Introduction
Korla fragrant pear (Pyrus
sinkiangensis Yu) is a traditional
high-quality fruit grown in Xinjiang province, China (Ma et al. 2019). It is popular with consumers because of the thin
exocarp, crispy flesh, high juice and sugar content and pleasant rich fragrance (Chen et al. 2020). At present, Korla fragrant pear is exported to many countries around the world and has high commercial value on the international
market due to its special flavour and nutritional qualities (Tian et al. 2014). Unfortunately,
Korla fragrant pear is prone to diseases such as blackhead disease, scab,
powdery mildew, brown spots, fire blight and other fungal diseases during
long-distance transportation or long-term storage (Cheng et al. 2019). Among these diseases, blackhead disease mainly occurs during storage and
its incidence rate can be as high as 10%, making blackhead the main storage disease of Korla fragrant pears.
Blackhead disease caused by Alternaria
alternata is a
fatal necrotrophic fungal disease that affects Korla fragrant pear
quality and production
(Chen et al. 2019). At the early
stage of fruit infection, the lesions first appear at the calyx end of the pear, the peel in
the diseased area turns brown-black, the flesh appears as a light brown
honeycomb and the uninfected pulp tissues look good but taste slightly bitter. As
the disease development progresses, the surface of the fruit collapses and
produces a sticky black juice-like substance. A distinct boundary appears
between the lesion and the internal good flesh and a white mold layer grows on
the peel that leads to a decline in pear fruit quality. At present, chemical
fungicides are the main method used to inhibit fungal disease (Dalcero et al. 1996). However, long-term and
large-scale use of chemical fungicides may lead to strong resistance in A. alternata and have an adverse effect
on the environment (Ma and Michailides
2005). Therefore, an improved description of the host-pathogen interaction and
the pear defense response against A. alternata infection will help
provide a reference for understanding the causes of blackhead disease and
biological control of the pathogen.
With
the rapid development of molecular biology and associated bioinformatics tools,
considerable progress has been made in understanding plant-pathogen
interactions. RNA-seq
technology is an important
tool to explore complex biological processes (Marguerat and Bähler 2010). At present, RNA-seq
technology has been
frequently used to study the interaction between plants and pathogens in
horticultural crops. For
example, RNA-seq analysis have provided valuable information about changes in
gene expression in host-pathogen interactions involving apple and A. alternata (Zhu et al. 2017), apple and powdery
mildew fungi (Tian et al. 2019), Callery
pear and A. alternata (Kan et al. 2017). In this study, RNA-seq was used to explore the transcriptomic profiles of Korla pears in response to fungal infection. Our objective
is to explore the potential causes of susceptibility of pears to A. alternata infection at the molecular
level, so as to provide support for the development of new storage and
preservation technologies for pear fruits.
Materials and Methods
Plant materials, A.
alternata culture and inoculation procedures
Five-year-old Korla fragrant pear plants were grown in a
pear orchard located in Xinjiang province, China. This
study cultured the A. alternata
fungus on potato dextrose agar (PDA; 20 g dextrose, 200 g potato extract and 20
g agar in 1 L of water) medium at 28°C in the dark. After 5 days, conidia were
collected in ~20 mL of distilled sterile water per plate by swirling gently to
detach the conidia. The conidial suspension was then centrifuged and the
conidia were diluted until reaching the concentration of 1×105
conidia/mL. The surface of pears was disinfected with 75% alcohol and then
rinsed in sterile distilled water for 3 times. After air drying, 3 holes were
punched in the surface of the pears (diameter = 1 mm; deep = 1 mm) and 20 μL of the conidial suspension was introduced in these
holes using a pipette. The control group (T0) was inoculated with sterile
water. There were four experimental pear groups, each consisting of 30
inoculated fruits. The fruits were incubated in sterile plastic chambers at
25°C under a 14 h light/10 h dark cycle and fruit tissues around the wounded
sites (1 cm deep x 1 cm diameter)) were taken at 24, 72 and 96 hours as
experimental groups T1, T3 and T5, respectively. After flash freezing in liquid
nitrogen, the fruit samples were stored at -80°C for later use during the
RNA-seq experiments. Each stage involved three parallel fruit samples which
were picked at the same time and represented three biological replicate
samples, respectively.
Total RNA extraction, library
construction and sequencing
The cetyltrimethyl
ammonium bromide (CTAB)-based method was used to isolate
total RNA and to improve the RNA isolation, column purification was performed using a RNAeasy Plant Mini kit
produced by Qiagen (Germany). The three
biological replicates at different stages of infection were combined to
construct a complementary DNA (cDNA) library. Next,
magnetic beads were used to purify the products and oligo (dT) magnetic beads
were used to enrich mRNAs. Then short fragments of ~200 bases were prepared by mixing the mRNAs were with fragmentation
buffer and they were used as templates to
synthesize first-strand cDNA and random
hexamer-primers. Second-strand cDNAs were synthesized using Buffer, dNTPs, RNase H and DNA polymerase I. The short two-stranded cDNAs were subject to purification via a QiaQuick PCR extraction kit. After repair, the cDNA fragment ends and poly (A) tails were
connected to the Illumina sequencing adapters. Fragments of different size ranges were separately recovered by agarose
gel electrophoresis and fragments of the appropriate sizes were enriched by PCR
amplification. The constructed libraries were subject to sequencing
using an n Illumina HiSeq™ platform.
