Abstract
Amorphophallus species are one of the main economic crops in the mountainous areas of southwest China. However, soft rot disease (Pectobacterium carotovorum ssp. carotovorum) is devastating for this crop. This study explored the Amorphophallus resistance mechanism against soft rot disease by analyzing transcriptome data using a weighted gene coexpression network analysis. The RNA sequencing of plants infected for 0, 12, 24, and 48 hours produced a total of 52.25 Gb of clean reads. A total of 29,096 genes were divided into 34 modules. Six modules of interest with the highest correlation with the target traits were selected to elucidate the resistance genes and pathways. The selected modules were enriched in the α-linolenic acid metabolism, phenylpropane biosynthesis, plant hormone signal transduction, and plant pathogen interaction pathways. Ultimately, AmBGLU, AmCAML, AmCDPK, AmLOX, and AmRBOHD were identified as genes of interest in the four significantly related metabolic pathways for real-time fluorescence quantitative polymerase chain reaction verification. The determination of salicylic acid (SA) and jasmonic acid (JA) in Amorphophallus muelleri and Amorphophallus konjac that suffered from soft rot disease showed that SA and JA were involved in the A. muelleri and A. konjac defense response against soft rot disease. Methyl jasmonate treatment delayed the onset of A. konjac soft rot disease. This study provides a reference for the interaction between Amorphophallus species and soft rot disease and the breeding of broad-spectrum and specific Amorphophallus cultivars that are resistant to soft rot disease.
Amorphophallus species are widely used in agriculture, industry, biomedicine, food processing, and other fields because they are rich in glucomannan (Liang et al. 2020; Wu et al. 2021; Zhang et al. 2020). Moreover, it is often used as a raw material, such as for konjac tofu and konjac noodles. With the increasing demand for Amorphophallus, it has become an economically important crop for farmers in southwest China. Yunnan Province is especially suitable for Amorphophallus cultivation because of its unique climatic conditions. According to statistics, the Amorphophallus planting area in Yunnan Province reached 400 km2 in 2021, accounting for approximately one-third of the total Amorphophallus planting area in China. However, soft rot disease is common in these planting areas; when it occurs, the Amorphophallus yield is reduced by 30% to 50% or more (Sun et al. 2019).
Soft rot disease is a devastating soilborne disease caused by Pectobacterium, Dicyeya, and Enterobacter (Charkowski 2018; Wei et al. 2020; Wu et al. 2016). The typical symptoms of Amorphophallus infection with the soft rot pathogen are the production of water stains at the initial stage, followed by the tissue becoming soft and rotten and emitting a foul odor. Soft rot disease starts from the infected site and gradually spreads to the surrounding area, eventually leading to plant death. However, there is no safe and effective control method.
Over the course of long-term evolution, plants have developed sophisticated molecular mechanisms to resist bacterial infection. Activating immune mechanisms is an important way to successfully immunize plants against pathogens. Pattern-triggered immunity (PTI) and effector-triggered immunity are two innate defense systems of plants, and the potentiation of PTI is an indispensable component of effector-triggered immunity during bacterial infection (Yuan et al. 2021). Plant immune signals are transduced by mitogen-activated protein kinases (Devendrakumar et al. 2018) and calcium-dependent protein kinases (Romeis and Herde 2014), which ultimately activate downstream transcription factors and induce the expression of defense genes (Seybold et al. 2017). Plant hormones also have an indispensable role in plant resistance to pathogen infection. The contents of ethylene, salicylic acid (SA), and jasmonic acid (JA) in plants tend to increase with pathogen infection (Bari and Jones 2009). Recent studies have shown the crosstalk of different hormones when regulating plant defense responses (Verma et al. 2016). In Brassica rapa, the JA, SA, and auxin (IAA) contents increased after infection with Pectobacterium carotovorum ssp. carotovorum (Pcc), and spraying methyl jasmonate (MeJA) enhanced its resistance to Pcc (Liu et al. 2019). In Solanum tuberosum, SA and JA are the necessary pathways to defend against Dickeya solani (Burra et al. 2015). Additionally, some microRNAs are involved in plant defense against soft rot disease. In Arabidopsis thaliana, miRNAs are involved in the response to Pcc (Djami-Tcha and Ntushelo 2017). However, the mechanism of Amorphophallus resistance to soft rot disease is still unclear.
