# Comptes Rendus
Biologies

Carine Chauveau and Dominique Roby

**Molecular complexity of quantitative immunity in plants: from QTL mapping to
functional and systems biology**

Volume 347 (2024), p. 35-44

Online since: 21 May 2024

https://doi.org/10.5802/crbiol.153

<figure><img src="image_1.png" /></figure>

This article is licensed under the

CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL LICENSE.

http://creativecommons.org/licenses/by/4.0/
Review article

# Molecular complexity of quantitative immunity in plants: from QTL mapping to functional and systems biology

Carine Chauveau<sup>a</sup> and Dominique Roby<sup>®,*,a</sup>

<sup>a</sup> Laboratoire des Interactions Plantes-Microbes-Environnement (LIPME), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France

E-mails: carine.chauveau@inrae.fr (C. Chauveau), dominique.roby.descazal@gmail.com (D. Roby)

**Abstract.** In nature, plants defend themselves against pathogen attack by activating an arsenal of defense mechanisms. During the last decades, work mainly focused on the understanding of qualitative disease resistance mediated by a few genes conferring an almost complete resistance, while quantitative disease resistance (QDR) remains poorly understood despite the fact that it represents the predominant and more durable form of resistance in natural populations and crops. Here, we review our past and present work on the dissection of the complex mechanisms underlying QDR in *Arabidopsis thaliana*. The strategies, main steps and challenges of our studies related to one atypical QDR gene, *RKS1* (Resistance related KinaSe 1), are presented. First, from genetic analyses by QTL (Quantitative Trait Locus) mapping and GWAs (Genome Wide Association studies), the identification, cloning and functional analysis of this gene have been used as a starting point for the exploration of the multiple and coordinated pathways acting together to mount the QDR response dependent on RKS1. Identification of RKS1 protein interactors and complexes was a first step, systems biology and reconstruction of protein networks were then used to decipher the molecular roadmap to the immune responses controlled by RKS1. Finally, exploration of the potential impact of key components of the RKS1-dependent gene network on leaf microbiota offers interesting and challenging perspectives to decipher how the plant immune systems interact with the microbial communities' systems.

**Keywords.** Plant–pathogen interactions, Immunity, Quantitative disease resistance (QDR), Protein–protein interactions, Networks, Signaling, Microbiota.

**Note.** Dominique Roby is the recipient of the price JAFFE – Fondation de l'Institut de France in Integrative Biology 2023.

Manuscript received 16 February 2024, revised 20 March 2024, accepted 28 March 2024.

## 1. Introduction

In nature, plants must defend themselves against various pathogenic microbes and diverse macroorganisms, that employ different virulence strategies. The molecular mechanisms which underlie disease resistance in plants, have been intensively studied during the last decades. The plant immune

system involves cell-surface and intracellular receptors that perceive pathogen-derived molecules or effectors, and activate various signaling cascades leading to downstream immune cellular responses. A significant body of research in this field has focused on the understanding of qualitative disease resistance mediated by a few genes conferring an almost complete resistance such as Nucleotide binding domain Leucine-Rich repeat (NLR) receptors (predominantly intracellular) or Pattern-Recognition

<footnote>* Corresponding author.</footnote>
Receptors (PRRs) (mainly extracellular). In contrast, quantitative disease resistance (QDR) remains poorly understood [1], despite the fact that it represents the predominant form of resistance in natural populations and crops, much more prevalent than *R* gene qualitative resistance [2, 3]. Quantitative disease resistance is characterized by a continuous distribution of host plant phenotypes in response to pathogen attack, the host exhibiting reduced disease symptoms but uncomplete resistance. In addition, this form of immunity has usually been shown to be much more durable (effective for a long time and in a large area where conditions are generally favorable to the disease) than qualitative, major-gene dependent resistance [4]. The complexity of the genetic architecture underlying natural variation of QDR, which involves many genes with moderate effects [2, 3, 5-7], explains this property but has limited the exploration of the underlying molecular mechanisms. Many QDR loci have been mapped over the past decades, but only a few genes have been cloned in recent years [1, 3, 6, 8]. QDR genes have different functions, some of them have been previously associated with plant disease resistance, such as pathogen perception and signal transduction. However, QDR genes are associated with other processes including metabolite biosynthesis and transport, membrane trafficking, ubiquitin related mechanisms, or other unknown functions [1]. Interestingly, even when QDR genes are associated with previously known disease mechanisms, the genes identified are rarely those previously identified in the context of qualitative resistance. This suggests that disease resistance in plants is not reduced to pathogen perception and signaling mechanisms as previously suggested for qualitative resistance, but relies on many other molecular mechanisms which constitute diverse plant strategies to reduce disease severity. In contrast with several recent reviews focused on the genetic and molecular QDR mechanisms identified over the last two decades [1, 3, 5, 6, 9], in this review, we will present the strategies, main steps and challenges of our studies more specifically related to one atypical QDR gene, *RKS1* (Resistance related KinaSe 1) [10]. This review will cover cloning and functional analysis of this gene as a starting point, the exploration of the multiple and coordinated pathways acting together to mount the QDR response dependent on RKS1, and finally future prospects about a potential impact of RKS1

gene network on systems of microbial communities (Figure 1).