RNA-seq data analysis
We converted the raw image data obtained from the
sequencing instrument into raw sequence reads and saved it in FASTQ file
format. Raw data were used so that high-quality
clear reads for subsequent analysis can be obtained and they were trimmed
to remove the sequencing adapters. Reads consisting
only of adapters, those with > 10% Ns (unknown bases) and low-quality reads
in which the percentage of bases with phred quality scores Q ≤ 20 was
>50% were removed from the data sets.
The relative gene
expression level was calculated using Fragments Per
Kilobase of transcript per Million mapped reads (FPKM). The criteria for identifying differentially expressed genes (DEGs) were
a False Discovery Rate (FDR) ≤ 0.05 and the absolute value of
|fold-change (FC)| ≥ 1.
Annotation
of proteins encoded by the DEGs was performed by GO functional classification
using the Blast2GO program. We then used WEGO software to analyze the
significant functional enrichment of the DEGs. Finally, the GO terms that showed significant enrichment in the DEGs was founded using the hyper-geometric test with
P ≤ 0.05
as the threshold. We also used the DEGs as queries to search the KEGG database
to analyze pathway enrichment using the same criteria described above.
qRT-PCR verification
We selected 10 random DEGs for qRT-PCR to analyze and
verify the RNA-seq data. Based on the sequence information of the selected
DEGs, we designed gene-specific primers for
qRT-PCR using the Primer Express software. Total RNA samples (1 μg) from pears were used for cDNA
synthesis by reverse transcription using the FastQuant RT Kit (Beijing, China)
as directed by the manufacturer. The qRT-PCR assays were used on an ABI StepOnePlus Real-Time PCR System (ABI, U.S.A.) and the reaction mixtures (20 μL) contained 0.4 μL of
the forward and reverse PCR primers (10 μM), 10 μL of qPCR Master Mix and 4 μL template cDNA. The amplification program was 95°C for 90 s
and then 40 cycles of 95°C for 5 s, 60°C for 15 s and 72°C for 20
s. Internal control on normalization
of gene expression was performed using the pear Actin
gene (Actin2/7) and the 2-△△Ct method was used to calculate the relative expression level of selected
unigenes. Three independent biological replicates were selected for each sample and Table 1 shows all the primers used.
Results
Microscopic analysis of Korla fragrant pears infected
with A. alternata
By observing the symptoms of A. alternata-infected pears at different time points, we found that
a few spores and hyphae were newly produced on the surface of the peel at 1 day
post infection (dpi) (Fig. 1A). At 3 dpi, the spotted brown lesions on the
surface of the pears were stained with white mycelia and the
early-stage symptoms of blackheads became apparent (Fig. 1B). After 5 dpi, noticeable lesions appeared in the
inoculated sites and were covered with large areas of white hyphae accompanied
by softening and depression of the peel and pulp tissue (Fig. 1C).
To further
determine the optimal sampling time and observe the process of A. alternata infection of fragrant
pears, scanning electron microscopy and lactophenol trypan blue staining were
used to observe the process of spore germination and hyphal production and
growth. Microscopic observation showed that the spores start to germinate at 1
d and formed embryo tubes. Three days after
inoculation, the germ tubes were further extended and the hyphae showed obvious
growth and adhered to the surface of the peel. On the fifth day following
inoculation, the surface of the peel including the
inoculation site was covered
with white hyphae and the hyphae had invaded the pulp tissue, causing symptoms
such as brown lesions, softening and surface depression (Fig. 1D–I).
RNA-seq data
and DEG profiles in response to A.
alternata infection
Changes in transcript levels in comparisons of the
non-inoculated control and the inoculated groups (T0 vs. T1, T0 vs. T3 and T0 vs. T5) were determined in an RNA-seq
experiment. We obtained a total of 196.25 million raw 300 bp paired end
sequencing reads. After filtering out the low-quality reads, 191.83 million
clean reads remained. Of these, 91.35 million (48.10%) were mapped to the pear
genome reference sequence and 87.20 million (44.09%) of the clean reads mapped
uniquely to the pear genome reference sequence. Based on these mapped reads,
the expression levels of 13,621 DEGs were calculated using the FPKM method.
Gene expression between the A. alternata
infected groups (T1, T3 and T5) and the control group (T0) were compared. The
results identified 5,877, 7,970 and 11,485 DEGs at 1, 3 and 5 d, respectively
(Fig. 2A). Among the DEGs, there was no significant difference in the number of
up-regulated genes, but the number of down-regulated genes increased gradually
with the severity of infection. For example, there were 3,499, 3,580 and 3,503
up-regulated genes at 1, 3 and 5 d, but 2,378, 4,390 and 7,987 genes were
down-regulated at these three time points (Fig. 2B). Most of the DEGs were detected at 5 dpi, which suggests that this
is a critical stage in the host response to A. alternata infection in
pear fruits.
In addition, we visualized the number of
DEGs from the different comparisons in a Venn diagram, which clearly shows that both unique DEGs and shared
DEGs are present in the different
groups (Fig. 2C). Among them,
3,958 DEGs were shared among the three pairwise comparisons and the T0 vs. T1 comparison shared 4,494 and 4,308
DEGs with the T0 vs. T3 and T0 vs. T5 comparisons, respectively. Moreover,
there were 7,415 DEGs shared between the T0 vs.
T3 and T0 vs. T5 comparisons, which
is significantly higher than in the above two comparisons.
Functional annotation of the
DEGs
To explore the function of
the DEGs, a GO analysis was carried out by mapping
them to three major GO categories, biological process, molecular function and cellular component when
the corrected P-value was < 0.05. In
the biological process category, the GO terms “metabolic process” (2,577 DEGs), “cellular process”
(2,184 DEGs) and “single-organism process” (1,699 DEGs) were the most
enriched groups.
