Cereal cyst nematodes (CCNs) are
the most widely distributed parasites of cereals and grasses, which have caused substantial economic losses to wheat production (Smiley and Nicol 2009). In China, CCNs are known to be distributed across
16 provinces (Chen et al.
1991; Peng et al. 2012). Among the CCNs, Heterodera avenae and H.
filipjevi negatively impact the wheat production,
resulting in significant reductions of 15–80% in the yield annually (Li et al. 2010).
Most CCNs are able to complete only one generation of
their life cycle during each crop season. The J2s (infective second-stage
juveniles) penetrate the epidermal and cortical cells using their robust
stylet, following which they migrate intracellularly
toward the vascular stele (Jung
and Wyss 1999). After the “migration period”, the nematodes select an
initial syncytium cell (ISC) and stay motionless for approximately eight hours.
When this stage, referred to as the “feeding-preparation period”, is completed, it is followed by the “feeding site
period” which begins at approximately 18 h post-inoculation (hpi). When the nematodes enter into the ISC, they
release cell-wall modifying proteins from their esophageal glands, inducing the
formation of enlarged feeding cells. Thereafter, the nematodes feed on the
water- and nutrient-conductive tissues in the host root, marking the completion
of their life cycle (Sobczak
and Golinowski 2009).
The infection of wheat roots with CCN leads to impairments in the
physiological aspects and growth of the infected host plants. Several
histological changes such as the formation of the syncytium, dissolution of
surrounding cell walls, enlargement of nucleoli, accumulation of endoplasmic
reticulum, deposition of callose, increase in the number of vacuoles, and
appearance of nematode secretions near the stylet during the period between 18
hpi and 4 dpi have been reported (Mahalingam and Skorupska 1996). The CCN infection elicits the response of the
host defense system, along with tremendous alterations in the gene expression
in wheat cells. Signaling within both the nematode and the plant is necessary for
the formation and maintenance of the syncytium (Davis et al.
2008; Gheysen and Mitchum 2009). Identification of active,
differentially-expressed genes might provide insight into the molecular
mechanisms of interaction between nematode and plant, and propose technologies
for crop protection (Williamson and Kumar 2006; Klink and Matthews 2009). GeneChip microarray technology serves as a useful
platform for studying alterations in gene expression during the process of
nematode infection. A study was conducted on soybean–SCN (soybean cyst
nematode) interaction using soybean array, focusing on the sensitive response
of soybean to the SCN infection (Puthoff et al.
2003; Ithal et al.
2007a). Other related studies focused on SCN infection
in susceptible and resistant varieties (Klink et al.
2007b; Mazarei et al.
2011). In the present study, customized Agilent 4 × 44
K wheat whole-genome oligo microarray, containing 43,803 probe sets and
spanning over 42,000 transcripts, was utilized for investigating early gene
expression in the wheat roots infected with H.
filipjevi. The present research would assist in understanding the wheat–CCN
interaction during the onset of the syncytium establishment.
Materials and Methods
Experimental materials
An H. filipjevi-susceptible wheat cultivar Chinese Spring was used as
a host in the present study (Zhang et al.
2012). The seeds from the host plant were
surface-disinfected by soaking in 95% ethanol for 3 min, followed by further
treatment with 10% sodium hypochlorite for 10 min in a laminar flow hood using
sterile culture techniques. Seedlings of wheat were grown in sterile sand in
flats (20 × 20 × 10 cm3) in a growth chamber for one week, following
which they were pulled out softly from the sterile sand, rinsed with sterile
water, and dried using bibulous paper.
The cysts of H. filipjevi (pathotype Hfc-1) were obtained from Xuchang, Henan province,
China (34.04°N, 113.74°E) (Li et al. 2010).
Treatments
Hatching of the cysts was achieved
by following the method described ahead. The full cysts with bright color were
separated, sterilized using 0.5% NaClO for 10 min, and washed several times
with sterile water. The selected cysts were added into tubes containing 50 mL
sterile water and stored at 4°C in a refrigerator for two months to retain
moisture. Subsequently, the cysts were incubated at 15°C in an artificial
climate incubator.
In order to prepare the pi-J2s (pre-infective
second-stage juvenile nematodes), the hatched cysts inside the tubes were
poured on a 250-µm mesh cloth and
washed with sterile water. The washed pi-J2s were carefully harvested and
placed into a clean flask, followed by the addition of sterile water until the
volume of 100 mL was reached. After proper mixing, 100 µL of the sterile water containing pi-J2s were pipetted into a hemacytometer to evaluate the exact number of pi-J2s
using a microscope. Ten repeats were averaged for the calculation of the
concentration of J2s in the suspension. Finally, the J2s were diluted to a
final concentration of 4000 individuals mL–1.
Seedlings were placed on moistened
filter paper placed inside plastic trays. The inoculum (nematode suspension)
was added directly to the roots, at a final concentration of 1000 J2s/root. The
blank control (mock-inoculated) was inoculated with the same amount of sterile
water. The roots were then covered with a moistened sheet of germinating paper
and placed in a plastic tray (size: 45 × 50 × 20 cm3) with 1 cm
water level at the bottom for humidity. The tray was then covered with a
translucent plastic film and was maintained under a 16-h light/8-h dark
photoperiod. Using the knowledge from previous experiments, the inoculated
wheat roots were grown for 24 hpi only. The roots of the inoculated and
mock-inoculated were washed; one inoculated seedling root–as a sample, and four
samples were prepared as technical replicas using the same method. Four control
samples were prepared using the same method. Finally, the root tissues were
frozen using liquid nitrogen and stored at −80°C.