A weighted gene coexpression network analysis (WGCNA) is an effective method of studying the relationship between genes and traits by using a large amount of biological data (Langfelder and Horvath 2008). It has been widely used to detect the coexpressed genes that participate in metabolite fluxes between floral color and scent in Narcissus tazetta flowers (Yang et al. 2021), the abiotic stress response in Oryza stativa (Lv et al. 2019; Zhu et al. 2019), and the biotic stress response in A. thaliana (Dobin et al. 2013).
To explore the defense mechanism of Amorphophallus species against soft rot disease, Amorphophallus muelleri was used as the experimental material. In this study, the gene expression profiles of the control and different infection time points were analyzed using the WGCNA. Moreover, the contents of JA and SA in A. muelleri infected with Pcc were measured, and the effects of Amorphophallus konjac resistance to Pcc under exogenous MeJA treatment were identified. These data provide significant information for the future selective breeding of Amorphophallus resistance to soft rot disease.
Materials and Methods
Plant growth and plant infection.
Forty 3-month-old seedlings of disease-free A. muelleri and 20 3-month-old seedlings of disease-free A. konjac were selected and transplanted into the matrix after high-temperature sterilization. All plants were kept under growth conditions of 28 °C with a 16-h/8-h (light/dark) photoperiod and relative humidity of ∼80%. The Pcc strain was inoculated onto Luria-Bertani broth solid medium and incubated at 28 °C overnight. A single colony was placed into Luria-Bertani broth liquid medium, cultivated at 180 rpm and 28 °C overnight, and eventually diluted to 108 cfu/mL. Pcc bacterial fluid inoculum or Luria-Bertani broth (mock) (100 μL) was injected into the base of the petiole of A. muelleri. Each time point was one treatment, and each treatment contained 10 A. muelleri seedlings. Different A. muelleri seedlings were randomly sampled at 0, 12, 24, and 48 h after Pcc inoculation. The stems and leaves 2 cm away from the injection site were cut, immediately frozen in liquid nitrogen, and stored at −80 °C until use.
Total RNA extraction and mRNA library construction.
Total RNA from Amorphophallus was extracted using pBIOZOL plant total RNA extraction reagent (Beijing Bomax Biotechnology Development Co., Ltd., Beijing, China) according to the manufacturer’s instructions. Subsequently, the total RNA was qualified and quantified using a spectrophotometer (NanoDrop; Thermo Fisher Scientific, Waltham, MA, USA) and bioanalyzer (model 2100; Agilent Technologies Co., Ltd., Beijing, China). The extracted RNA was purified with oligo(dT)-attached magnetic beads. Then, the purified mRNA was fragmented into small pieces using fragment buffer and reverse-transcribed into first-strand complementary DNA (cDNA) using random hexamer priming. Next, second-strand cDNA was synthesized. Afterward, A-Tailing enzyme mix (Vazyme Biotechnology Co., Ltd., Nanjing, China) and RNA Index Adapters (Vazyme Biotechnology Co., Ltd.) were added at the end of the second-strand cDNA for end repair. The cDNA obtained from the previous step was amplified by polymerase chain reaction (PCR) and purified with magnetic particles (AMPure XP Beads; Beckman Coulter Life Sciences, Indianapolis, IN, USA). After quality control of the obtained product, heat denaturation and circularization by the splint oligo sequence were performed to obtain the final library. The single-strand circular cDNA obtained from the previous step was used as the final library. Then, the final library was amplified with phi29 to make DNA nanoballs, which had more than 300 copies of one molecule. DNA nanoballs were loaded into the patterned nanoarray, and single-end 50-base reads were generated on the Beijing Genomics Institute (Beijing, China) sequencing 500 platform.
RNA-Seq data analysis.
