Skip to main content
Download PDF

Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum

BMC Genomics volume 24, Article number: 553 (2023) Cite this article

Abstract

Background

Black spot disease caused by the necrotrophic fungus Alternaria spp. is one of the most devastating diseases affecting Chrysanthemum morifolium. There is currently no effective way to prevent chrysanthemum black spot.

Results

We revealed that pre-treatment of chrysanthemum leaves with the methy jasmonate (MeJA) significantly reduces their susceptibility to Alternaria alternata. To understand how MeJA treatment induces resistance, we monitored the dynamics of metabolites and the transcriptome in leaves after MeJA treatment following A. alternata infection. JA signaling affected the resistance of plants to pathogens through cell wall modification, Ca2+ regulation, reactive oxygen species (ROS) regulation, mitogen‐activated protein kinase cascade and hormonal signaling processes, and the accumulation of anti-fungal and anti-oxidant metabolites. Furthermore, the expression of genes associated with these functions was verified by reverse transcription quantitative PCR and transgenic assays.

Conclusion

Our findings indicate that MeJA pre-treatment could be a potential orchestrator of a broad-spectrum defense response that may help establish an ecologically friendly pest control strategy and offer a promising way of priming plants to induce defense responses against A. alternata.

Peer Review reports

Background

Plants possess innate immune systems that rely on a broad range of constitutive, inducible anti-fungal molecules and a large-scale transcriptional reprogramming in the host plant that is activated via a complex signaling network. Initially, pathogens must overcome the plant's physical barriers, such as the waxy cuticle and the cell wall, which leads to cell wall damage [1, 2]. Pathogen-associated molecular patterns (PAMPs) are recognized by plant cell-surface pattern-recognition receptors (PRRs) that induce signals through plasma-membrane-associated co-receptor kinases and intracellular protein kinases. Through the activation of NADPH oxidases encoded by respiratory burst oxidase homologue (RBOH) genes, mitogen-activated protein kinases (MAPKs), and the induction of downstream cellular immune responses, ligand-dependent association between PRRs and protein kinases causes an influx of Ca2+ and the production of ROS [3]. The defense response can be directly induced by gene expression and indirectly by the stimulation and fine-tuning of hormones such as jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) [4, 5]. Previous research has shown that SA is a major hormone against biotrophic and hemi-biotrophic pathogens, which rely on living plant tissue for nutrients [6,7,8]. In contrast, the JA/ET pathway is critical for plant defense against necrotrophic diseases [9, 10].

JA biosynthesis begins with the release of α-linolenic acid from membrane lipids in the chloroplast [11, 12]. Subsequently, the bioactive hormone jasmonoyl-isoleucine (JA-Ile) is created when JA is conjugated to isoleucine. Moreover, inductive signals like PAMPs are recognized by PRRs at the cell surface to trigger de novo synthesis of JA-Ile from plastid lipids. JA-Ile acts as the major bioactive JA to activate core JA signaling by binding with its coreceptor, the Skp1-Cullin1-F-box-type (SCF) protein ubiquitin ligase complex SCFCOI1-JAZ [13]. At present, many studies have reported that exogenous feeding or external stimuli, can induce endogenous JA synthesis and signal transduction to activate JA signaling [14, 15]. Thereafter, JA signaling activates multiple downstream signaling pathways and transcription factors (TFs) to affect cell wall modification, the production of pathogenesis-related proteins, and the accumulation of anti-fungal molecules that regulate resistance to necrotrophic pathogens and associated stress responses [16,17,18]. Generally, many defense secondary metabolites, such as phenylpropanoids, flavonoids, and phytoalexins, which serve as signal molecules in plant–pathogen interactions, have anti-fungal or anti-oxidant characteristics [19,20,21].

Several studies have explored the mechanism of host–pathogen interactions for Alternaria alternata. For example, in Nicotiana attenuate, JA signaling regulates the biosynthesis and accumulation of phytoalexin scopoletin through MYC2, and activated abscisic acid (ABA) signaling promotes stomatal closure to enhance resistance to A. alternata [22]. The latest evidence suggests that NaWRKY3 is an important factor in scopoletin synthesis, which transcriptionally regulates Rboh-mediated stomatal closure [23]. In apple (Malus domestica Borkh.), ET, JA, and SA signaling and pathogen-induced release of elicitors from the cell wall also contribute to A. alternata resistance [24]. In chrysanthemum, the cross-talk between hormone and Ca2+ signal transduction pathways is the most effective defense response against A. alternata infection [25, 26]. Moreover, the transgenic silencing of the Mildew Resistance Locus O gene (CmMLO17) in chrysanthemum regulates ABA and Ca2+ signaling pathways, resulting in reduced susceptibility to A. alternata infection [27].

C. morifolium is an important member of Asteracese that has ornamental, medical, and edible value. However, both the quality and quantity of chrysanthemum are severely affected by fungal diseases. A. alternata is a necrotrophic fungus that is ubiquitously found on various plant species. It causes a black spot disease that severely affects chrysanthemum cultivation. The disease usually occurs in mature leaves [28, 29], and once established, it can spread rapidly through plant residue, soil, and atmosphere, especially in warm and humid environmental conditions. In the early stage of the infection, A. alternata damages plant tissues by secreting toxins and cell wall degrading enzymes (CWDEs) to help pathogens invade. Then, pathogen obtains nutrients from decaying tissue, and lead to round spots with dark mildew layer. Finally, premature aging and even death were occurred on the plant. The widespread occurrence of this disease causes economic losses and hinders crop production. Broad spectrum fungicides are currently less effective against this disease, cause serious environmental pollution, and have a high cost and energy consumption. Therefore, it is necessary for elicitors or organic compounds to promote resistance by triggering the host's defense mechanism against infections [30].

Using transcriptomic analysis, we reported that A. alternata activates the transcription of JA biosynthesis and signaling genes in chrysanthemum [31]. However, the molecular mechanism associated with JA-induced defense responses against A. alternata in chrysanthemum is largely unknown. Here, we revealed that MeJA pre-treatment of the chrysanthemum significantly reduced its susceptibility to A. alternata infection. By keeping track of the large-scale metabolomic and transcriptomic changes in the leaves after MeJA pre-treatment and A. alternata infection, we pinpointed potential defense response underpinning mechanisms. We also indicated that MeJA treatment has a little effect on gene expression but does induce regulatory genes associated with the defense response such as receptor kinases, Ca2+ regulation, TFs, and phytohormones. When leaves were pretreated with MeJA before infection with A. alternata, we noticed transcriptional reprogramming of genes related to the cell wall, resistance proteins, and Ca2+, MAPK, ROS, and hormonal signaling processes, as well as transcription factors (TFs) associated with defense responses. Furthermore, the levels of anti-fungal metabolites were regulated by MeJA pre-treatment and A. alternata infection. To verify the function of these hub genes, inoculation assays with CmWRKY6 transgenic strains showed that CmWRKY6 positively regulated resistance to black spot disease in the chrysanthemum. Our research presents insight into the overall mechanism of JA signaling that drives defense responses against A. alternata in chrysanthemum, which will aid the screening of relevant candidate anti-fungal elicitors that could serve as ecologically friendly disease control agents.

Results

MeJA-treated chrysanthemum leaves reduced A. alternata susceptibility

JAs has been shown to reduce the susceptibility to necrotrophic pathogen infection [32, 33]. To test whether MeJA treatment of chrysanthemum leaves could reduce susceptibility to A. alternata, we exogenously sprayed whole C. morifolium plants with MeJA before leaves were inoculated with A. alternata. After 48 h post-inoculation (hpi), the leaves treated with 100 μM MeJA displayed significantly lower decay in comparison to the control; however, the sensitivity increased with the higher MeJA concentrations (Figs. 1, S1). These results indicated that exogenous 100 μM MeJA induced A. alternata resistance in Chrysanthemum.