Root
penetration assay
The inoculated roots were harvested
at 12 h, 24 h, and 48 h post-inoculation (hpi), followed by soaking in 1.5% sodium hypochlorite
solution for 15 min, and then washing with tap water to remove the excess
sodium hypochlorite. The roots were stained using 1 mL of 3.5% acid fuchsin (Bybd et al. 1983). The solution was heated until boiling, followed
by cooling to room temperature and then rinsing with tap water to remove the
excess stain. The stained roots were used to prepare slide specimens, which
were then observed under a microscope. Ten seedlings were selected at each
sampling time point, and for each of these seedlings, 3 root tips were selected
randomly, followed by counting the number of J2s in the root tissue. In order
to analyze the number of J2s in the root tissue, one-way ANOVA, and Bonferroni
multiple comparison test were performed using S.P.S.S. 20.0 software.
RNA extraction and agilent GeneChip
hybridization
The time points selected for the
collection of the materials were all within the period between the infusion of nematode
in wheat and the formation of syncytium cells. Therefore, rather than the
syncytium cells, the inoculated roots and the mock-inoculated roots were collected
as the experimental materials. Total RNA was extracted from the experimental
group and the control group using the Trizol reagent in accordance with the
manufacturer's instructions (Life Technologies, Carlsbad, CA, USA). The
concentration and purity of the extracted RNA were measured using a
spectrophotometer, and the RNA Integrity Number (RIN) was verified using
Agilent Bioanalyzer 2100 (Agilent).
Probes were prepared using Low
Input Quick Amp Labeling Kit-plus for One-color (Agilent). An aliquot of two
micrograms of total RNA from each sample was converted into complementary RNA
labeled with fluorophore Cyanine 3-CTP (CY3c). The labeled cRNAs were further purified using RNeasy Mini Kit
(QIAGEN) and RNase-Free DNase Set (QIAGEN, GmBH, Germany). The probes were evaluated for yield, concentration, amplification
efficiency, and abundance of CY3c, using Nanodrop ND–1000 spectrophotometer
(Thermo Scientific, USA) at A260 and A550.
The Agilent 4 × 44 K wheat
whole-genome oligo microarray, with 43,803 probe sets and spanning over 42,000
transcripts (design ID: 022297), was constructed on the basis of the wheat
transcriptome information available in WormBase, RefSeq, Unigene, and TIGR
databases. A total of eight microarrays of single dye were considered for each
sample following the manufacturer's instructions (Agilent Technologies
One-Color Microarray-Based Gene Expression Analysis). In brief, each slide was
hybridized with 1.65 µg Cy3-labeled
cRNA, in a hybridizing oven at 65°C and 10 rpm, using a gene expression
hybridization Kit (Agilent Technologies, Santa Clara, C.A., U.S.). After 17 h
of hybridization, the slides were washed with gene expression Buffer Kit
(Agilent) in a staining dish (Thermo Shandon, Waltham, MA, USA).
Data analysis and gene
annotation
Agilent chip scanner G2565CA was
used with default settings for hybridized slides scanning at 550 nm. The image
processing program of the feature extraction software 10.7 (Agilent) was
utilized to process the scanned .tiff files and generate the standard data for
statistical analysis. The original data were normalized using the quantile
algorithm, followed by the processing of all the samples from the baseline to
median using Gene Spring Software 11.0 (Agilent). Detected, undetected, and
leaked data were marked as P, A, and M, respectively, and the standardized data
were transformed into log2 values (Quackenbush 2002). Coefficients of variations (CVs) were calculated
using the signal readings obtained from the ten replicates of the
probe spots and were used to determine the stability in the Agilent array
system. The detection percentages were also calculated from the
number of detected spots (with flags, except A) and the number of total spots.
All the CVs were below 10% (range: 4.13−6.19%), while the detection
percentages ranged from 70.10 to 76.21%.
The Diffgene programs, including t-test
and SAM (significant analysis of microarray) (Tusher
et al. 2001) from Shanghai Biochip Co., Ltd. (SBC) analysis system
(http://sas.ebioservice.com/, SAS), were used to screen the differentially
expressed genes. The program was connected to the R-software (The R Project for
Statistical Computing, http://www.r-project.org), the Gene Ontology website
(http://www.geneotology.org), NCBI Entrez Gene, KEGG, Biocarta, MINT, among
others. The average expression values for different groups were used to
calculate the ratio of expression changes between the treated samples and the
control samples. The criteria for the identification of the differentially
expressed genes induced by the CCN-infection were as follows: (i) differential
expression of genes between nematode-infected plants and mock-infected plants was
statistically significant (P ≤
0.05); (ii) the fold change (FC) in the expression of inoculated sample and
control sample was ≥ 2 (up-regulated) or ≤ 0.5 (down-regulated);
and (iii) the flag/call value for each probe site “A” did not occur in either
group.
The genes exhibiting a change of greater than 2-fold or less than 0.5-fold
in response to the CCN infection were classified according to their logarithmic
transformation rate using the hierarchical clustering method (Anderberg 1973). The annotation information was obtained from GenBank. BLASTx search
was applied on three databases, which included these genes assigned to the
known genes of rice or Arabidopsis–the TIGR rice pseudomolecules
database, the KOME peptide sequence database, and the TIGR Arabidopsis
annotation database (ATH1). Accession numbers of the wheat mRNA (with certain
exceptions such as those for rice) were identified through BLAST search in
NCBI. Gene classification based on gene ontology (GO) was performed using the
hierarchical clustering program in R software. In the
present analysis, the GO terms with an FDR (false discovery rate)-adjusted P-value of ≤ 0.05 were retained.
