DISCUSSION
Given that pathogens are prevalent across all ecosystems, an
individual’s reproductive success and survival will depend on its
ability to resist infection. Natural host populations have been shown to
support considerable diversity in resistance (Laine et al., 2011;
Salvaudon et al., 2008), and theory predicts that this variation is
maintained by pathogen-imposed selection. However, empirical support for
the role of selection in generating resistance diversity is to date
still scarce. With recent advances uncovering the molecular
underpinnings of resistance, it is becoming increasingly feasible to
study resistance also in non-model systems.
Here, we established a high-quality de novo transcriptome
assembly of P. lanceolata to investigate the gene expression and
processes activated in different plant genotypes in response to
inoculation of the same pathogen strain. In our study, all five plant
genotypes showed unique gene expression patterns. This was clearly
demonstrated in the principal component analysis where the genotypes
separate, while the inoculated and mock-inoculated replicates of the
same genotype cluster together. Significant variation in gene expression
patterns among plant genotypes has also been discovered in other studies
(Burghardt et al., 2017; Muller, Kersten, Fladung, & Schroeder, 2019).
In the redundancy analysis (RDA), inoculation explains only 4 % of the
total variation, while genotype-by-inoculation interactions contributes
46 %, suggesting that the genotypes have highly unique responses to
pathogen attack. While such genotype specificity may be expected between
susceptible and resistant genotypes, the split to resistant versus
susceptible phenotypes explains only 9 % of the variance, with
considerable transcript expression pattern differences between
phenotypes. Overall, the plant genotypes also differ by the number, fold
change and the function of the transcripts which are differentially
expressed in response to the pathogen. Furthermore, even though the gene
expression shows the known induction of JA, SA and ABA signaling
pathways, they show highly varying activation patterns with JA and ABA
significantly contributing to the genotype by experiment differences in
RDA. This suggests that different plant genotypes use different
strategies in response to the same pathogen, which could be an important
mechanism generating phenotypic resistance diversity. One possible
explanation for the diverse responses is the extremely high genetic
variation within the species; overall, the transcriptome had very high
Watterson θ=0.068 and nucleotide diversity π= 0.077, suggesting
effective population sizes in the order of millions. The high genetic
diversity where, on average, eight nucleotides out of 100 differ between
any two individuals, is likely manifested also in the diverse responses.
The experimental take home message of genotype-specific expression
pathways to treatment is that including multiple genotypes in
experiments and avoiding pooling for RNA-Seq is essential to uncover
variation relevant for phenotypic differentiation.
The pathways induced by the pathogen were also visible in the gene
expression data. The common responses included the induction of specific
nitrate transport genes in susceptible phenotypes as well as elevated
expression of photosynthesis-associated genes and related biological
processes taking part in chloroplast in all genotypes. The powdery
mildew fungi have a contracted carbohydrate metabolism, for example they
are not able to degrade pectin, an essential component of plant cell
walls (Liang et al., 2018), whereas the lipid metabolism is intact,
suggesting that their main source of energy is from lipids. Chlorosis is
another hallmark sign of a successful pathogen attack. In our results,
the fact that specific nitrate transporters show elevated expression
levels as well as chloroplast processes in general, suggests elevated
chlorophyll biosynthesis. Together, this suggests that, at least at the
early stage of infection, P. plantaginis may target the
chloroplast lipids of Plantago to obtain its nutrients, most likely the
lipids involved in chlorophyll.
NLRs play an important role in pathogen recognition and downstream
defense responses, defense signaling, as well as activation of a hyper
sensitive response (Monteiro & Nishimura, 2018). In our study, we
discovered 543 NLR isoforms from a transcriptome that is a combination
of the five different Plantago genotype NLR repertoires. Majority
of these transcripts were expressed at least at some level in all five
plant genotypes. Presence-absence polymorphism in a subset of NLRs has
been demonstrated across Arabidopsis accessions (Van de Weyer et al.,
2019), and hence it could contribute to the slight differences in the
numbers of NLRs detected in the genotypes. Overall, the putative number
of NLR genes in P. lanceolata, based on the identified 543 NLR
isoforms, seems to be quite high compared to the range reported in other
species (Baggs et al., 2017). The high count could result from
preferential retention of NLR genes after Plantaginaceae specific whole
genome duplication event 46-49 million years ago (Li, Zhang, et al.,
2019), since it has been proposed that numerous and highly diversified
defense genes are particularly favored in long-lived plants to avoid
infection by their rapidly evolving pathogens (Plomion et al., 2018).