In the molecular function category, “catalytic activity” (2,544 DEGs) and “binding” (1,690 DEGs) were
remarkably enriched and we found that these two terms were highly involved in
the process of plant hormone signal transduction. For cell component, the
significantly enriched terms were “cell”
(983 DEGs), “cell part” (983 DEGs), “macromolecular complex” (305 DEGs),
“membrane” (784 DEGs), Table 1: Primers used for qPCR
|
Gene ID |
Gene Name |
Forward primer (5'--3') |
Reverse primer (5'--3') |
|
- |
Actin2/7 |
CTCCCAGGGCTGTGTTTCCTA |
CTCCATGTCATCCCAGTTGCT |
|
LOC103936633 |
EMB1144 |
CTGTGCCGATGGTGGAAG |
AGTTGCTGCCTCCGCT |
|
LOC103939953 |
PLD1 |
CCCCCTTCCATTCACTTTTCAG |
ACCACCTTGCTTTCTCCACC |
|
LOC103943393 |
IMP1 |
ATTCGCTTGCTCAGTTCCTCT |
CCTTATCAGTTTCCGTGACCAG |
|
LOC103948139 |
FBA3 |
GACGAACTCCTCCTAACTGCC |
CTTCCCATCGGTTGTAGACTG |
|
LOC103951141 |
ADH |
CACCACCACAGGCAAATGAAG |
TGTCACGCCCTCACCAATAC |
|
LOC103951803 |
At5g47720 |
ATGGGTGGTTTTCTGGGTGC |
CTGTATGCTCTGTGCTGCGA |
|
LOC103957712 |
Cht5 |
AACAGGTCAAGTTTCGGTGG |
CAAGAAAAGATTGCCGTGTGTAG |
|
LOC103958948 |
FQR1 |
TTGCTGAGGCTGATGGGATA |
GCTGTCAAGGGGGTAGTCTC |
|
LOC103965128 |
PDC4 |
GGGACGCACAGGATTCTTCA |
CCTGATAGCAAGTCACGGTCTG |
|
LOC103966218 |
DPE2 |
GTCTGCTACTGAGCCCTGTC |
ACATTTGAAGCCCTTTGGAAC |
%201173-1186_files/image002.jpg)
“membrane part” (616 DEGs), “organelle” (654 DEGs) and “organelle
parts” (336 DEGs) (Fig. 3).
To identify pathways that play key roles in
plant-pathogen interactions, 8,512 DEGs were further assigned to 45 different
categories by KEGG pathway analysis. The results showed that several secondary metabolite pathways involved in defense,
such as those involved in synthesis of terpenoid derivatives and flavonoids, were enriched.
These pathways included “tropane, piperidine and pyridine alkaloid
biosynthesis”, “ubiquinone and other terpenoid-quinone biosynthesis”,
“monoterpenoid biosynthesis” and “terpenoid backbone biosynthesis” (Table 2).
Moreover, the pathways for defense signaling transduction and plant pathogen recognition,
which are related to “plant hormone signal transduction”, “cutin, suberine and
wax biosynthesis” and “plant-pathogen interaction” were also enriched. However, the timing of gene induction
was different. For instance, the pathways “plant hormone signal transduction”
and “cutin, suberine and
wax biosynthesis” were induced earlier on the first day, while the
“plant-pathogen interaction” pathway was induced later on the 3rd and 5th days.
DEGs involved in plant cell wall integrity
The plant
cell wall is an important physical barrier against pathogen infection and is the main composition of the monitoring system in the plant innate immune
system. There are many genes involved in plant cell wall biosynthesis,
including HTH (Hothead)
(Kurdyukov et al. 2006), WSD (Wax-ester synthase/ diacylglycerol
o-acyltransferase) (Li et al. 2008), GPAT (Glycerol-3-phosphate
acyltransferase) (Gidda et al. 2009), CER (Eceriferum) (Aarts et al. 1995), PME
(Pectin methylesterase), (Bethke et al. 2014) and
XTH (xyloglucan endotransglycosylase/hydrolase) (Rose et al. 2002). In this study, the RNA-seq data showed that expression of two GPAT6 (LOC103931179; LOC103949465), one HTH1 (LOC103929758), two CER1
(LOC103932050;
LOC103942391), three WSD1
(LOC103928845;
LOC103941920; LOC103961411) and 13 XTH %201173-1186_files/image004.jpg)
Fig. 2: DEGs between
samples. (A) Scattered plot of
differential expression. (B) Numbers
of DEGs compared between two samples (T0 vs.
T 1 dpi, T0 vs. T3 dpi, T0 vs. T5 dpi and with T0 dpi as the
control). Red shows up-regulated, green shows down-regulated. (C) Venn diagram analysis of the DEGs in
Korla fragrant pear after A. alternata
infection
%201173-1186_files/image006.png)
Fig.
3: GO categories of DEGs in Korla
fragrant pear in response to A. alternata
infection (red for up-regulated, green for down-regulated)
genes was down-regulated during the infection process (Table 2). In addition, we found that expression of 10 PME genes showed an upward trend after plants were
infected with A. alternata (Fig. 4).
