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\DOI{10.5802/crbiol.153}
\datereceived{2024-02-16}
\daterevised{2024-03-20}
\dateaccepted{2024-03-28}
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\dateposted{2024-05-21}
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\title{Molecular complexity of quantitative immunity in~plants: from
QTL mapping to functional and systems biology}

\alttitle{Exploration de la complexit\'{e} mol\'{e}culaire de
l'immunit\'{e} quantitative des plantes : d'une cartographie de QTL
\`{a} l'analyse fonctionnelle de g\`{e}nes et de r\'{e}seaux}

\author{\firstname{Carine} \lastname{Chauveau}}
\address{Laboratoire des Interactions Plantes-Microbes-Environnement
(LIPME), Institut National de Recherche pour l'Agriculture,
l'Alimentation et l'Environnement (INRAE), CNRS, Universit\'{e} de
Toulouse, 31326 Castanet-Tolosan, France}
\email[C. Chauveau]{carine.chauveau@inrae.fr}

\author{\firstname{Dominique} \lastname{Roby}\CDRorcid{0000-0001-8414-1799}\IsCorresp}
\addressSameAs{1}{Laboratoire des Interactions
Plantes-Microbes-Environnement (LIPME), Institut National de Recherche
pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), CNRS,
Universit\'{e} de Toulouse, 31326 Castanet-Tolosan, France}
\email[D. Roby]{dominique.roby.descazal@gmail.com}

\keywords{\kwd{Plant--pathogen
interactions}\kwd{Immunity}\kwd{Quantitative disease resistance
(QDR)}\kwd{Protein--protein
interactions}\kwd{Networks}\kwd{Signaling}\kwd{Microbiota}}

\altkeywords{\kwd{Interactions plantes -- agents
pathog\`{e}nes}\kwd{Immunit\'{e}}\kwd{R\'{e}sistance quantitative
(QDR)}\kwd{Interactions
prot\'{e}ines--prot\'{e}ines}\kwd{R\'{e}seaux}\kwd{Signalisation}\kwd{Microbiote}}

\def\thanksname{Note}
\thanks{Dominique Roby is the recipient of the price JAFFE -- Fondation
de l'Institut de France in Integrative Biology 2023.}

\begin{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 \textit{Arabidopsis thaliana}. The
strategies, main steps and challenges of our studies related to one
atypical QDR gene, \textit{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.
\end{abstract}

\begin{altabstract} 
Dans la nature, les plantes se d\'{e}fendent contre les attaques
pathog\`{e}nes en activant tout un arsenal de m\'{e}canismes de
d\'{e}fense. Au cours des d\'{e}cennies pass\'{e}es, la recherche s'est
principalement focalis\'{e}e sur la compr\'{e}hension de la
r\'{e}sistance qualitative m\'{e}di\'{e}e par quelques g\`{e}nes
majeurs conf\'{e}rant une r\'{e}sistance quasi compl\`{e}te, alors que
la r\'{e}sistance quantitative (QDR) demeure peu comprise bien qu'elle
repr\'{e}sente la forme de r\'{e}sistance pr\'{e}dominante et la plus
durable dans les populations naturelles ou les cultures. Nous donnons
ici une revue de nos travaux pass\'{e}s et pr\'{e}sents sur la
dissection des m\'{e}canismes complexes qui sous-tendent la QDR chez
\textit{Arabidopsis thaliana}. Les strat\'{e}gies, \'{e}tapes cl\'{e}s
et d\'{e}fis de nos \'{e}tudes concernant un g\`{e}ne QDR atypique,
\textit{RKS1} (Resistance related KinaSe~1), sont rapport\'{e}s. En
premier lieu, \`{a} partir d'analyses g\'{e}n\'{e}tiques par
cartographie de QTL et GWA, l'identification, le clonage et l'analyse
fonctionnelle de ce g\`{e}ne ont \'{e}t\'{e} utilis\'{e}s comme point
de d\'{e}part \`{a} l'exploration des voies multiples et
coordonn\'{e}es agissant ensemble pour le d\'{e}veloppement de la
r\'{e}ponse QDR d\'{e}pendante de RKS1. L'identification des
interacteurs et complexes prot\'{e}iques impliquant RKS1 a \'{e}t\'{e}
une premi\`{e}re \'{e}tape, la biologie des syst\`{e}mes et la
reconstruction de r\'{e}seaux d'interactions
prot\'{e}ines-prot\'{e}ines ont ensuite \'{e}t\'{e} mises en {\oe}uvre
pour d\'{e}coder les voies mol\'{e}culaires conduisant aux r\'{e}ponses
immunitaires contr\^{o}l\'{e}es par RKS1. Finalement, l'exploration de
l'impact potentiel de composantes cl\'{e}s du r\'{e}seau de g\`{e}nes
d\'{e}pendant de RKS1 sur le microbiote, offre des perspectives
int\'{e}ressantes et ambitieuses pour comprendre comment le syst\`{e}me
immunitaire de la plante interagit avec le syst\`{e}me des
communaut\'{e}s microbiennes.
\end{altabstract} 