Fig. 1
figure 1

Decreased susceptibility of chrysanthemum leaves to A. alternata after pre-treatment with 100 μM MeJA. a C. morifolium ‘Jinba’ was pre-treated with 100 μM MeJA and inoculated with A. alternata. Controls were treated with distilled water. b Leaf damage caused by A. alternata 48 hpi in control and MeJa-treated leaves. c Disease severity was determined by measuring the lesion area (mm2) of leaves 48 hpi. Data are presented as the mean ± standard error of four biological replicates. *P ≤ 0.0001 compared to control, as calculated by two-way ANOVA. d Pathogens of A. alternata. The left part shown that the conidiophores morphology of A. alternata at a magnification of × 10. The right part shown that the colony of A. alternata growing on potato dextrose agar medium after a week

Full size image

MeJA treatment of chrysanthemum caused transcriptional reprogramming following A. alternata infection

To further explore the function of JA signaling in A. alternata defense responses at the transcriptome level, we utilized RNA-seq analysis of C. morifolium plants sprayed with 100 µM MeJA or deionized water as a mock with or without A. alternata inoculation. Approximately 90% of the reads for each sample were mapped to the reference chrysanthemum genome (https://doi.org/10.6084/m9.figshare.21655364.v2) (Table S1), and the biological replicates for each treatment showed good correlation (Fig. 2a). The differentially expressed genes (DEGs) were identified by comparing JA group versus MOCK group (JA vs. MOCK), MOCK-I group versus MOCK group (MOCK-I vs. MOCK), and JA-I group versus JA group (Fig. 2b). The JA vs. MOCK showed a minimal impact on gene expression, with only 1101 and 428 genes exclusively upregulated and downregulated, respectively. The MOCK-I group showed a significant change in gene expression, with 2902 and 2983 genes exclusively upregulated and downregulated, respectively (Figs. 2c, d), which were self-activated genes independent of JA signaling after inoculation. In contrast, MeJA pre-treatment prevented this change in gene expression post-infection (JA-I vs. JA), with 1904 and 1845 genes upregulated and downregulated, respectively, that were affected by the JA-I treatment. Clearly, pre-treatment with JA reduced infection-induced gene expression, which resulted in a decreased susceptibility to the fungus. The JA-treated leaves are relevant candidates for understanding the mechanism of reduced susceptibility to A. alternata. The fact that 6680 DEGs were upregulated both in MOCK-I vs. MOCK and in JA-I vs. JA groups suggested that inoculation induced a response similar to that of the JA-I group and that these genes are pivotal to elucidating JA signaling-mediated defense responses against A. alternata (Fig. 2c).

Fig. 2
figure 2

The DEGs, Venn diagrams, and enrichment analysis of chrysanthemum after JA pre-treatment and A. alternata infection. a Heatmap depicting pairwise Pearson correlation of gene expression values of all samples. b Bar graph showing total number of upregulated (orange) and downregulated (green) DEGs in JA vs. MOCK, MOCK-I vs. MOCK, and JA-I vs. JA groups. c Venn diagrams presenting the distribution of up-regulated DEGs after JA pre-treatment and A. alternata infection. d Venn diagrams presenting the distribution of down-regulatedDEGs after JA pre-treatment and A. alternata infection. e KEGG and (f) GO enrichment analysis of the upregulated genes shared between MOCK vs. MOCK-I and JA vs. JA-I groups. g Heat map presenting the normalized to Log2 (FPKM + 1) of DEGs. Rows are centered based on the average FPKM. From left to right is MOCK, MOCK-I, JA, and JA-I groups

Full size image

The differentially expressed genes, including those upregulated by MeJA pre-treatment, in MOCK-I, and JA-I groups were classified by gene ontology (GO) analysis and mapped onto metabolic and regulatory pathways using the MAPMAN tool [34]. The MeJA pre-treatment affected the expression of genes associated with metabolic and regulatory pathways and upregulated genes involved in cell wall integrity (cell wall and lipids), terpenes, flavonoids, phenylpropanoids, and phenolic metabolism (Fig. S2a). In JA vs. MOCK group, up-regulated regulatory genes involved in protein modification and degradation, receptor kinases, Ca2+ regulation, TFs, and phytohormones (Fig. S2b). Additionally, changes in the expression of secondary metabolic genes involved in anti-oxidative and anti-fungal molecules, including phenylpropanoids, flavonoids and derivatives, glucosinolates, lignin and lignans were observed (Fig. S3a). In mock pre-treatment leaves, MAPMAN-based analysis of the genes that were upregulated following infection revealed a dramatic effect in almost all metabolic and regulatory pathways (Fig. S4a). In contrast, in the MeJA pre-treated group, infection only affected the expression of small number of metabolic genes, including the upregulation of genes involved in cell wall integrity, such as those associated with the biosynthesis of wax, flavonoids, and lipids (Fig. S4c). It is worth noting that almost all regulatory pathways, such as receptor kinases, Ca2+ regulation, TFs, and phytohormones, in plants pre-treated with MeJA before infection were positive regulation compared to controls. These results showed that A. alternata infection caused significant changes in metabolic pathways, whereas pre-treatment with MeJA prevented this reaction and maintained the expression of defense-related regulatory genes.

We focused on the common up-regulated genes in JA-I vs. JA and MOCK-I vs. MOCK to explore the mechanism of JA signaling-mediated defense responses against A. alternata. The 6680 commonly up-regulated genes were classified by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GO enrichment analysis to assess biological functions (Figs. 2e, f, g, S3b). The results of the analysis showed that MAPK signaling pathway-plant (ko04016, 582 DEGs), phenylpropanoid biosynthesis (ko00940, 275 DEGs), zeatin biosynthesis (ko00908, 102 DEGs), alpha-linolenic acid metabolism (ko00592, 82 DEGs), flavonoid biosynthesis (ko00941, 86 DEGs), anthocyanin biosynthesis (ko00942, 34 DEGs), and peroxisome (ko04146, 91 DEGs) genes were significantly enriched in common up-regulated genes (Fig. 2e). Moreover, GO analysis showed that the DEGs were considerably enriched in oxidative stress processes, including oxidoreductase activity (GO:0016491, 1026 DEGs), hydrogen peroxide catabolic processes (GO:0042744, 95 DEGs), peroxidase activity (GO:0004601, 110 DEGs), ROS metabolic processes (GO:0004601, 96 DEGs), and the ET response pathway, including ethylene-activated signaling pathway (GO:0009873, 63 DEGs), cellular response to ethylene stimulus (GO:0071369, 63 DEGs), and response to ethylene (GO:0009723, 64 DEGs) (Fig. 2f). The MAPMAN-based analysis of the genes showed that common up-regulated genes had a great effect in secondary metabolic pathways, especially phenylpropanoids, phenols, flavonoids and derivatives, lignin and lignans (Fig. S3b). These results indicated that JA could regulate the complex biological pathways of chrysanthemum inoculated with A. alternata.

The genes in the major enrichment pathways were primarily involved in MAP kinases, Ca2+ signaling, ROS regulation, JA and ET signaling, and phenylpropanoid biosynthesis (Fig. 2g). For instance, regulatory genes encoding the cyclic nucleotide gate channel calcium-binding protein, ROS scavenging enzyme-like L-ascorbate oxidase (AAO), catalase (CAT1), superoxide dismutase (SOD), peroxidase (POD), and glutathione S-transferase (GST); as well as genes encoding key enzymes of phenylpropanoid biosynthesis such as phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), shikimate O-hydroxycinnamoyl transferase, caffeoyl-CoA O-methyltransferase, cinnamyl-alcohol dehydrogenase, ferulate-5-hydroxylase, ferulate-5-hydroxylase, 5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase, cinnamoyl-CoA reductase and POD; genes encoding key enzymes of the JA synthesis pathway, such as phospholipase A1, lipoxygenase, allene oxide synthase, 12-oxophytodienoic acid reductase, OPC-8:0 CoA ligase 1, enoyl-CoA hydratase/3-hydroxyacyl-CoA dehydrogenase, and acetyl-CoA acyltransferase 1. Among the common genes, 23 genes that were associated with the defense response showed a higher induction in JA-I vs. JA than that in MOCK-I vs. MOCK, including pathogenesis-related proteins (PDF1.2, PR10, PR1, RPS4, and RPS2), proteins associated with strengthening of the cell wall barrier (GT61, CESA, and ChiB), and defense-related molecular chaperones (HSP90) (Fig. S3; Table S3). This analysis suggested that these genes might contribute to resistance to A. alternata.

The metabolomics results also verified that downstream metabolic changes were involved in JA biosynthesis, lignin biosynthesis, and oxidative stress processes. Exogenous MeJA treatment in plant leaves led to the accumulation of numerous derived phenolics, phenylpropanoids, and flavonoids (Fig. 3). In this study, MeJA pre-treatment caused a significant increase in endogenous JA levels such as methyl jasmonate (61.1-fold) and (˗)-trans-methyl dihydrojasmonate (23.24-fold), and increased downstream metabolites included shikimic acid (1.29-fold), phenylacetaldehyde (1.27-fold), caffeic acid (1.51-fold), 3,5-dicaffeoylquinic acid (0.72-fold), syringin (1.27-fold), quercetin (0.94-fold), taxifolin (1.28-fold), and cyanidin (0.75-fold) when compared with the controls. These findings suggested that MeJA pre-treating the leaves led to an absorption of JA and subsequent activation of downstream metabolites. The primary impact of MeJA pre-treatment was noticed after fungal infection. In mock-treated leaves, fungal infection led to a substantial reduction in the phenylalanine derived volatile eugenol (reduced by 50%) and flavonoid derived naringenin (reduced by 40%), pethidine (reduced by 55%), and quercitrin (reduced by 39%). However, luteolin (2.65-fold), 3,5-dicaffeoylquinic acid (2.3-fold), taxifolin (1.55-fold), and naringenin chalcone (2.47-fold) increased in the infected leaves of non-treated leaves. Interestingly, MeJA pre-treatment increased or further promoted the accumulation of these metabolites (Figs. 3b and S5), suggesting that fungal infection can negatively affect the antioxidant system of plants, whereas MeJA pre-treatment prevented the decline in these metabolites and maintained intracellular ROS homeostasis. In MeJA-treated leaves, the metabolic impacts following infection included an accumulation of flavonoid-derived peonidin (66.46-fold), pethidine (6.24-fold), and monolignols 4,5-dicaffeoylquinic acid (5.06-fold) and 5-hydroxyferulic acid (0.42-fold) (Figs. 3b, S5).