First, the raw data reads containing adapter, poly-N, and low-quality sequences were removed to obtain high-quality clean reads (quality control value >20%) for subsequent data analysis. The remaining clean reads from the 12 samples (ML0: Pcc-inoculated A. muelleri at 0 h; ML12: Pcc-inoculated A. muelleri at 12 h; ML24: Pcc-inoculated A. muelleri at 24 h; ML48: Pcc-inoculated A. muelleri at 48 h) were assembled to make a transcriptome reference using Trinity. A total of four isoforms were identified. The reference sequence was annotated based on National Center for Biotechnology Information (Bethesda, MD, USA) nonredundant protein sequences, National Center for Biotechnology Information nonredundant nucleotide sequences, protein families, clusters of orthologous groups of proteins, a manually annotated and reviewed protein sequence database, the Kyoto Encyclopedia of Genes and Genomes orthologue database (KEGG), and Gene Ontology. The gene expression levels of clean data were mapped back onto the assembled transcriptome, and read counts for each gene were obtained from the mapping results estimated by RNA-Seq by expectation maximization (Li and Dewey 2011). Differential expression analyses of the four treatments were conducted using DESeq2 software (Love et al. 2014). Genes with an adjusted P < 0.05 and an absolute fold change of two or more were considered differentially expressed genes (DEGs). The KEGG enrichment analysis of genes is an important method used to understand gene function (Kanehisa et al. 2008). Therefore, to understand the functions of DEGs, KO-based Annotation System (Mao et al. 2005) software was used for KEGG enrichment analysis.
WGCNA.
The coexpression network was constructed using the WGCNA package (version 1.47) (Langfelder and Horvath 2008) in R (R Foundation for Statistical Computing, Vienna, Austria). Using the transcriptome data of 12 A. muelleri-infected Pcc at different stages, the expression abundance of each gene in different samples was calculated. To determine the significant modules related to soft rot resistance, module eigengenes were used to calculate the correlation coefficient with samples. The intramodular connectivity and module correlation degree of each gene were calculated using the R package WGCNA, and genes with high connectivity tended to be hub genes that might have important functions. A correlation analysis was performed using module eigengenes with data for specific traits or phenotypes. Pearson correlations between each gene and trait data under the module were also calculated for the most relevant module corresponding to each phenotype data, and the gene significance value was obtained. The networks were visualized using Cytoscape_3.3.0 (Shannon et al. 2003). Ultimately, we selected the significance-related modules, screened out the modules related to A. muelleri resistance to soft rot, and enriched and analyzed the genes in the modules using KEGG pathway enrichment.
Quantitative reverse-transcription PCR validation.
Quantitative reverse-transcription PCR was used to validate the RNA-Seq data for five different genes (Supplemental Table S1). Total RNA was used to synthesize cDNA, and a PCR system (Step OnePlus Real-Time Fluorescent Quantitative PCR system; Qingke Biotechnology Co., Ltd., Beijing, China) was used to monitor the amount of cDNA. Assays of each gene were repeated three times. The actin gene was used as an internal reference gene. The quantification was evaluated using the 2−(ΔΔCt) method.
Measurement of JA and SA contents.
A. muelleri tissues infected with Pcc for 0, 24, and 48 h were homogenized with phosphate buffer, followed by centrifugation at 4 °C and 5000 gn. Then, the supernatant was used as the test sample. The JA and SA contents were determined with an enzyme-linked immunosorbent assay kit (Enzyme Linked Biotechnology Co., Ltd., Shanghai, China). The specific operational steps were as follows: first, standard wells and testing sample wells were set; second, 50 μL of standard was added to the standard well and 10 μL of testing sample was added; third, 40 μL of sample diluent was added to the testing sample well, followed by the addition of 100 μL of horseradish peroxidase-conjugated reagent to each well; and fourth, the wells were covered with an adhesive strip and incubated for 1 h at 37 °C. Moreover, after the plate was washed, chromogen solution A (50 μL) and chromogen solution B (50 μL) were added to each well and incubated in the dark for 15 min at 37 °C. Finally, the reaction termination solution was added, and the optical density was read at a wavelength of 450 nm.
Exogenous MeJA treatment of A. konjac seedlings.
