Abstract
Background
Rye (Secale cereale L.) is a cereal crop highly tolerant to
environmental stresses, including abiotic and biotic stresses (e.g.,
fungal diseases). Among these fungal diseases, leaf rust (LR) is a
major threat to rye production. Despite extensive research, the genetic
basis of the rye immune response to LR remains unclear.
Results
An RNA-seq analysis was conducted to examine the immune response of
three unrelated rye inbred lines (D33, D39, and L318) infected with
compatible and incompatible Puccinia recondita f. sp. secalis (Prs)
isolates. In total, 877 unique differentially expressed genes (DEGs)
were identified at 20 and 36 h post-treatment (hpt). Most of the DEGs
were up-regulated. Two lines (D39 and L318) had more up-regulated genes
than down-regulated genes, whereas the opposite trend was observed for
line D33. The functional classification of the DEGs helped identify the
largest gene groups regulated by LR. Notably, these groups included
several DEGs encoding cytochrome P450, receptor-like kinases,
methylesterases, pathogenesis-related protein-1, xyloglucan
endotransglucosylases/hydrolases, and peroxidases.
The metabolomic response was highly conserved among the genotypes, with
line D33 displaying the most genotype-specific changes in secondary
metabolites. The effect of pathogen compatibility on metabolomic
changes was less than the effects of the time-points and genotypes.
Accordingly, the secondary metabolome of rye is altered by the
recognition of the pathogen rather than by a successful infection. The
results of the enrichment analysis of the DEGs and differentially
accumulated metabolites (DAMs) reflected the involvement of
phenylpropanoid and diterpenoid biosynthesis as well as thiamine
metabolism in the rye immune response.
Conclusion
Our work provides novel insights into the genetic and metabolic
responses of rye to LR. Numerous immune response-related DEGs and DAMs
were identified, thereby clarifying the mechanisms underlying the rye
response to compatible and incompatible Prs isolates during the early
stages of LR development. The integration of transcriptomic and
metabolomic analyses elucidated the contributions of phenylpropanoid
biosynthesis and flavonoid pathways to the rye immune response to Prs.
This combined analysis of omics data provides valuable insights
relevant for future research conducted to enhance rye resistance to LR.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12870-024-04726-0.
Keywords: Biotic stress, Fungal disease, Plant immune response,
RNA-seq, Differentially accumulated metabolites
Background
Rye (Secale cereale L.) is considered to be the cereal crop most
tolerant to abiotic and biotic stresses, including fungal diseases
[[39]1]. Among the rye diseases caused by fungi, leaf rust (LR) also
known as brown rust, which is an airborne disease caused by the
obligate biotrophic basidiomycete Puccinia recondita f. sp. secalis
(Prs) (Roberge ex Desmaz), is responsible for significant yield and
economic losses [[40]2]. The genetic basis of the resistance to this
disease remains relatively unknown and is a major interest of breeders.
To date, 16 dominant Pr genes (Pr1-5, Pr-d–f, Pr-i–l, Pr-n, Pr-p, Pr-r,
and Pr-t) on five of the seven rye chromosomes (1R, 2R, 4R, 6R, and 7R)
have been identified using Mendelian-based methods [[41]2–[42]5]. The
release of rye reference genome sequences (Lo7 and Weining) [[43]6,
[44]7] has allowed researchers to conduct precise analyses at the
molecular level. For example, Vendelbo et al. [[45]8, [46]9] performed
a genome-wide association study and mapped five LR
resistance-associated quantitative trait loci (QTLs) on chromosome arms
1RS, 1RL, 2RL, 5RL, and 7RS; the two QTLs on chromosome arms 1RS and
7RS were especially important for LR resistance. The main
resistance-associated marker on chromosome arm 1RS was physically
co-localized with molecular markers delimiting the previously
characterized Pr3 gene. The second region on 7RS contained several
nucleotide-binding leucine-rich repeat (NLR)-encoding genes, one of
which (provisionally designated as Pr6) was similar (at the protein
level) to the wheat LR resistance gene Lr1, which is on chromosome arm
5DL. However, these results have not been supported by an analysis at
the transcriptome level.
In addition to typical resistance genes, the genes controlling
benzoxazinoid (BX) biosynthesis (Table S1) are also affected by Prs
infections [[47]10]. For example, the expression level of ScBx4, which
encodes a cytochrome P450 monooxygenase, reportedly increases in
infected plants at 8, 17, 24, and 48 h post-treatment (hpt). This is in
accordance with an earlier finding that a single nucleotide
polymorphism (ScBx4_1583) in ScBx4 is stably associated with the field
resistance of adult plants to LR [[48]11]. Transcriptome sequencing
(RNA-seq) is a powerful experimental technique for exploring global
changes in gene expression in response to various stimuli (e.g.,
developmental changes and responses to abiotic and biotic stresses)
[[49]12]. It has been widely used for studying plant host–pathogen
interactions and various diseases, including LR [[50]13–[51]15] in
wheat, which is a close relative of rye. Poretti et al. [[52]14]
identified 753 genes with expression levels that were uniquely
down-regulated in the susceptible isogenic line Thatcher following an
infection with LR and powdery mildew. An enrichment analysis of these
genes indicated that six major biochemical pathways (nuclear transport,
alternative splicing, DNA damage response, ubiquitin-mediated
proteolysis, phosphoinositol signaling, and photosynthesis) were
suppressed by the diseases. Therefore, the authors concluded that both
pathogens can overcome plant immune responses by repressing programmed
cell death and responses to cellular damage.
Ji et al. [[53]15] identified 1,455 differentially expressed genes
(DEGs) in the wheat–Agropyron cristatum 2P addition line II-9-3
infected with LR; most of these DEGs were wheat genes. The Kyoto
Encyclopedia of Genes and Genomes (KEGG) analysis and gene set
enrichment analysis (GSEA) assigned the DEGs to several pathways,
including the following: plant–pathogen interaction, MAPK signaling
pathway–plant, plant hormone signal transduction, glutathione
metabolism, and phenylpropanoid biosynthesis. Among the A. cristatum
DEGs, there were many defense-related genes, including genes encoding
NLRs, receptor kinases, and transcription factors.
To date, the RNA-seq method has been used to identify genes associated
with rye responses to fungal diseases. In 2020, Mahmood et al. [[54]16]
conducted an RNA-seq analysis to identify rye DEGs linked to an ergot
infection caused by Claviceps purpurea. By performing a Gene Ontology
(GO) enrichment analysis, the authors detected 228 genes associated
with metabolic processes, hydrolase activities, pectinesterase
activities, and cell wall modifications. These over-represented groups
of genes were considered to be critical for successful parasitism.
Tsers et al. [[55]17] detected several genes related to the
resistance/susceptibility to Microdochium nivale. Their results
identified flavonoid-related genes as the most important group of genes
mediating the resistance to this pathogen. The susceptibility of plants
to M. nivale is apparently influenced by the expression of genes
encoding lipases and proteins associated with lipase activities. For
LR, only one RNA-seq analysis identified rye orthologs of wheat Lr
genes [[56]18]. The authors determined that ScLr1_3 and ScLr1_4 (on
chromosome 7R) as well as ScLr1_8 and ScRga2_6 (on chromosome 6R) are
differentially expressed in three unrelated rye inbred lines infected
with LR. Unfortunately, it is unknown whether these genes are the
counterparts to Pr2 and Pr6, which are also on chromosome 7R. Apart
from these four genes, no other Lr genes, including those identified by
Vendelbo et al. [[57]8, [58]9], were differentially expressed. Peng and
Yang [[59]19] stated that in wheat infected with LR some NLR are known
to be extremely weakly expressed.
In addition to alterations at the transcriptome level, plant immune
responses also involve the synthesis of immunity-related metabolites
[[60]20]. Phenylpropanoids and their downstream metabolites
(flavonoids) are scavengers of reactive oxygen species (ROS) generated
in response to environmental stresses [[61]20, [62]21]. Moreover,
several phenolics and the glycosidic forms of flavonols are inhibitors
of fungal growth in cereals [[63]22, [64]23]. There has recently been
considerable interest in the antifungal properties of indole-derived
BXs [[65]10]. The accumulation of BXs has been correlated with the
resistance to various diseases of grasses, including LR in rye
[[66]10], head blight caused by Fusarium ssp. [[67]24], and corn leaf
blight [[68]25]. Nevertheless, the genes and end-products in the BX
pathway respond inconsistently to pathogens, indicative of a complex
system regulating BX biosynthesis. Accordingly, the relationship
between BXs and plant defenses will need to be clarified. The largest
and most diverse group of cereal immunity-related metabolites are
terpenoids [[69]26]. The thoroughly characterized diterpenes involved
in rice–pathogen interactions are momilactones and oryzalexins [[70]27,
[71]28]. Moreover, biogenic amines and their phenol-containing
conjugates accumulate rapidly during the interaction between pathogens
and plants, including rye [[72]29], barley [[73]30], and wheat
[[74]21].
An earlier analysis of the rye metabolome profile led to the
identification of groups of defensive metabolites responsive to a
nematode attack [[75]31]. A metabolomic analysis was also performed to
compare resistant and susceptible wheat genotypes infected with LR
[[76]32], thereby revealing metabolite functions potentially related to
wheat stripe rust resistance [[77]33].
The objective of this study was to identify and characterize DEGs and
differentially accumulated metabolites (DAMs) in three unrelated rye
inbred lines infected with compatible and incompatible isolates of Prs
using several analytical methods, including RNA-seq, quantitative
reverse transcription PCR (RT-qPCR), and liquid chromatography and mass
spectrometry (LC-MS)-based untargeted metabolomics combined with
dedicated bioinformatics approaches. We hypothesize that compatible and
incompatible plant-pathogen interactions induce specific changes at the
transcriptome and metabolome levels.
Methods
Plant materials and inoculation procedure
The following three unrelated rye inbred lines were included in this
study: D33 and D39 (both bred by Danko Plant Breeding Ltd., Poland) and
L318 (bred in our department). These lines were selected according to
their BX contents in early spring (in the developmental stage GS 20–24
according to the Zadoks Cereal Growth Stage, about two weeks after the
start of the vegetation) and disease rating performed at maximum brown
rust epidemic intensity (end of May), under field conditions (Table
S[78]1). Ten germinating seeds (2 days at 22 °C) were added to a
sterilized peat substrate in plastic pots, which were then incubated
for 10 days in a growth chamber set at 22 °C with a 16-h light (60 µmol
m^−2 s^−1)/8-h dark cycle. For each rye line, a compatible (CP) and
incompatible (ICP) Prs strain was selected after preliminary screening
15 single-spore isolates (Table S[79]2). The strains were selected on
the basis of detached-leaf inoculations [[80]10] followed by an in
planta confirmation. The infection types were described by Murphy’s
scale, which utilize a 0–4 scale where 0 corresponds to “immune” (no
visible reaction); 1 corresponds to “resistant” (minute uredinia
surrounded by chlorosis or necrosis); 2 corresponds to “moderately
resistant” (small pustules surrounded by chlorosis); 3 corresponds to
“moderately susceptible” (moderately large pustules surrounded by
chlorosis); and 4 corresponds to “susceptible” (moderately large to
large pustules with little or no chlorosis). Prior to inoculating
12-day-old rye plants, each selected Prs isolate was resuspended in
Novec 7100 engineered fluid (1 mg/ml) in a glass diffuser (Roth, Basel,
Switzerland). The control (mock) plants were inoculated with Novec 7100
engineered fluid, but they were otherwise treated the same as the
Prs-inoculated plants. Immediately after the inoculation, the plants
were covered with black boxes to maintain dark and humid (100%)
conditions during the 24-h incubation at 18 °C. The plants were then
transferred to a growth chamber. The experiment was completed using
three biological replicates comprising five plants from one pot. Plant
tissue was collected at 20 and 36 hpt, immediately frozen in liquid
nitrogen, and stored at − 80 °C. The time points we selected were the
same as before [[81]18]. We decided for these time points because at
the 20th hpt only haustorium mother cells were observed at the
infection site, while at the 36th hpt an additional micronecrotic
reaction was observed, indicating an active plant response. Pathogens
secrete effectors from specialized feeding structures – haustoria that
affect the expression of many genes related to the immune response
against fungal pathogens [[82]34, [83]35].
