INTRODUCTION
Pathogens and herbivores play an important role in maintaining
biodiversity in natural populations (Bever, Mangan, & Alexander, 2015;
Kursar et al., 2009). The threats imposed by pathogens on humans and on
managed, food production systems have motivated research that aims to
predict where pathogens will occur and how risks of infection evolve
(Gilligan, 2002; Koff, 1992; Woolhouse, Taylor, & Haydon, 2001).
Pathogens can only occur where they have susceptible hosts, and hence,
resistance diversity is the key determinant of disease dynamics. Thus,
our ability to understand how diversity in resistance is generated and
maintained underlies our ability to predict and prevent disease
emergence and epidemics. In agriculture increasing the diversity of
crops - even from a monoculture to a mixture of two cultivars - has been
shown to reduce disease levels significantly (Mundt, 2002b; Zhu et al.,
2000). Natural host populations typically support diversity in
resistance phenotypes (Laine, Burdon, Dodds, & Thrall, 2011; Salvaudon,
Giraud, & Shykoff, 2008), and limited data available to date show that
increasing resistance diversity decreases disease risk also in the wild
(Jousimo et al., 2014a).
Hosts and pathogens are assumed to coevolve through Red Queen dynamics,
where the pathogen overcomes host’s defenses and the host in turn
responds with new counter-defenses (Hamilton, 1980; Jaenike, 1978).
Theory predicts such reciprocal coevolutionary selection to be a
powerful mechanism for maintaining diversity in both host and parasite
populations, as the selection rate for resistance depends on the
frequency of parasite alleles, and vice versa, in a negative indirect
frequency‐dependent manner (Bergelson, Kreitman, Stahl, & Tian, 2001;
Leonard, 1977). There are numerous examples of pathogens overcoming host
resistance mechanisms, both from agriculture and from the wild (Mundt,
2002a, 2014). While evidence of resistance evolving under pathogen
attack in the wild is scarce (Laine, 2006), there is ample support for
coevolution from local adaptation studies where parasite/host fitness is
measured in sympatry vs. allopatry (Greischar & Koskella, 2007;
Hoeksema & Forde, 2008). To date, a handful of ground-breaking studies
have demonstrated that fluctuations in resistance and infectivity in
natural systems match the predictions of coevolutionary selection
(Decaestecker et al., 2007; Gómez & Buckling, 2011; Thrall et al.,
2012).
The interaction between plants and their pathogens is mediated by
complex defense mechanisms, incorporated in several layers of defense.
Thick and waxy cell walls are an example of a mechanical defense barrier
against pathogen invasion (Miedes, Vanholme, Boerjan, & Molina, 2014).
Upon the arrival of a pathogen, the pathogen-associated molecular
patterns (PAMPs) trigger the so-called PAMP-triggered immunity (PTI)
response, which can stop the pathogen infection even before it begins.
If the pathogen succeeds in overcoming these first two defense layers,
the next layer is effector triggered immunity (ETI), involving the
recognition of pathogen virulent factors (effector proteins), either
directly or indirectly by recognizing the modifications that they make
to the plant proteins (Jones & Dangl, 2006). Many of the proteins
involved in intracellular pathogen recognition belong to
nucleotide-binding–leucine-rich repeat (NLR) receptors (Monteiro &
Nishimura, 2018). After pathogen recognition a multitude of different
signaling pathways, including production of reactive oxygen species,
elevated Ca2+ and MAP kinases lead to activation of
plant defenses. These defenses include the induction of stress hormones
salicylic acid, jasmonic acid and ethylene, as well as extensive
transcriptional re-programming ultimately resulting in the production of
defensive compounds, such as toxic secondary metabolites and
antimicrobial chemicals and enzymes. Against some pathogens, plants also
activate the hyper-sensitive response, programmed cell death, in which
the plant rapidly kills the cells surrounding the infection to prevent
the spread to nearby tissues (Coll, Epple, & Dangl, 2011; Egorov &
Odintsova, 2012).
Within these defense responses, NLR proteins play a key role. They are
involved in recognition of the pathogen’s effector proteins both
directly and indirectly, as well as in triggering the plant immune
response (Meunier & Broz, 2017). NLRs have also been shown to be
involved in signaling and transcript regulation (Chisholm, Coaker, Day,
& Staskawicz, 2006; Jones & Dangl, 2006). Moreover, NLR genes play an
important role in local adaptation and habitat expansion of plants (Stam
et al., 2017; Thrall et al., 2012). The antagonistic interaction between
plant NLR and pathogen effector proteins is considered to have a
profound effect on the evolution of both organisms, shaping their
genomes and gene repertoire (Upson, Zess, Bialas, Wu, & Kamoun, 2018).
NLRs usually form large tandemly arrayed gene families and hence
questions regarding their origins and evolutionary history have been
under active research in both plants and animals (Andolfo et al., 2019;
Borrelli et al., 2018). The numbers of identified NLRs differ
substantially within and between plant families (Baggs, Dagdas, &
Krasileva, 2017), for example Arabidopsis thaliana (Arabidopsis)
contains between 165 to 251 NLR genes (Shao et al., 2016; Van de Weyer
et al., 2019) and crop species such as wheat, barley, rice, tomato and
potato contain 627, 224, 438,137 and 309 NLRs, respectively (Sarris,
Cevik, Dagdas, Jones, & Krasileva, 2016). In A. thaliana there
is evidence of widespread positive selection in the core NLRs shared
among accessions, especially in the canonical NLR domains (Van de Weyer
et al., 2019), while a pioneering study on wild tomato revealed high NLR
diversity with a small subset of NLR genes driving local adaptation to
pathogens (Stam et al., 2017).
A current key challenge in molecular ecology is to understand the role
of pathogen-imposed selection on generating NLR diversity. Exploring the
breadth and depth of plant NLR natural variation has the potential to
increase our understanding of how NLR diversity is generated and
maintained, and to establish a toolbox of deployable disease resistance
traits (Monteiro & Nishimura, 2018). In natural plant populations,
neither pathogen epidemiology nor host resistance is under human
management in contrast to agricultural systems where disease is managed
both via resistance breeding and fungicides. Hence, natural populations
have the potential to offer unique insight into the processes generating
NLR diversity. Our study is focused on the interaction betweenPlantago lanceolata and its fungal pathogen Podosphaera
plantaginis . Previous studies have detected considerable phenotypic
variation in P. lanceolata resistance against P.
plantaginis (Laine, 2004); diversity is shown to accumulate in the
well-connected populations across the landscape (Hockerstedt, Siren, &
Laine, 2018), and has a direct negative impact on disease dynamics
(Jousimo et al., 2014b). Moreover, there is evidence of coevolution in
this interaction (Laine, 2005, 2006, 2008).
Here, we carried out a controlled experiment where five P.
lanceolata individuals were inoculated with the same P.
plantaginis strain. The aim of our study was to characterize the
transcriptional responses and the expression pathways activated in
response to the pathogen treatment, as well as in resistant versus
susceptible phenotypes. For this purpose, we carried out the firstde novo transcriptome assembly for P. lanceolata and used
it to characterize the induced NLR repertoire in P. lanceolataand test whether any particular NLRs show signs of selection. We found
that each plant genotype demonstrated a unique gene expression profile
in response to the pathogen and discovered a diverse NLR repertoire inP. lanceolata which is consistent with the high phenotypic
resistance diversity uncovered in earlier studies.