A. konjac seedlings were sprayed with 50 mL MeJA (50 μM) or deionized water and used as a control. After 24 h of treatment, petioles of A. konjac were inoculated with Pcc. Disease symptoms were documented at 24 h and 48 h.
Results
Overview of transcriptomic analysis.
During our previous study, the physiological indices of A. muelleri infected with Pcc showed that the disease resistance enzyme activity and the malondialdehyde content of A. muelleri were significantly induced after inoculation with Pcc for 12 h, and the enzyme activity and malondialdehyde content showed different trends within 48 h (Wei et al. 2022). Therefore, the samples infected with Pcc at 0, 12, 24, and 48 h were selected as the research materials, and the RNA-Seq results were analyzed to explore the mechanism of the A. muelleri response to soft rot disease.
A total of ∼52.25 Gb of clean bases were generated from 12 biological samples (9 infected samples and 3 control samples). The percentages of raw reads with a quality score more than 20 (Q20) or a quality score more than 30 (Q30) were ∼98.16% and 98.29%, respectively, and the guanine (G) and cytosine (C) contents were between 49.15% and 49.92%, indicating that the sequenced fragments had high quality (Supplemental Table S2). A Pearson correlation analysis of the samples showed that all the replicates exhibited similar expression patterns (Supplemental Fig. S1), indicating the high reliability of our sequencing data. These data showed that the sequencing data can be used for subsequent analysis.
To obtain a comprehensive view of the gene expression profile associated with the response of A. muelleri to Pcc infection, DESeq2 was used to identify DEGs. Based on the filtering parameters of a false discovery rate <0.05 and |log2FC| >1, the expression levels of 1415, 1560, and 1532 genes were found to differ significantly in ML0-VS-ML12, ML0-VS-24, and ML0-VS-ML48, respectively. There were 1568 DEGs expressed in all comparisons. To understand the function of DEGs, a KEGG enrichment analysis of DEGs during Pcc infection was performed. Among these DEGs, genes involved in “protein processing in endoplasmic reticulum,” “plant–pathogen interaction,” “plant hormone signal transduction,” other biosynthesis of secondary metabolite pathways, and other plant development processes were identified (Fig. 1). These results showed that Pcc infection activated the defense system in A. muelleri and that the “plant–pathogen interaction” and “plant hormone signal transduction” pathways may have an important role in the reaction of A. muelleri to Pcc.
Kyoto Encyclopedia of Genes and Genomes bubble diagram of differentially expressed genes in Amorphophallus muelleri infected with Pectobacterium carotovorum ssp. carotovorum at 48 h post-inoculation.
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
WGCNA.
To discover the functions of the DEGs associated with Pcc infection, transcriptome profiling of A. muelleri infected with Pcc at different time points was performed. A total of 29,096 genes were analyzed. WGCNA was used for specific modules and key genes of A. muelleri in response to Pcc at different infection time points. A total of 34 modules were obtained (Supplemental Fig. S2). Modules with a correlation coefficient more than 0.8 and P < 0.05 were defined as sample-specific modules; five target modules were obtained from 12 samples. Mediumpurple3 and green yellow were positively correlated with ML12–1 and ML12–2, respectively. Brown was positively correlated with ML24–3. Light steel blue1 and grey60 were positively correlated with ML48–2 and ML48–3, respectively (Table 1).
Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the differentially expressed genes from five special modules.