Calcofluor white staining to visualize the plant–pathogen interaction
The rye lines were inoculated with the selected Prs isolates as
described above. Leaf samples were collected at 20, 36, and 72 hpt,
fixed, and stained with calcofluor white [[84]10]. The stained leaf
materials were examined using the Diaphot fluorescence microscope
(Nikon) to detect germinating spores, appressoria, haustorium mother
cells (HMCs), and micronecrosis. The infection sites were defined as
the sites with an appressorium as well as HMC and/or micronecrosis.
Sites containing only an appressorium were not considered. Observations
were made for an average of 60 infection sites per leaf sample, usually
in three to four replicates (plants). The following three profiles were
used to describe plant–pathogen interactions: i, appressorium + HMC;
ii, appressorium + HMC + micronecrosis; and iii,
appressorium + micronecrosis. Profiles were expressed as percentages.
RNA isolation
Total RNA was extracted from approximately 100 mg frozen leaves of the
mock- and Prs-inoculated rye lines (D33, D39, and L318) using the
mirVana miRNA Isolation Kit and the Plant RNA Isolation Aid (Thermo
Fisher Scientific, Waltham, MA, USA) for the RNA-seq analysis. The
GeneMATRIX Universal RNA Purification kit (EURx, Gdańsk, Poland) was
used to isolate total RNA for the RT-qPCR analysis. The RNA
concentration and purity were estimated using the NanoDrop 2000
spectrophotometer and the Qubit® 2.0 fluorometer (Invitrogen, Waltham,
MA, USA).
RNA-seq analysis
The extracted RNA was sent to Genomed S.A. (Warsaw, Poland) for the
RNA-seq analysis, sequence assembly, and primary gene expression
analysis. The RNA-seq libraries were prepared using the NEBNext Ultra
II Directional RNA Library Prep Kit for Illumina kit (NEB, Ipswich, MA,
USA). A total of 54 cDNA libraries.
[3 lines × 3 treatments (CP Prs, ICP Prs, and mock) × 2 time-points (20
and 36 hpt) × 3 biological replicates] were prepared for the RNA-seq
analysis, which was completed using the NovaSeq 6000 system (Illumina)
in the PE150 mode. The sequencing reads were filtered using the
Cutadapt (version 3.0) program [[85]36] and then quality reports were
generated using the FASTQC (version 0.11.8) software [[86]37]. The
reads were mapped using the HISAT2 (version 2.2.0) program [[87]38]. As
required by HISAT2, short reads (˂20 bp) were removed. Next, the reads
were mapped to the S. cereale Lo7 reference genome [[88]6]. The
following option of the HISAT2 program was applied: library preparation
--rna-strandness RF. The number of read pairs mapped to individual
genes was determined using the HTseq program [[89]39], with
differentiation due to the transcript strand (--stranded = reverse).
The genes were annotated on the basis of the gene description file
(gff3 file) for the S. cereale Lo7 genome [[90]6]. The results were
statistically analyzed – detailed information is provided in the
section “statistical analysis”. The raw RNA-seq (fastq) data were
deposited in the NCBI database (BioProject: PRJNA888031). Several genes
were selected for the validation of the RNA-seq data via an RT-qPCR
analysis performed using a standard protocol as previously described
[[91]10] (Fig. S[92]1; Table S[93]3).
Metabolomic analysis
To extract metabolites, 2.5 µL of pure DMSO (Sigma–Aldrich, Steinheim,
Germany) was added to 1 mg leaf samples, after which 0.5 mM
camphorsulfonic acid and 0.5 mM lidocaine (Sigma–Aldrich) were added as
internal standards in proportion of 1 µL of standard for 200 µL of
DMSO. All frozen samples were homogenized using 1.0 mm zirconia beads
(BioSpecProducts, Bartlesville, OK, USA) and Precellys Evolution tissue
homogenizer (Bertin Corp, Montigny-le-Bretonneux, France), with a cycle
of 2 × 30 s at 8,000 rpm. The samples were centrifuged (15,000 rpm at
4 °C) and the supernatants were collected for the LC-MS analysis, which
was performed using the UltiMate 3000 RS system (Dionex, ThermoFisher
Scientific) linked to the TIMS-TOF mass spectrometer (Bruker Daltonics,
Hamburg, Germany). The chromatographic separation was completed using
the BEH RP C18 column (2.1 × 150 mm, 1.8 μm particle size) at 30 °C,
with a mobile phase flow rate of 0.25 mL/min. The elution was conducted
using water containing 0.1% formic acid (Sigma–Aldrich) (solvent A) and
acetonitrile (VWR Chemicals, Fontenay-sous-Bois, France) containing
0.01% formic acid (solvent B). The gradient elution was as follows:
0–5 min, 10–30% B; 5–12 min, 30–100% B; 12–15 min, maintained at 100%
B; and 15–15.5 min, the system was returned to starting conditions and
re-equilibrated for 5 min. The mass spectrometer was calibrated using
sodium formate salt clusters as the internal calibrant prior to each
analysis. The mass spectrometer was operated using the following
settings: ion source voltage, − 4.5 kV or 4.5 kV; nebulization of
nitrogen, 2.2 bar (pressure); and gas flow rate, 10 L/min. The ion
source temperature was 220 °C. The spectra were scanned in positive and
negative ionization fragmentation modes (ddMSMS) at a range of
95–1,000 m/z and a resolution of > 30,000 full width at half maximum
(FWHM). Data were acquired using the Compass HyStar (version 6.0)
software (Bruker Daltonic). The raw LC-MS data were processed using
MS-DIAL (version 4.74) [[94]40]. The processing steps included peak
detection, annotation according to spectral MSMS public metabolomic
libraries ([95]http://prime.psc.riken.jp/compms/msdial/main.html),
adduct elimination, alignment, and gap filling by compulsion. The raw
data from the positive and negative ionization modes transformed to the
universal mzXML format are available online
([96]https://box.pionier.net.pl/d/4e8c093c332b41b2ab6e/).
Statistical analysis
The DEGs and DAMs in each rye line were detected on the basis of the
following four comparisons: CP vs. mock and ICP vs. mock at two
time-points (20 and 36 hpt) (Table [97]1).
Table 1.
Comparisons for the transcriptome and metabolome analyses
Line Comparison description Comparison name
D33 CP vs mock, 20 hpt D33_CP_20
CP vs mock, 36 hpt D33_CP_36
ICP vs mock, 20 hpt D33_ICP_20
ICP vs mock, 36 hpt D33_ICP_36
D39 CP vs mock, 20 hpt D39_CP_20
CP vs mock, 36 hpt D39_CP_36
ICP vs mock, 20 hpt D39_ICP_20
ICP vs mock, 36 hpt D39_ICP_36
L318 CP vs mock, 20 hpt L318_CP_20
CP vs mock, 36 hpt L318_CP_36
ICP vs mock, 20 hpt L318_ICP_20
ICP vs mock, 36 hpt L318_ICP_36
[98]Open in a new tab
CP Compatible Prs isolate, ICP Incompatible Prs isolate, hpt hours post
treatment
The final RNA-seq results were analyzed in the R environment (version
3.6.3) [[99]41] using DESeq2 (version 1.26.0) [[100]42]. In line with
DESeq2 default parameters, the statistical significance of differential
expression was tested using a Wald test, and the obtained p-values were
corrected for multiple testing using the Benjamini and Hochberg method
to calculate false discovery rate (FDR). In all our analysis, genes
were considered as differentially expressed (DEGs) if meet the
following parameters: |log2(fold-change)| ≥ 2, (FDR) < 0.01, and
BaseMean (BM) - the average of the normalized count values ≥ 50. These
criteria were selected to avoid false positives. For the KEGG analysis
of significant DEGs, the KEGG internal annotation tool BlastKOALA
(database: Eukaryotes) was used [[101]43]. The GO enrichment analysis
was performed using the software available on the Triticeae-Gene Tribe
website [[102]44]. For the analysis, p-values were adjusted according
to the Bonferroni-Hochberg correction method and an FDR of 0.05 was
applied. The Venn online tool
([103]https://bioinformatics.psb.ugent.be/webtools/Venn/) was used to
visualize the relationships between the comparisons of DEGs and DAMs.
The processed metabolomic data were normalized via a log[2]
transformation and missing values were replaced (1/10 of the minimum
peak height for all samples). Experimental samples were compared with
“blank” samples containing only extraction buffer to eliminate
background noise due to the buffer. Curated data tables for the
positive and negative ionization modes were combined using MSCleanR and
subjected to an ANOVA with FDR correction. The signal intensities were
visualized using MetaboAnalyst [[104]45]. For each comparison, the DAMs
between the inoculated and control plants were selected according to
the following criteria: FDR < 0.01 and |log[2](fold-change)| > 0.58.
The processed LC-MS data are available in Table S[105]4.
Integration of transcriptomic and metabolomic analyses
Signals corresponding to the DAMs and DEGs were imported into the
Joint-Pathway Analysis module in MetaboAnalyst 5.0 to screen for
relationships between the DAMs and DEGs. The DAMs were imported into
MetaboAnalyst as a peak list profile with fold-change values and
p-values for scoring, whereas the DEGs (fold-change values) were
imported by Entrez ID. Metabolites over-represented at the pathway
level were ranked, with a mass tolerance of 5 ppm. Additionally, the
Mummichog 2 algorithm was used, with the p-value cutoff set at 0.05 on
the basis of the Oryza sativa japonica reference metabolome in the KEGG
database [[106]46]. Significantly enriched metabolic pathways were
identified using Fisher’s method involving an integrated pathway
p-value < 0.1 across multiple comparisons (Table [107]3) with at least
one DAM and one DEG associated with the pathway.
Table 3.
Joint-pathway enrichment analysis^a of the DAMs and DEGs
[108]graphic file with name 12870_2024_4726_Tab3_HTML.jpg
[109]Open in a new tab
^aThe analysis was performed on the basis of the enriched KEGG
metabolic pathways for all 12 comparisons. Only pathways containing
both metabolites and transcripts were selected (merged p-value < 0.1).
The complete functional enrichment results and annotated compounds are
provided in Table S[110]10. D33, D39, and L318 refer to rye inbred
lines; CP and ICP represent compatible and incompatible isolates,
respectively; 20 and 36 refer to the plants harvested at 20 and 36 hpt,
respectively. Blue color indicates metabolic pathways enriched for both
CP and ICP treatments, green color indicates metabolic pathways
enriched for CP treatments and yellow color indicates metabolic
pathways enriched for ICP treatments
Correlation network comprising DEGs and DAMs
A combined correlation network was constructed on the basis of three
separate correlation networks (for DAMs, DEGs, and both DAMs and DEGs)
using the WGCNA package in R [[111]47] and then visualized using
Cytoscape [[112]48]. The most highly correlated compounds were grouped
according to the first two networks mentioned above, whereas the
connections between the compounds were determined on the basis of the
third network. To construct each network, the Pearson correlation
matrix was transformed into an adjacency matrix using a power function
of 9, 8, and 14 for the DAMs, DEGs, and both DAMs and DEGs,
respectively, according to the scale-free topology criterion [[113]47].