Fig. 3
figure 3

Metabolic changes in chrysanthemum leaves after JA-treatment and A. alternata infection. a Changes in JA synthesis-related metabolites levels in chrysanthemum leaves in MOCK, MOCK-I, JA, JA-I groups. b Changes in phenolic acid levels in chrysanthemum leaves in MOCK, MOCK-I, JA, JA-I groups. Changes in the levels of metabolites were analyzed by LC–MS/MS. Values represent means from three biological replications ± standard error. Green bars indicate metabolite levels in non-infected leaves and orange bars represent metabolite levels after infection. MOCK, control; JA, MeJA pre-treated. Note: the KEGG pathways were retrieved from the Kanehisa Laboratories [35]

Full size image

Identification of TFs involved in JA treatment and A. alternata infection

TFs form the core of the gene regulatory network, mediating transcriptional reprogramming in the reaction to phytopathogens. It has been demonstrated that members of the TF family, such as WRKY, AP2/EREBP, NAC, and MYB, contribute to defense against A. alternata [36,37,38]. In our study, a large number of TFs were identified through DEG analysis that were specifically or commonly upregulated. Therefore, the host defense response could be significantly impacted by variations in TF expression. These differentially expressed TFs included WRKY, AP2/EREBP, MYB, and NAC (Fig. 4a). Of the 6680 common genes, 425, including 87 WRKYs and 104 AP2/EREBPs, encoded TFs (Fig. 4a). Most of these WRKYs belonged to WRKY33 and WRKY22 families. The AP2/EREBPs belonged to the EREBP subfamily, such as ethylene response factor (ERF), dehydration response element binding protein (DREB), and other proteins (EREBP-like; Fig. 4b). We identified 122 TFs specifically in the JA-I group, including 14 WRKYs and 13 AP2/EREBPs (Figs. 4a, S8). One forty five genes of the DEGs exclusively found in MOCK-I were classified as TFs. MYB (15 members) and AP2/EREBP (24 members) were the two TFs with the most annotations.

Fig. 4
figure 4

Differentially expressed transcription factors in response to JA-treatment and A. alternata infection. a Classification of transcription factors. b Heat map of the normalized Log2 (FPKM + 1) of WRKY and AP2-EREBP transcription factors in the common upregulated DEGs. From left to right is MOCK, MOCK-I, JA, and JA-I. MOCK, control; MOCK-I, control infected group; JA-I, MeJA pre-treated and infected group; JA, MeJA-pre-treated group

Full size image

Validation of differential gene expression using reverse transcription quantitative PCR (RT-qPCR)

To validate the RNA-seq results, 12 genes were randomly selected from a total 6680 common genes for RT-qPCR. The expression of WRKY29 (evm.TU.scaffold_1046.374), WRKY33 (evm_model_scaffold_9028_9), WRKY6 (evm.TU.scaffold_9505.8), and CERK1 (evm_model_scaffold_673_83) was induced by A. alternata infection. The expression of LRR receptor-like kinase (BGI_novel_G004336), VSP2 (evm.model.scaffold_1548.86), JAZ (evm.TU.scaffold_6916.92), ESD1 (evm_model_scaffold_895_116), CYP94A (evm.TU.scaffold_268.169), PAL (BGI_novel_G004149), and CCA1 (evm.model.scaffold_11180.134) was induced by MeJA-treatment. Similar upregulation or downregulation expression patterns were seen in the qRT-PCR and RNA-seq data (Fig. 5), indicating that our transcriptome data was reliable.

Fig. 5
figure 5

Changes in gene expression levels after JA pre-treatment and A. alternata infection. The left vertical axis represents relative gene expression level from RT-qPCR (orange) and the right vertical axis represents FPKM from RNA-seq (green). MOCK, control; MOCK-I, control infected group; JA-I, MeJA pre-treated and infected group; JA, MeJA-pre-treated group

Full size image

Overexpression of CmWRKY6 confers chrysanthemum resistance to black spot disease

To further verify the reliability of the results, we choose WRKY6 (evm.TU.scaffold_9505.8), which was induced by A. alternata, to generate overexpressed and silenced (RNA interference [RNAi]) CmWRKY6 chrysanthemum plants [39]. Inoculation assays demonstrated that compared with wild-type (WT), the CmWRKY6 overexpressing (OX-CmWRKY6) lines had enhanced resistance to black spot disease, with a lesion area that was reduced by 55%. Conversely, the CmWRKY6 silenced lines (RNAi-CmWRKY6) displayed enhanced susceptibility, with a lesion area that increased by 40% (Fig. 6). These results further validated the upregulated genes that were identified by comparing MOCK-I vs. MOCK with JA-I vs. JA groups, indicating that they are relevant candidates for understanding the mechanism of JA-induced A. alternata resistance.

Fig. 6
figure 6

Overexpression of CmWRKY6 confers resistance to black spot disease in chrysanthemum ‘Jinba’. a The A. alternata infection phenotypes in inoculated leaves of WT, OX-CmWRKY6 and RNAi-CmWRKY6 plants, respectively. b Disease severity was determined by measuring the lesion area (mm2) of leaves 48 hpi. Data are presented as the mean of four replicates ± standard error. Asterisks (*) depict statistically significant differences for each time interval between the different treatments, as calculated by a two-way ANOVA (*P ≤ 0.05, ** P ≤ 0.01)

Full size image

Discussion

JA enhances resistance to fungal pathogen in plants

To respond to fungal attacks, plants produce defense-related compounds and different phytohormones. High-throughput data obtained through liquid chromatography tandem mass spectrometry (LC–MS/MS) and RNA-seq technology can objectively and comprehensively reflect the global metabolic change and transcriptional expression associated with pathogen responses [40], such as the resistance induced by different elicitors or natural molecules. At present, many studies have shown that exogenously applied elicitors or natural molecules can induce plant defense responses. For instance, high phenylalanine concentrations reduce the susceptibility to Botrytis cinerea in petunia, Arabidopsis, tomato leaves, and chrysanthemum flowers [41, 42]. Exogenous MeJA also induced resistance to fungal pathogen in potato [14] and rose leaves [15]. Here, we showed that MeJA pre-treatment decreased the susceptibility of chrysanthemum leaves to A. alternata (Fig. 1), indicating that treatment with elicitors or natural molecules is an effective mode of enhancing pathogen resistance in a range of plant species. However, the mechanisms connecting A. alternata infection with JA signaling are not completely clear, especially in chrysanthemum plants. Therefore, we monitored the dynamics of metabolites and transcriptomes in leaves after MeJA pre-treatment and A. alternata infection to explore JA-dependent cross-talk, signaling, and defense responses in disease-resistance systems. These results further deepen our understanding of the JA-mediated mechanisms underlying resistance to A. alternata infection in chrysanthemum.

JA enhances the secondary metabolism in chrysanthemum after A. alternata infection

Similar to a previous study, MeJA pre-treatment influenced the expression levels of JA synthesis genes and induced the accumulation of endogenous JA such as methyl jasmonate and (˗)-trans-methyl dihydrojasmonate (Figs. 3a, S2b). Inducing the expression of genes associated with secondary metabolism (Fig. S3a) further increased the concentrations of secondary metabolites [14, 15], specifically phenylpropanoids and flavonoids, including shikimic acid, caffeic acid, 3,5-dicaffeoylquinic acid, syringin, quercetin, and taxifolin (Fig. 3b), which have anti-fungal and anti-oxidative activity [42]. The MeJA pre-treatment prevented the reduction in the levels of the volatiles, such as phenylacetaldehyde and eugenol in the JA-I group (Fig. 3b). This could be as a result of the enhanced carbon flow towards the formation of eugenol in MeJA-pretreated leaves following infection, consistent with the release of volatiles being associated with pathogen resistance [43]. Additionally, MeJA pre-treatment resulted in transcriptional reprogramming of a group of genes involved in the cell wall, lipids, Ca2+ signaling processes, and hormonal signaling processes, which are connected to plant defense responses to pathogen attack (Fig. S2). These results confirmed the JA can regulate specific primary and secondary metabolic processes [14, 15].