DEGs involved in plant hormone signaling pathways
Plant hormones,
including salicylic acid (SA), jasmonic acid (JA), ethylene (ET), abscisic acid
(ABA) and brassionosteroids (BRs), are critical factors involved
in the plant disease defense response (Tian et
al. 2019). To identify DEGs associated with hormonal responses in pears infected with A. alternata, we analyzed the hormone
signal transduction pathways. In this study, three DEGs involved in SA
signaling (PR-1, Pathogenesis-related
proteins) and six DEGs in
JA signaling (JAZ, Jasmonate zim domain) were
up-regulated (Fig. 5A- B); three DEGs involved in
ET signaling encoding ERF1/2 (Ethylene response factor
1/2) were up-regulated in the three stages (Fig.
5C); In addition, the
genes involved in
the ABA signal perception and transduction pathway, for example PYR/PYL (pyrabactin
resistance1/PYR1-like) were up-regulated (Fig. 5D); DEGs involved in
the BR-response, such as BRI1 (BRI-associated receptor
kinase 1), BSK
(brassionosteroid insensive) and TCH4
(Xyloglucan endotransglucosylase, also known as Touch 4 ) that showed
the same expression pattern were significantly upregulated at 5 dpi (Fig. 5E).
DEGs involved in plant-pathogen interactions
To identify DEGs
associated
with plant-pathogen interactions in the pears infected
with A. alternata, we analyzed the plant-pathogen interaction pathways. In the present study,
three DEGs encoding CDPK
(calcium-dependent
protein kinases)
and two DEGs encoding Rbohs
(respiratory
burst oxidase homologue)
were up-regulated in the three stages, inducing a hypersensitive response and
cell wall reinforcement. In addition, 16 CNGC (cyclic
nucleotide-gated ion channel)
and 31 CaM/CML
(calmodulin/cam-like) genes were regulated and
among them, four DEGs
encoding CNGC and six DEGs encoding CaM/CML
were strongly up-regulated at 3 dpi and 5 dpi. Furthermore, four DEGs encoding RPM1 (RPM1-interacting protein) were also up-regulated
at 3 and 5 dpi (Fig. 6). In addition two transcription factor genes, WRKY33 and WRKY29, showed changes in
expression; these genes encode proteins that participate in the MAPK
(mitogen-activated protein kinase) signaling pathway and induce plant disease
resistance.
DEGs encoding transcription factors (TFs)
%201173-1186_files/image008.jpg)
Fig. 4: Heatmaps of DEGs
involved in plant cell wall integrity. The log2|Foldchange|
was colored using OriginPro 2020 (red for
up-regulated, green for down-regulated), each horizontal row represents a DEG
with its gene ID and the vertical columns represent 1, 3 and 5 dpi from left to
right. (A) DEGs related to stratum
corneum; (B) Genes related with PME;
(C) Genes related with XTH
%201173-1186_files/image010.png)
Fig. 5: Heatmaps of DEGs
Involved in Plant Hormone Signaling Pathway. The log2|Foldchange|
was colored using OriginPro 2020 (red for
up-regulated, green for down-regulated), each horizontal row represents a DEG
with its gene ID and the vertical columns represent 1, 3 and 5 dpi from left to
right. (A) DEGs related to SA; (B) Genes related with JA; (C) Genes related with ET; (D) Genes related with ABA; (E) Genes related with BRs
TFs
are important regulatory proteins that can regulate gene transcription by binding to specific sequence
motifs in the promoter regions of downstream
target genes (Vidhyasekaran 2016). In plants, WRKY, MYB, ERF, Hsfs, ZIP and NAC are all important transcriptional regulators of
plant defense responses (Pandey and Somssich 2009; Dezar et
al. 2011; Pajerowska-Mukhtar et al.
2012). In this study,
several TF-encoding genes belonging to different families were either up- or down-regulated,
including 54 WRKY
genes, 29 MYB genes, 12 ERF genes, 17 bHLH
genes, nine ARF genes,
six
Hsf genes,
two ZIP genes and two DOF genes (Fig. 7). Interestingly, more DEGs
were down-regulated than up-regulated in the MYB, ERF, ARF, Hsfs, ZIP and bHLH
families. The expression
of related TF genes in pear fruits after A. alternata infection is shown in Fig. 7.
