Abstract
Gummy stem blight (GSB), a severe and widespread disease causing great
losses to cucurbit production, is a major threat to melon production.
However, the melon-GSB interaction remains largely unknown, which
significantly impedes the genetic improvement of melon for GSB
resistance. Here, full-length transcriptome and widely targeted
metabolome were used to reveal the early defense responses of
resistant (PI511890) and susceptible
(Payzawat) melon to GSB. Differentially expressed genes were
specifically enriched in the secondary metabolite biosynthesis and MAPK
signaling pathway in PI511890, while in carbohydrate metabolism and
amino acid metabolism in Payzawat. More than 1000 novel genes were
identified in PI511890, which were enriched in the MAPK signaling
pathway.
There
were 11,793 alternative splicing events identified and involved the
defense response to GSB. A total of 910 metabolites were identified,
with flavonoids as the dominant metabolites. Integrated full-length
transcriptome and metabolome analysis showed that eriodictyol and oxalic
acid may be used as marker metabolites for GSB resistance in melon.
Moreover, post-transcription regulation was widely involved in the
defense response of melon to GSB.
These results improve our
understanding of the interaction between melon and GSB and may
facilitate the genetic improvement of GSB resistance of melon.
Key words: Melon; Gummy stem blight; Full-length transcriptome;
Metabolome
Introduction
Melon (Cucumis melo L.), an important horticultural crop with
great economic significance, is widely grown for fresh consumption.
According to the statistics of Food and Agriculture Organization, the
global melon production reached 28.6 million tons in 2021 (FAOSTAT,
www.fao.org/faostat). China is the
leading producing country of melon, accounting for approximately half of
the total production (14.1 million
tons), followed by Turkey, India,
and Kazakhstan, whose annual production was
1.4–1.6 million tons in 2021.
However, the yield and quality of melon are faced with serious threats
of diseases caused by pathogen
attack.
Gummy stem blight (GSB) caused by Stagonosporopsis
cucurbitacearum (syn. Didymella bryoniae ) is a prevalent and
devastating fungal disease of melon throughout the world (Li et al.,
2017; Stewart et al., 2015). It has been reported that GSB pathogens can
attack 37 species of the Cucurbitaceae family (Rennberger & Keinath,
2018). Under favorable environmental conditions, the pathogen can infect
all aboveground parts of susceptible plants throughout the whole growth
period, causing the formation of necrotic spots and serious reduction of
yield and quality. The incidence of GSB can reach up to 80% for melon
cultivated in greenhouse, and the yield loss can reach 100% once
infected (Rennberger & Keinath, 2018; Virtuoso et al., 2022).
Currently, chemical control, particularly fungicides, is the most widely
used method to control GSB. However, excessive application of fungicides
inevitably causes negative impacts on the environment and food safety.
In addition, the effect is declining due to increasing resistance of
certain pathogenic isolates to chemicals (Keinath & Zitter, 1998;
Hassan et al., 2018).
Breeding of resistant cultivars is the most efficient approach for
disease control. In recent years,
some research efforts have been
devoted to screening GSB-resistant melon germplasm (Wolukau et al.,
2007; Zhang et al., 1997). A review has summarized the currently
identified melon GSB-resistant resources (Luo et al., 2022). Another
study investigated the inheritance of GSB-resistant traits, resulting in
the identification of five independent monogenic resistance loci from PI
accessions of PI140471, PI157082, PI511890, PI482398, and PI482399,
which were designated as Gsb-1 , Gsb-2 , Gsb-3 ,Gsb-4 , and gsb-5 , respectively (Frantz & Jahn, 2004).
Only a limited number of molecular markers associated with GSB
resistance have been developed for maker-assisted selection of melon
(Hassan, Rahim et al., 2018; Hassan, Robin et al., 2018; Wolukau et al.,
2009). By using an ultra-dense genetic map, a previous study mapped GSB
resistance QTLs from an inbred line of Cucumis melo spp.conomon into a 108-kb interval on chromosome 4 and proposed a
candidate gene (Hu et al., 2018). Recently, Gsb-7(t) conferring
GSB resistance was mapped on chromosome 7 and MELO3C010403-T2 was
proposed as the candidate gene (Ma et al., 2023). However, functions of
these candidate genes have not been validated yet (Seblani et al.,
2023).