The influence of MeJA pre-treatment, both at the transcriptomic and metabolic levels suggested that the JA priming of plant leaves enabled a rapid immune response after A. alternata infection through the activation of a diverse set of defense mechanisms. In this study, plants that received the MeJA pre-treatment showed small transcriptional changes after A. alternata infection in contrast to the drastic increase in mock-treated leaves after infection (Figs. 2b, c). This difference may be related to the overall early pathogen defense response induced by exogenously applied JA [14]. To further explore the mechanism of JA-induced resistance against A. alternata, we focused on the 6680 genes upregulated in both MOCK-I vs. MOCK and in JA-I vs. JA groups. The common DEGs played a central role in coordinating different pathways, and accounted for 78% of the DEGs in JA-I and 70% of the DEGs in MOCK-I (Fig. 2c), reflecting the relationship between JA signaling and A. alternata. The RNA-seq analysis showed that the commonly upregulated DEGs were involved in MAPK signaling pathway, secondary metabolism (flavonoid, anthocyanin, and phenylpropanoid biosynthesis) and JA biosynthesis (α-linoleic acid metabolism). The GO term enrichment analysis highlighted that the DEGs were mainly associated with oxidative stress processes (Figs. 2d, e). These findings indicated that various metabolic production and defense pathways are possibly induced through the accumulation of secondary metabolites and ROS scavenging in chrysanthemum.

JA enhanced the cell wall integrity maintenance system

The production of ROS often initiates various defense responses that help plants in fighting off pathogen attacks, which can include enhancing the cell wall barrier and regulating Ca2+ signaling, as ROS can function as Ca2+ sensors [44]. We found the upregulation of Ca2+ signaling and ROS metabolic genes in the JA and JA-I groups (Fig. 2g), suggesting the interplay between ROS and Ca2+ signals contributed to the defensive response. The overexpression of ROS can promote cell injury; therefore, maintaining intracellular ROS homeostasis can promote plant defense response [45]. Previous reports have provided evidence that JA prevents excess ROS generation [46] and promotes accumulation of antioxidant enzymes [47]. Our findings show that the DEGs that encoded antioxidant enzymes (two CAT1s, four SODs, one AAO, four GSTs, and four PODs) were significantly upregulated in the JA-I group (Fig. 2g). Furthermore, the levels of anti-oxidative metabolites, including flavonoids (luteolin and quercetin), phenolic acids (3,5-dicaffeoylquinic acid), and anthocyanins (cyanidin and pethidine), were increased (Figs. 3b, S5b).

ROS and Ca2+ signaling also mediate defense mechanisms through cell wall strengthening and re-organization [48], and the induced expression of genes related to cell wall biosynthesis and wax release may also act as signals for cell wall remodeling to defend against pathogen infection [49]. Different classes of flavonoids and lignin are synthesized via the phenylpropanoid pathway [50]. Lignin is a structural component of the cell wall and the expression of its biosynthetic pathway genes enhance plant defense [51,52,53]. The expression of genes encoding key enzymes of the phenylpropanoid pathway was significantly upregulated following A. alternata infection in chrysanthemum (Fig. 2g). Notably, one PAL (BGI_novel_G003750), one 4CL (evm.TU.scaffold_320.366), and three POD (evm.TU.scaffold_550.84, BGI_novel_G000132, evm.TU.scaffold_11661.112) genes were upregulated in JA-I vs. JA compared with that in MOCK-I vs. MOCK (Table S2). The downstream lignin synthesis substrate monolignols also accumulated in the MeJA-treated plants (Fig. S5a) after enzymatic oxidization and the subsequent radical coupling in the cell walls and formed a heterogenous polymer that constitutes lignin [54], suggesting that this might contribute to the stronger physical barrier for plant defense.

JA enhanced pathogen‐induced MAPKs signaling

MAPK cascades participate in many signal‐transferring processes, are essential signaling modules downstream of receptors or sensors that detect endogenous stimuli like PAMPs and effectors, and play crucial roles in signal transduction in response to phytohormones and environmental challenges [55,56,57,58]. MAPKs further transmit and amplify these signals through the stepwise phosphorylation of mitogen-activated protein kinase kinases (MAPKKs) and mitogen-activated protein kinase kinases (MAPKKKs). The A. alternata infection in chrysanthemum caused the induction of MAPKs, MAPKKs, and MAPKKKs signaling events (Fig. 2g). In Arabidopsis, MPK3/MPK6 and their orthologs were proposed to share a subset of defense responses [59, 60] that affect many downstream transduction pathways. For example, MPK3/MPK6 cascade and Ca2+ signaling pathway crosstalk regulate the biosynthesis of camalexin [61, 62]. MPK3/MPK6 regulate ET biosynthesis during pathogen attack [63, 64]. Additionally, the functions of the MKK4/MKK5–MPK3/MPK6 module in plant immunity have been identified [65], and the search for the MAPKKK(s) upstream of the MKK4/MKK5–MPK3/MPK6 module has progressed in recent years [66, 67], but there is still a gap in our understanding of its mechanism. In this study, MPK3 (evm.TU.scaffold_1634.185 and evm.TU.scaffold_155.197), MKK4/5 (evm.TU.scaffold_9357.64 and evm.TU.scaffold_10165.15), and ANP1 (evm.TU.scaffold_10099.27 and evm.TU.scaffold_826.7) were up-regulated by A. alternata and were more significantly up-regulated in the JA-I group (Fig. 2g; Table S2). This indicated that JA signaling may regulate Ca2+ signaling and the synthesis of phytoalexin or activate ET signaling by upregulating the expression of MPK3 to produce disease resistance. Conversely, it suggests that JA signaling might occur via an ANP1-MKK4/5–MPK3 cascade to activate immune signaling, which could be related to oxidative signal transduction. Early studies have shown that H2O2 can activate the specific Arabidopsis MAPKKK, ANP1, to initiate a phosphorylation cascade involving the stress MAPKs, AtMPK3 and AtMPK6 [68]. Recently, MKK4/MKK5 has been reported to regulate plant defense pathways, including ROS production and the synthesis of ET and SA [69], suggesting that ANP1-MKK4/MKK5–MPK3/MPK6 is an oxidative stress-activated mitogen-activated protein kinase cascade in plants. Conclusively, JA signaling is one of the main pathways of JA-induced resistance, which can further amplify ROS regulation and ET signaling through the ANP1-MKK4/MKK5–MPK3 cascade to regulate immune responses.

The role of transcription factors in the JA signaling-induced defense response

TFs play key roles in coordinating large-scale transcriptional reprogramming that resolve plant immune mechanisms [70, 71]. Numerous TFs that act as essential players in JA signal transduction have been discovered using forward and reverse genetic methods [72]. In our study, scanning through the shared DEGs between the JA-I and MOCK-I groups, we identified 104 AP2-EREBP and 87 WRKY TFs, which accounted for the two largest proportions of overlapping TFs (Fig. 4), suggesting that AP2-EREBP and WRKY family members play direct roles in JA-triggered immunity. At present, studies have indicated that AP2-EREBP TFs regulate the signal transduction pathways of numerous phytohormones, including ET, ABA, cytokinin (CTK), and JA [73,74,75], activating inducible defense responses in plants. The AP2-EREBP family is divided into five subfamilies: AP2, ERF, DREB, ABI3/VP1 (RAV)-related, and other EREBP-like [76]. In the present study, most of the AP2-EREBP TFs induced by JA and A. alternata infection belonged to ERF (60 members, 57.7%, Fig. 4b). Previous studies reported that JA and ET synergistically activate defense signaling against necrotrophic pathogens [77, 78]. Moreover, WRKY TFs participate in phytohormone-mediated signaling pathways and transcriptional reprogramming associated with plant defense responses like the MAPK signaling cascade [79, 80]. NaWRKY3 and NaWRKY6 can regulate the synthesis of JA to mediate pathogen defense responses [81]. WRKY33 positively regulates target genes involved in the biosynthesis of the antimicrobial compound camalexin and JA/ET downstream signaling [82, 83], and functions as a key transcriptional regulator required for immunity in Arabidopsis towards Botrytis cinerea [84, 85]. In this study, most of the WRKY TFs that regulate immune responses induced by JA and A. alternata belonged to WRKY33 (42 members, 48%), followed by WRKY22 (22 members, 25%), and WRKY29 (8 members, 14%) (Fig. 4b). The WRKY33 and WRKY22/29 TFs were significantly upregulated in JA-I compared with those in the MOCK-I group (Fig. S7). This is potentially the reason that MeJA pre-treatment had a stronger induction of resistance genes than the MOCK-I group (Fig. S6, Table S2). Three TFs were annotated as WRKY53 (evm.TU.scaffold_1554.315, evm.TU.scaffold_1317.150 and evm.TU.scaffold_1315.60) in chrysanthemum, which were more significantly up-regulated in JA-I group (Figs. 4b, S7), suggesting that WRKY53 may be mediated by JA signaling in response to pathogenic fungal infection. Additionally, 13 AP2-EREBP and 14 WRKY TFs were detected specifically in JA-I, which may play important positive regulatory roles in mediating the JA signal pathway against A. alternata in chrysanthemum. Our recent research shows that CmWRKY6 negatively regulates the resistance to Fusarium oxysporum [39], and in the present study, WRKY6 was upregulated in both JA-I vs. JA and MOCK-I vs. MOCK groups, and the CmWRKY6 overexpression line had reduced susceptibility to black spot disease compared to the control. Differences in pathogenicity between these reports might be because F. oxysporum is a soilborne plant pathogen whose hypha penetrates plant roots rather than invading leaves like A. alternata.