\maketitle
{\vspace*{.25pc}}

\twocolumngrid

\end{noXML}

\section{Introduction} \label{sec1}

\looseness=-1
In nature, plants must defend themselves against various pathogenic
microbes and diverse macro-organisms, 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 Receptors (PRRs) (mainly
extracellular). In contrast, quantitative disease resistance (QDR)
remains poorly understood~\cite{Gouetal2023}, despite the fact that it
represents the predominant form of resistance in natural populations
and crops, much more prevalent than \textit{R} gene qualitative
resistance~\cite{Polandetal2008, Corwinetal2017}. 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~\cite{Cowgeretal2019}. The complexity of the genetic
architecture underlying natural variation of QDR, which involves many
genes with moderate effects~\cite{Polandetal2008, Corwinetal2017, 
Niksetal2015, Rouxetal2014, StClair2010}, 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~\cite{Gouetal2023, Corwinetal2017,
Rouxetal2014, Frenchetal2016}. 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,
\mbox{ubiquitin} related mechanisms, or other unknown
functions~\cite{Gouetal2023}. 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 mecha{\hbreak}nisms which constitute diverse plant strategies to
reduce disease severity. In contrast with several recent reviews
focused on the genetic and mole{\hbreak}cular QDR mechanisms identified over the
last two decades~\cite{Gouetal2023, Corwinetal2017,
Niksetal2015, Rouxetal2014, Nelsonetal2018}, in this review, we will present the
strategies, main steps and challenges of our studies more specifically
related to one atypical QDR gene, \textit{RKS1} (Resistance related
KinaSe~1)~\cite{HuardChauveauetal2013}. 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~\ref{fig1}).
{\vspace*{-.3pc}}

\begin{figure*}[t!]
{\vspace*{.6pc}}
\includegraphics{fig01}
\caption{\label{fig1}A schematic view of the strategies, main steps and
challenges of the studies related to the atypical Quantitative Disease
Resistance gene, \textit{RKS1} (Resistance related KinaSe~1). Genetic
approaches including QTL analysis and GWAs combined to molecular
methodologies allowed to identify \textit{RKS1} as the gene underlying
a major QTL of resistance in \textit{Arabidopsis thaliana} in response
to \textit{Xanthomonas campestris} pv.\ \textit{campestris}, a vascular
bacterial pathogen. \textit{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.}
{\vspace*{.85pc}}
\end{figure*}

\section{Quantitative trait locus (QTL) and genome wide association
studies (GWAS) to unravel the genetic and molecular bases of QDR:
identification of \textit{RKS1}, a gene underlying a major QTL of
resistance to \textit{Xanthomonas campestris} pv.\ \textit{campestris},
encodes an atypi{\hbreak}cal 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
\textit{Arabidopsis thaliana} (\textit{A.~thaliana}) to the bacterial
pathogen \textit{Xanthomonas campestris} pv.\ \textit{campestris}
(\textit{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
\textit{A.~thaliana}~\cite{Bartolietal2018, Kniskernetal2007,
Williams1980}. It colonizes the vascular system of plants. Previous
work on this interaction revealed that different sources of resistance
and tole{\hbreak}rance to \textit{Xcc} exist in Arabidopsis~\cite{Buelletal1997,
Godardetal2000, Lummerzheimetal1993, Tsujietal1991}, 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~\cite{Meyeretal2005}.