Real-time qRT-PCR verification
A total of six differential genes from various
functional categories were selected randomly for the validation of the
microarray data using real-time quantitative reverse transcription PCR
(qRT-PCR). Gene-specific primers based on the sequences of probe sets in the
microarray were designed using Primer Premier 6.0, and the specificity of the
primer pairs was verified using the blast program in NCBI (Table 1). The
wheat actin gene was used for data normalization. Total RNA samples were treated with DNase to remove residual any traces
of genomic DNA before the synthesis of the first strand of cDNA from the Oligo
dT primer labeled with SuperScript III RT (Invitrogen). PCR was performed in a
96-well plate inside Bio-Rad iCycler real-time PCR system (Bio-Rad, Hercules, CA) using SYBR Green I PCR Master Mix (Bio-Rad).
The PCR reaction conditions were as follows: 95°C for 10 s, followed by 40
cycles at 94°C for 5 s, 60°C for 25 s, and 72°C for 31 s. In order to evaluate
the overall specificity, the non-cDNA template was used as a negative control.
Each experiment was performed in triplicate. Gene expression was quantified using
the relative quantitative method (2−ΔΔCT) and was compared
with Table 1: Primer sets used in the qRT-PCR analysis
|
Gene
symbol |
Probe
set |
Primer sequence |
Product
size (bp) |
|
|
Forward |
Reverse |
|||
|
EF368363 |
A_99_P124345 |
GTCGTCGGGAGGAAGAAAGG
|
AGCCGTCGTCGAGGATGT
|
91 |
|
JX679079 |
A_99_P489907
|
AGTCGGAGCTACAGCGTGTG |
GAGGACGGCTGGTTGTTGTAG |
163 |
|
DQ013358 |
A_99_P072835 |
CCGAGAACAGAGTCCCGAGTT |
CCATCCAGCAAGACCAACGA |
175 |
|
EU082065 |
A_99_P158407 |
CGATGATATTCAGGGCACAGC |
CCAGCACCAAGGAAGAGGTAAG |
108 |
|
DQ334410 |
A_99_P195090
|
CGGAGTTTATGTAGCTGATATGACTG
|
CCCTGCCGTTGTTGTGC |
139 |
|
EF368361 |
A_99_P148642 |
CGACTACTCGCTGCTTCCG |
CCGCTCGTACATGTTCATCG |
64 |
|
AB181991 |
Wheat
actin |
TCCAATCTATGAGGGATACACGC |
GCCAGCAAGGTCCAAACGA |
58 |
%20610-620_files/image002.png)
Fig. 1: Micrographs showing progression of H.
filipjevi infection in Wheat cultivar Chinese Spring roots that were infected
with H. filipjevi (pathotype Hfc-1)
and grown for different times. Nematodes in wheat roots were stained with acid fuchsin
stain. Wheat roots sections were bleached with sodium hypochlorite. A, at 12 hpi, a small amount of J2s
already penetrated the wheat root; B,
at 24 hpi, more J2s penetrated wheat root and reached the vascular tissue; C, 48 hpi, almost entirely invaded J2s
reached the vascular tissue. N=nematode and R=wheat root
internal control. Data from the real-time PCR populations were analyzed
through an independent t-test in S.P.S.S. 20.0 using an alpha of 0.05 (Canales
et al. 2006).
Results
Time course
of H. filipjevi infection in wheat roots
The time course of J2s infection was monitored, and the results are
presented in Figs. 1 and 2. At 12 hpi and 24 hpi, J2s penetrated the wheat
roots and migrated toward the vascular tissue (Fig. 1A and 1B). Subsequently, the nematodes established infection by selecting
a cell as the feeding site. The number of J2s in the root tissue at 48 hpi was
significantly higher than that at 12 hpi (Fig. 1C). The number of J2s in the
root tissue at 24 hpi exhibited no significant differences from that at 12 hpi
and 48 hpi (Fig. 2). Therefore, the 24 hpi time
point was selected for subsequent gene profiling analyses, which is also
consistent with the time at which the CCN began forming the syncytium.
Gene
expression profiling
The microarray analysis identified
820 transcripts that exhibited differential accumulation in the susceptible
cultivar with a fold change ratio of ≥ 2 or ≤ 0.5 with a P-value ≤ 0.05; in total, 496
transcripts were up-regulated, while 324 transcripts were down-regulated. In
all the transcripts exhibiting differential expression, 336 up-regulated
transcripts and 292 down-regulated transcripts exhibited an FC ratio between two
and four, 109 up-regulated transcripts, and 32 down-regulated transcripts
exhibited an FC ratio between four and eight, and 51 up-regulated transcripts
exhibited an FC ratio above eight. When a P-value
of ≤ 0.01 was used, a total of 595 differentially-expressed transcripts were identified,
among which 235 were up-regulated and 205 were down-regulated with an FC ratio
between two to four, 84 were up-regulated and 23 down-regulated with an FC
ratio between four to eight, and 48 were up-regulated at an FC ratio greater
than eight (Fig. 3).