To understand the function of the target gene module, a KEGG enrichment analysis was performed for the genes in the six highly correlated modules. The KEGG analysis of the brown module indicated that amino acid metabolism pathways (“phenylalanine, tyrosine, and tryptophan biosynthesis,” “cysteine and methionine metabolism,” and “biosynthesis of amino acids”), “phenylpropanoid biosynthesis,” “plant hormone signal transduction,” and “alpha-linolenic acid metabolism,” among others, were significantly enriched, suggesting that the genes in the brown module contribute to Pcc infection by regulating the content of intracellular free amino acids, phenylpropane biosynthesis, and JA synthesis. The KEGG analysis of green yellow module genes showed that “aminoacyl-tRNA biosynthesis,” “porphyrin and chlorophyll metabolism,” and “glycine, serine, and threonine metabolism,” among others, were enriched, indicating that the amino acid metabolism and primary metabolic activity of A. muelleri were affected after being infected with Pcc. The KEGG analysis of medium purple3 module genes indicated that “steroid biosynthesis,” “peroxisome,” and “plant–pathogen interaction,” among others, were enriched, suggesting that the genes in the medium purple3 module regulate resistance and antioxidant stress responses of A. muelleri in response to Pcc infection. The KEGG analysis of light steel blue1 module genes indicated that “taurine and hypotaurine metabolism” and “protein processing in endoplasmic reticulum” were significantly enriched. The KEGG analysis of grey60 module genes indicated that “tight junction,” “circadian rhythm—plant,” and “glutathione metabolism” were enriched, indicating that after infection with Pcc, antioxidant stress-related metabolic pathways of A. muelleri were activated (Table 1). These data showed that the “plant hormone signal transduction,” “alpha-linolenic acid metabolism,” “phenylpropanoid biosynthesis,” and “plant pathogen interaction” pathways were involved in the defense response of A. muelleri to Pcc.
To screen the genes involved in the A. muelleri response to Pcc, heatmaps of genes annotated as “plant hormone signal transduction,” “alpha-linolenic acid metabolism,” “phenylpropanoid biosynthesis,” and “plant pathogen interaction” pathways in the six specific modules were generated. Plant hormone crosstalk is the key to plant defense against pathogens and insects. Molecular plant hormone signals trigger and regulate plant resistance to pathogens through complex signal transduction networks (Yang et al. 2015). WGCNA showed that a total of eight genes were annotated to participate in the plant hormone signal transduction process. These eight genes primarily involved IAA-related, abscisic acid-related, and brassinolide-related genes, and they were induced after Pcc infection (Fig. 2A, Supplemental Table S3). Alpha-linolenic acid metabolism is the main metabolic pathway involved in JA synthesis in plants. During our study, the expression of a total of 18 genes was induced, including six lipoxygenase genes (AmLOXs), two OPC-8:0 CoA ligase 1 genes (AmOPCL1s), three alpha-dioxygenase genes (AmDOX1s), three enoyl-CoA hydratase/3-hydroxyacyl-CoA dehydrogenase genes (AmmFP2s), four acyl-CoA oxidase genes, and one allene oxide cyclase gene (Fig. 2B, Supplemental Table S3). These results suggest that Pcc infection activated the signal transduction involved in plant hormones, enhanced the biosynthesis of JA, and jointly participated in the A. muelleri defense response to Pcc.
Heatmaps of differentially expressed genes (DEGs) from the plant hormone signal transduction pathway (A), DEGs from the alpha-linolenic acid metabolism pathway (B), DEGs from the phenylpropanoid biosynthesis pathway (C), and DEGs from the plant–pathogen interaction pathway (D) in Pectobacterium carotovorum ssp. carotovorum-infected Amorphophallus muelleri seedlings at 12, 24, and 48 h post-inoculation.
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
The phenylpropane biosynthesis pathway is one of the important secondary metabolic pathways involved in plant defense against pathogens. There were 25 DEGs associated with Pcc infection, namely, one cinnamoyl-CoA reductase gene (AmCCR), three phenylalanine ammonia-lyase genes (AmPALs), one transcinnamate 4-monooxygenase gene (AmCYP73A), four 4-coumarate–CoA ligase genes (Am4CLs), three coumaroylquinate (coumaroylshikimate) 3′-monooxygenase genes (AmCYP98A3s), one caffeoylshikimate esterase gene (AmCSE), seven beta-glucosidase (AmBGLUs), three peroxidase genes (AmPODs), and two caffeoyl-CoA O-methyltransferase genes (Fig. 2C, Supplemental Table S3). Among these genes, some are involved in the biosynthesis and degradation of metabolites in the phenylpropane biosynthesis pathway, and some are involved in A. muelleri defense against Pcc by encoding disease resistance and antioxidant proteins. The results showed that Pcc infection could lead to the deterioration of A. muelleri tissue and to oxidative stress in its cells. A. muelleri participated in the resistance response to Pcc by regulating the expression of AmBGLUs, AmPODs, and genes related to the biosynthesis and degradation of secondary metabolites.