Clarifying the defense response of host to pathogen infection is
important for understanding the disease resistance mechanism.
High-throughput omics technologies have become powerful tools for
studying plant defense response to biotic stresses, among which
transcriptome is widely employed to identify the genes, signal
transduction pathways, and regulatory networks involved in
plant-pathogen interaction. For example, a transcriptomic analysis in a
recent study demonstrated that an apyrase-like gene plays an important
role in the defense response of pumpkin to GSB (Zhao et al., 2022).
Alternative splicing (AS), which is usually identified by full-length
transcriptome, is an important post-transcriptional regulatory mechanism
that increases the diversity of transcripts and proteins
(Ule & Blencowe, 2019). Several
studies have shown that many genes undergo AS in response to biotic
stresses in plants (Zhang et al., 2019). Functional analysis of
alternative transcripts has become a powerful tool to develop new
strategies for improvement of plant tolerance to environmental stress
(Kufel et al., 2022). Metabolome can act as a bridge between genotypes
and phenotypes, and is also a powerful tool for decoding plant-pathogen
interaction (Serag et al., 2023). Disease infection can cause great
perturbation on plant metabolism. The widely targeted metabolome allows
comprehensive metabolic profiling of plants during pathogen attack. It
is known that plant secondary metabolites, including phenolic compounds,
alkaloids, glycosides, and terpenoids, play pivotal roles in
plant-pathogen interaction (Anjali et al., 2023). However, gene
expression profiles, AS landscape, and metabolites involved in the
defense response of melon to GSB remain largely unknown.
In this study, we selected two melon accessions with contrasting
resistance to GSB, and determined their defense responses to GSB based
on full-length transcriptome and widely targeted metabolome. We also
characterized the novel genes, AS events, differentially expressed genes
(DEGs), and differentially accumulated metabolites (DAMs) involved in
the defense response of melon to GSB. The results are expected to
provide a comprehensive understanding on the defense response of melon
to GSB at transcriptomic and metabolic levels, as well as valuable
information for elucidating the mechanism for the resistance of melon to
GSB.
Materials and methods
Plant materials and artificial inoculation
PI511890 (C. melo var. chito ) from Mexico and Payzawat
from China (C. melo var. inodorus ) were used as the plant
materials in this study. PI511890 is a wild melon accession and exhibits
resistance to GSB (Frantz & Jahn, 2004). Payzawat is a widely
cultivated landrace and susceptible to GSB. The seeds were firstly
sterilized with 1.5% sodium hypochlorite, and then sown in pots
containing sterilized peat-perlite substrate (2: 1,
v/v).
The pots were placed in a greenhouse and seedling management followed
the commercial production practices. At the third true leaf stage,
uniform and healthy seedlings were selected for subsequent experiments.
Pathogenic fungi were isolated from melon stem with typical GSB symptoms
and identified as Stagonosporopsis cucurbitacearum . Purified
fungi were inoculated on potato dextrose agar (PDA) culture medium,
cultured at 25℃ under darkness for three days, then treated with 12 h
photoperiod under ultra-violet light (280–360 nm) for five days, and
maintained at darkness for two days to obtain the spores. The spores on
the medium were washed off and filtered to obtain the spore suspension.
The inoculum suspension was adjusted to 5 × 105spores/mL by adding ddH2O. For inoculation, the spore
solution was sprayed on the seedlings until reaching the point of
runoff. After inoculation, the seedlings were covered with a plastic
tunnel and the relative humidity was kept over 90% with the temperature
varying from 25℃ to 30℃. At the same
time, spraying of distilled water was performed for the other seedlings
to be used as the controls. Completely randomized block experimental
design with three biological replicates was adopted for the treatments
and controls, with each replicate consisting of 20 seedlings. Leaves
were sampled at 0, 12, 24, 36, 48, 60, 72 h after inoculation (hpi) for
subsequent analyses. For transcriptome and metabolome analyses, the
leaves were immediately frozen in liquid nitrogen and then stored in a
refrigerator (–80°C).