Table
2: Significantly
enriched KEGG pathway of DEGs in response to A. alternata
|
Pathway |
Number of DEGs at each time point |
Pathway ID |
||
|
T0 vs. T1 |
T0 vs. T3 |
T0 vs. T5 |
||
|
Biosynthesis of secondary metabolites |
396 |
556 |
693 |
ko01110 |
|
Terpenoid backbone biosynthesis |
22 |
35 |
46 |
ko00900 |
|
Microbial metabolism in diverse environments |
127 |
193 |
239 |
ko01120 |
|
Taurine and hypotaurine
metabolism |
13 |
|
17 |
ko00430 |
|
Biosynthesis of antibiotics |
164 |
234 |
294 |
ko01130 |
|
Glycolysis / Gluconeogenesis |
45 |
65 |
83 |
ko00010 |
|
Circadian rhythm - plant |
|
28 |
38 |
ko04712 |
|
Photosynthesis |
|
|
37 |
ko00195 |
|
Metabolic pathways |
576 |
842 |
1080 |
ko01100 |
|
Carotenoid biosynthesis |
17 |
23 |
31 |
ko00906 |
|
Pyruvate metabolism |
|
46 |
63 |
ko00620 |
|
Porphyrin and chlorophyll metabolism |
|
|
36 |
ko00860 |
|
alpha-Linolenic acid metabolism |
35 |
40 |
43 |
ko00592 |
|
Regulation of autophagy |
|
23 |
31 |
ko04140 |
|
Sulfur metabolism |
|
25 |
30 |
ko00920 |
|
Biosynthesis of amino acids |
92 |
128 |
155 |
ko01230 |
|
Folate biosynthesis |
|
10 |
14 |
ko00790 |
|
Phenylalanine, tyrosine and tryptophan biosynthesis |
27 |
29 |
37 |
ko00400 |
|
Vitamin B6 metabolism |
10 |
10 |
14 |
ko00750 |
|
Sesquiterpenoid and triterpenoid biosynthesis |
|
18 |
19 |
ko00909 |
|
Ubiquinone and other terpenoid-quinone biosynthesis |
|
|
27 |
ko00130 |
|
Phenylalanine metabolism |
|
22 |
28 |
ko00360 |
|
Pentose phosphate pathway |
|
30 |
35 |
ko00030 |
|
Arginine and proline metabolism |
23 |
30 |
36 |
ko00330 |
|
Tropane, piperidine and pyridine alkaloid biosynthesis |
16 |
19 |
20 |
ko00960 |
|
Plant-pathogen interaction |
|
97 |
114 |
ko04626 |
|
Steroid biosynthesis |
|
22 |
27 |
ko00100 |
|
Tyrosine metabolism |
22 |
25 |
27 |
ko00350 |
|
Selenocompound metabolism |
|
10 |
12 |
ko00450 |
|
Monobactam biosynthesis |
8 |
7 |
8 |
ko00261 |
|
Flavonoid biosynthesis |
18 |
30 |
|
ko00941 |
|
Ribosome |
152 |
161 |
|
ko03010 |
|
Galactose metabolism |
21 |
38 |
|
ko00052 |
|
Isoquinoline alkaloid biosynthesis |
13 |
15 |
|
ko00950 |
|
Monoterpenoid biosynthesis |
8 |
10 |
|
ko00902 |
|
Cysteine and methionine metabolism |
41 |
54 |
|
ko00270 |
|
Carbon fixation in photosynthetic organisms |
30 |
39 |
|
ko00710 |
|
Pantothenate and CoA biosynthesis |
|
21 |
|
ko00770 |
|
Carbon metabolism |
|
118 |
|
ko01200 |
|
beta-Alanine metabolism |
21 |
23 |
|
ko00410 |
|
Glutathione metabolism |
|
44 |
|
ko00480 |
|
Fatty acid degradation |
20 |
|
|
ko00071 |
|
Butanoate metabolism |
11 |
|
|
ko00650 |
|
Cutin, suberine and wax
biosynthesis |
15 |
|
|
ko00073 |
|
Plant hormone signal transduction |
87 |
|
|
ko04075 |
DEGs encoding defense-related
proteins
Exposure to abiotic and biotic stresses triggers the expression of various defense-related proteins with
antibacterial activity, which leads to
other defense-related responses such as cell death, the HR and cell wall
rigidification in plants. In our
results, a number of DEGs encoding defense-related proteins
belonging to different families were up-regulated at three detection time points; these included three PR-1 genes, 10 CHT (chitinase) genes, six TLP
(thaumatin-like protein) genes and seven
POD (peroxidase)
genes (Fig. 8). In contrast to
the above four TF families, genes encoding HSP family proteins were
significantly down-regulated (Fig. 8).
qRT-PCR verification
In order to verify the RNA-seq results, we randomly selected 10 DEGs from two time points (T0 and T3) of pear
fruit infection for qRT-PCR analysis. The genes were shown to be either up- or
down-regulated in the T0 vs. T3
comparison and our analysis showed that the qRT-PCR and RNA-seq data expression
results were consistent and showed a significant positive correlation, verifying the accuracy and reliability of the RNA-seq data (Fig. 9).
Discussion
%201173-1186_files/image012.jpg)
Fig. 6: Plant-Pathogen
Interaction Pathway. The red for up-regulated, green for down-regulated (red
for up-regulated, green for down-regulated), each horizontal row represents a
DEG with its gene ID and the vertical columns represent 1, 3 and 5 dpi from
left to right. (A) DEGs related to
PTI; (B) DEGs related to ETI
%201173-1186_files/image014.jpg)
Fig. 7: Heatmaps of DEGs
Involved in Transcription Factors. The log2|Foldchange|
was colored using OriginPro 2020 (red for
up-regulated, green for down-regulated), each horizontal row represents a DEG
with its gene ID and the vertical columns represent 1, 3 and 5 dpi from left to
right. (A) DEGs related to WRKY; (B) Genes related with MYB; (C) Genes related with Hsfs; (D) Genes
related with ERF; (E) Genes related
with bHLH. (F)
Genes related with ARF
The Korla fragrant pear is a type of fruit
that has both a high nutritional value and a high commercial value. However, during
storage, fragrant pears are readily infected by A. alternata, which
leads to great losses. Therefore, we studied the transcriptome changes in
fragrant pear in response to A. alternata infection in order to explore
the potential reasons for the susceptibility of pears to blackhead disease at
the molecular level and to provide support for the development of new storage
technology for pear fruits. At present, the reference genome of Chinese white pear
maintained by NCBI has a total of 42,194 genes (Wu et al. 2013). In this study, 25,877 (61.33%) genes were compared in
all of the sample groups. The results of the enrichment analysis showed that
the DEGs are mainly involved in
metabolic pathways related to resistance, such as plant cell wall metabolic
pathways, plant hormone signaling pathways, plant-pathogen interaction pathways
and transcription factor regulation pathways, among others. Compared with other
fruits, Korla fragrant pear has a number of unique characteristics. There is a
dense wax layer on the surface of pear, which is very effective at maintaining
fruit quality and controlling pathogenic microorganisms. The wax layer can
further assist the plant cell wall and protect plant cells from microbial
infection %201173-1186_files/image016.jpg)
Fig. 8: Heatmaps of DEGs
Involved in Defense-related proteins. The log2|Foldchange| was
colored using OriginPro 2020 (red for up-regulated,
green for down-regulated), each horizontal row represents a DEG with its gene
ID and the vertical columns represent 1, 3 and 5 dpi from left to right. (A) DEGs related to PR-1; (B) Genes related with chitinase; (C) Genes related with TLP; (D) Genes related with HSP
%201173-1186_files/image018.png)
Fig.