## 2. Quantitative trait locus (QTL) and genome wide association studies (GWAS) to unravel the genetic and molecular bases of QDR: identification of *RKS1*, a gene underlying a major QTL of resistance to *Xanthomonas campestris* pv. *campestris*, encodes an atypical kinase

A large number of studies have been reported in the past, including QTL mapping experiments, but led only recently to the identification and functional analysis of the genes underlying these loci. Here, QTL analysis together with genome wide association experiments were combined to unravel the immune response of the model plant *Arabidopsis thaliana* (*A. thaliana*) to the bacterial pathogen *Xanthomonas campestris* pv. *campestris* (*Xcc*). This bacterium, which causes the black rot disease, possibly the most important disease of crucifers, is one of the most prevalent bacterial pathogens in natural populations of *A. thaliana* [11–13]. It colonizes the vascular system of plants. Previous work on this interaction revealed that different sources of resistance and tolerance to *Xcc* exist in Arabidopsis [14–17], but no information was available about the genes involved in this resistance or tolerance. In addition, interactions between plants and vascular pathogens were largely overlooked at that time, due to the difficulties of reproducing natural pathogen invasion conditions in reproducible and quantitative pathological tests [18].

To identify the genetic bases of the plant immune response to this pathogen, we subjected natural accessions of *A. thaliana* to infection by the *Xcc* strain 568 (*Xcc568*) and observed substantial genetic variation for resistance to this strain in a core collection of natural accessions of *A. thaliana* [10]. Then, we used two complementary approaches: (i) a QTL (Quantitative Trait Locus) analysis using a F6 Recombinant Inbred Line (RIL) population of 115 lines derived from Columbia (Col-5) and Kashmir-1 (Kas-1) accessions, which exhibit contrasting phenotypes to *Xcc*; (ii) a GWA (Genome Wide Association) mapping of a population in *A. thaliana* containing 381 natural accessions including both worldwide accessions and French accessions. While QTL analysis revealed
<figure><img src="image_2.png" /></figure>

**Figure 1.** A schematic view of the strategies, main steps and challenges of the studies related to the atypical Quantitative Disease Resistance gene, *RKS1* (Resistance related KinaSe 1). Genetic approaches including QTL analysis and GWAs combined to molecular methodologies allowed to identify *RKS1* as the gene underlying a major QTL of resistance in *Arabidopsis thaliana* in response to *Xanthomonas campestris* pv. *campestris*, a vascular bacterial pathogen. *RKS1* gene cloning was used as a starting point for functional analysis of this gene and exploration of the downstream multiple and coordinated pathways acting together to mount the QDR response dependent on RKS1. A challenge is now to explore the potential impact of RKS1-dependent QDR components on the leaf microbiota composition, diversity and structure.

one major QTL on chromosome 3, *QRX3* (Quantitative Resistance to Xcc568, explaining 50 to 60% of

the phenotypic variance) and four minor QTLs, GWA mapping revealed a unique large peak of association
on chromosome 3 that overlaps with QRX3. Map-
based cloning together with mutant analysis led to
the identification of two genes encoding putative
protein kinases that belong to a cluster of putative
kinases. Complementation tests performed with ge-
nomic fragments on the only mutant showing altered
resistance, allowed to identify the Arabidopsis RKS1
(Resistance KinaSe 1) gene. Finally, gene silencing
using artificial microRNA confirmed that RKS1 was
responsible for quantitative resistance to Xcc. RKS1
encodes an atypical kinase since it lacks some critical
domains in the kinase catalytic core that are essen-
tial for catalysis, and no kinase activity could be de-
tected until now. Only the HRD (His-Arg-Asp) motif,
including the catalytic Asp residue that functions as
a base acceptor to achieve proton transfer, is present
in RKS1 [10]. Interestingly, atypical kinases (or pseu-
dokinases) are known to be signaling components
of gene networks [7], and act as allosteric modula-
tors, scaffolds for assembly of protein complexes or
modulation of signaling pathways [19,20]. For exam-
ple, the atypical kinase ILK1 promotes disease resis-
tance in Arabidopsis by interacting with cation trans-
porters [21]. Moreover, the major locus revealed by
GWA study corresponded to an allelic series at RKS1
at the species level. Interestingly, a negative relation-
ship between disease index and RKS1 transcription
level was found in different transgenic lines and nat-
ural accessions. Regulation of RKS1 expression might
be a major component of QDR to Xcc568 [10]. RKS1
is involved in the restriction of bacterial spread from
the infection site, without visible cell death pheno-
type, a resistance mechanism particularly adapted
to counteract the infection strategy of the vascu-
lar bacterial pathogen Xanthomonas. Finally, RKS1
confers resistance to different races and all patho-
vars of Xanthomonas campestris, similarly to other
broad-spectrum QDR genes [10]. Considering these
findings, RKS1 represents a starting point for explo-
ration of the signaling pathways leading to QDR to
Xcc in A. thaliana. More recently, RKS1 has also been
shown to be a component of an NLR resistosome
(here the ZAR::RKS1:PBL2 activated complex which
presents different three-dimensional structures) [22,
23]. These results suggest that RKS1 might be part
of a signaling platform in response to recognition of
various pathogen determinants and a key element
to coordinate gene expression of the plant immune
system.