Among a total of 820
differentially-expressed genes that were identified, 317 genes had been
annotated, and approximately 86% of these annotated genes were identified as
having known or speculative functions. Analysis of the annotated data revealed
that greater than 17% of these genes were associated with amino acid/protein
metabolism, energy metabolism, carbohydrate metabolism, and fatty acid, and
lipid metabolism. Further, 24% of these were involved in transcription
regulation, 12% were involved in translation, 15% were involved in signal
%20610-620_files/image004.png)
Fig. 3: Venn diagrams depicting the numbers of differential transcripts in the
Chinese Spring (susceptible) after H.
filipjevi infection. A, General numbers of transcripts that
displayed differential accumulation at different false discovery rate (FDR) of p values and FC ≥ 2
(up-regulated) or FC ≤ 0.5 (down-regulated). ↑=
up-regulated and ↓= down-regulated. B, Transcripts that displayed up-regulated
or down-regulated accumulation with different FC ratio cutoff at an FDR with a P value of ≤ 0.05. C, Transcripts
that displayed up-regulated or down-regulated accumulation with different FC
ratio cutoffs at an FDR with a P
value of ≤ 0.01
%20610-620_files/image006.png)
Fig. 2: Number of J2 penetrating into roots of H. filipjevi -infected assayed at 12, 24 and 48 hpi. Values are
means ±SE. Data were analyzed using one-way analysis of variance and the
Bonferroni multiple comparison test in S.P.S.S. 20.0
transduction, and the rest were
related to cell structure, stress/defense, and transport (Fig. 4).
In order to explore the potential pathogenic mechanisms and resistance
resources, the focus of the present study was maintained on the functional
classifications related to cell structure, stress/defense, transcription factors,
signal transduction, and metabolism.
Major
functional categories of response genes in wheat roots
Cell wall,
defense, and stress response-related genes: Cell wall strengthening and
development are generally viewed as a part of early defense response. Genes
related to the cell wall, such as the cellulose synthetase (CESA) gene, play a
key role in the defense response to nematode infection. In the microarray
analysis conducted in the present study, nine cell wall-related genes exhibited
significant changes. Among these genes, glycosyl
hydrolase family 5 (JV987084), and glucuronosyltransferase (JV945131), which are involved in the
synthesis of glucuronoxylan hemicellulose in secondary cell walls, as well as
pectin lyase (AK335586) and hydroxyproline-rich glycoprotein (DR737925) that
are involved in cell walls maintenance and synthesis, were observed to be
up-regulated. In contrast, the cellulose synthesis-related gene cellulose
synthase-like H1 (AK332242), pectin lyase (AK335102), and hydroxyproline-rich
glycoprotein (AK072978*, rice GenBank accession), which are involved in cell
wall synthesis and maintenance, were observed to be down-regulated (Table 2).
Several genes in wheat were also
differentially expressed under the cyst pathogen stresses. Among these alterations, the gene (JV948284) similar to the Arabidopsis
gene, which encodes proline-rich extensin, peroxidase (EF028783, JW017812), and
monocopper
oxidase (JP837828) and performs a [hypothetical] role in the
general defense response of plants, was up-regulated. In contrast, two genes,
including the pathogenesis-related protein gene (JP233598)
and thylakoid-bound ascorbate peroxidase (AF532972), were
down-regulated (Table 2). However, a few other defense-response genes were observed, usually 2–8 days after the SCN
invasion, and were absent during the 24 h after challenge with H. filipjevi.
Three genes encoding the NBS-LRR
disease-resistance proteins (GAJL01182435, CJ848889, and CV767657), the
rust-resistance protein gene (JP940665), and the mildew-resistance gene (MLO5, GAJL01189557) were up-regulated. Notably, the
gene encoding a protein similar to the resistance protein candidate (CV767657)
was up-regulated, exhibiting an increase of greater than 10.0 folds
and an FC ratio of 15.3837, while the other two genes corresponding
to the NBS-LRR disease-resistance proteins (DP000010*, rice GenBank accession;
AK336044) were down-regulated (Table 2).
Transcription
factors and signal transduction-related genes: The wheat genome is known to
contain at least 1,127 predicted transcription factors (TFs), which
have been classified into 57 families (Wheat Transcription Factor
database-PlantTFDB;
http://planttfdb_v1.cbi.pku.edu.cn:9010/web/index.php?sp=ta). In the
present study, 43 differentially expressed TFs were identified, which belonged
to approximately 18 families, namely, ABI3-VP1, AP2-EREBP,
ARF, ARID, bZIP, C2H2, C3HC4, CCAAT, FHA, HB, HLH, HMG,
MYB, NAC, TCP, TLP, trihelix, and WRKY. Among these differentially expressed
TFs, most of the members of the WRKY, MYB, and AP2-EREBP families were
up-regulated, while the members of C2H2, C3HC4, HB, and TLP families were
either up-regulated or down-regulated.
Forty-eight signaling-related genes were identified as infection
responsive genes, 32 of which were up-regulated. Twenty-one
of the signaling-related genes were identified as protein kinase genes based on
the query against the database of rice kinase
(http://phylomics.ucdavis.edu/kinase/). All the protein kinases in the database
are divided into the following seven groups: AGC kinases, CaM kinases, CK1,
CMGC, STE, TKL, and the non-classified. Among the differentially-expressed protein kinase genes, ten genes
belonging to the TKL family were up-regulated, while one gene from the uridine
kinase family was up-regulated remarkably with an FC ratio of 11.8762. Three
genes from the CMGC family and one gene from the CK1 family were
down-regulated.