To identify the genetic bases of the plant immune response to this
pathogen, we subjected na{\hbreak}tural accessions of \textit{A.~thaliana} to
infection by the \textit{Xcc} strain~568 (\textit{Xcc568}) and observed
substantial genetic variation for resistance to this strain in a core
collection of natural accessions of
\textit{A.~thaliana}~\cite{HuardChauveauetal2013}. 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 \textit{Xcc}; (ii)~a GWA (Genome Wide
Association) mapping of a population in \textit{A.~thaliana} containing
381 natural accessions including both worldwide accessions and French
accessions. While QTL analysis
revealed one major QTL on chromosome~3, \textit{QRX3} (Quantitative
Resistance to \textit{Xcc}568, explaining 50 to 60\% of the phenotypic
variance) and four minor QTLs, GWA mapping revealed a unique large peak
of \mbox{association} on chromosome~3 that overlaps with \textit{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 genomic fragments on the only mutant showing altered
resistance, allowed to identify the Arabidopsis \textit{RKS1}
(\textit{Resistance KinaSe~1}) gene. Finally, gene silencing \mbox{using}
artificial microRNA confirmed that \textit{RKS1} was responsible for
quantitative resistance to \textit{Xcc}. \textit{RKS1} encodes an
atypical kinase since it lacks some critical domains in the kinase
catalytic core that are essential for catalysis, and no kinase activity
could be detected 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~\cite{HuardChauveauetal2013}. Interestingly, atypical kinases (or
pseudokinases) are known to be signaling components of gene
networks~\cite{StClair2010}, and act as allosteric modulators,
scaffolds for assembly of protein complexes or modulation of signaling
pathways~\cite{Boudsocqetal2010, Maceetal2021}. For example, the
atypical kinase~ILK1 promotes disease resistance in Arabidopsis by
interacting with cation transporters~\cite{Braueretal2022}. Moreover,
the major locus revealed by GWA study corresponded to an allelic series
at \textit{RKS1} at the species level. Interestingly, a negative
relationship between disease index and \textit{RKS1} transcription
level was found in different transgenic lines and natural accessions.
Regulation of \textit{RKS1} expression might be a major component of
QDR to \textit{Xcc568}~\cite{HuardChauveauetal2013}. RKS1 is involved
in the restriction of bacterial spread from the infection site, without
visible cell death phenotype, a resistance mechanism particularly
adapted to counteract the infection strategy of the vascular bacterial
pathogen \textit{Xanthomonas}. Finally, \textit{RKS1} confers
resistance to different races and all pathovars of \textit{Xanthomonas
campestris}, similarly to other broad-spectrum QDR
genes~\cite{HuardChauveauetal2013}. Considering these findings, RKS1
represents a starting point for exploration of the signaling pathways
leading to QDR to \textit{Xcc} in \textit{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)~\cite{Wangetal2015, Burdettetal2019}.
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 \mbox{system}. 

\section{What molecular roadmap to the immune responses controlled by
\textit{RKS1}? Identification of RKS1 protein interactors and complexes
as a first step}\vspace*{-.15pc}

During the last two decades, the immune system has been viewed as
constituted of two layers activated through two types of receptors, as
mentioned above (see Section~\ref{sec1}). 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 mechanisms recently found~[3,~1]. However, even if recent
reports led to mechanistic insight into the quantitative resistance
loci by finding the causal genes, only a few studies, focused on
canonical immune components, reported more in depth understanding of
QDR mechanisms~\cite{Deppeetal2018, Lietal2019, Wangetal2021}. Studies
reported until recently are mainly limited to the identification of the
QDR gene of interest and its implication in plant
resistance.\looseness=-1

A way to explore the QDR mechanisms, especially 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 protein
interactors is in line with a potential participation to protein
complexes for modulation of immune signaling pathways, as suggested for
pseudokinases~\cite{Boudsocqetal2010, Maceetal2021}. Using a Yeast two
hybrid screen and a mutated version of RKS1
($\text{RKS1}^{\mathrm{D191A}}$) as a bait against a cDNA library
generated from leaves inoculated by
\textit{Xcc}~\cite{Froidureetal2010}, we identified 43 candidate
proteins~\cite{Delplaceetal2020}, including 21 proteins related to
metabolic functions and 6 signaling components (e.g., EFR, KIN11, SGT1a,
MKP1, RANBP1 and NLP7)~\cite{Delplaceetal2020}. Among them a fragment
of the kinase domain of MIK2 (MDIS1-Interacting receptor-like Kinase~2)
as a putative interactor of RKS1~\cite{Delplaceetal2024}. 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{\hbreak}rodimerizing with MDIS1 and MDIS1-Interacting
Receptor-like Kinase~1~\cite{Wangetal2016}. It is also involved in the
perception of the peptide SCOOP12, a phytocytokine, through
heterodimerizing with BAK1~\cite{Rhodesetal2021}. By co-localization,
Bimolecular Fluorescence Complementation (BiFC) and
co-immunoprecipitation experiments, 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 \textit{mik2} mutants exhibit
increased susceptibility to \textit{Xcc} as compared to the wild type
and that MIK2 catalytic activity is required for QDR to \textit{Xcc}.
Finally, we generated a double mutant \textit{mik2-1}/\textit{rks1-1}
which showed a similar phenotype in response to \textit{Xcc} as the
single mutants, suggesting that both genes might belong to the same
pathway~\cite{Delplaceetal2024}.