3. What molecular roadmap to the immune re-
sponses controlled by RKS1? Identification
of RKS1 protein interactors and complexes
as a first step

During the last two decades, the immune system
has been viewed as constituted of two layers acti-
vated through two types of receptors, as mentioned
above (see Section 1). However, this rather simplistic
view of immune mechanisms, mainly focused on
pathogen perception mechanisms, revealed to be
rather limited in view of the complexity of mecha-
nisms recently found [3, 1]. However, even if recent
reports led to mechanistic insight into the quant-
titative resistance loci by finding the causal genes,
only a few studies, focused on canonical immune
components, reported more in depth understanding
of QDR mechanisms [24–26]. Studies reported until
recently are mainly limited to the identification of
the QDR gene of interest and its implication in plant
resistance.

A way to explore the QDR mechanisms, espe-
cially for the atypical functions found for some QDR
genes, is to combine diverse molecular and genomic
approaches. In the case of RKS1, a search for pro-
tein interactors is in line with a potential partici-
pation to protein complexes for modulation of im-
mune signaling pathways, as suggested for pseu-
dokinases [19, 20]. Using a Yeast two hybrid screen
and a mutated version of RKS1 (RKS1D191A) as a
bait against a cDNA library generated from leaves
inoculated by Xcc [27], we identified 43 candidate
proteins [28], including 21 proteins related to meta-
bolic functions and 6 signaling components (e.g.,
EFR, KIN11, SGT1a, MKP1, RANBP1 and NLP7) [28].
Among them a fragment of the kinase domain of
MIK2 (MDIS1-Interacting receptor-like Kinase 2) as a
putative interactor of RKS1 [29]. MIK2 is an LRR-RLK
(leucine rich repeat receptor-like kinase) which is
involved in the perception of the peptide LURE1, a
chemotactic attractant of the pollen tube, by hete-
rodimerizing with MDIS1 and MDIS1-Interacting
Receptor-like Kinase 1 [30]. It is also involved in
the perception of the peptide SCOOP12, a phytocy-
tokine, through heterodimerizing with BAK1 [31]. By
co-localization, Bimolecular Fluorescence Comple-
mentation (BiFC) and co-immunoprecipitation ex-
periments, we demonstrated a physical interaction
between RKS1 and the kinase domain of MIK2 in
the plasma membrane, one of the subcellular compartments where RKS1 has been localized (with the nucleus and cytoplasmic tracks). We also showed that *mik2* mutants exhibit increased susceptibility to *Xcc* as compared to the wild type and that MIK2 catalytic activity is required for QDR to *Xcc*. Finally, we generated a double mutant *mik2-1/rks1-1* which showed a similar phenotype in response to *Xcc* as the single mutants, suggesting that both genes might belong to the same pathway [29].

In plants, receptor kinases (RKs) play an essential role in environment perception, including pathogen detection at early stages of infection and through detection of different pathogen epitopes. For example, the RK Flagellin Sensitive 2 (FLS2) is implicated in the detection of flg22, a conserved peptide of bacteria flagellin [32, 33]. The EF-Tu receptor (EFR) is involved in the perception of the peptide elf18, the N-terminal peptide of EF-Tu [34, 35]. However, the number of perception systems with specificity for pathogen determinants is difficult to estimate, and it is highly probable that only a limited number of RKs have been reported yet [36]. Recently, a complex and dynamic network of LRR-RKs interactions has been described in *A. thaliana* [37]. The association of LRR-RKs in heterodimers might participate to a large detection of diverse epitopes involved in various plant processes. Activation of immune responses requires assembly of receptors (RLKs), co-receptors, or signaling proteins such as kinases or pseudokinases which reside in different regulatory scaffolds [38-42]. These events take place first in plasma membrane protein complexes. Taking these recent findings into account, we investigated whether the two proteins MIK2 and RKS1 might require other perception/signaling proteins and do not act isolated to orchestrate QDR. To test this hypothesis, proteins experimentally demonstrated to physically interact with MIK2 were first identified from the literature: a MIK2-centralized network was reconstructed including 25 RLKs, BSK3 and RKS1. These RLKs have been described in literature to be involved in either plant immunity or plant development. Then, in order to evaluate the implication of some of the MIK2-RKS1 network components in QDR to *Xcc*, 17 mutants (corresponding to 16 genes) were collected and phenotyped for their response to *Xcc568*. The results indicate that at least seven RLKs, highly connected to MIK2 and/or RKS1, have a partial effect in QDR to

*Xcc568* [29]. These findings reveal the complexity of the molecular mechanisms involved in MIK2/RKS1-dependent signaling pathways, in good agreement with the genetic and molecular complexity of QDR. They also suggest that the RKS1/MIK2 complex, in relation with a number of RLKs, might participate to a perception system and act as a scaffold hub to coordinate assembly of multiple perception complexes.