The expressions of several other genes relevant to cell signaling and
communication were altered as well. Two genes associated with synaptic
signaling (GAEF01091554 and GAJL01128233), two ras-related protein genes (CD877975 and GAJL01223381), two
signal-transducing G protein genes (JW026210 and BT008975), three calcium-related genes (JV954788,
HE996494, and JV998694), the protein phosphatase 2C gene (GAJL01007051), and one cyclin-like F-box domain-containing
protein gene (AK332287) were up-regulated. The GTPase-activating protein gene (AK332208)
and the remorin protein gene (AK330902) were down-regulated (Table 3).
%20610-620_files/image008.png)
Fig. 4: Functional classification of the
317 annotated genes out of 820 significantly regulated genes
in H. filipjevi -infected wheat roots.
A, The bar chart representation
of Gene Ontology (GO) classification of the 317 annotated
genes.
It includes three main categories: biological processes,
cellular components, and molecular functions. The y-axis on the left indicates the number of up-regulated
genes and down-regulated genes in a category, respectively. The y-axis on the right indicates the
percent of a specific category of up-regulated genes and down-regulated genes in
that main category, respectively. B, Functional distribution of the
317 annotated genes in different tentative
functional categories. Of the 317 annotated genes, 14% are unclassified for
function unknown. C, The bar chart representation of the 317 annotated genes in
different tentative functional categories. The y-axis indicates the number of up-regulated
and down-regulated annotated genes.
The genes encoding nine different secondary
transporters (CJ628086; C99253*, rice GenBank accession; JW033866;
AP008217*, rice GenBank accession; AB539586;
JP210959; JW014975; CA486682; AK331727), as well as the five ATP-dependent
transporter genes (JV979550; BT008921; AK332850; JV980384; AK066618*, rice GenBank accession) were down-regulated,
while three genes associated with multidrug resistance (AK332498; GAJL01218900;
AP008212*, rice GenBank accession) were
up-regulated (Table 3).
Genes involved in metabolism: Metabolic enzymes are classified
into six groups: oxidoreductases, transferases, hydrolases, lyases, isomerases,
and ligases. A total of 55 genes encoding metabolic enzymes were identified as
being differentially expressed, among which 30 genes were up-regulated,
including the 14 genes associated with protein and amino acid metabolism and
the 14 hydrolase encoding genes. Genes encoding Glucan
endo–1,3-β-glucosidase (JP861804), NADP-dependent malic enzyme (JP845417), galactose oxidase (AK100645*, rice GenBank accession; GAEF01030908), and 2-oxo acid dehydrogenase acyltransferase domain-containing
protein Table 2: The selected genes from the 317 annotated genes that involved are involved in cell structure, defense, and transcription factor in wheat
roots at 24 hpi after H. filipjevi inoculation. GenBank accession with “*”is
rice gene accession
|
Functional
category |
Fold change |
Description |
|
|
Cell structure |
|
|
|
|
Up-regulated |
DR737925 |
7.237 |
Similar to
Hydroxyproline-rich glycoprotein DZ-HRGP precursor |
|
|
AK335586 |
2.7477 |
Pectin lyase fold family
protein |
|
|
JV945131 |
2.0119 |
Probable
glucuronosyltransferase involved in the synthesis of glucuronoxylan
hemicellulose in secondary cell walls |
|
|
JV987084 |
2.0098 |
Cellulase, glycosyl
hydrolase family 5 |
|
Down-regulated |
AK072978* |
0.4707 |
Hydroxyproline-rich
glycoprotein family protein |
|
|
AK335102 |
0.3277 |
Pectin lyase fold family
protein |
|
|
AK332242 |
0.2467 |
Cellulose synthase-like H1 |
|
Stress/defense related |
|
|
|
|
Up-regulated |
CV767657 |
15.3837 |
Similar to NBS-LRR
Resistance protein candidate |
|
|
GAJL01189557 |
7.4513 |
Mildew resistance gene, MLO5 |
|
|
JP940665 |
4.319 |
Rust resistance protein |
|
|
JW017812 |
3.4493 |
Similar to Peroxidase (EC
1.11.1.7), |
|
|
JP837828 |
2.5819 |
Monocopper oxidase |
|
|
CJ848889 |
2.2945 |
NBS-LRR type resistance
protein |
|
|
JV948284 |
2.1583 |
Proline-rich extensin-like
family protein |
|
|
EF028783 |
2.0491 |
Similar to Peroxidase
precursor (EC 1.11.1.7) |
|
|
GAJL01182435 |
2.0401 |
NBS-LRR disease resistance
protein |
|
Down-regulated |
JP852719 |
0.4913 |
Similar to Thylakoid-bound
ascorbate peroxidase (EC 1.11.1.11) |
|
|
JP233598 |
0.4531 |
Similar to Steroid membrane
binding protein-like, OsFBT7-F-box and tubby domain-containing protein |
|
|
AK336044 |
0.4076 |
Similar to NBS-LRR disease
resistance protein homolog |
|
|
DP000010* |
0.328 |
Similar to NBS-LRR type
resistance protein |
|
Transcription factor |
|
|
|
|
Up-regulated |
JF951950 |
84.2977 |
MYB-like DNA-binding domain containing protein |
|
|
GAJL01041979 |
42.8642 |
Similar to AP2 domain containing protein RAP2 |
|
|
EU665453 |
5.2611 |
Similar to WRKY transcription factor 59 |