Modules, which are groups of highly correlated compounds, were detected
by clustering using the dynamic tree cut algorithm and the topological
overlap matrix (TOM) for the DAMs and DEGs. Thus, the interconnections
between nodes were determined on the basis of the correlation network
for the DAMs and the correlation network for the DEGs, which included
circles for the nodes and the module names [[114]49]. The nodes divided
into eight modules are provided in Table S[115]11. All connections
between nodes were derived from the network with nodes for both DAMs
and DEGs. The connections between compounds as well as their Pearson
correlation coefficients and the TOM values are presented in Table
S[116]11. Hubs, which were defined as highly connected metabolites and
genes, were selected as nodes with the most connections between
metabolites or genes according to the network for both DAMs and DEGs.
The degree of each node is listed in Table S[117]11. Moreover,
intergroup hubs were defined as follows.
A simple graph
[MATH: G :MATH]
consists of a non-empty finite set
[MATH: V :MATH]
of elements called nodes (or vertices) and a finite set
[MATH: E :MATH]
of distinct unordered pairs of distinct elements of
[MATH: V :MATH]
called edges. We call
[MATH: V :MATH]
the node set and
[MATH: E :MATH]
the edge set of
[MATH: G :MATH]
. An edge
[MATH: {v,w} :MATH]
is said to join the nodes
[MATH: v :MATH]
and
[MATH: w :MATH]
. The degree of a node
[MATH: v :MATH]
of
[MATH: G :MATH]
is the number of edges incident with
[MATH: v :MATH]
, and is written as
[MATH: degv :MATH]
[[118]50].
Let
[MATH: G(V1∪V2,E) :MATH]
denote the correlation network graph with
[MATH:
V1∪V2<
/mn> :MATH]
nodes and
[MATH: E :MATH]
edges, where
[MATH: V1 :MATH]
denotes the nodes belonging to one group and
[MATH: V2 :MATH]
denotes the nodes belonging to the other group. Each of the nodes
[MATH:
vi,n∈Vi :MATH]
for
[MATH:
i=1,2,n
=1,2,⋯,<
/mo>Ni :MATH]
, where
[MATH: Ni :MATH]
is the number of nodes in the
[MATH: i :MATH]
th group, belongs to one of the disjoint subgraphs (modules)
[MATH:
Mi,k
:MATH]
for
[MATH:
k=1,2,⋯
,Ki :MATH]
, where
[MATH: Ki :MATH]
is the number of groups in
[MATH: i :MATH]
th group containing only nodes from set
[MATH: Vi. :MATH]
The external degree of node
[MATH:
vi,n∈Vi :MATH]
is defined as the number of its incident edges connecting it to the
nodes belonging to the other set
[MATH: Vj :MATH]
, where
[MATH: j≠i :MATH]
, and is denoted as
[MATH: outdegvi,n. :MATH]
The external degree of module
[MATH:
Mi,k∈Vi :MATH]
is defined as the number of nodes from set
[MATH: Vj, :MATH]
where
[MATH: j≠i :MATH]
, that are connected by an incident edge to the nodes belonging to
module
[MATH:
Mi,k
:MATH]
. The external degree of the module is defined as
[MATH: outdegMi,k :MATH]
.
We call node
[MATH:
vi,n∈Mi,k<
/msub> :MATH]
an intergroup hub if and only if
[MATH: outdegvi,noutdegMi,k·100%≥
20%,and outdegvi,n≥3. :MATH]
Without a loss of generality, the definition can be generalized to N
groups (e.g., into three groups by adding proteins to metabolites and
genes). All analyses were performed using our in-house scripts in R.
Results
Puccinia recondita profiles on rye inbred lines inoculated with compatible
and incompatible isolates
Three rye inbred lines were infected with the following four isolates
derived from single spores: 83/2/2.2_5x (compatible), 1.1/6
(incompatible for lines D33 and D39), 88/o/1_5x (compatible for line
L318), and 81/r/5_5x (partially incompatible for line L318, infection
type – “3”) (Fig. [119]1). Of all the isolates tested so far, isolate
81/r/5_5x induced the highest resistance of line L318 to Prs.
Fig. 1.
Fig. 1
[120]Open in a new tab
Types of interactions between rye inbred lines and Prs isolates.
Macroscopic examination of LR symptoms at 10 days after the inoculation
of rye inbred lines, D33, D39 and L318, with compatible and
incompatible Prs isolates. The infection types determined using the
following 0–4 scale [[121]51] are provided in parentheses: 0 = immune
(no visible reaction); 1 = resistant (minute uredinia surrounded by
chlorosis or necrosis); 2 = moderately resistant (small pustules
surrounded by chlorosis); 3^* = moderately susceptible (moderately
large pustules surrounded by chlorosis); and 4 = susceptible
(moderately large to large pustules with little or no chlorosis); ^*)
for line L318, an infection type “3” was treated as a partially
incompatible reaction
Plant–pathogen interactions were analyzed at 20, 36, and 72 hpt. At 20
hpt, no differences in plant-pathogen interaction were observed between
lines or between compatible and incompatible reactions (Fig. [122]2).
At 36 hpt, differences between compatible and incompatible reactions
were observed in line D39, these were the first micronecrosis in the
incompatible reaction (profile ii: compatible − 0%, incompatible
− 15.5%). At 72 hpt, very large differences between compatible and
incompatible reactions were observed in line D39 (profile ii:
compatible − 0%, incompatible − 61.3%; and profile iii: compatible
− 0%, incompatible − 6.2%) and large differences in line D33 (profile
ii: compatible − 0.4%, incompatible − 18.4%; profile iii: compatible
− 0.3%, incompatible − 3.8%). In line L318, the differences between
compatible and incompatible responses were very small. Our analysis was
supported by the susceptibility profiles determined at 10 days
post-inoculation (Fig. [123]1).
Fig. 2.
[124]Fig. 2
[125]Open in a new tab
Compatible and incompatible interactions between three rye inbred lines
(D33, D39, and L318) and Prs. A Plant–pathogen interaction profiles for
the seedlings of rye inbred lines D33, D39, and L318 inoculated with
compatible and incompatible Prs isolates. The results show the average
percentage of infection sites with profiles i – iii, with standard
deviation. Observations were made for average of 60 infection sites per
leaf sample (minimum 18, maximum 200) in three – four replicates. Bar
colours: grey – profile i (appressorium + HMC), blue – profile ii
(appressorium + HMC + micronecrosis) and green - profile iii
(appressorium + micronecrosis). B Example of the profiles at 72 hpt in
line D39 inoculated with isolate 1.1/6; samples were stained with
calcofluor white and examined using a fluorescence microscope. A:
appressorium; HMC: haustorium mother cell; N: micronecrosis. Bar = 100
μm
Considering these results, we examined the changes at the molecular
level during the infection of rye inbred lines D33, D39, and L318 by
compatible and incompatible Prs isolates to clarify the mechanisms
underlying the immune responses of the susceptible and resistant rye
genotypes infected with Prs.
Transcriptomic analysis of rye infected with leaf rust
We sequenced 54 libraries and generated more than 2,642 million raw
150-bp paired-end reads (approximately 48 million reads per sample).
After trimming reads and filtering for quality, we obtained
2,639 million high-quality paired-end clean reads (average of
48 million reads per sample). The average GC content was 53.2%.
Approximately 90.6% of the high-quality reads were mapped to the Lo7
rye reference genome [[126]6]. Of these mapped reads, approximately 88%
were uniquely mapped to a single locus. The data were processed
appropriately for an RNA-seq analysis. A transcriptomic approach was
used to investigate the differences in the responses of the three rye
inbred lines to compatible and incompatible Prs isolates. We selected
two time-points because they corresponded to different Prs
developmental stages (Fig. [127]2). In total, 877 unique differentially
expressed genes (DEGs) were identified at 20 and 36 h post-treatment.
Of 877 unique DEGs, 562 (64%) were present only once in only one of 12
comparisons and the remaining 315 (36%) - appeared in more than one
comparison, usually in two or three comparisons; maximum six in case of
gene SECCE7Rv1G0460350 encoding ammonium transporter. As a result, the
total number of transcripts was 1255 (Table S[128]5, S[129]7).
The plant–pathogen profiles were similar among the analyzed rye lines.
Additionally, approximately 100 DEGs were detected in the compatible
and incompatible interactions. The L318_CP_20 comparison had the fewest
DEGs (37), whereas the L318_ICP_20 comparison had the most DEGs (233)
(Fig. [130]3A; Table S[131]6). These findings (i.e., relatively few
DEGs) were due to the very restrictive filtering parameters that were
applied, which allowed us to identify important genes affected by LR in
response to the compatible and incompatible Prs isolates. For the
comparisons involving lines D39, up-regulated genes were the
predominant DEGs (especially in the D39_ICP_20 and D39_ICP_36
comparisons), while only in half of the comparisons involving line L318
up-regulated DEGs dominated. Conversely, for the comparisons involving
line D33, down-regulated genes were the main DEGs (especially in the
D33_CP_20 and D33_CP_36 comparisons) (Fig. [132]3A). These differences
suggest the observed changes may depend on the genetic background of
the lines and the type of reaction (Fig. [133]3C).
Fig. 3.
[134]Fig. 3
[135]Open in a new tab
Number of DEGs after infections with CP and ICP Prs isolates (20 and 36
hpt). A Total number of DEGs (1255) responsive to Prs. B Venn diagram
presenting the DEGs grouped according to the changes in their
expression relative to the type of response and time-point.
C Similarities in the transcriptomic responses to both CP and ICP Prs
isolates among the three rye genotypes and the two time-points. D
Heatmap representing changes in the expression of the most important
DEGs
Molecular signature of the rye response to compatible and incompatible Prs
isolates
To clarify the complexity of rye susceptibility and resistance to LR
and reveal the differences between the reaction types, we analyzed gene
regulatory networks associated with the rye response to compatible and
incompatible Prs isolates. Among the identified DEGs, there were more
genes affected by CP Prs than genes affected by ICP Prs (394 vs. 291
genes, with 192 overlapping genes). The proportions were similar for
the up-regulated genes after the infection with CP Prs (257 vs. 105
genes, with 114 overlapping genes). However, the analysis of the
down-regulated genes indicated that the infection with CP Prs decreased
the expression of fewer genes than the infection with ICP Prs (169 vs.
211, with 64 overlapping genes; Table S[136]7).
The analysis of the expression dynamics following the Prs infection
revealed the changes in expression [i.e., log[2](fold-change)] ranged
from 7.86 to − 6.80 (Fig. [137]3D; Table S[138]8). The most strongly
up-regulated gene was that encoding a kaurene synthase
(SECCE7Rv1G0520210) found in D39 line during compatible interaction at
20 hpt. The most strongly down-regulated gene encoded an expansin
protein family member (SECCE1Rv1G0032070) and was detected in line D33
during the incompatible interaction at 36 hpt. The up-regulated genes
were major fraction of DEGs in lines D39 and L318 infected with CP Prs.
Two of these genes (both in line L318), namely SECCE2Rv1G0073350 and
SECCE4Rv1G0283900, encoded a peroxidase (PO) and acid invertase 1,
respectively. In addition to their high log[2](fold-change) values,
they also had a high BM value (> 600). In contrast, the down-regulated
genes were the dominant DEGs in line D33 infected with ICP Prs. In this
group, the genes with the highest log[2](fold-change) values had
relatively low BM values (from 50 to 3022; BM mean is 377 and median -
141).