**4. What molecular roadmap to the immune responses controlled by RKS1? Systems biology and reconstruction of protein networks**

While the work described above constitutes a strong basis for deciphering molecular mechanisms involved in the perception systems related to QDR, in the case of RKS1, its localization in multiple subcellular compartments including the nucleus, suggests different functions. These functions include a contribution not only to pathogen perception events, but also to signaling pathways (potentially in the cytoplasm) and transcriptional activation of immunity related genes (in the nucleus). Besides, the complexity of the genetic architecture underlying natural variation of QDR, which involves several QTLs with minor to moderate effects, suggests more complex molecular mechanisms. These mechanisms predominantly include downstream defense mechanisms [3], and not only perception events. In this context, one way to understand the components of the complex responses associated with this form of resistance, is a transcriptomic approach on QDR genes. Such an analysis focused on a QDR gene-dependent pathway might lead to underestimation of multiple and coordinated pathways acting together to mount the QDR response. However, combined with a genome-wide modular network analysis, it should help to decipher QDR molecular complexity, as shown for more characterized plant immune responses [43]. Thus, plant immunity should rather be seen as a distributed and highly connected molecular network which includes different functions to fine-tune plant defense expression in response to pathogens [28]. This is a relatively new emerging concept, in rupture with previous studies mainly focused on qualitative resistance, a simple form of immunity, and with reductionist points of view.
Thus, in response to extracellular signals, plants deploy cell surface receptor networks to mount carefully balanced responses [37, 44–46]. In the same line, signaling networks have been described, allowing plant cells to modulate defense responses according to the perceived signals/organisms, and to avoid any conflicting responses. This balance is notably enabled by hormonal pathways and crosstalks [47, 48]. Finally, the plant immune response, after perception and signaling events, is governed by transcriptional reprogramming during pathogen attack. In this case, the understanding of this reprogramming at the systems level is still under development, for example through co-expression and gene regulatory network modeling [49]. In summary, the immune system, from perception to defense expression, is organized through a complex interplay of different functions and includes multiple interacting components organized in regulatory networks. Study of network properties (referred to as “emergent properties”) of these complex biological networks is also of prime importance, as it allows to predict key and novel immune functions. One of these essential properties is robustness (the capacity of a system to maintain its functions against internal and external perturbations), which is particularly of interest in the case of the immune system [50]. Another property is tunability, also important for the efficiency of the immune system, submitted to attack by various bioagressors (biotrophs, necrotrophs, microorganisms, nematodes, insects, etc.) and to diverse environmental conditions. Finally, dynamics of the immune system network is crucial, but its study is still very limited. These approaches have proven to be fruitful for elucidation of components of the mammalian immune system, through topology-driven network analysis [51] in combination with mutant (RNAi, CRISPR) screening [52]. Such strategies remain scarce in plants and have never been applied to the dissection of QDR mechanisms.

In the case of RKS1, we used a combination of omics, network reconstruction, and mutational approaches, and showed that the RKS1-dependent gene network was constituted of multiple regulatory modules controlling QDR to *Xcc* in *A. thaliana*, and exhibiting differential robustness [28]. First, transcriptomic analysis was performed by RNA sequencing, using RKS1 deregulated lines expressing contrasted phenotypes in response to *Xanthomonas*.

To identify differentially expressed genes (DEGs) specifically associated with RKS1, we focused on genes expressed early (6 h post inoculation), up- or down-regulated in the RKS1-OE (overexpressor) line and in the opposite way in the RKS1-si (silenced) line and the *rks1-1* mutant. A total of 268 DEGs were identified, whose functions belong to three main cellular activities: cellular responses, transport, and metabolism. Interestingly, a very low proportion (less than 15%) of these genes were found in common with previously identified immunity-associated genes (ETI and/or PTI), suggesting that RKS1 controls different immune pathways, distinct from those already described. Then, to decipher the signaling pathways downstream from RKS1, we reconstructed a model of protein-protein interaction (PPI) network by looking for known interactors of the proteins encoded by the 268 DEGs and by including the RKS1 putative interactors previously identified. The final PPI network is a highly interconnected (1330 nodes (proteins) involved in 1876 interactions) and distributed RKS1-dependent network [28]. The network integrates 49% of the DEGs and is organized in five gene modules of different biological functions: vesicle-mediated and small molecule transport, signaling and regulation of cellular process and protein and small molecule metabolism. In order to validate the complex and distributed nature of the RKS1-dependent QDR network, we tested seventy-one T-DNA mutants (corresponding to 41 genes) affecting the different modules. And 76% of the genes and all gene modules were shown to participate partially in RKS1-mediated resistance. Interestingly, the 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, and suggesting a potential compensation between genes belonging to these different modules. We then focused on a subnetwork including RKS1 and comprising essential signaling and regulatory proteins, located in the plasma membrane, nucleus and cytoplasm. The validation of the function in QDR of a large part of the genes involved in this subnetwork, and of protein-protein interactions, is currently underway. In addition, the dynamics of such a complex immune response together with the importance of the subcellular location of RKS1, are currently under study by using system-level approaches combined with
transcriptomics and molecular approaches. This
should undoubtedly help to decipher the defense
regulators and their precise contribution to QDR in
response to Xanthomonas. Finally, in the context
of climate change, the adaptation of the plant im-
mune system to environmental conditions repre-
sents a major issue, which might be resolved using
systems biology approach. However, at this stage, the
studies conducted on this question remain mainly
descriptive.

5. Towards a more ecological view: what about a potential impact of RKS1 gene network on systems of microbial communities?