Conclusions

We have presented experimental evidence that MeJA pre-treatment is an effective strategy to control black spot in plants. JA enrichment promotes multilayered defense responses in plant tissues and improves host immunity by mediating the expression of receptor kinases, TFs, and proteins involved in Ca2+ signaling, hormone signaling, cell wall and lipid metabolism pathways, in coordination with the production of anti-fungal and anti-oxidant metabolites. Our results suggest that MeJA pre-treatment mediates the transcriptional reprogramming of defense response activation before fungal infection. During pathogen attack, MeJA pre-treatment probably activates the ANP1-MKK4/MKK5–MPK3 cascade, ROS and ET signaling events, and induces TFs that mediate gene regulatory networks in response to A. alternata, along with promoting the production of anti-fungal and anti-oxidant metabolites. Therefore, the role of JA signaling in positively regulating plant immunity is dependent on crosstalk among multiple signaling pathways (Fig. 7). The findings of the present study identified promising candidates with anti-fungal and anti-oxidant characteristics that may serve as ecologically friendly pathogen control agents.

Fig. 7
figure 7

Hypothetical model of the jasmonate-induced defense mechanism. JA signaling activates endogenous Ca2+, ROS, MAPK, and TF signaling transduction and the accumulation of anti-fungal molecules, which enhances resistance to A. alternate

Full size image

Materials and methods

Plant material and A. alternata culture

The chrysanthemum cultivar ‘Jinba’ was provided by the Chrysanthemum Germplasm Resource Preserving Center at Nanjing Agricultural University (Nanjing, China). Rooting cuttings of similar growth stage were transplanted in a 3:1 mixture of vermiculite and perlite without fertilizer. Chrysanthemums were cultivated in an illumination incubator with a photoperiod of 16 h light/8 h dark at 28 °C and 70% humidity. After the transplants had up to 10 mature leaves, they were used in the experiments.

The A. alternata strain used for this study was isolated and identified from typical infected leaves of Chrysanthemum ‘Fubaiju’ at our laboratory. The test strain was moved to potato dextrose agar solid medium, where it was grown for around a week at 28 °C. Thereafter, fungal cakes were transferred into 200 mL potato dextrose water liquid medium and grown overnight at 28 °C with shaking at 180 rpm before inoculation assays were conducted.

Inoculation with A. alternata and MeJA treatments

C. morifolium leaves were sprayed with distilled water or 100 µM MeJA (Sigma-Aldrich, Darmstadt, Germany) until all leaves of each plant were wet. Twenty-four hours after exogenous elicitation, two leaves on each plant were inoculated with A. alternata (Fig. 1a). Each inoculation site was about 1 cm in diameter. There were four treatments: 100 µM MeJA pre-treatment (JA group), 100 µM MeJA pre-treatment and inoculation (JA-I group), mock (control, MOCK group), mock and inoculation (MOCK-I group). This procedure ensured that each spot was inoculated with a quantitative amount of mycelium. Each groups were cultivated in a controlled environment with a photoperiod of 16 h light/8 h dark at 28 °C and 70% humidity. The lesion area was observed at 48 hpi.

RNA extraction and RNA-seq library construction

Total RNA was isolated from leaves from the MOCK, MOCK-I, JA, and JA-I groups (three samples per group) at 48 hpi using the RNA extraction kit (Huayueyang Biotechnology, Beijing, China) following the manufacturer’s protocol. All 12 libraries were constructed and sequenced using the using an DNBSEQ platform at BGI (Shenzhen, China) to generate sequence reads.

Analysis of RNA-seq datasets

To create clean read data, the original raw data was filtered, adapter sequences and poly-N and poor-quality reads were eliminated. After filtering, the clean data were mapped by HISAT (v2.1.0) [86] to the chrysanthemum genome [87], matched to reference gene sequences by Bowtie2 [88], and the gene expression level of each sample was determined using RSEM [89]. Differential expression analysis was performed with DESeq2 [90], using the negative binomial distribution model, the hypothesis test probability (P value) was calculated to identify differences in the gene expression data [91]. Genes with fold change ≥ 2, and adjusted P value (Q value) ≤ 0.001 were designated as DEGs. Based on the GO (http://geneontology.org) and KEGG (http://www.genome.jp/kegg) notes genes and classifications, the DEGs were functionally classified, and the phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) in the R software was applied for KEGG enrichment analysis, whereas the TermFinder package was utilized for GO enrichment analysis (https://metacpan.org/pod/GO::TermFinder). The cutoff point for defining candidate genes as significantly enriched was set at Q value ≤ 0.05.

Extraction and LC–MS/MS profiling of metabolites

Leaves from three biological replicates of the MOCK, MOCK-I, JA, and JA-I groups were clipped to eliminate the lesions and immediately stored at liquid nitrogen. The freeze-dried leaves were crushed into a fine powder and used for metabolite extraction. The extracts were used for a further LC–MS/MS analysis after absorbing and filtering. Raw LC–MS/MS data were collected for peak extraction and identification to obtain peak area and identify metabolites, respectively. Data preprocessing was performed using metaX [92]to obtain and identify the isolated metabolic compounds. The identified metabolites were categorized and functionally annotated using KEGG ID, HMDB ID, category, and KEGG pathway in the KEGG and HMDB databases.

Validation of RNA-seq data by (RT-qPCR)

Twelve DEGs were chosen at random for RT-qPCR. Primers were designed using PRIMER 5 software (Table S3). RT-qPCR was carried out using an Eppendorf Mastercycler Ep RealPlex 2S fluorescence quantifier (Hamburg, Germany). The manufacturer's instructions were followed while using the 2 SYBR Green qPCR master mix (Bimake) in the reactions. A total of 10.0 μL of SYBR® Premix Ex TaqTM II, 1.0 μL of each 10 μM forward and reverse primer, and 2.0 μL of cDNA template were used in each reaction. The following described the reaction conditions: 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s, 60 °C for 15 s, and 72 °C for 20 s. CmEF1α (GenBank: AB548817.1) was used as a reference gene, and the 2−ΔΔCT method was used to calculate each gene's relative expression level [93].

Availability of data and materials

The datasets generated during the current study were submitted to the NCBI repository, bioproject PRJNA982184. Chrysanthemum genome used in the study is from the website https://doi.org/10.6084/m9.figshare.21655364.v2.

References

  1. Rodriguez PA, Rothballer M, Chowdhury SP, Nussbaumer T, Gutjahr C, Falter-Braun P. Systems biology of plant-microbiome interactions. Mol Plant. 2019;12(6):804–21.

    CAS  PubMed  Google Scholar 

  2. Ma H, Zhang B, Gai Y, Sun X, Chung KR, Li H. Cell-wall-degrading enzymes required for virulence in the host selective toxin-producing necrotroph Alternaria alternata of Citrus. Front Microbiol. 2019;10:2514.

    PubMed  PubMed Central  Google Scholar 

  3. Couto D, Zipfel C. Regulation of pattern recognition receptor signalling in plants. Nat Rev Immunol. 2016;16(9):537–52.

    CAS  PubMed  Google Scholar 

  4. De Cremer K, Mathys J, Vos C, Froenicke L, Michelmore RW, Cammue BP, De Coninck B. RNAseq-based transcriptome analysis of Lactuca sativa infected by the fungal necrotroph Botrytis cinerea. Plant Cell Environ. 2013;36(11):1992–2007.

    PubMed  Google Scholar 

  5. Burketova L, Trda L, Ott PG, Valentova O. Bio-based resistance inducers for sustainable plant protection against pathogens. Biotechnol Adv. 2015;33(6 Pt 2):994–1004.

    CAS  PubMed  Google Scholar 

  6. Cao H, Bowling SA, Gordon AS, Dong X. Characterization of an arabidopsis mutant that is nonresponsive to inducers of systemic acquired resistance. Plant Cell. 1994;6:1583–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Thomma BP, Eggermont K, Penninckx IA, Broekaert WF. Separate jasmonate–dependent and salicylate–dependent defense–response pathways in Arabidopsis are essential for resistance to distinct microbial pathogens. PANS. 1998;95:15107–11.

    CAS  Google Scholar 

  8. Wildermuth MC, Dewdney J, Wu G, Ausubel FM. Isochorismate synthase is required to synthesize salicylic acid for plant defence. Nature. 2001;414:562–5.

    CAS  PubMed  Google Scholar 

  9. Jia H, Zhang C, Pervaiz T, Zhao P, Liu Z, Wang B, Wang C, Zhang L, Fang J, Qian J. Jasmonic acid involves in grape fruit ripening and resistant against Botrytis cinerea. Funct Integr Genomics. 2016;16(1):79–94.