9: Validation of RNA-seq data by qRT-PCR.
10 DEGs were selected for validation and they showed a similar tendency with
RNA-Seq. Left vertical coordinate is RPKM of RNA-Seq; right vertical coordinate
is relative expression level of qRT-PCR
(Bellincampi
et al. 2014). Many genes are involved
in the biosynthesis of the plant cell wall and epidermal wax layer, including HTH,
WSD, GPAT, CER, PME and XTH. Our results show that the homologs of
these genes in pear, GPAT6, HTH1, CER1 and WSD1 all
showed down-regulated expression during the infection process. Previous studies
have shown that the down-regulation of HTH1 and GPAT6 may have
adverse effects on the formation of the stratum corneum (Ya et al. 2017), while the down-regulation of
CER1 and WSD1 could affect the synthesis of the cuticular wax
layer (Li et al. 2008). Therefore,
the down-regulation of these genes in this study implies that the biosynthesis
of the cuticle and wax is impaired in infected pears making it easier for A.
alternata to penetrate, which may be the main reason why pears are more
susceptible to pathogen infection. In addition, we found that 10 PME
genes showed an upward expression trend after plants were infected with A. alternata (Fig. 4). It is well known that PME can
catalyze the de-methylesterification of its pectin substrate. Therefore, the
up-regulated expression of PME genes in this study may indicate that the
degradation of plant cell wall components is accelerated.
Plant hormones
are a general class of signaling molecules that play key regulatory roles in
plant growth, development and defense responses. The complex interactions
between different plant hormones via
signaling pathways, called hormone crosstalk, can lead to changes in
plant-specific metabolic pathways (Robert-Seilaniantz et al. 2011). The plant
hormones usually involved in crosstalk are SA, JA, ET, ABA and BRs, which
activate the corresponding defense reactions by regulating specific
physiological responses, thereby preventing and resisting infection by
pathogenic microorganisms (Bari and Jones 2009). In this study, genes involved in the SA (PR-1),
JA (JAZ, MYC2), ABA (PYR/PYL), ET (ERF1/2) and BRs (BRI1, BSK, TCH4) %201173-1186_files/image020.jpg)
Fig. 10: Molecular network underlying the defense response to A. alternata in pear
signaling
pathways were up-regulated when pears were infected with A. alternata. Some studies have
shown that overexpression of PR-1 may play a positive role in enhancing
plant immunity to pathogens (Tian et al.
2019). We found that several DEGs associated with SA (PR-1) were significantly upregulated in response to A. alternata infection, suggesting
that SA might participate in regulating the response to A. alternata in pears. Previous
studies have shown that JA and ET are mainly involved in the defense response
against necrotrophs (Zhu et al. 2017). In this experiment, the genes involved in JA (JAZ,
MYC2) and ET (ERF1/2) signaling were up-regulated when the pears
were infected by A. alternata, which
is consistent with previous studies. ABA is an important regulator of the interaction between
plants and pathogenic microorganisms (Laurens et al. 2017). Many studies have shown that ABA often interferes
with defense signaling pathways such as the SA/JA/ET pathway, thus negatively
regulating plant resistance (Zhu et al.
2017). In this study, the genes
involved in ABA signaling pathways, for example PP2C, SnrK2 and ABF
were down-regulated while PYR/PYL were up-regulated in response to A. alternata infection, which revealed
that the ABA signaling pathway was significantly inhibited after infection by A. alternata. This result is consistent
with the response of apple leaves to A. alternata infection (Zhu et al. 2017). Finally, BRs play a
complex and positive role in plant innate immunity (Tian et al. 2019). In this study, several
DEGs associated with BR signaling were significantly upregulated in response to
A. alternata infection, suggesting
that BRs might participate in regulating the response to A. alternata infection in pear fruits.
In the
process of resisting pathogen infection, plants mainly use two defense
mechanisms. On the one hand, they trigger pathogen triggered immunity (PTI) by
recognizing a broad range of pathogens with conserved molecular pattern on
their surface and on the other hand, specific R genes that contain nucleotide binding
site (NBS) and leucine-rice repeat (LRR) domains recognized specific pathogen
proteins to trigger effector-triggered immunity (ETI) (Sun et al. 2013). In this study, we detected several genes involved in
PTI and ETI that showed differential expression in response to A. alternata
infection. BAK1 is one of the best studied receptor-like protein kinases
(RLKs). A previous study has shown that flg22 induces BAK1 as a co-receptor and initiates immune
signaling during the heterodimerization of FLS2 and BAK1 (Sun et al. 2013). In our research, the genes encoding BAK1 and FLS2 were
up-regulated after inoculation, suggesting
that BAK1 and FLS2 may promote the immune response in pear and lead to resistance to A.
alternata. MAPK cascades play an important role as signaling
modules of a high conservation level in the response to abiotic and biotic
stress and activate downstream defense-related genes (Colcombet and Hirt 2008). The MEKK1-MKK1/2-MPK4 signaling
cascade in Arabidopsis thaliana affects both
plant defense responses and the acquisition of basal resistance (Su et al. 2013). Our RNA-seq data revealed
that the genes encoding the downstream targets of the MAPK cascades were
up-regulated, which may be help pear activate
the innate immune system in response to pathogen infection to produce a related
immune response. Pathogens
usually secrete pathogen effectors to inhibit FLS2 recognition of flg22, thus
enhancing the colonization and proliferation of pathogens by overcoming PTI (Crabill et al.