Beyond the exploration of the networks underlying
the immune pathways, an important question, even
more complex, is to explore the interplay between
the plant and the pathogen, and not only the plant
partnership. Combining host and pathogen tran-
scriptomic analyses in a dual-transcriptomic net-
work would allow to identify potential interactions
between the components of plant host innate im-
mune and pathogen virulence systems. This has
been tempted in some cases [53–55], but co-network
modeling is still a challenging approach. In addition,
pathogens represent only a part of the plant micro-
biome. So, a major challenge now would be to ex-
plore the potential impact of key components of the
RKS1-dependent network on the leaf microbiota, as a
first step towards a more ambitious challenge to de-
cipher how the plant immune systems interact with
the microbial communities’ systems.

Although host-microbial communities (micro-
biota) are more and more documented, most of the
studies focused on their composition and potential
use for biological control using growth-promoting
microbial associations [11, 56, 57]. Plant-associated
microbes can indeed improve access to nutrients, ac-
tivate or prime the immune system, and/or compete
with pathogens. Despite this potential agronomical
interest, the influence of plant genetic and molec-
ular factors on the composition and functioning of
microbial communities remains poorly understood.
However, there are some reports on the influence of
Arabidopsis genetic traits on microbial community
composition and abundance, through plant mutant
analysis (mutations affecting diverse plant func-
tions such as primary and secondary metabolism,

symbiosis, immunity, or development), and plant
genetic variation through GWAs notably [58]. More
recently, Brachi et al. [59] determined how the host
genotype affects different microbial community
members (microbial hubs) and thus shapes the over-
all microbiome, and that these effects contribute to
host fitness. Surprisingly, the potential effect of QDR
genes, which underlie the most common and more
durable form of disease resistance in crops and natu-
ral populations [6, 60], has not been tested yet. By us-
ing the RKS1 deregulated lines described in our work,
and the knowledge accumulated on dissecting the
immune network and its emergent properties, such
an approach is now underway in our lab (Figure 1).
Such studies, realized in ecologically realistic con-
texts, might lead to a better understanding of the host
control on the microbiota and the identification of
microbial targets of RKS1. These microbes might be
direct or indirect targets, particular microbes being
pathogens, other being known to improve growth or
resistance to pathogens through direct or indirect ef-
fects [11, 61, 62]. In conclusion, there is an important
challenge through these studies not only for know-
ledge acquisition, but also for improvement of plant
health for agricultural and conservation purposes
(including harnessing the microbiome towards the
improvement of plant health for example) [58, 60].

Declaration of interests

The authors do not work for, advise, own shares in, or
receive funds from any organization that could bene-
fit from this article, and have declared no affiliations
other than their research organizations.

Acknowledgements

This article reports works which have been per-
formed during the last years by many young and
less young researchers, cited along the text. Here
I thank them for their respective contributions and
their trust. I would like also to thank M. Delseny for
the invitation to write this review and for his encour-
agements by the past. In addition, this article is dedi-
cated to Sarah Ranty-Roby who is starting research in
the field of plant–pathogen interactions, with all my
best wishes.
## References

[1] M. Gou, P. Balint-Kurti, M. Xu, Q. Yang, "Quantitative disease resistance: multifaceted players in plant defense", *J. Integr. Plant Biol.* **65** (2023), p. 594-610.

[2] J. A. Poland, P. J. Balint-Kurti, R. J. Wisser, R. C. Pratt, R. J. Nelson, "Shades of gray: the world of quantitative disease resistance", *Trends Plant Sci.* **14** (2008), p. 1360-1385.

[3] J. A. Corwin, D. J. Kliebenstein, "Quantitative resistance: more than just perception of a pathogen", *Plant Cell* **29** (2017), p. 655-665.

[4] C. Cowger, J. K. M. Brown, "Durability of quantitative resistance in crops: greater than we know?", *Ann. Rev. Phytopathol.* **57** (2019), p. 253-277.

[5] R. E. Niks, X. Qi, T. Marcel, "Quantitative resistance to biotrophic filamentous plant pathogens: concepts, misconceptions, and mechanisms", *Annu. Rev. Phytopathol.* **53** (2015), p. 445-470.

[6] F. Roux, D. Voisin, T. Badet, C. Balagué, X. Barlet, C. Huard-Chauveau, D. Roby, S. Raffaele, "Resistance to phytopathogens *e tutti quanti*: placing plant quantitative disease resistance on the map", *Mol. Plant Pathol.* **15** (2014), p. 427-432.

[7] D. A. St Clair, "Quantitative resistance and quantitative resistance loci in breeding", *Annu. Rev. Phytopathol.* **48** (2010), p. 247-268.

[8] E. French, B. S. Kim, A. S. Iyer-Pascuzzi, "Mechanisms of quantitative disease resistance in plants", *Semin. Cell Dev. Biol.* **6** (2016), p. 201-208.

[9] R. Nelson, T. Wiesner-Hanks, R. Wisser, P. Balint-Kurti, "Navigating complexity to breed disease-resistant crops", *Nat. Rev. Genet.* **19** (2018), p. 21-33.