|
|
GAEF01013195 |
4.8624 |
WRKY transcription factor 74 |
|
|
JV951032 |
4.8347 |
MYB DNA-binding domain containing protein |
|
|
JP923124 |
3.2649 |
Ethylene-responsive factor,AP2 |
|
Down-regulated |
GAEF01037412 |
0.4614 |
Homeobox protein knotted-1–1, HB |
|
|
JP848030 |
0.4278 |
OsFBT7 – F-box and tubby domain containing
protein, TLP |
|
|
HP633405 |
0.3291 |
Zinc finger, C3HC4 type, domain containing protein |
|
|
AK335450 |
0.2873 |
Zinc knuckle domain containing protein, C2H2 |
(BJ270556) were up-regulated, while
seven sugar-related genes were down-regulated (Table 3). Six of the identified genes were involved in lipase metabolism, among
which four genes encoding fatty acid desaturase (JW031840), thioesterase
(AP003572*, rice GenBank accession), hydrolase (JP844213), and lipase family
protein (CA635498) were down-regulated. In contrast, those encoding
lipase-related protein (BJ222289) and esterase/lipase/thioesterase
domain-containing protein (JP901515) were upregulated (Table 3). With the only exception of
cytochrome P450 (CK206961), most of the genes associated with drug metabolism
were up-regulated at 24 hpi. Most of the genes encoding chloroplast-related
enzymes were up-regulated, while those encoding photosystem I subunit L (JP832184) and porphobilinogen deaminase (AK333284) were
down-regulated (Table 3). Taken together, these results indicated that when
comparing the genes associated with catabolism and those associated with
anabolism, the number of up-regulated genes was higher [by a small number] in
the former group. This possibly reflected the situation around the
establishment of the feeding site.
Real-time
qRT-PCR to verify microarray data: In order to verify the microarray
data and study the dynamic changes occurring in the gene expression, the
expression profiles of the six genes that were altered dramatically (five up-regulated
and one down-regulated) were analyzed using real-time qRT-PCR
(Fig. 5). The results demonstrated that
cell wall-associated kinase 4 (WAK4), NADP-dependent malic enzyme (NADP-ME),
ethylene-responsive factor-like transcription factor ERFL 2b, WRKY 80, and WRKY
10 were significantly up-regulated after the H. filipjevi infection, which was consistent with the microarray data. Nevertheless,
certain exceptions were observed. The expression of the WRKY 72-b gene in the
real-time qRT-PCR analysis was less than that in the microarray data. However,
in general, the data from real-time qRT-PCR confirmed the expression trend of
the corresponding genes observed from the microarray analysis.
Discussion
Infection in host plants with the cyst nematode is a complicated process,
which could be generally divided into several distinct stages. Although two dpi
or three dpi are often selected for observing the
development of syncytium, the previously proposed time point for the initial
establishment of the feeding cell was 18 hpi (Sobczak and Golinowski 2009). In the present study, 24 hpi was selected as an important time point for
the screening of early responses of wheat to CCN infections. Traditional
techniques for evaluating gene expression could detect a limited number of
genes. With the employment of a recently-developed microarray to analyze the
alterations in gene transcription in susceptible wheat roots at 24 hpi, the
present study became a pioneer in demonstrating
the differentially expression of genes in wheat after the CCN infection using a
whole-genome microarray. This analysis generated huge data that provided
indications regarding the expressions of a large number of wheat genes during
the critical period of infection when the CCN began to establish feeding sites.
This data also revealed the
commonality and uniqueness in the gene expression profiles between the
compatible wheat–CCN interaction and soybean–SCN interaction at similar
time-points (Puthoff et al. 2003; Khan et al. 2004).
A few studies investigated the alterations in
gene expression after infection with SCN in both susceptible and resistant
soybean cultivars, during an extended period of six hpi to eight dpi (Alkharouf et al. 2006). These studies demonstrated that the gene expression patterns of soybean
varied remarkably with progress in nematode infection. Remarkably
down-regulated gene expression was observed at 24 hpi in the susceptible
soybean infected with SCN. Similar to the above-stated finding, the present study
also revealed that the number of up-regulated and down-regulated genes in
susceptible wheat at 24 hpi accounted for 60 and 40% of the total
differentially-expressed genes, respectively, thereby exhibiting a unique gene
expression profile. The present study also suggested that the down-regulation
of genes upon CCN infection might be as important as the up-regulation of genes
for the successful establishment of the feeding site and the subsequent
successful parasitism.