2010). At this time, in order to resist pathogen
infection, plants will further induce ETI to trigger the HR response by recognizing viral
effectors through specific disease-resistance proteins (Guo et al.
2009). In the present study, the
genes encoding RIN4, RPM1, RPS2 and EDS1 were all
up-regulated.
Previous studies have reported that the
effectors AvrRpm1 and AvrB secreted by Agrobacterium tumefaciens during
infection of Nicotiana benthamiana plants can phosphorylate the
RIN4 protein to relieve its negative regulation of the disease resistance (R)
protein RPM1, which limits the occurrence of disease. AvrRpt2 abolishes the
inhibition of RPS2 by RIN4 by removing the physical connection between RIN4 and
RPS2, ultimately triggering an R protein-mediated HR response (Axtell and Staskawicz 2003). Therefore, the upregulation of genes encoding RPM1 and RPS2 may help to inhibit
pathogen infection and activate the ETI immune system. EDS1 is a
positive regulator of ETI and the up-regulated expression of EDS1-encoding
genes also activates programmed cell death (Bhattacharjee et al. 2011). Based on these results, we can infer that PTI and ETI play roles in
resisting pathogen infection during the infection and colonization of pears by A. alternata.
Transcription
factors (TF) are widely involved in plant responses to biotic stresses and
regulate the expression of defense-related genes at the transcriptional level (Sun et al.
2013). In our RNA-seq data, WRKY TF family
genes comprised the largest group and most of the WRKY genes were
up-regulated. Previous studies have shown that WRKY family genes act as
positive regulators in both the Arabidopsis response to Pectobacterium
carotovorum ssp. carotovorum infection and in the response to A.
alternata infection in apple leaves (Zhu et al. 2017). These results indicate that up-regulated expression
of WRKY family genes in pear may play an important role in the response
to A. alternata infection.
MYB family
genes are mainly involved in various functions such as anthocyanin
biosynthesis, morphogenesis and abiotic stress responses, among others (Wang et al. 2004). Zhang et al. (2020) found that MYB family genes in wheat can
mediate host resistance to the fungal pathogen Bipolaris sorokiniana by regulating
the SA signaling pathway and defense-related genes. Zhu et al. (2017) reported that MYB family genes might play a
regulatory role in the responses of the ‘Starking Delicious’ pear cultivar to A.
alternata attack. A total of 29 pear MYB genes were found to be
either up- or down- regulated in response to A. alternata infection in
this experiment (Fig. 7), which suggests that these genes might also play a
regulatory role in the response of Korla pear fruits to A. alternata infection. However,
further research is needed to confirm this hypothesis.
Heat shock
transcription factors (Hsfs) participate in the response to biotic and abiotic
stresses by regulating the expression of heat shock-related genes (Yu et al. 2019). A total of six pear Hsf
genes were either up- or down- regulated by A. alternata infection in
this experiment (Fig. 7). Interestingly, we found that one of the
down-regulated DEGs, LOC103937244, encodes a protein that is highly similar to AtHsfB2b
from Arabidopsis. Kumar et al. (2009)
reported that knockout of AtHsfB2b in Arabidopsis can significantly
improve resistance to the necrotrophic fungal pathogen A. brassicicola. We therefore speculate that the
down-regulation of this gene in pear can improve disease resistance. The other
four Hsf genes (LOC103962963, LOC103960544, LOC103960440, LOC103960090) also showed down-regulated expression, suggesting that
the defense signal transduction pathway mediated by Hsf TFs may be
compromised in Korla fragrant pear, leading to A. alternata infection.
Ethylene-responsive element binding factors (ERFs) are
one family of TFs that are found only in plants (Cao et al. 2018). Yang et al.
(2005) reported that the ERF4 and ERF12 genes in Arabidopsis encode transcriptional repressors
that can modulate ethylene and abscisic acid responses. In this study, the genes encoding ERF4 (LOC103944178), ERF12 (LOC103944179) and ERF17 (LOC103944180) were significantly up-regulated (Fig. 7). Up-regulation of these genes negatively regulates
ethylene and abscisic acid reactions, which may be related to the
down-regulation of most genes in the abscisic acid and ethylene pathways (Fig. 5).
In addition,
our results show that some other transcription factor family genes (bHLH
and ARF) were either up- or down- regulated by A. alternata
infection (Fig. 7). However, various studies have shown that bHLH and ARF
TFs play key roles in plant growth, development and stress tolerance, but are
not stronly correlated with disease resistance (Zhang et al. 2020). Therefore, we will not give more details on these TF
families here.