Histochemical staining
Trypan
Blue staining was performed for the leaves to determine the growth of
spores and hyphae according to the previous reports (Bhadauria et al.,
2010; van Wees, 2008). Briefly, the
leaves were punched to discs with a diameter of 10 mm and soaked in the
Trypan Blue staining solution, then immediately heated in 90℃ water for
10 min. After the solution was allowed to cool down to room temperature,
the staining solution was discarded and the leaf discs were decolorized
using 2.5 mg/mL chloral hydrate solution, which was replaced after every
24 h until the leaf discs were completely decolorized.
Accumulation of H2O2 and
O2- in leaves was measured using 3,3’-diaminobenzidine
(DAB) and nitro blue tetrazolium chloride (NBT) staining methods,
respectively (Daudi & O’Brien, 2012). In brief, the leaves were punched
into several discs with a diameter of 10 mm. For each biological
replicate, 10 discs were selected and immersed in 1 mg/mL DAB solution
under 25 ℃/light for 5 h and 0.5 mg/mL NBT solution under 25 ℃/dark for
5 h, respectively. Then, the leaf discs were decolorized with 95%
ethanol under 95℃ for 20 min and immersed in anhydrous ethanol for store
and photo. The staining results were observed under a light optical
microscope (OLYMPUS C × 41) with an ocular magnification lens at
40 × (400 um scale). The staining
areas were calculated using
Image
J with the formula of (stained leaf area/leaf disc area) × 100%
(Schneider et al., 2012). The larger staining area means higher
accumulation of H2O2 or
O2-. ANOVA was used to test the differences in
staining areas among treatments and the least significant difference was
used for multiple comparisons of the means. The agricolae package of R
was used for statistical analysis.
Full-length transcriptome
analysis
Samples at 24 hpi were selected for full-length transcriptome analysis,
which included GSB-inoculated
samples of PI511890 (TRT) and Payzawat (TST), and controls of PI511890
(TRC) and Payzawat (TSC). Extraction of RNA and construction of
sequencing libraries were performed according to the protocols providing
by the Oxford Nanopore Technologies
(ONT). The libraries were sequenced on a PromethION platform to obtain
the full-length transcriptome according to the standard protocol of ONT.
The pipeline for full-length transcriptome analysis is shown in
Supplementary Figure 1. The short fragments and low-quality reads
(length < 100 bp, Qscore < 7) were filtered out
using NanoFilt (v2.8.0; Coster et al., 2018). The clean data were then
processed with Pychopper (v2.4.0) to identify and orient full-length
sequences. The melon genome of DHL92 (v4.0) was used as the reference
(http://cucurbitgenomics.org/v2/organism/23). The full-length sequences
were mapped to the reference genome using minimap2 (v2.17-r941; Li,
2018). Samtools (v1.7) was used to extract the uniquely mapped reads
with a minimum quality score of 10. After polishing and clustering the
full-length sequences, the consensus sequence was obtained using Pinfish
pipeline (v0.1.0; Chen et al., 2021). The resulting consensus
transcripts were then mapped to the reference genome using minimap2.
Transcript isoforms were identified for the full-length transcriptome.
All consensus transcripts were merged and assembled to obtain a
non-redundant transcript set using StringTie (Pertea et al., 2015). The
assembled transcripts were compared with the reference genome using
gffcompare (v0.12.1; Pertea & Pertea, 2020). After filtering
transcripts with single exon, transcripts with class codes (”u”, ”x”,
”i”, ”j”, ”o”) and length longer than 200 bp were defined as novel
transcripts. The novel transcripts were further classified into isoforms
of novel genes (”u”, ”x”, ”i”) and novel isoforms of known genes (”j”,
”o”).
Ballgown was used to estimate transcript abundance (v2.26.0; Pertea et
al., 2016). DEGs were identified using DEseq2 with
|log2FoldChange| > 1 and
adjusted p < 0.05 (Liu et al., 2021). Enrichment analyses of
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
for DEGs were performed using clusterProfiler with the cutoff of p
< 0.05 (Yu et al., 2012).
