S1), and all presented increased resistance onstrates that transcriptome approaches on focused QDR genes to Xcc568, the Xc strain used for the identification of RKS1 can help to understand some components of the complex responses (Fig. 1 A–C and SI Appendix,Fig.S2A). This phenotype was as- associated with this form of resistance. However, one major limi- sociated with an absence of leaf bacterial invasion as shown by tation is that an individual analysis of an identified pathway might using an Xcc568 strain harboring the Photorhabdus luminescens lux lead to the possible underestimation of multiple and coordinated operon (30) and by in planta bacterial growth (Fig. 1B and SI pathways acting together to mount the QDR response. Appendix,Fig.S2B). In this context, genome-wide modular network analysis has For transcriptome analysis, we harvested leaves after inoculation been recently used to decipher such biological complexity and first with the bacterial strain Xcc568 and focused on early responses applied to the plant immune responses ETI (effector-triggered (0, 1.5, 3, and 6 h postinoculation [hpi]). Three independent immunity) and PTI (PAMP-triggered immunity) (12, 15–17). experiments were performed, and RNA was sequenced with an Comparison at different scales indicated a densely connected average of 20.6 million reads per sample. The major driver of network shared between ETI and PTI (12). Important interactions differential gene expression was the time (hpi/time 0, accounting between these two immune responses were identified, one sig- for 78.3% of the total variance; SI Appendix, Fig. S3). The line/ naling subnetwork mediating the hypersensitive response being time effect accounted for 13.7% of total variance and revealed inhibited by PTI signaling (18). QDR, considered as the result of an that significant differences among lines were mainly observed at interplay between multiple pathogen
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