    CAS  PubMed  Google Scholar 

  10. Zhang L, Zhang F, Melotto M, Yao J, He SY. Jasmonate signaling and manipulation by pathogens and insects. J Exp Bot. 2017;68:1371–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Browse J. Jasmonate passes muster: a receptor and targets for the defense hormone. Annu Rev Plant Biol. 2009;60:183–205.

    CAS  PubMed  Google Scholar 

  12. Wasternack C, Hause B. Jasmonates: biosynthesis, perception, signal transduction and action in plant stress response, growth and development. An update to the 2007 review in Annals of Botany. Ann Bot. 2013;111(6):1021–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Sheard LB, Tan X, Mao H, Withers J, Ben-Nissan G, Hinds TR, Kobayashi Y, Hsu FF, Sharon M, Browse J, et al. Jasmonate perception by inositol-phosphate-potentiated COI1-JAZ co-receptor. Nature. 2010;468(7322):400–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang Y, Yang X, Guo X, Hu X, Dong D, Li G, Xiong X. Exogenously applied methyl Jasmonate induces early defense related genes in response to Phytophthora infestans infection in potato plants. Hortic Plant J. 2022;8(4):511–26.

    CAS  Google Scholar 

  15. Ren H, Bai M, Sun J, Liu J, Ren M, Dong Y, Wang N, Ning G, Wang C. RcMYB84 and RcMYB123 mediate jasmonate-induced defense responses against Botrytis cinerea in rose (Rosa chinensis). Plant J. 2020;103(5):1839–49.

    CAS  PubMed  Google Scholar 

  16. Hu X, Li W, Chen Q, Yang Y. Early signal transduction linking the synthesis of jasmonic acid in plant. Plant Signal Behav. 2009;4(8):696–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhang Y, Xue J, Liu L, Sun X, Zhou G, Chen M, Shao Z, Hang Y. Divergence and conservative evolution of XTNX genes in land plants. Front Plant Sci. 2017;8:1844.

    PubMed  PubMed Central  Google Scholar 

  18. Wang F, Yu G, Liu P. Transporter–mediated subcellular distribution in the metabolism and signaling of jasmonates. Front Plant Sci. 2019;10:390.

    PubMed  PubMed Central  Google Scholar 

  19. Sun H, Song N, Ma L, Li J, Ma L, Wu J, Wu J. Ethylene signalling is essential for the resistance of Nicotiana attenuata against Alternaria alternata and phytoalexin scopoletin biosynthesis. Plant Pathol. 2017;66:277–84.

    CAS  Google Scholar 

  20. Vanholme B, Houari IE, Boerjan W. Bioactivity: phenylpropanoids’ best kept secret. Curr Opin Biotechnol. 2019;56:156–62.

    CAS  PubMed  Google Scholar 

  21. Song N, Ma L, Wang W, Sun H, Wang L, Baldwin IT, Wu J. An ERF2–like transcription factor regulates production of the defense sesquiterpene capsidiol upon Alternaria alternata infection. J Exp Bot. 2019;70:5895–908.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Sun H, Hu X, Ma J, Hettenhausen C, Wang L, Sun G, Wu J. Requirement of ABA signaling mediated stomatal closure for resistance of wild tobacco to Alternaria alternata. Plant Pathol. 2014;63:1070–7.

    CAS  Google Scholar 

  23. Xu Z, Zhang S, Wu J. NaWRKY3 is a master transcriptional regulator of the defense network against brown spot disease in wild tobacco. J Exp Bot. 2023;74(14):4169–88.

  24. Liu K, Liang Z, Yang A, Yan J, Cong P, Han X, Zhang C. Comparative transcriptome analysis of apple cultivars reveals key genes and pathways in response to Alternaria alternata apple pathotype infection. Hortic Plant J. 2023. https://doi.org/10.1016/j.hpj.2023.02.008.

  25. Zhao X, Song L, Jiang L, Zhu Y, Gao Q, Wang D, Xie J, Lv M, Liu P, Li M. The integration of transcriptomic and transgenic analyses reveals the involvement of the SA response pathway in the defense of chrysanthemum against the necrotrophic fungus Alternaria sp. Hortic Res. 2020;7(1):80.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu Y, Xin J, Liu L, Song A, Guan Z, Fang W, Chen F. A temporal gene expression map of Chrysanthemum leaves infected with Alternaria alternata reveals different stages of defense mechanisms. Hortic Res. 2020;7:23.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Xin J, Liu Y, Li H, Chen S, Jiang J, Song A, Fang W, Chen F. CmMLO17 and its partner CmKIC potentially support Alternaria alternata growth in Chrysanthemum morifolium. Hortic Res. 2021;8(1):101.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Cheng J, Sun W. Study on variety resistance of tobacco developing period to brown spot and the intergrated management techniques. Acta Phytopathologica Sinica. 2001;28:44–8.

    CAS  Google Scholar 

  29. Zhang CX, Tian Y, Cong PH. Proteome analysis of pathogen-responsive proteins from apple leaves induced by the alternaria blotch Alternaria alternata. PLoS One. 2015;10(6):e0122233.

    PubMed  PubMed Central  Google Scholar 

  30. Romanazzi G, Sanzani SM, Bi Y, Tian S, Gutiérrez Martínez P, Alkan N. Induced resistance to control postharvest decay of fruit and vegetables. Postharvest Biol Technol. 2016;122:82–94.

    CAS  Google Scholar 

  31. Liu L, Chen F, Chen S, Fang W, Liu Y, Guan Z. Dual species dynamic transcripts reveal the interaction mechanisms between Chrysanthemum morifolium and Alternaria alternata. BMC Genomics. 2021;22(1):523.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Lecompte F, Nicot PC, Ripoll J, Abro MA, Raimbault AK, Lopez-Lauri F, Bertin N. Reduced susceptibility of tomato stem to the necrotrophic fungus Botrytis cinerea is associated with a specific adjustment of fructose content in the host sugar pool. Ann Bot. 2017;119:931–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. He Y, Han J, Liu R, Ding Y, Wang J, Sun L, Yang X, Zeng Y, Wen W, Xu J, et al. Integrated transcriptomic and metabolomic analyses of a wax deficient citrus mutant exhibiting jasmonic acid-mediated defense against fungal pathogens. Hortic Res. 2018;5:43.

    PubMed  PubMed Central  Google Scholar 

  34. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004;37(6):914–39.

    CAS  PubMed  Google Scholar 

  35. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51:D587–92.

    CAS  PubMed  Google Scholar 

  36. Song N, Ma L, Wang W, Sun H, Wang L, Baldwin IT, Wu J. An ERF2-like transcription factor regulates production of the defense sesquiterpene capsidiol upon Alternaria alternata infection. J Exp Bot. 2019;70(20):5895–908.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Ma L, Li R, Ma L, Song N, Xu Z, Wu J. Involvement of NAC transcription factor NaNAC29 in Alternaria alternata resistance and leaf senescence in Nicotiana attenuata. Plant Divers. 2021;43(6):502–9.

    PubMed  Google Scholar 

  38. Fu H, Chung KR, Gai Y, Mao L, Li H. The basal transcription factor II H subunit Tfb5 is required for stress response and pathogenicity in the tangerine pathotype of Alternaria alternata. Mol Plant Pathol. 2020;21(10):1337–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Miao W, Xiao X, Wang Y, Ge L, Yang Y, Liu Y, Liao Y, Guan Z, Chen S, Fang W, Chen F, Zhao S. CmWRKY6–1–CmWRKY15-like transcriptional cascade negatively regulates the resistance to Fusarium oxysporum infection in Chrysanthemum morifolium. Hortic Res. 2023;10:uhad101.

    PubMed  PubMed Central  Google Scholar 

  40. Pan LY, Zhou J, Sun Y, Qiao BX, Wan T, Guo RQ, Zhang J, Shan DQ, Cai YL. Comparative transcriptome and metabolome analyses of cherry leaves spot disease caused by Alternaria alternata. Front Plant Sci. 2023;14:1129515.

    PubMed  PubMed Central  Google Scholar 

  41. Oliva M, Hatan E, Kumar V, Galsurker O, Nisim-Levi A, Ovadia R, Galili G, Lewinsohn E, Elad Y, Alkan N, Oren-Shamir M. Increased phenylalanine levels in plant leaves reduces susceptibility to Botrytis cinerea. Plant Sci. 2020;290:110289.

    CAS  PubMed  Google Scholar 

  42. Varun K, Hatan E, Bar E, Davidovich-Rikanati R, Doron-Faigenboim A, Spitzer-Rimon B, Elad Y, Alkan N, Lewinsohn E, Oren-Shamir M. Phenylalanine increases chrysanthemum flower immunity against Botrytis cinerea attack. Plant J. 2020;104:226–40.

    Google Scholar 

  43. Quintana-Rodriguez E, Rivera-Macias LE, Adame-Alvarez RM, Torres JM, Heil M. Shared weapons in fungus-fungus and fungus-plant interactions? Volatile organic compounds of plant or fungal origin exert direct antifungal activity in vitro. Fungal Ecol. 2018;33:115–21.