The plant–pathogen interaction pathway primarily occurs via the plant perception of pathogens through a series of signal transduction processes that activate the downstream defense response. In the present study, two calmodulin genes (AmCMLs), five LRR receptor-like serine/threonine-protein kinase genes (AmFLS2s), one mitogen-activated protein kinase kinase 1 (AmmAPK1), 10 calcium-dependent protein kinase genes (AmCDPKs), one cyclic nucleotide-gated channel gene (AmCNGCs), two calcium-binding protein genes (AmCAMLs), and one respiratory burst oxidase gene (AmRBOHD) were induced to respond to Pcc infection (Fig. 2D, Supplemental Table S3). These results showed that A. muelleri could recognize Pcc and transmit pathogenic signals through multiple signal transduction pathways. Moreover, AmRBOHD gene expression is induced, indicating that Pcc infection leads to oxidative stress in A. muelleri and activates the corresponding resistance response.
Validation of candidate DEGs based on quantitative reverse-transcription PCR analysis.
To screen the disease resistance-related genes in A. muelleri in response to Pcc, the hub gene in the target gene module was screened, and the results showed that the AmLOX gene (DN33994), AmRBOHD gene (DN4623), and AmBGLU gene (DN10877) had high module connectivity and correlation with the gene modules of interest. Moreover, these three genes are annotated in the alpha-linolenic acid metabolic pathway, the plant–pathogen interaction pathway, and the phenylpropane biosynthesis pathway.
To understand whether the candidate DEGs were involved in the A. muelleri response to Pcc, we screened five genes in the four aforementioned metabolic pathways for quantitative reverse-transcription analysis, including three hub genes (the AmLOX, AmRBOHD, and AmBGLU genes) and two calcium-mediated metabolic pathway genes [AmCAML (DN14546) and AmCDPK genes (DN6732)]. As shown in this research, after infection with Pcc, the expression of the AmLOX, AmRBOHD, AmCDPK, and AmBGLU genes was significantly increased (Fig. 3). This outcome indicated that these four genes can be used as candidates to further study the A. muelleri response to Pcc.
Verification of the expression profiles of five unigenes using a quantitative reverse-transcription polymerase chain reaction (PCR) analysis. The relative expression changes of AmBGLU (A), AmCAML (B), AmCDPK (C), AmLOX (D), and AmRBOHD (E) at 0, 24, and 48 h post-inoculation (hpi). Different letters indicate significant differences according to Student’s t test (P < 0.05).
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
Determination of JA and SA contents.
Plant hormones have an important role in plant resistance to various stresses. To understand whether Pcc infection caused changes in the endogenous hormone contents in A. muelleri and A. konjac, the JA and SA contents were measured in A. muelleri and A. konjac infected with Pcc for 0, 24, and 48 h. The results showed that 24 h after inoculation with Pcc, the JA content increased significantly and returned to a normal level at 48 h. The SA content increased significantly at 24 h and 48 h after inoculation with Pcc (Fig. 4). These results indicate that after Pcc infection, the JA and SA contents of A. muelleri and A. konjac increased. Therefore, JA and SA may be involved in the defense response against pathogenic bacteria.
The contents of jasmonic acid (JA) and salicylic acid (SA) after Amorphophallus muelleri (A, B) and Amorphophallus konjac (C, D) inoculation with Pectobacterium carotovorum ssp. carotovorum at 0, 24. and 48 h post-inoculation (hpi); ck = inoculated with sterile water. Different letters indicate significant differences according to Student’s t test (P < 0.05).
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
Exogenous MeJA treatment improved A. konjac resistance to Pcc.
Previous resistance identification showed that A. konjac was susceptible to Pcc. To identify whether MeJA was involved in the A. konjac resistance response to Pcc, MeJA was used to treat A. konjac seedlings. The results of the control and MeJA treatment resistance identification showed that in the control group, after inoculation with Pcc for 24 h, all infected A. konjac seedlings produced obvious water stains, and liquid flowed out from the inoculation site and extended downward from the inoculation site after inoculation with Pcc for 48 h. Notably, in the exogenous MeJA treatment group, there were no obvious symptoms at the inoculation site for all treatment A. konjac seedlings after inoculation with Pcc for 24 h; however, there were obvious disease spots at the inoculation site 48 h after inoculation with Pcc (Fig. 5, Supplemental Fig. S3). This result showed that exogenous MeJA treatment could delay the occurrence of A. konjac disease to some extent.