[10] C. Huard-Chauveau, L. Perchepied, M. Debieu, S. Rivas, T. Kroj, I. Kars, J. Bergelson, F. Roux, D. Roby, "An atypical kinase under balancing selection confers broad spectrum disease resistance in Arabidopsis", *PLoS Genet.* **9** (2013), article no. e1003766.

[11] C. Bartoli, L. Frachon, M. Barret, M. Rigal, C. Huard-Chauveau, B. Mayjonade, C. Zanchetta, O. Boucher, D. Roby, S. Carrère, F. Roux, "*In situ* relationships between microbiota and potential pathobiota in *Arabidopsis thaliana*", *ISME J.* **12** (2018), no. 8, p. 2024-2038.

[12] J. M. Kniskern, M. B. Traw, J. Bergelson, "Salicylic acid and jasmonic acid signaling defense pathways reduce natural bacterial diversity on *Arabidopsis thaliana*", *Mol. Plant-Microbe Interact.* **20** (2007), p. 1512-1522.

[13] P. H. Williams, "Black rot: a continuing threat to world crucifers", *Plant Disease* **64** (1980), p. 736-742.

[14] C. R. Buell, S. C. Somerville, "Use of Arabidopsis recombinant inbred lines reveals a monogenic and a novel genic resistance mechanism to *Xanthomonas campestris* pv *campestris*", *Plant J.* **12** (1997), p. 21-29.

[15] F. Godard, M. Lummerzheim, P. Saindrenan, C. Balagué, D. Roby, "hxc2, an Arabidopsis mutant with an altered hypersensitive response to *Xanthomonas campestris* pv. *campestris*", *Plant J.* **24** (2000), p. 749-761.

[16] M. Lummerzheim, D. de Oliveira, C. Castresana, F. C. Miguens, E. Louzada, D. Roby, M. Van Montagu, B. Timmerman, "Identification of compatible and incompatible interactions between *Arabidopsis thaliana* and *Xanthomonas campestris* pv. *campestris* and characterization of the hyper-sensitive response", *Mol. Plant-Microbe Interact.* **6** (1993), p. 532-544.

[17] J. Tsuji, S. C. Somerville, R. Hammerschmidt, "Identification of a gene in *Arabidopsis thaliana* that controls resistance to *Xanthomonas campestris* pv. *campestris*, Physiol. Mol. Plant Pathol. **38** (1991), p. 57-65.

[18] D. Meyer, E. Lauber, D. Roby, M. Arlat, T. Kroj, "Optimization of pathogenicity assays to study the *Arabidopsis thaliana*-*Xanthomonas campestris* pv. *campestris* pathosystem", *Mol. Plant Pathol.* **6** (2005), p. 327-333.

[19] M. Boudsocq, M. R. Willmann, M. McCormack, H. Lee, L. Shan, P. He, J. Bush, S.-H. Cheng, J. Sheen, "Differential innate immune signalling via Ca(2+) sensor protein kinases", *Nature* **464** (2010), p. 418-422.

[20] P. D. Mace, J. M. Murphy, "There's more to death than life: noncatalytic functions in kinase and pseudokinase signaling", *J. Biol. Chem.* **296** (2021), article no. 100705.

[21] E. K. Brauer, N. Ahsan, G. V. Popescu, J. J. Thelen, S. C. Popescu, "Back from the dead: the atypical kinase activity of a pseudokinase regulator of cation fluxes during inducible immunity", *Front Plant Sci.* **13** (2022), article no. 931324.

[22] G. Wang, B. Roux, F. Feng, E. Guy, L. Li, N. Li, X. Zhang, M. Lau-tier, M. F. Jardinaud, M. Chabannes, M. Arlat, S. Chen, C. He, L. Noël, J.-M. Zhou, "The decoy substrate of a pathogen effec-tor and a pseudokinase specify pathogen-induced modified-self recognition and immunity in plants", *Cell Host Microbe* **18** (2015), p. 285-295.

[23] H. Burdett, A. R. Bentham, S. J. Williams, P. N. Dodds, P. A. Anderson, M. J. Banfield, B. Kobe, "The plant resistosome: structural insights into immune signaling", *Cell Host Microbe* **26** (2019), p. 193-201.

[24] J. P. Deppe, R. Rabbat, S. Hörtensteiner, B. Keller, E. Martinoia, R. L. Lopéz-Marqués, "The wheat ABC transporter Lr34 modifies the lipid environment at the plasma membrane", *J. Biol. Chem.* **293** (2018), p. 18667-18679.

[25] G. Li, J. Zhou, H. Jia, Z. Gao, M. Fan, Y. Luo, P. Zhao, S. Xue, N. Li, Y. Yuan, S. Ma, Z. Kong, L. Jia, X. An, G. Jiang, W. Liu, W. Cao, R. Zhang, J. Fan, X. Xu, Y. Liu, Q. Kong, S. Zheng, Y. Wang, B. Qin, S. Cao, Y. Ding, J. Shi, H. Yan, X. Wang, C. Ran, Z. Ma, "Mutation of a histidine-rich calcium-binding-protein gene in wheat confers resistance to Fusarium head blight", *Nat. Genet.* **51** (2019), p. 1106-1112.