The investigation of the genetic factors underlying the compatible and
incompatible interactions with Prs revealed intriguing expression
patterns for specific genes. Our analysis identified seven common genes
among the three inbred rye lines that were associated with compatible
interactions at all time-points. Within this group, the expression
levels of the following three genes were consistently up-regulated in
all lines: SECCE7Rv1G0520220 (glycosyltransferase), SECCE6Rv1G0429310
(beta-1,3-glucanase), and SECCE7Rv1G0520230 (cytochrome P450).
Conversely, only SECCE7Rv1G0457810 (thiopurine S-methyltransferase) had
a down-regulated expression level in all lines. Interestingly, there
were no common gene groups for the incompatible interactions among the
analyzed rye lines (Table S[139]5; S[140]9). Several genes common to
all lines (Table S[141]9) were selected for the RT-qPCR analysis
performed to validate our RNA-seq data. The RT-qPCR data were highly
consistent with the RNA-seq data (Fig. [142]S1).
In addition to the DEGs that were common to all three rye inbred lines
at every time point, we identified four genes (SECCE1Rv1G0039520,
SECCE4Rv1G0273590, SECCE5Rv1G0367900, and SECCE7Rv1G0491620) coding for
the same type of protein, specifically the NAC domain-containing
protein. These genes were specific to CP response. Similarly, five
genes (SECCE1Rv1G0057050, SECCE4Rv1G0232880, SECCE7Rv1G0507990,
SECCE7Rv1G0508030, and SECCE7Rv1G0508100) encoding xyloglucan
endotransglucosylase/hydrolases (XTH), which are typically associated
with ICP, were differentially regulated in all three rye inbred lines.
A number of genes were exclusively found in the D33 line, including ten
genes (SECCE1Rv1G0043580, SECCE1Rv1G0052820, SECCE1Rv1G0058340,
SECCE3Rv1G0200810, SECCE4Rv1G0250540, SECCE5Rv1G0361700,
SECCE6Rv1G0378580, SECCE6Rv1G0378610, SECCE6Rv1G0378630 and
SECCEUnv1G0564610) encoding chlorophyll a/b-binding proteins (CabBP),
and four genes (SECCE1Rv1G0027760, SECCE2Rv1G0116400, SECCE7Rv1G0454360
and SECCE7Rv1G0496900) encoding Aquaporin and Aquaporin-like proteins,
which showed down-regulation in the CP and ICP interactions,
respectively (Table S[143]5).
Time-point-specific DEGs related to the rye responses to compatible and
incompatible Prs isolates
Time-point-specific DEGs related to the rye responses to compatible and
incompatible Prs isolates.
Our goal was to identify time-point-specific transcriptomic changes
caused by the compatible and incompatible Prs isolates. A total of 229
and 432 unique DEGs were detected for the compatible interaction at 20
and 36 hpt, respectively. Similarly, 213 and 301 unique DEGs were
detected for the incompatible interaction at 20 and 36 hpt,
respectively (Table S[144]5). One intriguing group consisted of genes
associated with a particular response to LR (Fig. [145]3B).
Specifically, for the compatible interaction, we identified 108 and 265
LR response-related DEGs at 20 and 36 hpt, respectively. For the
incompatible interaction, there were 101 and 180 LR response-related
DEGs at 20 and 36 hpt, respectively. At both time-points of the
incompatible interaction, the genes were mostly down-regulated. For the
compatible interaction, the DEGs at 20 hpt were mainly down-regulated
genes, but at 36 hpt, the DEGs were primarily up-regulated genes. In
group 108 (CP_20hpt unique), the significantly enriched GO categories
included translation, response to light, and rRNA binding. In contrast,
the main enriched GO categories in group 265 (CP_36hpt unique) were
glutathione metabolism, salicylic acid (SA) signaling, and jasmonic
acid (JA) signaling. Notably, during the incompatible interactions,
completely different GO categories were enriched, reflecting the
specificity of the rye response to Prs. Specifically, after 20 hpt, the
GO categories enriched among the genes in group 101 (ICP_20hpt unique)
were lipid transport and plant cell wall biogenesis, whereas these
categories were not enriched in group 180 (ICP_36hpt unique). Instead,
asparagine biosynthetic process and response to JA were slightly
enriched GO categories (Table S[146]6).
Our analyses identified several genes (15 DEGs) that were present in
all types of comparisons, encompassing both CP and ICP responses as
well as both time points. Except for two genes: SECCE7Rv1G0460350
(coding for ammonium transporter) and SECCE4Rv1G0248210 (coding for
cytochrome P450) which were identified as DEGs in all three rye lines,
all the remaining thirteen genes were differentially expressed in one
(D39 or L318) or two (usually D39 and L318) lines. This observation
suggests that these genes may play a substantial role in developmental
processes of the pathogen. The genes in this group encoded proteins
involved in cell wall modifications, including WIR1a,
endo-1,3-beta-glucanase, and 1-deoxy-D-xylulose 5-phosphate synthase
(DXS), as well as genes belonging to the CYP450 family involved in
NADPH- and/or O[2]-dependent hydroxylation reactions. Interestingly,
all of the genes common to the CP and ICP interactions were
up-regulated DEGs (Table [147]2; Table S[148]5).
Table 2.
Common pool of genes affected by LR identified in both interactions (CP
and ICP) and both time points (20 hpt and 36 hpt)
No Gene ID Encoded protein Differentially expressed in comparison:
1 SECCE7Rv1G0458590 Ice recrystallization inhibition protein-like
protein
D33_ICP_20; D33_CP_20
D33_CP_36; D39_CP_36
D39_ICP_36
2 SECCE3Rv1G0160690 basic helix-loop-helix (bHLH) DNA-binding
superfamily
D33_ICP_36; D33_CP_36
D39_ICP_20; D39_CP_20
3 SECCE7Rv1G0460350 Ammonium transporter
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
L318_ICP_36; L318_CP_36
4 SECCE2Rv1G0142080 1-deoxy-D-xylulose 5-phosphate synthase
D33_ICP_20; D39_ICP_20
D39_CP_20; D39_ICP_36
D39_CP_36
5 SECCE5Rv1G0322060 Indole-3-glycerol phosphate synthase
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
6 SECCE2Rv1G0117990 Cysteine protease
L318_ICP_20; L318_CP_20
L318_ICP_36; L318_CP_36
7 SECCE7Rv1G0460090 Aldo/keto reductase family protein
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
8 SECCE6Rv1G0429650 Endo-1,3-beta-glucanase
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
L318_CP_20; L318_CP_36
9 SECCEUnv1G0532270 tRNA-specific 2-thiouridylase MnmA
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
L318_CP_36
10 SECCEUnv1G0568520 WIR1a
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
L318_CP_36
11 SECCEUnv1G0532290 WIR1a
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
12 SECCE5Rv1G0302060 WIR1a
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
L318_CP_36
13 SECCE4Rv1G0285880 Cytochrome P450, putative
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
14 SECCE7Rv1G0462250 Cytochrome P450
D39_ICP_20; D39_CP_20
D39_ICP_36; D39_CP_36
15 SECCE4Rv1G0248210 Cytochrome P450
D33_ICP_36; D39_ICP_20
D39_CP_20; D39_CP_36
L318_CP_36
[149]Open in a new tab
Functional classification of leaf rust-regulated genes
There were many genes with changes in expression due to LR during
compatible and incompatible interactions. These genes covered a
substantial portion of the genome. To functionally classify the DEGs,
BlastKOALA was used and a GO enrichment analysis was performed. The
characterization of gene functions allowed us to visualize the
regulatory trends in different biological pathways affected by Prs
development.
First, we analyzed the enrichment of all 877 unique DEGs from all
comparisons to determine which processes are crucial for Prs
development independent of the reaction type. Using BlastKOALA, we
assigned 340 of the 877 unique DEGs to 18 KEGG categories. The
categories with the most DEGs were “Biosynthesis of other secondary
metabolites” (38 DEGs), “Carbohydrate metabolism” (30 DEGs), and
“Genetic information processing” (30 DEGs). The categories with the
fewest DEGs were “Cellular processes” (three DEGs) and “Nucleotide
metabolism” (two DEGs) (Table S[150]5).
In this study, we wanted to highlight the differences in the enriched
KEGG categories among the DEGs in the compatible and incompatible
interactions (Fig. [151]4). The CP DEGs were the dominant DEGs at 20
and 36 hpt and were assigned to most of the KEGG functional categories
(10 and 16 of the 18 KEGG categories, respectively). There was an equal
number of DEGs for the CP and ICP reactions only at 20 hpt and
exclusively in three categories, namely “Cellular processes”, “Lipid
metabolism”, and “Nucleotide metabolism”. At 20 hpt, there was a large
increase in the number of CP DEGs (2- to 6-times more) in the following
three categories: “Carbohydrate metabolism”, “Energy metabolism”, and
“Genetic information processing”. For the ICP DEGs at 20 hpt, the KEGG
categories with a substantial increase in the number of genes (at least
2-times more) were “Protein families: genetic information processing”
and “Protein families: metabolism”. The “Metabolism of other amino
acids” category lacked ICP DEGs, but it included one CP DEG
(Fig. [152]4A). At 36 hpt, CP DEGs were over-represented (i.e.,
> 2-times more abundant than ICP DEGs) in the following categories:
“Metabolism of cofactors and vitamins”, “Metabolism of other amino
acids” and “Organismal systems”. The ICP DEGs were > 2-times more
abundant than the CP DEGs in two categories at 36 hpt, namely “Amino
acid metabolism” and “Energy metabolism” but the opposite trend in
these categories was observed at 20 hpt. Four categories, including
“Metabolism of terpenoids and polyketides” comprised only CP DEGs
(Fig. [153]4B).
Fig. 4.
[154]Fig. 4
[155]Open in a new tab
KEGG-based functional classification of DEGs in CP and ICP interaction.
DEGs were analyzed separately (A) at 20 hpt and (B) at 36 hpt by using
BlastKOALA method; biosynth. - biosynthesis; met. - metabolism; PF -
protein families; proc. - processing; sec. – secondary
Metabolite profiling following an infection with Prs
To support our transcriptomic data that identified many DEGs related to
plant metabolism, we completed a comparative metabolomic analysis of
rye infected with Prs. The three rye inbred lines exhibited diverse
metabolomic responses to Prs. Line D33 infected with CP Prs had the
fewest number of DAMs at both time-points, whereas line L318 had the
highest number of DAMs. The infection with ICP Prs resulted in the
significant decrease in proportion of the number of up-accumulated
metabolites in D33 in comparison to CP response. The opposite effect
was observed in line D39 (i.e., increase in the up-accumulation of
metabolites with ICP in comparison to CP treatment) (Fig. [156]5A).
Fig. 5.
[157]Fig. 5
[158]Open in a new tab
Number and characteristics of DAMs. A Number of DAMs.
B Time-point-specific and common DAMs for the pooled genotypes infected
with CP or ICP Prs. C Similarities in the metabolomic responses to both
CP and ICP Prs isolates among the three rye inbred lines and at the two
time-points. D Heatmap representing changes in the expression of the
most important DAMs
The metabolomic profiles of L318 after Prs infection (i.e., identified
DAMs) is distant to profiles of the other two lines. The most diverse
immune response at the metabolomic level was detected for D39 at 36
hpt, whereas at 20 hpt, the response of D39 was similar to that of D33
(Fig. [159]5C).
