Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum

Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum Shuhuan Zhang, Weihao Miao, Ye Liu, Jiafu Jiang, Sumei Chen, Fadi Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3046091/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2023 Read the published version in BMC Genomics → Version 1 posted 8 You are reading this latest preprint version 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 plant hormone jasmonate (JA) significantly reduces their susceptibility to Alternaria alternata . To understand how JA treatment induces resistance, we monitored the dynamics of metabolites and the transcriptome in leaves after JA treatment following A. alternata infection. JA signaling affected the resistance of plants to pathogens through cell wall modification, Ca 2+ 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 JA 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 . JA signaling Alternaria alternata Chrysanthemum morifolium defense responses Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 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 Ca 2+ 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 – 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 SCF COI1 -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 – 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 – 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 abscise 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 Ca 2+ 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 Ca 2+ 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, which 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 JA pre-treatment of the chrysanthemum 'Jinba' significantly reduced its susceptibility to A. alternata infection. By keeping track of the large-scale metabolomic and transcriptomic changes in the leaves after JA pre-treatment and A. alternata infection, we pinpointed potential defense response underpinning mechanisms. We also indicated that JA treatment has a little effect on gene expression but does induce regulatory genes associated with the defense response such as receptor kinases, Ca 2+ regulation, TFs, and phytohormones. When leaves were pretreated with JA before infection with A. alternata , we noticed transcriptional reprogramming of genes related to the cell wall, resistance proteins, and Ca 2+ , 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 JA 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 ‘Jinba’. 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 JA-treated chrysanthemum leaves reduced A. alternata susceptibility JAs has been shown to reduce the susceptibility to necrotrophic pathogen infection [ 32 , 33 ]. To test whether methyl jasmonate (MeJA) treatment of chrysanthemum leaves could reduce susceptibility to A. alternata , we exogenously sprayed whole C. morifolium 'Jinba' 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 'Jinba'. JA 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. 2 a). 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. 2 b). 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. 2 c, d), which were self-activated genes independent of JA signaling after inoculation. In contrast, JA pre-treatment prevented this change in gene expression following 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. 2 c). The differentially expressed genes, including those upregulated by JA 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 JA 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. S2 a). In JA vs. MOCK group, up-regulated regulatory genes involved in protein modification and degradation, receptor kinases, Ca 2+ regulation, TFs, and phytohormones (Fig. S2 b). 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. S3 a). 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. S4 a). In contrast, in the JA 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. S4 c). It is worth noting that almost all regulatory pathways, such as receptor kinases, Ca 2+ regulation, TFs, and phytohormones, in plants pre-treated with JA 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 JA 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. 2 e, 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 (Figs. 2 e). 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. 2 f). 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. S3 b). 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, Ca 2+ signaling, ROS regulation, JA and ET signaling, and phenylpropanoid biosynthesis (Fig. 2 g). 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 JA treatment in plant leaves led to the accumulation of numerous derived phenolics, phenylpropanoids, and flavonoids (Fig. 3 ). In this study, JA 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 JA pre-treating the leaves led to an absorption of JA and subsequent activation of downstream metabolites. The primary impact of JA 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, JA pre-treatment increased or further promoted the accumulation of these metabolites (Figs. 3 b and S5), suggesting that fungal infection can negatively affect the antioxidant system of plants, whereas JA pre-treatment prevented the decline in these metabolites and maintained intracellular ROS homeostasis. In JA-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. 3 b, S5). 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 A. alternata defense [ 35 – 37 ]. 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. 4 a). Of the 6680 common genes, 425, including 87 WRKYs and 104 AP2/EREBPs, encoded TFs (Fig. 4 a). 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. 4 b). We identified 122 TFs specifically in the JA-I group, including 14 WRKYs and 13 AP2/EREBPs (Figs. 4 a, S8). 145 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. 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 JA-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. 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 [ 38 ]. Inoculation assays demonstrated that compared with wild-type (WT) ‘Jinba’, 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. 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 [ 39 ], 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 [ 40 , 41 ]. Exogenous JA also induced resistance to fungal pathogen in potato [ 14 ] and rose leaves [ 15 ]. Here, we showed that JA 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 JA 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, JA 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. 3 a, S2b). Inducing the expression of genes associated with secondary metabolism (Fig. S3 a) 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. 