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~\cite{Baueretal2001,
GomezGomezetal2000}. The EF-Tu receptor (EFR) is involved in the
perception of the peptide elf18, the N-terminal peptide of
EF-Tu~\cite{Kunzeetal2004, Zipfeletal2006}. 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~\cite{Tangetal2017}. Recently, a
complex and dynamic network of LRR--RKs interactions has been described
in \textit{A.~thaliana}~\cite{SmakowskaLuzanetal2018}. 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~\mbox{\cite{Adachietal2019,
Albertetal2020, Bonardietal2012, Boudeauetal2006, Holtonetal2015}}.
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 inclu{\hbreak}ding 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
\textit{Xcc}, 17 mutants (corresponding to 16 genes) were collected and
phenotyped for their response to \textit{Xcc568}. The results indicate
that at least seven RLKs, highly connected to MIK2 and/or RKS1, have a
partial effect in QDR to \textit{Xcc568}~\cite{Delplaceetal2024}. 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 scaffolder hub to coordinate
assembly of multiple perception complexes.

\section{What molecular roadmap to the immune responses controlled by
\textit{RKS1}? Systems bio{\hbreak}logy 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 seve{\hbreak}ral QTLs with
minor to moderate effects, suggests more complex molecular mechanisms.
These mechanisms predominantly include downstream defense
mechanisms~\cite{Corwinetal2017}, 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. \mbox{However,} combined
with a genome-wide modular network analysis, it should help to decipher
QDR mole{\hbreak}cular complexity, as shown for more characterized plant immune
responses~\cite{Dongetal2015}. Thus, plant immunity should rather be
seen as a distributed and highly connected molecular network which
includes diffe{\hbreak}rent functions to fine-tune plant defense expression in
response to pathogens~\cite{Delplaceetal2020}. 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~\cite{SmakowskaLuzanetal2018, Brustolinietal2017, Wuetal2018,
Wroblewskietal2018}. In the same line, signaling networks have been
described, allowing plant cells to modulate defense responses accor{\hbreak}ding
to the perceived signals/organisms, and to avoid any conflicting
responses. This balance is notably enabled by hormonal pathways and
crosstalks~\mbox{\cite{Noborietal2019, Tsudaetal2010}}. 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~\cite{Delplaceetal2022}. 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~\cite{Kitanoetal2004}. 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, dyna{\hbreak}mics 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~\cite{Kumaretal2020} in
combination with mutant (RNAi, CRISPR) screening~\cite{Shietal2020}.
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 \textit{Xcc} in \textit{A.~thaliana}, and
exhibiting differential robustness~\cite{Delplaceetal2020}. First,
transcriptomic analysis was performed by RNA sequencing, using RKS1
deregulated lines expressing contrasted
phenotypes in response to
\textit{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 \textit{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~\cite{Delplaceetal2020}. 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 mo{\hbreak}dules are more resilient than
others, and suggesting a potential compensation between genes belon{\hbreak}ging
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 \textit{Xanthomonas}. Finally, in the context of
climate change, the adaptation of the plant immune system to
environmental conditions represents a major issue, which might be
resolved using systems biology approach. However, at this stage, the
studies conducted on this question remain mainly{\break} descriptive.

\section{Towards a more ecological view: what about a potential impact
of \textit{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 transcriptomic analyses in a
dual-transcriptomic network would allow to identify potential
interactions between the components of plant host innate immune and
pathogen virulence systems. This has been tempted in some
cases~\cite{Caoetal2019, Zhangetal2019, Zhangetal2017}, but co-network
modeling is still a challenging approach. In addition, pathogens
represent only a part of the plant microbiome. So, a major challenge
now would be to explore 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 decipher how the plant immune systems
interact with the microbial communities' systems.

Although host-microbial communities (microbiota) are more and more
documented, most of the studies focused on their composition and
potential use for biological control using growth-promoting microbial
associations~\cite{Bartolietal2018, Finkeletal2017,
RamirezSanchezetal2023}. Plant-associated microbes can indeed improve
access to nutrients, activate or prime the immune system, and/or
compete with pathogens. Despite this potential agronomical interest,
the influence of plant genetic and molecular 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 functions such as
primary and secondary \mbox{metabolism}, \mbox{symbiosis}, immunity, or
development), and plant genetic variation through GWAs
notably~\cite{Bergelsonetal2021}. More recently, Brachi
{et~al.}~\cite{Brachietal2022} determined how the host genotype affects
different microbial community members (microbial hubs) and thus shapes
the overall 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 natural populations~\cite{Rouxetal2014, PiletNayeletal2017},
has not been tested yet. By using the \textit{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~\ref{fig1}). Such studies, realized in
ecologically realistic contexts, 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
effects~\cite{Bartolietal2018, Mulleretal2016, Vannieretal2019}. In
conclusion, there is an important challenge through these studies not
only for know{\hbreak}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)~\cite{Bergelsonetal2021, PiletNayeletal2017}. 

\section*{Declaration of interests}

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

\section*{Acknowledgements}

This article reports works which have been performed 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 encouragements by the past. In addition, this
article is dedicated to Sarah Ranty-Roby who is starting research in
the field of plant--pathogen interactions, with all my best wishes.

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