Because the time-point was revealed to have the largest effect on the
groupings among treatments, we determined the number of common and
specific DAMs, differentiating between the time-points and reaction
types. The metabolomic response was highly treatment-specific. The ICP
reaction resulted in the largest number of specific DAMs at 20 and 36
hpt for all pooled genotypes (Fig. [160]5B). Additionally, there were
more common DAMs between the two time-points for the ICP infection
(379) than between the two time-points for the CP infection (170).
There were considerably more common DAMs (166) than common DEGs (15).
These 166 common DAMs related to rye immunity included flavonoids
(e.g., catechins and glycosides of kaempferol and quercetin),
phenylpropanoids (e.g., sinapic and rosmarinic acids), as well as
polymine spermidine and spermine (Fig. [161]5B and D; Table S[162]12).
The amino acids threonine and asparagine were also identified among the
common metabolites. Interestingly, sulfo-jasmonate was a common DAM,
while JA was a DAM specific to the 20 hpt time-point of the CP
infection. Among the 170 DAMs common to both time-points of the CP
infection, benzoic acid derivatives (benzoic acid and gallic acid as
well as their hexosides) were specific to the CP infection. Both DIBOA
diglucoside and HMBOA glucoside were also associated with the CP
infection. This set of DAMs also included the flavonoids naringenin and
taxifolin. The ICP-specific DAMs (among 379 signals) included the
flavonoid chrysin and peptides (e.g., isoleucylglutamine,
isoleucylvaline, and S-adenosyl-homocysteine).
Functional interactions among DAMs and DEGs
To combine the RNA-seq and metabolomic profiling results, the DEGs and
DAMs from all comparisons were subjected to a joint-pathway enrichment
analysis involving the KEGG pathway database
([163]https://www.genome.jp/kegg/pathway.html) and MetaboAnalyst
(Table [164]3; Table S[165]4). First, the highly enriched pathways
identified on the basis of the annotation of only one data type
(metabolites or transcripts) were eliminated. Only two pathways
(“Phenylpropanoid biosynthesis” and “Diterpenoid biosynthesis”) were
commonly enriched among the genotypes. Most of the matched pathways
were specific to certain treatments. Phenylpropanoids were the most
commonly identified DAMs among treatments. Examples include
phenylalanine, tyrosine, hydroxycinnamic acids and their amines and
aldehyde derivatives, as well as chlorogenic acids. Monolignols
(annotated as syringin and coniferin and their derivatives) were also
matched. The PO-encoding DEGs were associated with the metabolites from
the “Phenylpropanoid biosynthesis” pathway. “Diterpenoid biosynthesis”
was mainly related to “Gibberellin biosynthesis.” The infection with
Prs induced changes in the accumulation of several metabolites related
to “Gibberellin biosynthesis,” but only one or two related transcripts
in this pathway were matched.
The interaction between the ICP isolate and lines D33 and L318 at 36
hpt had highly specific enriched pathways. The immune response of D33
to the ICP isolate at 36 hpt was exclusively associated with “Cutin,
suberine and wax biosynthesis”, “Flavone and flavonol biosynthesis” and
“Phenylalanine metabolism”. Similarly, the interaction between the ICP
isolate and line L318 at 36 hpt was reflected by the enrichment of
“Nitrogen metabolism”, “Alanine, aspartate and glutamate metabolism”,
“Flavonoid biosynthesis” and “Ascorbate and aldarate metabolism”.
Furthermore, the “Ascorbate and aldarate metabolism” pathway was
associated with the “Ascorbate biosynthesis” module. However, the
central metabolite ascorbic acid was not annotated. “Starch and sucrose
metabolism” was enriched for the interaction between line D33 and the
CP isolate at 20 hpt, while another sugar-related pathway (“Amino sugar
and nucleotide sugar metabolism”) was enriched for line D39.
Integration of the transcriptomic and metabolomic changes in rye in response
to Prs
To more comprehensively characterize the rye response to Prs, a
transcript–metabolite correlation network was constructed for all DEGs
and DAMs, but only the DEGs and DAMs connected to other features are
presented in Fig. [166]6.
Fig. 6.
[167]Fig. 6
[168]Open in a new tab
Correlation network of DEGs and DAMs. The genes are represented by
green squares with labels, whereas the metabolites are represented by
blue ellipses with labels. Hubs are indicated in orange, with
intergroup hubs containing a yellow border. Edges link highly
correlated compounds. Modules of compounds are indicated by circles.
Only edges corresponding to elements of the topological overlap matrix
(greater than 0.55) are shown, both within and between modules; pink
and blue edges indicate positive and negative correlations,
respectively. Only one negative correlation was detected
The correlations among the DEGs, DAMs, and their interactions were
mostly positive. Only one negative correlation, which was between the
dentin sialophosphoprotein-related gene (SECCE3Rv1G0174240) and the UGT
gene (SECCE7Rv1G0459340), was detected in Module 1. The UGT gene
(SECCE7Rv1G0459340) was both a hub and an intergroup hub of Module 1.
The SECCE6Rv1G0420800 and SECCE7Rv1G0526280 genes were also intergroup
hubs of this module. There were four modules for the correlations among
the DAMs and for the correlations among the DEGs. The largest module
within the DEGs was Module 1, which included mostly genes responsible
for cell wall remodeling. Strong interactions were observed within
modules. More specifically, the strongest interactions were detected
among the DAMs in Module 4, which included flavonoids and lipids, while
among DEGs, the strongest interactions were observed in Module 2, which
included genes belonging to GO classes such as: response to biotic
stimulus, oxidoreductase activity and lipid metabolic process. Although
Module 1 was only slightly less correlated within the module. However,
the inter-modular interactions DEG-DEG and DAM-DAM were relatively
weak. The correlations between the DAMs and DEGs were more complex and
extended beyond the modules. The module with the most DAMs was Module
2. Many of these DAMs were correlated with one DEG from Module 3
encoding an early light-induced protein (SECCE1Rv1G0044910; ELIP). ELIP
(being the intergroup hub of Module 3) was more strongly correlated
with other compounds than gene encoding ADP-ribosylation factor
(SECCE1Rv1G0063470). The DAMs associated with ELIP included apigenin
glycoside and 5-hydroxyindoleacetaldehyde. In contrast, only a few DAMs
in Module 3 (e.g., phenylpropanoid derivatives, including syringetin
hexosides, rosmarinic acid hexoside, sinapaldehyde glucoside, and
(epi)gallocatechin) were connected to several DEGs from Module 1. There
were strong correlations between the DAMs from Module 1 (e.g.,
feruloylphenyllactic acid isomers) and the DEGs from Module 2,
including genes encoding teosinte branched 1 (SECCEUnv1G0548560) and
kaurene synthase (SECCE7Rv1G0520210). Gen SECCEUnv1G0548560 was both
the hub and intergroup hub of Module 2. In addition, this module
contained six other intergroup hubs (including the SECCE7Rv1G0520210
gene). Only one DEG, which encodes glutathione S-transferase
(SECCE4Rv1G0282280), in Module 4 was linked with the Module 4 DAMs, of
which lipidsoleic acid from lipids and p-coumaroylquinic acid and
myricetin O-hexoside from phenylpropanoids and flavonoids,
respectively, were identified. Neither Module 2 nor Module 4 among the
DAMs had intergroup hubs.
Our analysis reveals a complex network of gene and metabolite
interactions and revealed both strong intra-module interactions and the
more complex inter-module relationships.
Discussion
Despite many years of research, the genetic basis of rye resistance to
LR is still poorly understood, with the available information mostly
derived from Mendelian-based analyses [[169]4, [170]5]. The recent
sequencing of rye genomes [[171]6, [172]7] and the publication of two
articles describing the molecular basis of the rye immune response to
the pathogen responsible for LR [[173]8, [174]9] have verified the
findings of earlier related research, while also further elucidating
the rye response to LR. Specifically, several QTLs and regions
containing NLR-encoding gene clusters were identified in the Lo7
genome, two of which include Pr genes (Pr3 on 1RS and Pr6, which is
similar to wheat Lr1 in terms of the encoded protein). Unfortunately,
the gene functions have not been confirmed at the transcriptome or
metabolome level. That’s why, we decided to combined transcriptomic and
metabolomic analyses supported by microscopy-based examinations of
plant–pathogen interactions to more comprehensively characterize the
mechanisms mediating rye defenses against LR.
This study involved three highly inbred rye lines (D33, D39, and L318)
that were previously analyzed in terms of their immune response to LR
under field and laboratory conditions. However, in earlier studies,
these lines were either infected with a mixture of LR isolates present
at a given time and at a given location [[175]11] or with the isolate
associated with the most uniform host–pathogen interaction among all
tested rye lines [[176]10]. Therefore, we used four carefully selected
Prs isolates to investigate the rye responses to compatible and
incompatible Prs isolates.
Phenotyping of the rye-Prs interaction
The specificity of the plant–pathogen interaction was clarified on the
basis of microscopic and macroscopic examinations, which enabled the
classification of compatible and incompatible plant–pathogen
interactions. In the compatible interaction, there was unrestricted
pathogen growth, while in the incompatible interaction, micronecrosis
were observed, indicative of the inhibitory effects of the plant on
pathogen growth. The earlier micronecrosis in line D39 than in line D33
was suggestive of a more effective inhibition of pathogen growth, which
was confirmed by the evaluation of the infection types (Fig. [177]2A).
These observations correlate very well with results in the wheat – leaf
rust interaction, where micronecrosis was observed very early in highly
resistant lines (TcLr9 or TcLr26 - infection type 0), while
micronecrosis did not occur in the compatible interaction (Thatcher –
infection type 4) [[178]52]. Necrosis, described as hypersensitive cell
death, were also observed in the wheat – yellow rust interaction in
both incompatible and compatible interactions [[179]53]. This reaction
was earlier and more extensive in incompatibles, whereas in compatible
it was few and only at the initial stage of infection, as in compatible
interaction D33 (72 hpt) in our experiment. Observed micronecrosis in
an incompatible reaction is a hypersensitivity reaction that is crucial
for effective resistance [[180]54].
Hallmarks of LR revealed by a transcriptome analysis
The changes in the rye transcriptome following an infection by Prs were
detected using an RNA-seq approach. In recent years, RNA-seq analyses
have become common in various biological research fields. For example,
it has been used to identify plant resistance genes [[181]14, [182]15].
To identify the significant DEGs, we analyzed the transcriptomic data
using the following strict selection criteria to eliminate the genes
with only a minor role in the rye defense response to LR: FDR < 0.01,
|log[2](fold-change)| ≥ 2, and BM > 50. Although transcriptome analyses
are typically conducted using less restrictive criteria (e.g.,
FDR < 0.05 and |log[2](fold-change)| ≥ 0.5–1) [e.g. [[183]14, [184]15,
[185]55]], some previous studies used similarly strict selection
criteria. For example, Coram et al. [[186]56] used strict selection
criteria to analyze the transcripts associated with race-specific
resistance to stripe rust in wheat. Furthermore, a third parameter
(i.e., BM) must also be considered, but it is often overlooked despite
its importance for characterizing the expression level of a specific
gene. We believe that in certain instances, DEGs selected solely on the
basis of other parameters may be unreliable and lack genuine biological
significance. The proportions of the up-regulated and down-regulated
DEGs were almost the same (52% vs. 48%). In contrast, only 15% of the
identified DEGs were down-regulated in the wheat–Agropyron cristatum 2P
addition line infected with LR in a recent study [[187]15]. The
proportions of up-regulated and down-regulated DEGs varied among the
three rye inbred lines, with up-regulated genes representing the main
group of DEGs in line D39, whereas the opposite trend was detected for
line D33 (even for the infection with ICP Prs). For line L318, there
were more up-regulated DEGs than down-regulated DEGs, but the
difference was less than that detected in line D39. Poretti et al.