3 b), which have anti-fungal and anti-oxidative activity [ 41 ]. The JA pre-treatment prevented the reduction in the levels of the volatiles, such as phenylacetaldehyde and eugenol in the JA-I group (Fig. 3 b). This could be as a result of the enhanced carbon flow towards the formation of eugenol in JA-pretreated leaves following infection, consistent with the release of volatiles being associated with pathogen resistance [ 42 ]. Additionally, JA pre-treatment resulted in transcriptional reprogramming of a group of genes involved in the cell wall, lipids, Ca 2+ 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 JA 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 JA pre-treatment showed small transcriptional changes after A. alternata infection in contrast to the drastic increase in mock-treated leaves after infection (Figs. 2 b, 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. 2 c), 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. 2 d, 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 Ca 2+ signaling, as ROS can function as Ca 2+ sensors [ 43 ]. We found the upregulation of Ca 2+ signaling and ROS metabolic genes in the JA and JA-I groups (Fig. 2 g), suggesting the interplay between ROS and Ca 2+ signals contributed to the defensive response. The overexpression of ROS can promote cell injury; therefore, maintaining intracellular ROS homeostasis can promote plant defense response [ 44 ]. Previous reports have provided evidence that JA prevents excess ROS generation [ 45 ] and promotes accumulation of antioxidant enzymes [ 46 ]. 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. 2 g). 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. 3 b, S5b). ROS and Ca 2+ signaling also mediate defense mechanisms through cell wall strengthening and re-organization [ 47 ], 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 [ 48 ]. Different classes of flavonoids and lignin are synthesized via the phenylpropanoid pathway [ 49 ]. Lignin is a structural component of the cell wall and the expression of its biosynthetic pathway genes enhance plant defense [ 50 – 52 ]. The expression of genes encoding key enzymes of the phenylpropanoid pathway was significantly upregulated following A. alternata infection in chrysanthemum (Fig. 2 g). 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 JA-treated plants (Fig. S5 a) after enzymatic oxidization and the subsequent radical coupling in the cell walls and formed a heterogenous polymer that constitutes lignin [ 53 ], 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 [ 54 – 57 ]. 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. 2 g). In Arabidopsis, MPK3/MPK6 and their orthologs were proposed to share a subset of defense responses [ 58 , 59 ] that affect many downstream transduction pathways. For example, MPK3/MPK6 cascade and Ca 2+ signaling pathway crosstalk regulate the biosynthesis of camalexin [ 60 , 61 ]. MPK3/MPK6 regulate ET biosynthesis during pathogen attack [ 62 , 63 ]. Additionally, the functions of the MKK4/MKK5–MPK3/MPK6 module in plant immunity have been identified [ 64 ], and the search for the MAPKKK(s) upstream of the MKK4/MKK5–MPK3/MPK6 module has progressed in recent years [ 65 , 66 ], 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. 2 g; Table S2 ). This indicated that JA signaling may regulate Ca 2+ 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 H 2 O 2 can activate the specific Arabidopsis MAPKKK, ANP1, to initiate a phosphorylation cascade involving the stress MAPKs, AtMPK3 and AtMPK6 [ 67 ]. Recently, MKK4/MKK5 has been reported to regulate plant defense pathways, including ROS production and the synthesis of ET and SA [ 68 ], 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 [ 69 , 70 ]. Numerous TFs that act as essential players in JA signal transduction have been discovered using forward and reverse genetic methods [ 71 ]. 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 [ 72 – 74 ], 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 [ 75 ]. 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. 4 b). Previous studies reported that JA and ET synergistically activate defense signaling against necrotrophic pathogens [ 76 , 77 ]. Moreover, WRKY TFs participate in phytohormone-mediated signaling pathways and transcriptional reprogramming associated with plant defense responses like the MAPK signaling cascade [ 78 , 79 ]. NaWRKY3 and NaWRKY6 can regulate the synthesis of JA to mediate pathogen defense responses [ 80 ]. WRKY33 positively regulates target genes involved in the biosynthesis of the antimicrobial compound camalexin and JA/ET downstream signaling [ 81 , 82 ], and functions as a key transcriptional regulator required for immunity in Arabidopsis towards Botrytis cinerea [ 83 , 84 ]. 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. 4 b). 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 JA 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. 4 b, 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 [ 38 ], 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 JA 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 Ca 2+ 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 JA pre-treatment mediates the transcriptional reprogramming of defense response activation before fungal infection. During pathogen attack, JA 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. 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 JA 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. 1 a). 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) [ 85 ] to the chrysanthemum genome [ 86 ], matched to reference gene sequences by Bowtie2 [ 87 ], and the gene expression level of each sample was determined using RSEM [ 88 ]. Differential expression analysis was performed with DESeq2 [ 89 ], using the negative binomial distribution model, the hypothesis test probability (P value) was calculated to identify differences in the gene expression data [ 90 ]. 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 [ 91 ]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) 12 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 [ 92 ]. 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. 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. Competing interests The authors declare that they have no competing interests. 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. Authors' 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. Acknowledgements Not applicable. Authors' information (optional) References 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. Ma H, Zhang B, Gai Y, Sun X, Chung KR, Li H. 