Table 3: The selected genes from the 317 annotated genes that are
involved in signal
transduction, transport, and metabolism
in wheat roots at 24 hpi after H. filipjevi inoculation. GenBank accession with
“*”is rice gene accession
|
Functional category |
GenBank
accession |
Fold
change |
Description |
|
Signal
transduction |
|
|
|
|
Up-regulated |
CD877975 |
134.1545 |
Ras-related
protein |
|
|
AK332287 |
7.3067 |
Cyclin-like
F-box domain containing protein |
|
|
JV998694 |
4.9537 |
calcium
ion binding |
|
|
BT008975 |
4.2439 |
signal-transducing
G protein |
|
|
JV954788 |
2.7883 |
Calreticulin
family protein |
|
|
HE996494 |
2.5925 |
IQ
calmodulin-binding region domain containing protein |
|
|
GAJL01128233 |
2.5116 |
synaptic
transmission |
|
|
JW026210 |
2.5073 |
signal-transducing
G protein |
|
|
GAJL01007051 |
2.4449 |
Similar
to Protein phosphatase 2C-like |
|
|
GAJL01223381 |
2.2173 |
Ras-related
protein |
|
|
GAEF01091554 |
2.1911 |
synaptotagmin |
|
Down-regulated |
AK332208 |
0.4977 |
GTPase-activating
protein |
|
|
AK330902 |
0.3848 |
Remorin protein |
|
Transport |
|
|
|
|
Up-regulated |
AK332498 |
75.3024 |
Multidrug resistance protein |
|
|
AP008212* |
3.696 |
Similar to multidrug resistance associated
protein 1 |
|
|
GAJL01218900 |
3.2219 |
Multidrug resistance protein |
|
Down-regulated |
AK066618* |
0.4859 |
F-ATPase |
|
|
CA486682 |
0.446 |
Secondary transporter |
|
|
JW014975 |
0.4427 |
Secondary transporter |
|
|
AK331727 |
0.4408 |
Secondary transporter |
|
|
JP210959 |
0.4036 |
Secondary transporter |
|
|
AB539586 |
0.3931 |
Secondary Transporter |
|
|
AP008217* |
0.3845 |
Secondary transporter |
|
|
JV980384 |
0.3718 |
P-ATPase |
|
|
AK332850 |
0.3347 |
ATP-Dependent ABCB, ABC, ABCB |
|
|
BT008921 |
0.3235 |
ATP-Dependent ABCB, ABC, ABCC |
|
|
JV979550 |
0.2769 |
ATP-Dependent ABCB, ABC, ABCB |
|
|
JW033866 |
0.2622 |
Secondary transporter |
|
|
C99253* |
0.2473 |
Secondary transporter |
|
|
CJ628086 |
0.2135 |
Secondary transporter |
|
Metabolism |
|
|
|
|
Up-regulated |
AK100645* |
7.325 |
Galactose
oxidase |
|
|
BJ270556 |
6.8057 |
2-oxo acid
dehydrogenases acyltransferase domain containing protein |
|
|
BJ222289 |
4.1343 |
Lipase-related |
|
|
GAEF01030908 |
2.8625 |
Galactose
oxidase |
|
|
JP845417 |
2.4679 |
NADP-dependent
malic enzyme |
|
|
JP901515 |
2.4033 |
Esterase/lipase/thioesterase
domain containing protein |
|
|
JP861804 |
2.3658 |
Glucan
endo–1,3-beta-glucosidase precursor |
|
Down-regulated |
CK206961 |
0.4998 |
Similar
to Cytochrome P450 76C2 (EC 1.14.-.-) |
|
|
CA635498 |
0.4956 |
Lipase
class 3 family protein |
|
|
JP844213 |
0.4392 |
hydrolysis |
|
|
JP832184 |
0.4075 |
PSAL
(photosystem I subunit L) |
|
|
AP003572* |
0.3598 |
Thioesterase
family protein |
|
|
JW031840 |
0.3454 |
Fatty
acid acyl-CoA desaturase family protein |
|
|
AK333284 |
0.2012 |
Similar
to Porphobilinogen deaminase |
%20610-620_files/image010.png)
Fig. 5:
Quantitative Real-Time PCR verification of the six differential genes expression
level. Transcript levels were presented as relative values that were normalized
with respect to the level of wheat actin gene
The present
study revealed an up-regulation of two peroxidase precursors (EF028783 and JW017812)
and one monocopper oxidase (JP837828), and the down-regulation of
thylakoid-bound ascorbate peroxidase (JP852719) and a protein similar to peroxidase I (JP233598). It is known that among the numerous defense-related proteins, class III
peroxidase (Prx, EC 1.11.1.7) is involved in auxin metabolism, cell wall
elongation, and stiffening, and the protection of plants against abiotic and
biotic stresses (Kawano 2003; Cosio and Dunand 2009). Several studies have demonstrated that peroxidase is involved in the
defense response of H. avenae (Al-Doss et al. 2010; Simonetti et al. 2012). Seven groups of peroxides have been identified in wheat, three of which
were induced in both susceptible and resistant lines at four dpi and seven dpi,
respectively, in a previous study (Simonetti et al. 2012). In addition, ascorbate peroxidases (APX, EC 1.11.1.11), which are class
I peroxidase members, were demonstrated to be induced in similar and higher
magnitudes in the anti-infection genotypes and the susceptible genotypes,
respectively (Simonetti et al. 2010). Monocopper oxidases are also supposed to participate in cell wall
expansion. Plants defend against nematodes by using various mechanisms, such as
the pathogenesis-related (PR) proteins, production of phyto-alexins and
hypersensitive response (H), lignification, oxidative burst, and reinforcement
of cell wall (Almagro et al. 2009).
When plants are under biotic or abiotic stress, the expression of defense
response-related genes is altered significantly after signal burst (Whitham et al. 2006; Użarowska et al. 2009). The findings of the
present study confirmed that peroxidases and monocopper oxidases were important
pathogenesis-related proteins (Almagro et al. 2009).
Besides Prxs and cellulose (AK332242), one wheat gene (JP233598), which
is similar to the pathogenesis-related protein gene in Arabidopsis, was down-regulated. The expression of genes encoding
SAM22, Kunitz trypsin inhibitor, germin-like protein, chitinases, and
lipoxygenase, which have been previously demonstrated to often respond
dramatically in susceptible soybean (Alkharouf et al. 2006), was not identified in susceptible wheat.