[[188]14] observed that LR infections down-regulate the expression of
numerous genes in susceptible lines. In our previous studies, D33 and
L318 were respectively the most and least resistant lines, while D39
exhibited an intermediate resistance, following an infection with a
semi-compatible Prs isolate under field [[189]11] and laboratory
conditions [[190]10]. However, in the current study, these lines
responded differently to compatible and incompatible Prs isolates. This
difference may be related to changes in sensitivity due to inbreeding;
in the last few years, there has been a progressive weakening of line
D33 under field conditions. Similar inbreeding depression has not been
observed in the other two lines. Notably, only two NBS-encoding genes
were identified using our strict selection criteria. Specifically,
SECCE6Rv1G0420800, which was localized to chromosome 6R and encodes an
NBS-LRR disease resistance protein, was detected in the L318_CP_36
comparison, whereas SECCE7Rv1G0523960, which was localized to
chromosome 7R and encodes an NB-ARC domain-containing protein, was
detected in the D39_CP_20 and D39_CP_36 comparisons. Both genes were
differentially expressed at a relatively low level in the compatible
plant–pathogen interaction. Perhaps SECCE6Rv1G0420800 is co-localized
with the Pr1 or Pr-e genes, while SECCE7Rv1G0523960 is co-localized
with Pr2 previously identified by Wehling et al. [[191]2] and Roux et
al. [[192]4], but this possibility will need to be experimentally
verified. None of the genes detected in our study matched the genes
identified by Vendelbo et al. [[193]8, [194]9]. However, the DEGs
identified using less stringent parameters 1 ≥ |log[2](fold-change)|≤
2, which are not provided herein, included SECCE5Rv1G0365570, which was
detected by Vendelbo et al. [[195]9] at position 807.97 Mb on
chromosome 5RL. This gene was differentially expressed exclusively in
line D33 infected with ICP Prs, but only at 36 hpt. The expression of
NBS-encoding genes at low levels does not reflect the contributions of
these genes to the rye immune response to Prs [[196]19], particularly
during the development of ETI.
Most common genes associated with the interactions between rye and CP and ICP
isolates
Among the 877 unique DEGs, three groups of genes encoding CYP450s,
RLKs, and POs, were more abundant than the other genes. The enzymes
encoded by these genes are the primary contributors to plant immune
responses to fungal pathogens, including Puccinia species [[197]10,
[198]57, [199]58]. Accordingly, their presence among the DEGs affected
by Prs was unsurprising.
The CYP450s, which belong to one of the largest enzyme families, are
crucial facilitators of NADPH- and/or O[2]-dependent hydroxylation
reactions in primary and secondary metabolism across various organisms.
In plants, they are also critical for responses to abiotic and biotic
stresses because they affect phytoalexin biosynthesis, hormone
metabolism, and the biosynthesis of some other secondary metabolites
such as BXs [[200]59, [201]60]. Dobon et al. [[202]61] reported that
the expression of CYP450-encoding genes increases in wheat infected
with Puccinia striiformis f. sp. tritici at 3 dpi, which coincides with
the timing of haustorium proliferation. In our study, two
CYP450-encoding genes (SECCE4Rv1G0248210 and SECCE7Rv1G0520230) were
common among the DEGs in all three inbred lines. The first gene
(SECCE4Rv1G0248210; up-regulated mostly after the CP Prs infection)
encodes dolabradiene monooxygenase, which converts dolabradiene to
dolabralexins, a class of defense-related diterpenoids. These compounds
accumulate in maize treated with the fungal pathogens Fusarium
verticillioides and Fusarium graminearum [[203]62]. In addition to
maize, dolabradiene monooxygenase has been detected in only a few
coniferous tree species in the families Araucariaceae and Cupressaceae.
The identification of SECCE4Rv1G0248210 as a DEG in all examined rye
lines indicates that dolabralexins are synthesized by rye and these
specialized diterpenoid metabolites may participate in the immune
response of rye to Prs. There is no information available regarding the
specific function of the second CYP450-encoding gene
(SECCE7Rv1G0520230) during the response to Prs. We previously
determined that the expression levels of two other CYP450-encoding
genes, namely SECCE5Rv1G0298500 (ScBx4) and SECCE5Rv1G0298490 (ScBx5),
are down-regulated by LR [[204]10]. The RNA-seq analysis conducted in
the current study indicated the expression of these genes was
down-regulated by LR, but only in the following four comparisons:
D33_CP_36 and D33_ICP_36 (SECCE5Rv1G0298500) and L318_CP_20 and
L318_CP_36 (SECCE5Rv1G0298490). However, in line L318 infected with the
ICP isolate, ScBx5 expression was up-regulated. This may be due to the
highest DIBOA content in the L318 line (compared to the other two
lines; Table S[205]1), in whose biosynthesis the ScBx4 gene is
involved. Besides ScBx4 and ScBx5 genes, another gene controlling BX
biosynthesis detected by us previously [[206]10], namely Scglu
(SECCE2Rv1G0138870), was also identified in the RNA-seq analysis as a
downregulated DEG, only in the D33 line.
The receptor-like kinases (RLKs) serve as pattern recognition receptors
that perceive signals or specialized elicitors secreted by pathogens
known as PAMPs, thereby activating PTI [[207]63]. Therefore, it is
somewhat surprising that RLK-encoding genes have not been the subject
of transcriptomic studies on LR or other rusts. There are only a few
published articles on this topic. According to RNA-seq analysis
performed by Zou et al. [[208]58], in Triticum urartu infected with
stripe rust, the TuRLK1 expression level increases. The encoded RLK is
essential for the immune response to stripe rust, which is mediated by
the NLR protein YrU1. Additionally, Gu et al. [[209]64] investigated
the role of the cysteine-rich receptor-like kinase (CRK), which belongs
to a large subgroup of plant RLKs. They focused on TaCRK2 and its
expression during an incompatible interaction with LR, which is
dependent on Ca^2+. By decreasing TaCRK2 expression in wheat, they
observed a dramatic increase in the hypersensitive response and the
number of HMCs at the infection site. Considering the role of RLKs in
immune responses, one might expect that the expression of the
corresponding genes would increase in LR-infected rye. The up-regulated
expression of RLK-encoding genes was generally observed in two lines
(D39 and L318) infected with the compatible isolate of Prs. However, in
D33, only three of the 10 RLK-encoding genes had up-regulated
expression levels, which may help to explain the weak defensive
potential of this line. This may be related to its extensive
inbreeding.
The significance of POs in the induced plant defense against fungal
pathogens is associated with their role in reinforcing the cell wall
(i.e., physical barrier) and enhancing the production of ROS and
phytoalexins [[210]65]. The relationship between stem rust resistance
and increased PO activity was first reported in 1971 [[211]66].
However, there have been relatively few published reports describing
this relationship. Nevertheless, earlier research confirmed that the
total [[212]67] and intercellular PO [[213]68] activities increase in
response to LR. Dmochowska-Boguta et al. [[214]57] observed that two of
the four POs in wheat are strongly induced by LR. Moreover, in the
susceptible cultivar Thatcher and resistant isogenic lines with
different Lr resistance genes, there is a PO-dependent oxidative burst.
It was suggested that (class III) POs play a leading role in ROS
formation during the wheat response to LR. The importance of POs in
immune responses was also demonstrated in an earlier study on stem
rust-infected wheat by Moerschbacher et al. [[215]69]. More
specifically, PO activities increased in infected wheat plants
(compatible and incompatible interactions) from 16 to 48 hpt; after
this period, the PO activity in the resistant plants continued to
increase for up to 7 days (compatible interaction). Alternatively, the
PO activity either remained constant or slowly decreased beginning at 2
dpi (incompatible interaction) [[216]69]. In our analysis, the
expression of most PO-encoding genes was up-regulated (mostly after the
CP Prs infection at 36 hpt); two of these genes were also expressed at
very high levels at 36 hpt in line L318. Almost all cases of
down-regulated PO expression were detected in line D33.
In addition to the groups discussed above, we detected several others
that were more specific either for a given type of plant–pathogen
interaction and/or for a given time-point. Some of the genes that were
primarily induced by CP Prs were revealed to encode methylesterases
(MEs), which are essential enzymes that coordinate carbohydrate
metabolism, stress responses, and sugar signaling [[217]70]. Following
an infection by fungal pathogens, methyl esterifications can improve
plant resistance because highly methyl-esterified pectins may be less
susceptible to the hydrolytic activities of pectic enzymes, including
fungal endopolygalacturonases [[218]71, [219]72]. Wiethölter et al.
[[220]73] demonstrated that during a stem rust infection of wheat,
there is a significant difference in the homogalacturonan contents
between susceptible and resistant lines. This difference is associated
with a nonrandom and blockwise distribution of the MEs in the
susceptible lines, which is in contrast to the more random distribution
of these enzymes in the resistant lines. In the present study, only two
ME-encoding genes (SECCE3Rv1G0211990 and SECCE3Rv1G0211270) were common
DEGs in all three rye inbred lines. In D33 and L318, the expression of
these genes was up-regulated. Unexpectedly, the expression levels of
these genes were down-regulated at 36 hpt in D39 infected with
compatible isolates. Assuming that methyl esterifications enhance plant
resistance, it is reasonable to expect the expression of ME-encoding
genes to increase, at least during incompatible interactions. We
observed that in line D39, in which the majority of genes relevant to
defense responses to LR were up-regulated, these two genes were
down-regulated. Interestingly, only one gene (SECCE3Rv1G0193070) from
the ME family was up-regulated at 20 hpt in D33 infected with the ICP
isolate. Nevertheless, in Blumeria graminis f. sp. hordei, thiopurine
methyltransferase may be targeted by a fungal effector candidate
[[221]74, [222]75]. Hence, it remains possible that MEs play a
significant role in the rye–Prs interaction. Our transcriptome analysis
revealed a significant decrease in SECCE7Rv1G0457810 expression in all
inbred lines infected with a CP isolate, implying the encoded enzyme
may be targeted by Prs.
By analyzing our RNA-seq data, we identified pathogenesis-related
protein 1 (PR-1)-encoding genes as the only large group of genes with
up-regulated expression levels at 20 hpt in the samples infected with
both CP and ICP isolates. The PR-1 proteins can inhibit the growth of a
variety of fungal pathogens [[223]76]. In wheat, two PR-1 genes, namely
TcLr19PR1 [[224]77] and TaLr35PR1 [[225]78], confer resistance to LR.
Neugebauer et al. [[226]79] determined that increased PR-1 expression
(Acc. No [227]FJ815167) is related to the immune response of the
susceptible cultivar Teacher to LR infections. In our analysis, three
PR-1-encoding genes (SECCE5Rv1G0309790, SECCE5Rv1G0309810 and
SECCE5Rv1G0309830) were expressed at high levels in both types of plant
responses to Prs. The expression of these three genes was up-regulated
at 20 hpt in D39 infected with CP and ICP isolates, indicating they may
be involved in the initial activation of plant defense mechanisms.
Additionally, pathogenesis-related protein 1 (SECCE7Rv1G0464120)
expression was up-regulated regardless of the response type in both D33
and D39. Notably, the magnitude of the expression changes was greater
in D39 than in D33. In contrast, in L318, the expression of only two
PR-1-encoding genes was up-regulated at 36 hpt, namely
SECCE5Rv1G0359230 [log[2](fold-change) of 2.05] and SECCE7Rv1G0480890
[log[2](fold-change) of 2.79]. Interestingly, in D39 infected with CP
Prs, the expression levels of the genes encoding PR-1 proteins were
mainly up-regulated, but almost exclusively at 20 hpt.