AS events and fusion genes were identified. SUPPA2 (v2.3) was used to
generate seven main types of the local AS events, including retained
intron (RI), alternative 5’ splice-site (A5), alternative 3’ splice-site
(A3), skipping exon (SE), alternative first exon (AF), alternative last
exon (AL), and mutually exclusive exons (MX) (Trincado et al., 2018).
Salmon (v0.13.1) was used to calculate the transcript abundance (TPM),
which was then used to calculate the value of percentage spliced-in
(PSI) by SUPPA2 (Patro et al., 2017). Furthermore, diffSplice was
applied to identify differentially alternative splicing events with the
cutoff of p < 0.05 (Hu et al., 2013). Candidate fusion genes
were identified using the ToFU (fusion_finder.py) in cDNA_Cupcake
program (v29.0.0, https://github.com/Magdoll/cDNA_Cupcake).
Widely targeted metabolome analysis
The samples at 24 hpi were further selected for widely targeted
metabolome analysis, which included GSB-inoculated samples of PI511890
(MRT) and Payzawat (MST), and controls of PI511890 (MRC) and Payzawat
(MSC). Extraction, detection, identification, and quantification of
metabolites were performed according to the reported methods (Chen et
al. 2013). Briefly, the freeze-dried sample was crushed using a mixer
mill (MM 400, Retsch) with a zirconia bead for 1.5 min at 30 Hz, and
approximately 100 mg of powder was extracted with 70% aqueous methanol.
The sample extracts were analyzed using an ultra-performance liquid
chromatography-electrospray ionization-mass spectrometry
(UPLC-ESI-MS/MS) system (UPLC, Shim-pack UFLC SHIMADZU CBM30A system;
MS, Applied Biosystems 4500 Q TRAP). The analytical conditions were as
follows, UPLC: column, Agilent SB-C18 (1.8 µm, 2.1 mm × 100 mm); column
temperature, 40°C; flow rate, 0.35 mL/min; injection volume, 4 µL. LIT
and triple quadrupole (QQQ) scans were acquired on a triple
quadrupole-linear ion trap mass spectrometer (Q TRAP). Instrument tuning
and mass calibration were performed with 10 and 100 µmol/L polypropylene
glycol solutions in QQQ and LIT modes, respectively. A specific set of
MRM transitions were monitored for each period according to the
metabolites eluted within this period.
Based on the detected metabolites, principal component analysis (PCA)
was performed to reveal the relationships among the samples using
FactoMineR and factoextra packages in R. The orthogonal partial least
squares-discriminant analysis (OPLS-DA) was performed to determine the
DAMs with |log2FoldChange|
> 1 and variable importance in project (VIP) ≥1 (Eriksson
et al., 2006). Enrichment analysis for DAMs was conducted using the
Metabolites Biological Role (MBROLE) (v2.0; López-Ibáñez et al., 2016).
3. Results
3.1 Growth of GSBpathogenic fungi on melon
leaves
The growth process of S. cucurbitacearum on the leaves of
PI511890 and Payzawat was observed at 0, 12, 24, 36, 48, 60, and 72 hpi
using trypan blue staining method. The results showed
that the growth process of S.
cucurbitacearum consisted of germination of
conidia, formation and elongation of
germ tube, production of
appresorium, as well as growth and spread of hyphae (Figure 1A). On the
leaves of Payzawat (GSB-susceptible), germ tubes and hyphae were
observed at 12 hpi, followed by appressoria at 24 hpi. The hyphae were
apparently elongated at 60 hpi and even began to invade into epidermis
of Payzawat leaves at 72 hpi. However, on the leaves of PI511890
(GSB-resistant), germ tubes and appressoria appeared until 24 hpi and 36
hpi, respectively. Hyphae were observed at 60 hpi, which grew slowly
thereafter. These results indicated that the spore germination and
hyphal growth of S. cucurbitacearum on PI511890 leaves were
inhibited compared with those on Payzawat leaves. Moreover, 24 hpi was
the key time point to determine the different responses of PI511890 and
Payzawat to S. cucurbitacearum infection.