Typical appearance of mock-pretreated (sterile water) (A, B) and methyl jasmonate-pretreated (C, D) Amorphophallus konjac seedlings 24 and 48 h post-inoculation (hpi) after Pectobacterium carotovorum ssp. carotovorum infection. The red box indicates the location of the disease. The blue box indicates the inoculation site.
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
Discussion
RNA-Seq study in A. muelleri resistance to soft rot resistance.
RNA-Seq has become an effective tool to study the mechanism of plant disease resistance. Gao et al. (2021) revealed resistance genes and predicted the molecular resistance mechanism underlying the Cucumis sativus response to Pseudoperonospora cubensis through physiological index determination and transcriptome sequencing. Li et al. (2021) sequenced the transcriptome and metabolome of Zanthoxylum bungeanum cultivars with high resistance and susceptibility to stem canker and found that the flavonoid metabolic pathway was positively involved in their resistance to stem canker. Yao et al. (2020) combined a genome-wide association study and transcriptome analysis and noted that Zea mays plants participate in resistance to Fusarium rotundum by activating plant hormone signal transduction, the mitogen-activated protein kinase signaling pathway, pathogenesis-related proteins, protein-like receptor kinase, and heat shock-related proteins. In our research, the DEGs produced by A. muelleri infected with Pcc were significantly enriched in plant–pathogen interactions, plant hormone signal transduction, and other secondary metabolite biosynthesis pathways (Fig. 1). These results showed the function of DEGs after A. muelleri infection with Pcc and that A. muelleri had a complex defense mechanism against Pcc.
WGCNA of A. muelleri resistance to soft rot resistance.
WGCNA uncovers key genes or metabolic pathways by aggregating highly related gene groups in the transcriptome and associating them with sample characteristics (Langfelder and Horvath 2008; Peng et al. 2021; Wang et al. 2022). The five target modules of interest selected in our study were expressed significantly after inoculation with Pcc KEGG pathways enriched in α-linolenic acid metabolism, phenylpropane biosynthesis, plant hormone signal transduction, and plant–pathogen interaction pathways. Previous studies have shown that lipoxygenase can activate the fatty acid oxidation pathway and produce oxidized lipids and the plant hormone JA, both of which have a key role in plant development and defense (Shrestha et al. 2021). When Musa acuminata was infected with Fusarium oxysporum, the expression of seven MaLOX genes was induced (Liu et al. 2021). In Z. mays, the expression of the LOX gene was significantly increased after infection with Fusarium verticillioides, and the expression of the LOX gene in highly resistant cultivars was significantly higher than that in susceptible cultivars (Maschietto et al. 2015). In both B. rapa and A. thaliana, infection with Pcc promoted the expression of the AmLOX gene (Liu et al. 2022). The AmLOX gene is an indispensable gene in the α-linolenic acid metabolism pathway, suggesting that JA synthesis and the signal transduction pathway have an important role in the A. muelleri response to Pcc defense.
Plant hormones are important factors that allow plants to defend against pathogens; in B. rapa, IAA, JA, and SA are involved in the immune response to Pcc (Liu et al. 2019; Liu et al. 2022). In A. muelleri, the expression of IAA, abscisic acid, and brassinolide signal transduction pathway-related genes was induced, indicating that these hormone-mediated signal transduction pathways may be involved in the defense response to Pcc.