Surprisingly, several genes related to defense proteins were observed to be
dramatically regulated by the CCN infection, such as those related to the
NBS-LRR proteins (DP000010*, rice GenBank accession; AK336044;
GAJL01182435; CJ848889; CV767657) and the genes encoding proteins involved in
plant resistance (Alkharouf et al.
2006; Klink and Matthews 2009). The contrary finding in the present study
indicates the importance of down-regulation of resistance genes in susceptible
hosts during the formation of the functional syncytium.
The present study revealed that wheat genes
encoding proteins similar to Arabidopsis
thaliana proline-rich extension (JV948284), hydroxyproline-rich
glycoprotein (DR737925), glycosyl hydrolase family 5 (JV987084), and pectinesterase (AK335586) were up-regulated, which was consistent with the
previous findings in susceptible soybean at a similar time point, although the
gene encoding expansin was not altered at 24 hpi. The results demonstrated that
most cell wall-related genes were influenced by the different infection
patterns of nematodes, which was not completely consistent with the previous
studies on the induction patterns of cell wall structural proteins, mainly
extensins, in tobacco (Niebel et al. 1993) and soybean (Khan et al. 2004) in an interaction compatible with cyst nematode. Moreover, several cell
wall-related genes such as cellulose synthase-like H1 (AK332242), pectin lyase
fold family protein (AK335102), and hydroxyproline-rich glycoprotein (AK072978*,
rice GenBank accession) were down-regulated, indicating that the
down-regulation of these genes probably plays a major role in the establishment
of feeding site of nematodes. The migration and establishment of feeding site
often induce degradation of cell walls, triggering a series of alterations in
cell wall-related genes. Extensive changes in cell wall structure are the
markers for syncytial development (Ithal et al. 2007b). Recently, several molecular studies have demonstrated
that the genes encoding cell wall modifying proteins, including reversible
glycosylated polypeptides, different kinds of glycosyltransferases, xylose
glucan endotransferases, β-1,4-endoglucanases, α-expansion, and
repeated proline-rich proteins, are differentially expressed in response to
soybean cyst nematode (Ithal et al.
2007a; Mazarei et al.
2011). In
particular, xyloglucan endotransglycosylases were observed to be expressed
specifically within the cyst nematode-induced feeding cells (Ithal et al.
2007b). In soybean–SCN compatible interaction, the genes encoding repetitive
proline-rich protein extensin, cellulose, and expansin, were observed to be
up-regulated upon SCN infection (Ithal et al. 2007a; Klink et al. 2007b). These structural proteins might have a role in
strengthening the syncytium walls to protect the nutrient contents during a compatible
cyst nematode interaction (Khan et al.
2004; Alkharouf et
al. 2006).
In the present study, most
of the signal transduction-related genes (32/48) were regulated after CCN
infection. TFs belonging to WRKY, MYB, and AP2-EREBP families were
up-regulated, which was consistent with the finding of a previous study on
soybean that TFs from the WRKY family were persistently up-regulated in
susceptible soybean from six hpi to eight dpi (Alkharouf et al. 2006).
In the present study, genes for fifty-five
metabolic enzymes were identified as differentially expressed genes, among
which 30 genes were up-regulated. Seven sugar-related genes were down-regulated
in susceptible wheat at 24 hpi; this was inconsistent with the previously
reported observations in susceptible soybean at six dpi and eight dpi,
according to which, several sugar-related genes were up-regulated (Alkharouf et al. 2006). The findings indicating the
involvement of certain identified genes in lipase metabolism was contrary to
the previous findings in soybean (Alkharouf et al. 2006; Klink et al. 2007a, b). A few up-regulated genes were
identified to be associated with catabolism, which was contrary to previous
findings in soybean (Klink et al. 2007a, b); this might be related to the
establishment of the feeding site.
Furthermore, transporter-related genes were identified in the present
study, with the genes encoding ATP-dependent transporters and secondary
transporters being down-regulated and the genes encoding multidrug resistance
protein being strongly up-regulated. These findings could reflect the dramatic
pathological changes associated with the formation of syncytium.
In the present study, 1.1% of the identified genes
were up-regulated genes, and 0.7% were down-regulated genes, similar to the
previously reported ratios for the whole root sample of soybean (1.0 and 0.6%,
respectively) (Alkharouf et al. 2006). However, the complexity of the wheat–CCN interactions suggested more
gene expressions are involved in the process of compatible infection.
Conclusion
In conclusion, the results of the present study, first of all, unraveled
the gene expression profiles in the susceptible wheat after the CCN infection.
A total of 820 transcripts were functionally annotated. With the inclusion of
samples from CCN-infected wheat roots, the results obtained in the present
study revealed important information regarding the gene expression associated
with the wheat response to the infection, in terms of defense, cell structure,
and signal transduction. Accordingly, a number of transcripts and their
functional annotations provided information that would allow exploring the
molecular mechanism underlying CCN resistance in Tritium aestivum L. Furthermore, certain genes were identified to
be dramatically altered in the CCN-infected wheat, which indicated that further
investigation is required to understand the molecular mechanism occurring in
CCN-infected wheat.
Acknowledgments
The financial supports
for this work were provided by the National Key R&D Program of China (2017YFD0201700).
The funders had no role in study design, data collection and analysis, decision
to publish, and preparation of the manuscript.
Author Contributions
X.X. conceived and
designed the research; X.X., H.Y. and Y.G. performed the experiments. The
manuscript was written by X.X. with input and corrections from all coauthors.
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