The genes encoding chlorophyll a/b-binding proteins (CabBP) had a
specific down-regulated expression pattern in line D33, especially at
20 hpt in the plants infected with CP isolates. This down-regulated
expression was observed for 10 genes. In contrast, at the same
time-point during the ICP infection, the expression of only one gene
(SECCE3Rv1G0200810) was down-regulated in D33. In line L318 infected
with the CP isolate, the expression of only one gene from the CabBP
family was up-regulated (SECCE4Rv1G0250540) at 36 hpt. Unexpectedly,
there were no significant DEGs related to CabBP in D39. Earlier
research confirmed CabBPs, which are representative nuclear-encoded
chloroplast proteins, are components of the light harvesting complex of
photosystem II and are present in the thylakoid membrane of
photosynthesizing plants [[228]80]. By providing energy, photosynthesis
is closely integrated into the defense response to pathogens [[229]81].
The suppression of nuclear-encoded chloroplast proteins may allow
pathogens to overcome PTI [[230]82]. However, there is currently no
evidence of a relationship between these proteins and rust resistance.
Thus, to the best of our knowledge, this is the first study to show
that LR significantly inhibits the expression of CabBP-encoding genes
in a genotype-dependent manner.
The xyloglucan endotransglucosylase/hydrolases (XTHs) belong to a group
of enzymes specific to plant–pathogen interactions [[231]83–[232]85].
The expression of XTH-encoding genes was down-regulated following the
infection with ICP Prs. These genes were primarily expressed in line
D33 at 36 hpt. The functions of these enzymes related to the immune
response to certain fungal pathogens have been thoroughly
characterized. By catalyzing the cleavage and polymerization of
xyloglucan molecules, XTHs mediate cell remodeling and are considered
to be key enzymes for plant cell wall reconstruction [[233]83–[234]85].
Thus, the roles for XTHs during responses to cell wall-degrading
pathogens seem obvious. Indeed, the protective effects of XTHs have
been confirmed in plants infected with certain fungal pathogens,
including F. graminearum [[235]86], Pyrenophora teres [[236]87], and
Macrophomina phaseolina [[237]88]. The observed down-regulated
expression of XTH-encoding genes was in accordance with the findings of
earlier studies on the expression of these genes. There is currently no
evidence of an association between the increased accumulation of XTHs
and the increased expression of the related genes and immune response
to LR.
The glycosyltransferases (UGTs) are enzymes belonging to a multigenic
and highly diverse superfamily that is ubiquitous among living
organisms and is associated with disease resistance, including LR
resistance. According to Pujol et al. [[238]89], Ta.90050, which
encodes a UGT, is putatively involved in the late resistance response
of wheat to stem rust. In our RNA-seq analysis, we identified two
UGT-encoding genes, which had down-regulated expression levels
specifically in D33. The expression of the first gene
(SECCE7Rv1G0520220) was down-regulated during the interaction with the
CP isolate, while the expression of the second gene (SECCE6Rv1G0435050)
was down-regulated during the interaction with the ICP isolate.
Recently, Amo and Soriano [[239]90] used a meta-QTL analysis approach
to identify five up-regulated genes encoding GTs, of which
TraesCS7D02G217700 was proposed as a candidate gene mediating LR
resistance. In rye, the protective role of GTs may be associated with
the fact they catalyze the conversion of DIBOA to DIBOA glucoside,
which accumulated in two lines (D39 and L318) between 8 and 24 hpt and
in line D33 at 24 hpt in plants inoculated with the semi-compatible Prs
strain [[240]10]. Our RNA-seq data analysis indicated the up-regulated
expression of GT-encoding genes generally occurred at 36 hpt. However,
these genes were not specific to any of the analyzed inbred lines or
reaction types (compatible or incompatible). Because UGTs contribute to
the biosynthesis of cell wall polysaccharides and glycoproteins, these
genes may be important for plant defense responses to pathogens,
especially considering LR-induced plant cell wall modifications are
essential for HMC development.
Antifungal hydrolases (beta-1,3-glucanase; Glu) belonging to the PR-2
family reportedly influence plant defense responses to fungal
pathogens, including those responsible for LR [[241]77, [242]79,
[243]91, [244]92], stem rust [[245]93], and stripe rust [[246]94].
Münch-Garthoff et al. [[247]93] observed that the activation of glu
transcripts occurs very early, approximately 16 h before the typical
hypersensitive response is detectable, which is long before a tight
contact between the pathogen and a host cell is established. In wheat,
the expression of the Glu-encoding gene TcLr19Glu is induced by Pt
during both compatible and incompatible interactions, but the
expression levels are greater during incompatible interactions. The
TcLr19Glu expression level peaks between 24 and 48 hpt [[248]77].
Neugebauer et al. [[249]79] analyzed wheat cultivar Thatcher infected
with six Pt races. They detected a gradual increase in the expression
of a Glu-encoding gene as well as PR-1 and PR-5 thaumatin-like
protein-encoding genes between 1 and 3 dpi, which was followed by a
decrease in expression until 5 dpi and then another increase at 6 dpi.
This may indicate that specific changes in the production of
beta-1,3-glucanases influence whether an LR infection is successful.
During our transcriptomic analysis, we detected fluctuations in Glu
expression. For example, in D39 infected with the CP isolate, the
log[2](fold-change) for SECCE6Rv1G0429310 was 3.18 at 20 hpt, whereas
it was 2.15 at 36 hpt. In contrast, the log[2](fold-change) for
SECCE6Rv1G0429650 in D39 during the compatible interaction was 2.62 at
20 hpt and 2.83 at 36 hpt. This trend was even more pronounced for
SECCE6Rv1G0429680, with a log[2](fold-change) of 2.09 and 2.89 at 20
and 36 hpt, respectively. Several genes encoding beta-1,3-glucanases
had up-regulated expression levels, particularly during the compatible
interactions involving line D39.
Although trypsin inhibitors (TIs) are protease inhibitors that are
among the first PTI-related proteins to be activated in response to
pathogens [[250]95, [251]96], their relationship to rust resistance is
unknown. The only article describing the involvement of serine-type
protease inhibitors [[252]96] indicated that in wheat plants infected
with stripe rust, several genes encoding Bowman-Birk (BBI) protease
inhibitors are differentially expressed (usually up-regulated). The TI
encoded by SECCE5Rv1G0365990, which was selected on the basis of our
RNA-seq data, belongs to the BBI class. The genotype specificity of the
expression of this gene may be indicative of a role in the general
plant response to LR.
The above-mentioned findings highlight the complexity of the response
of rye inbred lines to LR, while also emphasizing the importance of
interpreting the results on the basis of the Prs isolate, time-point,
and genetic background of the plant.
Common and specific transcripts among the CP and ICP interactions
Fifteen DEGs were common to all comparisons (Fig. [253]4B; Table
S[254]5). These DEGs may encode proteins with critical effects on
pathogen development regardless of the reaction type, implying they are
“core genes” for the rye response to LR. Interestingly, the expression
levels of all of these genes were up-regulated by Prs. Some of these
genes encode proteins belonging to the CYP450, endo-1,3-beta-glucanase,
DXS, ammonium transporter, and aldo/keto reductase families. Moreover,
three of these genes belong to the WIR1a family. The members of this
family encode integral membrane proteins affecting the cell wall
structure [[255]97] and immune responses to fungal pathogens, including
those causing rust infections. As mentioned above, Coram et al.
[[256]56] determined that among the 28 stripe rust-induced genes at 24
and 48 hpt, the gene encoding a pathogen-induced WIR1A protein is the
fourth most important gene for plant responses to stripe rust (i.e.,
after the genes encoding a copper-binding protein, heat-stress
transcription factor, and kaurene synthase). In a previous study by
Chen et al. [[257]98], the common wheat genes associated with the
Yr39-mediated adult-plant resistance to high temperatures and the
Yr5-mediated all-stage resistance included a WIR1-encoding gene. In
plants, DXS is an essential enzyme for isoprenoid biosynthesis because
it catalyzes the conversion of pyruvate and glyceraldehyde 3-phosphate
to 1-deoxy-D-xylulose 5-phosphate, which is a key precursor of
important plant isoprenoids, including carotenoids, chlorophylls,
gibberellins, and essential oil constituents [[258]99]. However, there
is no evidence indicating any association between this specific cluster
of genes and the response to LR.
Ammonium transporters primarily facilitate the uptake, distribution,
and homeostasis of ammonium (NH[4]^+) in plants, but their specific
involvement in LR infections is unclear. Jiang et al. [[259]100]
detected the up-regulated expression of an AMT2-type ammonium
transporter gene (TaAMT2;3a) in wheat infected with a virulent Prs
isolate. Moreover, Prs growth is hindered by a decrease in TaAMT2;3a
expression, resulting in a decrease in the number of hyphal branches
and HMCs.
In the immune response to LR, differentially regulated genes, specific
only for a given line and/or a given type of interaction, are equally
important. Our analyzes detected four groups of such genes encoding NAC
domain-containing protein, specific for CP, XTH specific for ICP, CabBP
and Aquaporin, differentially regulated only in line D33. The role of
two of the above-mentioned proteins - XTH and CabBP in the immune
response to LR has been discussed above. The functions of the other two
proteins are also related to this reaction.
The plant NAC gene family has been suggested to play important roles in
stress response [[260]101]. For LR, the role of these proteins has been
proven in wheat [[261]102]. Using the same experimental approaches as
we have - RNA-seq and RT-qPCR, the authors identified the activation of
the TaNAC069 gene in response to Puccinia triticina and related
signaling molecules. Aquaporins are membrane channel proteins present
in all living organisms and having many physiological functions during
plant growth and development. They are assumed to play also an
important role in plant defense responses against biotic and abiotic
stresses including fungal diseases [[262]103]. Among wheat genes
affected by LR, Prasad et al. [[263]104] found genes encoded
aquaporins. However, its expression was the highest at the
pre-haustorial stage (6 and 12 h post inoculation), so much earlier
than in our experiment.
KEGG enrichment analysis in silico
The KEGG pathway enrichment analysis enabled us to identify specific
categories that were associated with different time-points and reaction
types, thereby providing insights into the plant defense response. At
20 hpt of the CP interaction, the enrichment of “Carbohydrate
metabolism” and “Energy metabolism” suggests that these processes play
a role in plant defense mechanisms. It can be assumed that the
synthesis and use of carbohydrates and energy are crucial for
sustaining the metabolic requirements associated with an effective
defense against pathogens [[264]105]. Interestingly, in the ICP
reaction at 20 hpt, a more general category (“Metabolism”) was
enriched, implying that various metabolic pathways may be activated
during the plant defense response, which reflects the complexity and
the interconnected nature of plant immune mechanisms. Furthermore, the
enrichment of “Genetic information processing” during the CP and ICP
interactions suggests that transcriptional reprogramming is critical
for plant immune responses to LR.
At 36 hpt during the CP interaction, the enrichment of “Metabolism of
cofactors and vitamins” and “Organismal systems” is indicative of the
activation of additional defense mechanisms. These categories encompass
several processes, such as the production of secondary metabolites,
reinforcement of physical barriers, and modulation of overall
physiological responses, that enhance plant resistance to pathogens.