    Google Scholar 

  44. Marcec MJ, Gilroy S, Poovaiah BW, Tanaka K. Mutual interplay of Ca2+ and ROS signaling in plant immune response. Plant Sci. 2019;283:343–54.

    CAS  PubMed  Google Scholar 

  45. Li Z, Wang N, Wei Y, Zou X, Jiang S, Xu F, Wang H, Shao X. Terpinen–4–ol enhances disease resistance of postharvest strawberry fruit more effectively than tea tree oil by activating the Phenylpropanoid metabolism pathway. J Agric Food Chem. 2020;68:6739–47.

    CAS  PubMed  Google Scholar 

  46. Zhou X, Gao H, Zhang X, Rahman MKU, Mazzoleni S, Du M, Wu F. Plant extracellular self–DNA inhibits growth and induces immunity via the jasmonate signaling pathway. Plant Physiol. 2023;192:2475–91.

  47. Wang J, Song L, Gong X, Xu J, Li M. Functions of jasmonic acid in plant regulation and response to abiotic stress. Int J Mol Sci. 2020;21(4):1446.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Haile ZM, Pilati S, Sonego P, Malacarne G, Vrhovsek U, Engelen K, Tudzynski P, Zottini M, Baraldi E, Moser C. Molecular analysis of the early interaction between the grapevine flower and Botrytis cinerea reveals that prompt activation of specific host pathways leads to fungus quiescence. Plant Cell Environ. 2017;40:1409–28.

    CAS  PubMed  Google Scholar 

  49. Ziv C, Zhao Z, Gao YG, Xia Y. Multifunctional roles of plant cuticle during plant–pathogen interactions. Front Plant Sci. 2018;9:1088.

    PubMed  PubMed Central  Google Scholar 

  50. Shirley BW. Flavonoid biosynthesis. A colorful model for genetics, biochemistry, cell biology, and biotechnology. Plant Physiol. 2001;126(2):485–93.

    Google Scholar 

  51. Chen C, Zhang K, Khurshid M, Li J, He M, Georgiev MI, Zhang X, Zhou M. MYB transcription repressors regulate plant secondary metabolism. Crit Rev Plant Sci. 2019;38(3):159–70.

    CAS  Google Scholar 

  52. Yao L, Zhong Y, Wang B, Yan J, Wu T. BABA application improves soybean resistance to aphid through activation of phenylpropanoid metabolism and callose deposition. Pest Manag Sci. 2019;76(1):384–94.

    PubMed  Google Scholar 

  53. Li F, Zhang Y, Tian C, Wang X, Zhou L, Jiang J, Wang L, Chen F, Chen S. Molecular module of CmMYB15-like-Cm4CL2 regulating lignin biosynthesis of chrysanthemum (Chrysanthemum morifolium) in response to aphid (Macrosiphoniella sanborni) feeding. New Phytol. 2023;237(5):1776–93.

    CAS  PubMed  Google Scholar 

  54. Boerjan W, Ralph J, Baucher M. Lignin biosynthesis. Annu Rev Plant Biol. 2003;54:519–46.

    CAS  PubMed  Google Scholar 

  55. Zhang M, Su J, Zhang Y, Xu J, Zhang S. Conveying endogenous and exogenous signals: MAPK cascades in plant growth and defense. Curr Opin Plant Biol. 2018;45:1–10.

    PubMed  Google Scholar 

  56. Tang D, Wang G, Zhou JM. Receptor kinases in plant–pathogen interactions: more than patternrecognition. Plant Cell. 2017;29:618–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Janczarek M, Vinardell JM, Lipa P, Kara’s M. Hanks–type serine/threonine protein kinases and phosphatases in bacteria: roles in signaling and adaptation to various environments. Int J Mol Sci. 2018;19:2872.

    PubMed  PubMed Central  Google Scholar 

  58. Zhang M, Zhang S. Mitogen-activated protein kinase cascades in plant signaling. J Integr Plant Biol. 2022;64(2):301–41.

    PubMed  Google Scholar 

  59. Bigeard J, Colcombet J, Hirt H. Signaling mechanisms in pattern-triggered immunity (PTI). Mol Plant. 2015;8:521–39.

    CAS  PubMed  Google Scholar 

  60. Thulasi Devendrakumar K, Li X, Zhang Y. MAP kinase signalling: Interplays between plant PAMP- and effector-triggered immunity. Cell Mol Life Sci. 2018;75:2981–9.

    CAS  PubMed  Google Scholar 

  61. Zhou JG, Wang XY, He YX, Sang T, Wang PC, Dai SJ, Zhang SQ, Meng XZ. Differential phosphorylation of the transcription factor WRKY33 by the protein kinases CPK5/CPK6 and MPK3/MPK6 synergistically regulates camalex in biosynthesis in Arabidopsis. Plant Cell. 2020;32:2621–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Yang L, Zhang Y, Guan R, Li S, Xu X, Zhang S, Xu J. Co-regulation of indole glucosinolates and camalexin biosynthesis by CPK5/CPK6 and MPK3/MPK6 signaling pathways. J Integr Plant Biol. 2020;62:1780–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Li G, Meng X, Wang R, Mao G, Han L, Liu Y, Zhang S. Dual-level regulation of ACC synthase activity by MPK3/MPK6 cascade and its downstream WRKY transcription factor during ethylene induction in Arabidopsis. PLoS Genet. 2012;8:e1002767.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Liu T, Cao L, Cheng Y, Ji J, Wei Y, Wang C, Duan K. MKK4/5-MPK3/6 cascade regulates agrobacterium-mediated transformation by modulating plant immunity in Arabidopsis. Front Plant Sci. 2021;12:731690.

    PubMed  PubMed Central  Google Scholar 

  65. Su J, Zhang M, Zhang L, Sun T, Liu Y, Lukowitz W, Xu J, Zhang S. Regulation of stomatal immunity by interdependent functions of a pathogen-responsive MPK3/MPK6 cascade and abscisic acid. Plant Cell. 2017;29:526–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Sun T, Nitta Y, Zhang Q, Wu D, Tian H, Lee JS, Zhang Y. Antagonistic interactions between two MAP kinase cascades in plant development and immune signaling. EMBO Rep. 2018;19:e45324.

    PubMed  PubMed Central  Google Scholar 

  67. Bi G, Zhou Z, Wang W, Li L, Rao S, Wu Y, Zhang X, Menke FLH, Chen S, Zhou JM. Receptor-like cytoplasmic kinases directly link diverse pattern recognition receptors to the activation of mitogen-activated protein kinase cascades in Arabidopsis. Plant Cell. 2018;30:1543–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Kovtun Y, Chiu WL, Tena G, Sheen J. Functional analysis of oxidative stress-activatedmitogen-activated protein kinase cascadein plants. Proc Natl Acad Sci U S A. 2000;97(6):2940–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Liu T, Cao L, Cheng Y, Ji J, Wei Y, Wang C, Duan K. MKK4/5–MPK3/6 Cascade Regulates Agrobacterium-Mediated Transformation by Modulating Plant Immunity in Arabidopsis. Front Plant Sci. 2021;12:731690.

    PubMed  PubMed Central  Google Scholar 

  70. Campos ML, Yoshida Y, Major IT, de Oliveira FD, Weraduwage SM, Froehlich JE, Johnson BF, Kramer DM, Jander G, Sharkey TD, et al. Rewiring of jasmonate and phytochrome B signalling uncouples plant growth-defense tradeoffs. Nat Commun. 2016;7:12570.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Zhou T, Cao L, Hu K, Yu X, Qu S. miR164-NAC21/22 module regulates the resistance of Malus hupehensis against Alternaria alternata by controlling jasmonic acid signaling. Plant Sci. 2023;330:111635.

    CAS  PubMed  Google Scholar 

  72. Qi T, Huang H, Wu D, Yan J, Qi Y, Song S, Xie D. Arabidopsis DELLA and JAZ proteins bind the WD–repeat/bHLH/MYB complex to modulate gibberellin and jasmonate signaling synergy. Plant Cell. 2014;26:1118–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Hu Y, Jiang L, Wang F, Yu D. Jasmonate regulates the inducer of cbf expression-C-repeat binding factor/DRE binding factor1 cascade and freezing tolerance in Arabidopsis. Plant Cell. 2013;25(8):2907–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Giri MK, Swain S, Gautam JK, Singh S, Singh N, Bhattacharjee L, Nandi AK. The Arabidopsis thaliana At4g13040 gene, a unique member of the AP2/EREBP family, is a positive regulator for salicylic acid accumulation and basal defense against bacterial pathogens. J Plant Physiol. 2014;171(10):860–7.

    CAS  PubMed  Google Scholar 

  75. Rashotte AM, Goertzen LR. The CRF domain defines Cytokinin Response Factor proteins in plants. BMC Plant Biol. 2010;10:74.