[26] F. Wang, X. Li, Y. Li, J. Han, Y. Chen, J. Zeng, M. Su, J. Zhuo, H. Ren, H. Liu, L. Hou, Y. Fan, X. Yan, S. Song, J. Zhao, D. Jin, M. Zhang, Y. Pei, "Arabidopsis P4 ATPase-mediated cell detoxification confers resistance to *Fusarium graminearum* and *Verticillium Dahliae*, Nat. Commun. **12** (2021), article no. 6426.

[27] S. Froidure, J. Canonne, X. Daniel, A. Jauneau, C. Brière, D. Roby, S. Rivas, "AtsPLA2-alpha nuclear relocalization by the Arabidopsis transcription factor AtMYB30 leads to repression of the plant defense response", *Proc. Natl. Acad. Sci. USA* **107** (2010), p. 15281-15286.

[28] F. Delplace, C. Huard-Chauveau, U. Dubiella, M. Khafif, E. Alvarez, G. Langin, F. Roux, R. Peyraud, D. Roby, "Robustness of plant quantitative disease resistance is provided by a de-
centralized immune network", Proc. Natl. Acad. Sci. USA **117**
(2020), p. 18099-18109.

[29] F. Delplace, C. Huard-Chauveau, F. Roux, D. Roby, "The Arabidopsis leucine rich repeat receptor-like kinase MIK2 interacts with RKS1 and participates to the control of quantitative disease resistance to the bacterial pathogen Xanthomonas campestris", 2024, bioRxiv 577741v1. https://doi.org/10.1101/2024.01.29.577741.

[30] T. Wang, L. Liang, Y. Xue, P. F. Jia, W. Chen, M. X. Zhang, Y. C. Wang, H. J. Li, W. C. Yang, "A receptor heteromer mediates the male perception of female attractants in plants", *Nature* **531** (2016), p. 241-244.

[31] J. Rhodes, H. Yang, S. Moussu, F. Boutrot, J. Santiago, C. Zipfel, "Perception of a divergent family of phytocytokines by the Arabidopsis receptor kinase MIK2", *Nat. Commun.* **12** (2021), article no. 705.

[32] Z. Bauer, L. Gómez-Gómez, T. Boller, G. Felix, "Sensitivity of different ecotypes and mutants of *Arabidopsis thaliana* toward the bacterial elicitor Flagellin correlates with the presence of receptor-binding sites", *J. Biol. Chem.* **276** (2001), p. 45669-45676.

[33] L. Gómez-Gómez, T. Boller, "FLS2: An LRR receptor-like kinase involved in the perception of the bacterial elicitor flagellin in Arabidopsis", *Mol. Cell* **5** (2000), p. 1003-1011.

[34] G. Kunze, C. Zipfel, S. Robatzek, K. Niehaus, T. Boller, G. Felix, "The N terminus of bacterial elongation factor Tu elicits innate immunity in Arabidopsis plants", *Plant Cell* **16** (2004), p. 3496-3507.

[35] C. Zipfel, G. Kunze, D. Chinchilla, A. Caniard, J. D. G. Jones, T. Boller, G. Felix, "Perception of the Bacterial PAMP EF-Tu by the Receptor EFR Restricts Agrobacterium-Mediated Transformation", *Cell* **125** (2006), p. 749-760.

[36] D. Tang, G. Wang, J. M. Zhou, "Receptor kinases in plant-pathogen interactions: more than pattern recognition", *Plant Cell* **29** (2017), p. 618-637.

[37] E. Smakowska-Luzan, G. A. Mott, K. Parys, M. Stegmann, T. C. Howton, M. Layeghifard, J. Neuhold, A. Lehner, J. Kong, K. Grünwald, N. Weinberger, S. B. Satbhai, D. Mayer, W. Busch, M. Madalinski, P. Stolt-Bergner, N. J. Provart, M. Shahid Mukhtar, C. Zipfel, D. Desveaux, D. S. Guttman, Y. Belkhadi, "An extracellular network of Arabidopsis leucine-rich repeat receptor kinases", *Nature* **553** (2018), p. 342-346.

[38] H. Adachi, K. Tsuda, "Convergence of cell-surface and intracellular immune receptor signalling", *New Phytol.* **221** (2019), p. 1676-1678.

[39] I. Albert, H. Chenlei, T. Nürnberger, R. N. Pruitt, L. Zhang, "Surface sensor systems in plant immunity", *Plant Physiol.* **182** (2020), p. 1582-1596.

[40] V. Bonardi, J. Dangl, "How complex are intracellular immune receptor signaling complexes?", *Front Plant Sci.* **3** (2012), article no. 237.

[41] J. Boudeau, D. Miranda-Saavedra, G. J. Barton, D. R. Alessi, "Emerging roles of pseudokinases", *Trends Cell Biol.* **16** (2006), p. 443-452.

[42] N. Holton, V. Nekrasov, P. C. Ronald, C. Zipfel, "The phylogenetically-related pattern recognition receptors EFR and XA21 recruit similar immune signaling components in monocots and dicots", *PLoS Pathog.* **11** (2015), no. 1, article no. e1004602.

[43] X. Dong, Z. Jiang, Y. L. Peng, Z. Zhang, "Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis", *Plant Physiol.* **167** (2015), p. 1186-1203.