Phenylpropanoid metabolism is considered to be one of the most
important metabolic processes in plants. For example, it influences the
interaction between plants and the environment by providing flavonoids
that “scavenge” the ROS induced by environmental stresses and many
defense-related specialized metabolites [[265]15, [266]20, [267]21,
[268]106, [269]107]. Phenylpropanoid metabolism is also important for
the defense response to LR. The KEGG analysis and GSEA performed by Ji
et al. [[270]15] assigned the DEGs in wheat–Agropyron cristatum 2P
addition line II-9-3 infected with LR to several pathways (e.g.,
phenylpropanoid biosynthesis). In our metabolomic analysis,
phenylpropanoids were identified as DAMs in most comparisons (discussed
later). Furthermore, Tsers et al. [[271]17] showed that the initial
reactions of the phenylpropanoid biosynthesis pathway may be induced in
rye infected with M. nivale. Conversely, during the ICP reaction, the
enrichment of “Amino acid metabolism” and “Energy metabolism” at 36 hpt
suggests that the plant intensifies its metabolic activities to cope
with the ongoing defense response. The synthesis and utilization of
amino acids and energy-rich molecules are likely vital for satisfying
the heightened metabolic demands associated with a successful defense
against pathogens [[272]108].
In summary, our KEGG pathway enrichement analysis identified genes
involved in carbohydrate and energy metabolism that are specific to the
CP interaction. The enrichment of diverse metabolic categories in the
ICP interaction may reflect the activation of a multifaceted defense
response, potentially involving the production and use of secondary
metabolites. Additionally, the enrichment of “Genetic information
processing” and various metabolic categories is indicative of the
activation of complex defense mechanisms and transcriptional
reprogramming during both compatible and incompatible interactions with
Prs.
High genotype- and treatment specificity of the metabolome-related immune
response
In our metabolomics studies we found a predominance of genotype- and
treatment specific treatment-specific DAMs over common DAMs in all
comparisons. The genotype-specific DAMs may be indicators of the
intra-species diversification of immune responses, making them
potentially useful metabolic biomarkers of LR resistance that can
optimize the selection of the most resistant cultivars [[273]107].
Among the tested lines, the line L318 had the most distinct immune
response to both Prs strains, which may be related to its relatively
low resistance [[274]109].
We identified several DAMs characteristic of both types of
interactions: for the infection with the CP isolate benzoic acid
derivatives and flavonoids were most specific when for ICP – these were
chrysin and peptides (such as isoleucylglutamine, isoleucylvaline and
S-adenosyl-homocysteine). The role of benzoic acid derivatives and
flavonoids in the immune response against fungal pathogens, including
rust fungi, is well known [[275]109]. However, Mashabela et al.
[[276]109] showed that in wheat infected with Puccinia striiformis f.
sp. tritici both defence metabolites are accumulated faster in
resistant cultivar (ICP interaction) compared to the susceptible (CP
interaction) cultivar. So it seems that rye has its own rye-specific
way of defending against LR. To our knowledge, the role of chrysin,
isoleucylglutamine, isoleucylvaline and S-adenosyl-homocysteine in
plant defense against LR has never been investigated. So, we are the
first to describe these compounds as defense metabolites synthetized by
rye in response against LR, and, additionally, specific for ICP
interactions.
Complex relationships between the rye transcriptomic and metabolomic
responses to LR
The significant correlations among the DAMs and DEGs reflected the
extensive reprogramming of rye metabolic activities during an infection
with Prs. The functional annotation of the DAMs and DEGs revealed
pathways mainly related to the modulation of ROS levels. The dominant
effects of phenylpropanoids in the metabolomic response of cereals
infected with Puccinia ssp. were previously noted for wheat [[277]32],
maize [[278]110], and barley [[279]111]. Metabolites related to the
“Phenylpropanoid biosynthesis” pathway are the precursors for
specialized compounds that scavenge ROS, regulate photosynthesis, and
inhibit fungal growth, thereby influencing plant immunity [[280]20,
[281]112]. Multiple roles for phenylpropanoids in plant defense
responses are consistent with the reported association between cereal
stress resistance and hydroxycinnamic acid derivatives esterified with
amides [[282]21, [283]106] and quinic acids [[284]30]. In plants,
phenylpropanoids are produced as soluble compounds or as cell wall
components; the analysis of the latter compounds requires an alkaline
extraction step. Therefore, only soluble metabolites were considered in
the current study [[285]21]. The identification of phenylpropanoids
with multiple functions is consistent with our detection of a strong
interaction between pathogen-triggered metabolites and genes. Such
complex relationships presumably reflect the diversity in the metabolic
mechanisms associated with these compounds. Intriguingly, the
phenylpropanoids assigned to “Flavone and flavonol biosynthesis” and
“Flavonoids biosynthesis” accumulated exclusively at 36 hpt in lines
D33 and L318 infected with the ICP isolate.
The induction of “Ascorbate and aldarate metabolism” as an immune
response in L318 infected with the ICP isolate was indicative of the
accumulation of effective antioxidants as well as regulators of
photosynthesis and transmembrane electron transport [[286]113]. Another
effective antioxidation system related to “Glutathione metabolism” was
detected in D33.
There was also a considerable enrichment of the diterpenoid-related
metabolic pathways in rye infected with Prs. Diterpenoids contribute
directly to plant defenses against pathogenic fungi through their
antibiotic activities [[287]114]. The accumulation of many terpenoid
compounds in rice, such as oryzalexins and momilactones, is positively
correlated with increases in the efficiency of basal defense responses
in rice [[288]27]. The current study revealed the relationships between
plant immunity and gibberellins, which are mainly known as regulators
of developmental processes throughout the plant life cycle.
Nevertheless, gibberellins contribute to plant immunity by modulating
the SA/JA cross-talk in the immune signaling network as well as the
scavenging of ROS [[289]115]. In rice, the accumulation of gibberellins
increases the resistance to necrotrophs and the susceptibility to
(hemi)biotrophs [[290]116]. Interestingly, the opposite effects were
observed in wheat and barley [[291]117]. In our experiments, the
metabolites in the gibberellin biosynthesis module mainly accumulated
in response to both types of Prs strains, suggestive of the complex
effects of gibberellins on rye immune responses.
The observed increase in the contents of the carbohydrates associated
with “Starch and sucrose metabolism” in D33 infected with the CP
isolate is likely related to a disturbance in sugar homeostasis and
their storage [[292]118]. Starch and sucrose metabolism is closely
related to soluble, galactose-derived oligosaccharides, which are
predicted to be antioxidants and ABA-related signaling molecules
[[293]119]. Moreover, the management of sugar levels modulates the
expression of defense-related phenylpropanoids [[294]118]. Therefore,
we speculate that the enrichment of these pathways is associated with
the regulation of secondary metabolites. This is supported by the
findings of an earlier study, which indicated biotrophic pathogens
consume significant amounts of carbohydrates from the host plant, which
disrupts normal carbohydrate and nucleotide sugar metabolism
[[295]120]. The enrichment of “Cutin, suberine and wax biosynthesis”
during the interaction between D33 and the ICP isolate may be related
to the reinforcement of the extracellular barrier by cutin and suberin
during pathogen infections [[296]121]. In addition, similar to our
results, a previous study involving wheat detected changes to the
nitrogen reservoir induced by developing pathogens [[297]122].
Conclusions
In summary, our transcriptomic and metabolomic analyses identified
multiple DEGs and DAMs following a Prs infection. This study is the
first to apply such a comprehensive approach to examine LR and rye.
Additionally, the importance of some genes for the immune response of
rye to LR, such as genes encoding dolabradiene monooxygenase,
thiopurine methyltransferase, XTH, and basic secretory proteins, was
revealed for the first time. This research is an important step toward
understanding the early reaction of rye to an infection with Prs, which
is responsible for one of the most damaging rye diseases including
identification of both common and specific DEGs and DAMs for CP and ICP
interaction. Likewise, a pool of genotype-specific and common DEGs for
all three rye lines was identified. The discrepancy found in the
dynamic changes of DEGs and DAMs between ICP and CP interactions
suggests that there is a complex response that leads to plant
resistance or susceptibility. Using various research approaches we were
able to unveils an intricate network of gene and metabolite
interactions, providing insights into potential key regulatory
components. This insight is derived from the identification of strong
intra-module interactions as well as more complicated inter-module
relationships. Furthermore, the generated data may form the basis of
genome- and metabolome-based selection to support rye breeding toward
increasing phenylpropanoids, flavonoids, terpenoids and gibberellins
accumulation as well as expression of genes encoding several more
important groups of proteins, such as UGTs, beta-1,3-glucanases,
cytochrome P450, PR-1 and POs.
Supplementary Information
[298]12870_2024_4726_MOESM1_ESM.xlsx^ (478.8KB, xlsx)
Additional file 1: Table S1. Characteristics of rye inbred lines, D33,
D39, and L318, chosen for experiments.
[299]12870_2024_4726_MOESM2_ESM.docx^ (13.1KB, docx)
Additional file 2: Table S2. Resistance reaction of three rye inbred
lines, D33, D39, and L318, determined based on detached-leaf test.
[300]12870_2024_4726_MOESM3_ESM.docx^ (13.9KB, docx)
Additional file 3: Table S3. List of primers used in RT-qPCR
experiments.
[301]12870_2024_4726_MOESM4_ESM.xlsx^ (10.7MB, xlsx)
Additional file 4: Table S4. log2(fold-change)|values for metabolites
in all 12 comparisons (LC-MS). LC-MS data processed.
[302]12870_2024_4726_MOESM5_ESM.xlsx^ (420.5KB, xlsx)
Additional file 5: Table S5. RNA-seq results for all DEGs (only
comparisons containing DEGs are listed below). List of all unique
DEGs. KEGG-based*) functional classification of all unique
Differentially Expressed Genes (DEGs).
[303]12870_2024_4726_MOESM6_ESM.xlsx^ (94.3KB, xlsx)
Additional file 6: Table S6. Common and unique DEGs for both types of
interactions and both time points. Gene onthology analysis for group of
common DEGs for both types of interactions and both time points. Gene
onthology analysis for group of unique DEGs for CP 20hpt. Gene
onthology analysis for group of unique DEGs for ICP 20hpt. Gene
onthology analysis for group of unique DEGs for CP 36hpt. Gene
onthology analysis for group of unique DEGs for ICP 36hpt.
[304]12870_2024_4726_MOESM7_ESM.xlsx^ (5.1MB, xlsx)
Additional file 7: Table S7. Common and unique DEGs; different
comparisons.
[305]12870_2024_4726_MOESM8_ESM.xlsx^ (10.1KB, xlsx)
Additional file 8: Table S8. The most strongly up- and down-regulated
genes.
[306]12870_2024_4726_MOESM9_ESM.xlsx^ (973.8KB, xlsx)
Additional file 9: Table S9. DEGs common for three rye inbred lines.
[307]12870_2024_4726_MOESM10_ESM.xlsx^ (34KB, xlsx)
Additional file 10: Table S10. The complete joint-pathway enrichment of
the DAMs and DEGs.
[308]12870_2024_4726_MOESM11_ESM.xlsx^ (73.3KB, xlsx)
Additional file 11: Table S11. Nodes of the correlation network and
their properties. Edges of the correlation network and their
properties.
[309]12870_2024_4726_MOESM12_ESM.xlsx^ (128.4KB, xlsx)
Additional file 12: Table S12. Common and unique DAMs selected on the
basis of Venn Diagram.
[310]12870_2024_4726_MOESM13_ESM.pptx^ (64.2KB, pptx)
Additional file 13: Fig. S1. Comparison between relative expression
levels determined by RNA-seq and RT-qPCR analyses. The sequences of
primers used in RT-qPCR analysis (including primers for reference gene)
are listed in Table S3. The asterisk (*) indicate statistically
significant difference with p < 0.05.
Acknowledgements