The
up-regulation of defense-related protein genes has been found in a variety of
plants and it is an inducible part of the plant's self-defense mechanisms (Jwa et al. 2006). Expression of genes that
encode members of the PR-1 protein family was detected in pears and most of
them were found to be regulated in A.
alternata-infected fruits. The PR-1 protein was first detected in tobacco
plants infected with Tobacco mosaic virus and is the main PR protein
induced by pathogen infection and SA (Loon and Kammen
et al. 1970). PR-1 homologues are
also found in wheat, corn and tomato plants infected by pathogens and elevated
PR-1 protein levels in host plants also increase resistance to pathogens
(Niderman et al. 1995). The genes
encoding PR-1 proteins were found to be up-regulated in our experimental
results and this may play a positive role in disease control. It is worth
noting that up-regulation of the PR-1 protein gene is induced by the
up-regulation of SA signaling pathway genes, suggesting that SA can increase
resistance to necrotrophic fungi infection by inducing PR-1 proteins.
Moreover,
chitinase degrades the fungal pathogen cell wall by hydrolyzing the
β-1,4-glucosidic bonds between chitin N-acetylglucosamine monomers to
further inhibit the infection (Okongo et
al. 2019). It has been reported that chitinase can improve resistance to
ear rot fungi in corn and resistance to the red rot pathogen (Colletotrichum
falcatum Went) in barley and can inhibit fungal spore germination and
mycelium growth (Dowd et al. 2018).
In this study, we found that expression of 10 chitinase-encoding genes showed
an up-regulation trend over the course of infection, indicating that chitinase
accumulates in the pear to cope with A.
alternata infection.
Thaumatin-like
proteins (TLPs) are widely distributed in many organisms such as plants, fungi
and insects (Meng et al. 2017).
Previous studies have shown that TLPs have
significant antifungal activity (Misra et
al. 2016) and mainly work
in two ways: (1) TLPs are directly inserted into the fungal plasma membrane to
form perforations, thereby destroying membrane permeability; (2) TLPs can cause enzymatic hydrolysis of
β-1,3-glucan, a major part of the fungal cell wall. We identified 10
TLP-encoding genes, of which seven were up-regulated following A. alternata infection, with TLP1
(LOC103935639) and TLP1a (LOC103962570, LOC103967809) showing
significant increases in mRNA levels. These results indicate that A. alternata infection causes the
up-regulated expression of genes encoding TLPs to resist pathogen infection in
pear. Furthermore, heat shock proteins (HSPs), which act as molecular
chaperones, can repair and remove the misfolded proteins produced by external
factors such as plant stress, thereby reconstructing cellular protein
homeostasis (Wang et al. 2004).
Previous studies have shown that Arabidopsis infection by four pathogenic
bacteria also caused the accumulation of HSP83, HSP70, HSP23.6, HSP17.6 A, and
HSP17.4 (Whitham et al. 2003). In our
experimental results, the genes encoding HSPs showed a down-regulated pattern
of expression throughout the infection phase, which is consistent with results
showing the down-regulation of genes encoding Hsfs transcription factors. It
shows that A. alternate, in the process of infecting pear
fruits, inhibits the expression of related transcription factors, thereby
disrupting normal protein homeostasis and inhibiting the expression of defense
responses. Down-regulation of the genes encoding HSP70 and HSP83 may be one of
the important pathogenic mechanisms during the infection of Fragrant Pear
fruits by A. alternata. Based on these results, we were able to draw a feasible molecular
network that can explain the defense response in pear to A. alternata infection (Fig. 10). First of all, when pears are
inoculated with A. alternata, the pathogen will destroy the integrity of the cell
wall and a series of defense responses such as plant hormone signaling pathways
(SA and JA, for example) and plant-pathogen interactions (PTI, ETI) are
activated. Subsequently, TFs (WRKY, MYB, ERF, Hsfs)
trigger the host responses to A. alternata infection by activating or
inhibiting the expression of downstream genes including CHT, HSP
and PR-1. Briefly, pears undergo specific changes in defense-related
gene expression through molecular networks after infection with A.
alternata and then produce related defense response.
Conclusion
In conclusion, in this study, a total of
25,877 DEGs were detected, and results
showed that the DEGs take
part in plant pathogen interactions and plant hormone signaling pathways, and
defense-related proteins were
up-regulated, suggesting a positive role
for these genes in the pear-A. alternata interaction. Moreover,
expression of the DEGs
involved in cutin and wax biosynthesis was
down-regulated, as are DEGs that encode TFs (WRKY, MYB, ERF, Hsfs) and HSP70
and HSP83, following infection of pear fruits by A. alternata, which may
result in the appearance of blackhead disease symptoms. This study explored the
defense mechanism and pathogenesis of pears in response to A. alternata
infection and our results are expected to provide support for the development
of new storage and preservation technologies for pears.
Acknowledgements
We acknowledge the financial supports of the National
Natural Science Foundation of China under Grant No. 32060561; Research project
of young and middle-aged leading scientists, engineers and innovators in
Xinjiang production and construction corps (2018CB024); Scientific and
technological research plan in key areas of the Corps, Research on the key
technology of cold chain logistics and deep processing and creation of new
products (2019AB024).
Author Contributions
Hui Ouyang and Guogang Chen conceived and designed the
experiments; Hui Ouyang and Tongrui Sun performed the experiments; Hui Ouyang,
Tongrui Sun, Minrui Guo, Weida Zhang analyzed the data; Ying Jiang contributed
reagents; Fund acquisition from Shaobo Cheng and Guogang Chen.
Conflict of Interest
There are no
conflicts to declare.
Data Availability
The data presented in this study are available on request from the
corresponding author.
Ethics Approval
There are no
researches conducted on animals or humans.
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