The predicted NLR transcripts resulted in 58 clusters of varying sizes,
with 22 transcripts in the largest cluster. We also found considerable
variation in the branch lengths among clusters, which could indicate
variation among clusters in their likelihood to accumulate mutations
(Tucker, Ackerman, Eads, Xu, & Lynch, 2013). Indeed, NLR genes are
among the fastest evolving gene families in plants. They often form
tandemly arrayed gene clusters, and this is believed to be critical for
the fast pace of their structural and functional diversification
(Meyers, 2003; Michelmore & Meyers, 1998). Frequent homologous
recombination events and errors produced during the process, followed by
diversifying selection, may generate the structural diversity needed to
match high effector evolution rates characteristics of the microbes they
encounter (Jacob, Vernaldi, & Maekawa, 2013; McDowell & Simon, 2006).
NLR genes are also under evolutionary pressure resulting from
inappropriate activation of cell death. If the plant cannot control
NLR-activated cell death, it leads to decreased fitness (Phadnis &
Malik, 2014). In particular, we found an increase in the number of
homologs of Arabidopsis RPP13 gene, which is involved in defense against
downy mildew (Peronospora parasitica ) in Arabidopsis, as well as
other defense processes and signaling (Bittner-Eddy, Crute, Holub, &
Beynon, 2000; Rentel, Leonelli, Dahlbeck, Zhao, & Staskawicz, 2008).
While none of the clusters had significant dN/dS values, we found
between one and twelve loci under selective pressure in 23 of the
clusters. The cluster with the highest number of loci under selection
has been suggested to be involved in defense against viruses in
Arabidopsis (Revers et al., 2003). The H statistic analysis confirms the
transcripts identified by the dN/dS analysis (10 transcripts), as well
as identifies 13 other NLR transcripts that may have been under putative
selection pressure, two of which show selection in the LRR domain
responsible for detecting the type of pathogen.
We find that the NLR transcripts are differentially expressed in
response to the pathogen treatment, and that this response varies
according to genotype. Transcripts of many NLR genes are known to
accumulate in response to defense induction or related stimuli (Lai &
Eulgem, 2018). For example, 75 of the 124 studied Arabidopsis NLR genes
were found to exhibit at least two‐fold higher transcript levels in
response to one or more of the 15 implemented defence‐related treatments
(Mohr et al., 2010). Up‐regulation of NLR transcripts after defence
induction has also been observed in other plant species, such asBrassica rapa , soybean and rice (Brechenmacher et al., 2015;
Chen, Pang, Chen, Zhang, & Piao, 2015; Ribot et al., 2008). While we
were more likely to observe up-regulation of NLR expression levels in
response to the pathogen treatment, this was not consistent across
transcripts and genotypes. This is in line with recent studies on crop
plants testing different genotypes in response to pathogen infection
(Cruz-Miralles, Cabedo-Lopez, Perez-Hedo, Flors, & Jaques, 2019; Sari
et al., 2018; Sari, Bhadauria, Vandenberg, & Banniza, 2017). Plants
have evolved mechanisms to stabilize their basal expression levels, and
to reduce the fitness costs of an overexpressed immune response that
could have more deleterious effects on plant fitness than the infection
(Fei, Xia, & Meyers, 2013). This may explain the down-regulation of
some of the NLR transcripts we observe in both susceptible and resistant
phenotypes.
Our results here are well in line with the extensive phenotypic
variation, and highly strain-specific disease resistance measured inP. lanceolata in earlier studies (Hockerstedt et al., 2018;
Jousimo et al., 2014a). High levels of variation in resistance seems to
be nearly ubiquitous across natural host populations that experience
pathogen-imposed selection without any human interference, in contrast
to agricultural systems (Laine et al., 2011; Salvaudon et al., 2008). We
show that phenotypic resistance may be generated by different
mechanisms. First, we discovered a large repertoire of candidate NLRs inP. lanceolata. We also find evidence of selection generating
diversity in a subset of the identified NLRs. Moreover, we discovered
that the genotypes have unique expression profiles in response to
pathogens, a mechanism which may further contribute to phenotypic
variation. Indeed, this high level of genetic and expression profile
diversity may be the key to successful defense against pathogens in
sessile plants that lack a long lasting immune memory (Hall et al.,
2009; Roux & Bergelson, 2016). Finding different mechanisms that
contribute to phenotypic resistance is nontrivial, given how effectively
this variation may be utilized to predict and control disease epidemics
(Mundt, 2002a). Moreover, resistance in agricultural crops is highly
prone to breakdown following pathogen adaptation, and for many
commercially important pathogens, the known effective resistance genes
are becoming limited. Wild plant populations are currently identified as
the most promising source of genes required for development of
sustainable agriculture (Fu et al., 2019). In conclusion, characterizing
the architecture of resistance in natural host populations may yield
unprecedented light on the potential of evolution to generate variation,
and it can have broad and long-lasting impacts in our food production
environments.