[44] O. J. B. Brustolini, J. C. F. Silva, T. Sakamoto, E. P. B. Fontes, "Bioinformatics analysis of the receptor-like kinase (RLK) superfamily", *Methods Mol. Biol.* **1578** (2017), p. 123-132.

[45] J. Wu, A. M. van der Burgh, G. Bi, L. Zhang, J. R. Alfano, G. B. Martin, M. H. A. J. Joosten, "The bacterial effector AvrPto targets the regulatory coreceptor SOBIR1 and suppresses defense signaling mediated by the receptor-like protein Cf-4", *Mol. Plant-Microbe Interact.* **31** (2018), p. 75-85.

[46] T. Wróblewski, L. Spiridon, E. C. Martin, A. J. Petrescu, K. Canvanaugh, M. José Truco, H. Xu, D. Gozdowski, K. Pawłowski, R. W. Michelmore, F. L. W. Takken, "Genome-wide functional analyses of plant coiled-coil NLR-type pathogen receptors reveal essential roles of their N-terminal domain in oligomerization, networking, and immunity", *PLoS Biol.* **16** (2018), article no. e2005821.

[47] T. Nobori, K. Tsuda, "The plant immune system in heterogeneous environments", *Curr. Opin. Plant Biol.* **50** (2019), p. 58-66.

[48] K. Tsuda, F. Katagiri, "Comparing signaling mechanisms engaged in pattern-triggered and effector-triggered immunity", *Curr. Opin. Plant Biol.* **13** (2010), p. 459-465.

[49] F. Delplace, C. Huard-Chauveau, R. Berthomé, D. Roby, "Network organization of the plant immune system: from pathogen perception to robust defense induction", *Plant J.* **109** (2022), no. 2, p. 447-470.

[50] H. Kitano, "Biological robustness", *Nat. Rev. Genet.* **5** (2004), p. 826-837.

[51] T. Kumar, L. Blondel, C. G. Extavour, "Topology-driven protein protein interaction network analysis detects genetic subnetworks regulating reproductive capacity", *eLife* **9** (2020), article no. e54082.

[52] H. Shi, K. K. Yan, L. Ding, C. Qian, H. Chi, J. Yu, "Network approaches for dissecting the immune system", *iScience* **23** (2020), article no. 101354.

[53] F. Y. Cao, M. Khan, M. Taniguchi, A. Mirmiran, W. Moeder, S. Lumba, K. Yoshioka, D. Desveaux, "A host-pathogen interactome uncovers phytopathogenic strategies to manipulate plant ABA responses", *Plant J.* **100** (2019), p. 187-198.

[54] W. Zhang, J. A. Corwin, D. Harrison Copeland, J. Feusier, R. Eshbaugh, D. E. Cook, S. Atwell, D. J. Kliebenstein, "Plant-necrotrophin co-transcriptome networks illuminate a metabolic battlefield", *Life* **8** (2019), article no. e44279.

[55] N. Zhang, B. Zhang, W. Zuo, Y. Xing, S. Konlasuk, G. Tan, Q. Zhang, J. Ye, M. Xu, "Cytological and molecular characterization of ZmWAK-mediated headsmut resistance in maize", *Mol. Plant-Microbe Interact.* **30** (2017), p. 455-465.

[56] O. M. Finkel, G. Castrillo, S. Herrera Paredes, I. Salas Gonzalez, J. L. Dangl, "Understanding and exploiting plant beneficial microbes", *Curr. Opin. Plant Biol.* **38** (2017), p. 155-163.

[57] D. Ramirez-Sanchez, C. Gibelin-Viala, F. Roux, F. Vailleau, "Genetic architecture of the response of *Arabidopsis thaliana* to a native plant-growth-promoting bacterial strain", *Front. Plant Sci.* **14** (2023), article no. 1266032.

[58] J. Bergelson, B. Brachi, F. Roux, F. Vailleau, "Assessing the
potential to harness the microbiome through plant genetics", *Curr. Opin. Biotechnol.* **70** (2021), p. 167-173.

[59] B. Brachi, D. Filiault, H. Whitehurst, P. Darme, P. Le Gars, M. Le Mentec, T. C. Morton, F. Rabanal, A. Anastasio, S. Duncan, F. Huang, R. Leff, P. Novikova, M. Perisin, T. Tsuchimatsu, R. Woolley, C. Dean, M. Nordborg, S. Holm, J. Bergelson, "Plant genetic effects on microbial hubs impact host fitness in repeated field trials", *Proc. Natl. Acad. Sci. USA* **119** (2022), no. 30, article no. e2201285119.

[60] M.-L. Pilet-Nayel, B. Moury, V. Caffier, J. Montarry, M.-C. Ker-

lan, S. Fournet, C.-E. Durel, R. Delourme, "Quantitative resistance to plant pathogens in pyramiding strategies for durable crop protection", *Front. Plant Sci.* **8** (2017), article no. 1838.

[61] D. B. Müller, C. Vogel, Y. Bai, J. A. Vorholt, "The plant microbiota: systems-level insights and perspectives", *Annu. Rev. Genet.* **50** (2016), p. 211-234.

[62] N. Vannier, M. Agler, S. Hacquard, "Microbiota-mediated disease resistance in plants", *PLoS Pathog.* **15** (2019), article no. e1007740.