Plants have formed complex defense mechanisms over the course of the long-term evolution process (Dodds and Rathjen 2010; Nejat and Mantri 2017). When plants are infected by pathogens, they activate a series of signal transduction processes and induce the expression of downstream disease resistance genes to defend against pathogens. In Citrus paradisi, the expression of the FLS2 gene was induced to increase after C. paradisi was infected with Xanthomonas citri ssp. citri, which activated the C. paradisi PTI response (Shi et al. 2016). In A. thaliana, resistance to Fusarium graminearum also depends on the upregulation of FLS2 gene expression (Sarowar et al. 2019). After A. muelleri was infected with Pcc, the expression of seven AmFLS2 genes was significantly increased, indicating that AmFLS2 genes were involved in A. muelleri resistance to Pcc. After recognizing Pcc, AmFLS2s triggered a series of cellular reactions, including the production of reactive oxygen species and the expression of AmCDPKs. This result showed that infection with Pcc activated the A. muelleri PTI immune reaction. Additionally, Ma et al. (2013) indicated that after plants recognize pathogen infections, the cyclic nucleotide-gated ion channel-Ca2+ signal cascade is activated to induce the production of nitric oxide and reactive oxygen species in response to pathogen signals. After A. muelleri was infected with Pcc, AmCNGC, AmCMLs, AmCAMLs, and AmCDPKs were activated; moreover, the expression of the AmRBOHD gene, which is downstream in the signaling pathway, was increased significantly, indicating that Pcc stress activates the cyclic nucleotide-gated ion channel-Ca2+ signaling cascade and leads to the accumulation of reactive oxygen species in infected tissue cells.
Phenols and lignin produced in the phenylpropane biosynthesis pathway have an important role in plant growth and development and resistance to biological stress. Chen et al. (2021) showed that Pichia galeiformis induces resistance by regulating the expression of phenylpropane biosynthetic pathway genes in postharvest C. paradisi. When A. muelleri is harmed by Pcc, the expression of its phenylpropane biosynthesis pathway genes, including AmPOD, Am4CLs, AmPALs, AmCOAs, and AmBGLU, is induced, indicating that these genes may protect A. muelleri by participating in the synthesis of related secondary metabolites and scavenging reactive oxygen species.
JA, SA, and Pcc resistance.
JA and SA belong to the innate immune mechanism of plants. When B. rapa is infected with Pcc, the JA and SA contents increase significantly, and exogenous MeJA treatment delays the onset of soft rot (Liu et al. 2019; Liu et al. 2022). Similar results were observed during this study. Further study of the role of JA in A. konjac resistance to Pcc showed that MeJA treatment could delay the onset of A. konjac soft rot. This finding is consistent with the results of JA resistance to soft rot disease in B. rapa.
In conclusion, this study focused on the A. muelleri defense response to soft rot disease. AmCDPK, AmRBOHD, AmBGLU, and AmLOX were identified during the response of A. muelleri to Pcc. Moreover, MeJA pretreatment enhanced the resistance of A. konjac to Pcc. These results not only create a foundation for the molecular mechanism of Amorphophallus resistance to soft rot disease but also provide a theoretical basis for the prevention and control of soft rot disease in the process of Amorphophallus planting.
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Pearson correlation coefficient among Amorphophallus muelleri samples infected with Pectobacterium carotovorum ssp. carotovorum at 0, 12, 24, and 48 h post-inoculation.
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
Hierarchical cluster tree showing Amorphophallus muelleri coexpression modules identified by a weighted gene coexpression network analysis.
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
Cross-section of healthy (A), sterile water-treated (B), and methyl jasmonate-retreated (C) Amorphophallus konjac seedlings 48 h after Pectobacterium carotovorum ssp. carotovorum inoculation.
Citation: Journal of the American Society for Horticultural Science 147, 6; 10.21273/JASHS05251-22
Quantitative reverse-transcription polymerase chain reaction primers used for verification of the expression profiles of five unigenes (AmBGLU, AmCAML, AmCDPK, AmLOX, AmRBOHD).
The RNA-Seq statistical data of Amorphophallus muelleri infected with Pectobacterium carotovorum ssp. carotovorum at 0, 24, and 48 h post-inoculation.
Annotation of differentially expressed genes from the plant hormone signal transduction pathway, alpha-linolenic acid metabolism pathway, phenylpropanoid biosynthesis pathway, and plant–pathogen interaction pathway in Pectobacterium carotovorum ssp. carotovorum-infected Amorphophallus muelleri seedlings at 12, 24, and 48 h post-inoculation.