    PubMed  PubMed Central  Google Scholar 

  76. Nakano T, Suzuki K, Fujimura T, Shinshi H. Genome–wide analysis of the ERF gene family in Arabidopsis and rice. Plant Physiol. 2006;140:411–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Bari R, Jones JD. Role of plant hormones in plant defence responses. Plant Mol Biol. 2009;69:473–88.

    CAS  PubMed  Google Scholar 

  78. Pieterse CM, Leon-Reyes A, Van der Ent S, Van Wees SC. Networking by small-molecule hormones in plant immunity. Nat Chem Biol. 2009;5(5):308–16.

    CAS  PubMed  Google Scholar 

  79. Rushton PJ, Somssich IE, Ringler P, Shen QJ. WRKY transcription factors. Trends Plant Sci. 2020;15:247–58.

    Google Scholar 

  80. Chen X, Liu J, Lin G, Wang A, Wang Z, Lu G. Overexpression of AtWRKY28 and AtWRKY75 in Arabidopsis enhances resistance to oxalic acid and Sclerotinia sclerotiorum. Plant Cell Rep. 2013;32:1589–99.

    CAS  PubMed  Google Scholar 

  81. Skibbe M, Qu N, Galis I, Baldwin IT. Induced plant defenses in the natural environment: Nicotiana attenuata WRKY3 and WRKY6 coordinate responses to herbivory. Plant Cell. 2008;20:1984–2000.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Liu S, Kracher B, Ziegler J, Birkenbihl RP, Somssich IE. Negative regulation of ABA signaling by WRKY33 is critical for Arabidopsis immunity towards Botrytis cinerea 2100. ELife. 2015;4:e07295.

    PubMed  PubMed Central  Google Scholar 

  83. Wang D, Xu H, Huang J, Kong Y, AbuQamar S, Yu D, Liu S, Zhou G, Chai G. The Arabidopsis CCCH protein C3H14 contributes to basal defense against Botrytis cinerea mainly through the WRKY33-dependent pathway. Plant Cell Environ. 2020;43(7):1792–806.

    CAS  PubMed  Google Scholar 

  84. Birkenbihl RP, Diezel C, Somssich IE. Arabidopsis WRKY33 is a key transcriptional regulator of hormonal and metabolic responses toward Botrytis cinerea infection. Plant Physiol. 2012;159:266–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Liu S, Ziegler J, Zeier J, Birkenbihl RP, Somssich IE. Botrytis cinerea B05.10 promotes disease development in Arabidopsis by suppressing WRKY33–mediated host immunity. Plant Cell Environ. 2017;40:2189–206.

    CAS  PubMed  Google Scholar 

  86. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Song A, Su J, Wang H, Zhang Z, Zhang X, Van de Peer Y, Chen F, Fang W, Guan Z, Zhang F, et al. Analyses of a chromosome-scale genome assembly reveal the origin and evolution of cultivated chrysanthemum. Nat Commun. 2023;14(1):2021.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Langmead B, Salzberg SL. Fast gapped–read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA–Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2011;11:R106.

    Google Scholar 

  91. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA–seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Google Scholar 

  92. Wen B, Mei Z, Zeng C, Liu S. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics. 2017;18:183.

    PubMed  PubMed Central  Google Scholar 

  93. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real–time quantitative PCR and the 2(–Delta Delta C(T)) Method. Methods. 2001;25:402–8.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was supported in part by grants from the National Natural Science Foundation of China (32171854), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of State Forestry and Grassland Administration On Biology of Ornamental Plants in East China, College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China

    Shuhuan Zhang, Weihao Miao, Ye Liu, Jiafu Jiang, Sumei Chen, Fadi Chen & Zhiyong Guan

  2. Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, Jiangsu, China

    Shuhuan Zhang, Weihao Miao, Ye Liu, Jiafu Jiang, Sumei Chen, Fadi Chen & Zhiyong Guan

Authors
  1. Shuhuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weihao Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ye Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiafu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sumei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fadi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhiyong Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SZ and ZG designed the research. SZ and WM performed the experiments. SZ, JJ, ZG and YL analyzed the data. SZ, JJ, ZG and YL wrote the manuscript. SC and FC edited the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Zhiyong Guan.

Ethics declarations

Ethics approval and consent to participate

Experimental research and field studies on plants (either cultivated or wild), including the collection of plant material, must comply with relevant institutional, national, and international guidelines and legislation. The collecting of these plant materials complies with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and is allowed by the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Fig. S1.

Decreased A. alternate susceptibility in chrysanthemum leaves pre-treated with MeJA. (a) Chrysanthemum morifolium ‘Jinba’ was pre-treated with 50, 100, and 200 μM MeJA and then inoculated with A. alternate before sampling after 48 hpi. Controls were treated with distilled water. (b) Disease severity is expressed as lesion area (mm2) of leaves after 48 hpi. Data are presented as the mean of four replicates ± standard error. Asterisks (*) indicate statistically significant differences evaluated for each time interval between the different treatments as calculated by two-way ANOVA (*P ≤ 0.05, ** P ≤ 0.01).

Additional file 2: Fig. S2.

Visualization of gene expression after treatment with JA. (a) Differentially expressed genes related to metabolic pathways. (b) Differentially expressed regulatory genes. The log2 (fold change) values of genes significantly upregulated in JA pre-treated leaves compared to controls (MOCK) are presented as red colored squares. Not all differentially expressed genes are presented in the map, only the genes related to metabolic pathways and regulation are displayed. CHO, carbohydrates; OPP, oxidative pentose phosphate pathway; TCA, tricarboxylic acid cycle.

Additional file 3: Fig. S3.

Visualization of the changes in the secondary metabolic gene expression following JA treatment and A. alternate infection. (a) Significant upregulation of secondary metabolic genes in the JA-treated group compared to MOCK. (b) Significant upregulation of secondary metabolic genes in the JA-I group compared to JA-treated group. The log2 (fold change) values of differentially expressed genes are represented by red squares.

Additional file 4: Fig. S4.

Visualization of the changes in metabolic and regulatory gene expression following A. alternate infection. (a) Exclusively upregulated metabolic genes in MOCK-I vs. MOCK. (b) Exclusively upregulated regulatory genes in MOCK-I vs. MOCK. (c) Exclusively downregulated metabolic genes in JA-I vs. JA. (d) Exclusively upregulated regulatory genes in JA-I vs. JA. The log2 (fold change) values of differentially expressed genes are represented by red squares. Not all differentially expressed genes are presented in the map, only the genes related to metabolic pathways and regulation are displayed. CHO, carbohydrates; OPP, oxidative pentose phosphate pathway; and TCA, tricarboxylic acid cycle.

Additional file 5: Fig. S5.

Changes in secondary metabolite levels in chrysanthemum leaves after JA treatment and A. alternate infection. (a) Changes in monolignol levels in Chrysanthemum leaves in MOCK, MOCK-I, JA, JA-I groups. (b) Changes in anthocyanin levels in Chrysanthemum leaves in MOCK, MOCK-I, JA, JA-I groups. Changes in the levels of metabolites were analyzed by GC/LC-MS. Values represent the means of three biological replicates ± standard error. Green bars indicate metabolite levels in non-infected leaves, while orange bars are the metabolite levels after infection. MOCK, control; MOCK-I, control infected group; JA-I, MeJA pre-treated and infected group; JA, MeJA-pre-treated group.

Additional file 6: Fig. S6.

Changes in shared defense-related gene expression levels in response to JA treatment and A. alternate infection. Heat map presenting normalized Log2 (FPKM+1) expression of genes that are more up-regulated in JA-I vs. JA compared to MOCK-I vs. MOCK. Rows are centered based on the average FPKM. MOCK, control; MOCK-I, control infected group; JA-I, MeJA pre-treated and infected group; JA, MeJA-pre-treated group.

Additional file 7: Fig S7.

Changes in shared TF expression levels due to JA treatment and A. alternate infection. Heat map presenting normalized Log2 fold change of WRKY TFs that are more up-regulated in JA-I vs JA when compared to MOCK-I vs. MOCK groups. MOCK-I, log2 (MOCK-I/MOCK) and JA-I, log2 (JA-I/JA).

Additional file 8: Fig. S8.

Changes in exclusively upregulated TF expression in JA-I vs. JA groups. Heat map of normalized Log2 (FPKM+ 1) of exclusively WRKY and AP2-EREBP TFs that are up-regulated in JA-I vs. JA groups. Rows are centered based on the average FPKM. From left to right is MOCK, MOCK-I, JA, JA-I. MOCK, control; MOCK-I, control infected group; JA-I, MeJA pre-treated and infected group; JA, MeJA-pre-treated group.

Additional file 9: Table S1.

Summary of reads mapping

Additional file 10: Table S2.

Primer sequences used in qRT-PCR for the validation of dual RNA-seq data.

Additional file 11: Table S3.

Shortlisted genes from differential expression analysis that have a potential role in defense response to A. alternate infection

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Miao, W., Liu, Y. et al. Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum. BMC Genomics 24, 553 (2023). https://doi.org/10.1186/s12864-023-09671-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-023-09671-0

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us