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Supplementary Files Additionalfile1.docx Additionalfile2.docx Additionalfile3.docx Additionalfile4.docx Additionalfile5.docx Additionalfile6.docx Additionalfile7.docx Additionalfile8.docx Additionalfile9.xlsx Additionalfile10.xlsx Additionalfile11.xlsx supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2023 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Major revision 09 Aug, 2023 Reviews received at journal 01 Aug, 2023 Reviewers agreed at journal 27 Jul, 2023 Reviewers invited by journal 27 Jul, 2023 Editor assigned by journal 24 Jul, 2023 Editor invited by journal 19 Jun, 2023 Submission checks completed at journal 19 Jun, 2023 First submitted to journal 10 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3046091","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":211091514,"identity":"05153f2d-f33b-4a82-8198-84b79e49c9fc","order_by":0,"name":"Shuhuan Zhang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuhuan","middleName":"","lastName":"Zhang","suffix":""},{"id":211091515,"identity":"459dd714-8138-4828-8782-d1f10e19e3bf","order_by":1,"name":"Weihao Miao","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weihao","middleName":"","lastName":"Miao","suffix":""},{"id":211091516,"identity":"cd6976ed-afd5-4b30-85ad-6634c873d93f","order_by":2,"name":"Ye Liu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Liu","suffix":""},{"id":211091517,"identity":"aca986a3-f556-4beb-8d3e-ce10ed3ce60e","order_by":3,"name":"Jiafu Jiang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiafu","middleName":"","lastName":"Jiang","suffix":""},{"id":211091518,"identity":"ed4d5401-7801-4b17-9650-fe39e2260c69","order_by":4,"name":"Sumei Chen","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sumei","middleName":"","lastName":"Chen","suffix":""},{"id":211091519,"identity":"15ec6e6c-2d96-48c0-9527-2f625544a9a2","order_by":5,"name":"Fadi Chen","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fadi","middleName":"","lastName":"Chen","suffix":""},{"id":211091520,"identity":"6c1299b3-041a-45dc-8cd3-14cfbdb33b85","order_by":6,"name":"Zhiyong Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYLACxgYGOQaGAyAmM/FajEnXktgAYRKhRT4i+dnDnzsOp29nPPxMgqHCOrGB/ewBvFoMb6SZG0ieOZy7s+GYmQTDmfTEBp68BPxaZiSYSRi2Hc7dcOCAmQRj2+HEBgkeAwJa0r9JJLYdTjc4cPybBOM/IrTIS+SYSRxsO5xgcOAM0JYGIrQY8Lwpk2xsSzfccOBMsUXCsXTjNp4cAra0p2+T/NlmLW9w4/jGGx9qrGX72c8QsOUAmGpmYJAAshKATDa86kG2NICpOgYG/gZCakfBKBgFo2CkAgDkKEqxfD6+RwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2023-06-10 08:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3046091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3046091/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-023-09671-0","type":"published","date":"2023-09-19T15:00:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39110171,"identity":"24c4f31b-b657-488e-b54e-011a7838499b","added_by":"auto","created_at":"2023-06-26 18:43:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":392602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecreased susceptibility of chrysanthemum leaves to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. alternata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e after pre-treatment with 100 μM MeJA.\u003c/strong\u003e (a) \u003cem\u003eC. morifolium\u003c/em\u003e ‘Jinba’ was pre-treated with 100 μM MeJA and inoculated with \u003cem\u003eA. alternata\u003c/em\u003e. Controls were treated with distilled water. (b) Leaf damage caused by \u003cem\u003eA. alternata\u003c/em\u003e 48 hpi in control and MeJa-treated leaves. (c) Disease severity was determined by measuring the lesion area (mm\u003csup\u003e2\u003c/sup\u003e) 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.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/df1ee438f31a31bceacbaa5f.jpg"},{"id":39110176,"identity":"48e8f1f6-7be7-4615-ace4-621d5d17648f","added_by":"auto","created_at":"2023-06-26 18:43:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":497042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe DEGs, Venn diagrams, and enrichment analysis of chrysanthemum after JA pre-treatment and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. alternata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infection.\u003c/strong\u003e (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 \u003cem\u003eA. alternata\u003c/em\u003e infection. (d) Venn diagrams presenting the distribution of down-regulatedDEGs after JA pre-treatment and \u003cem\u003eA. alternata\u003c/em\u003e 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 Log\u003csub\u003e2 \u003c/sub\u003e(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.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/151627a6981589d04b2fb53f.png"},{"id":39110175,"identity":"11fd1945-aea0-4e20-9eb8-c296c9e00096","added_by":"auto","created_at":"2023-06-26 18:43:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":357346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic changes in chrysanthemum leaves after JA-treatment and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. alternata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infection.\u003c/strong\u003e (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.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/5699ed4eb5f5fec3a98fc052.jpg"},{"id":39110177,"identity":"3114ce5b-0218-4764-9fb0-5200ac3cbf78","added_by":"auto","created_at":"2023-06-26 18:43:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":440186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed transcription factors in response to JA-treatment and\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e A. alternata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infection.\u003c/strong\u003e (a) Classification of transcription factors. (b) Heat map of the normalized Log\u003csub\u003e2 \u003c/sub\u003e(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.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/7d0ad74c45dd0a3c4f526483.jpg"},{"id":39111213,"identity":"ae942c6e-14d9-4984-a165-2958bb84df86","added_by":"auto","created_at":"2023-06-26 18:51:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":298579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in gene expression levels after JA pre-treatment and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA. alternata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infection.\u003c/strong\u003eThe 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.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/89e28a253e08b415cdc85e2f.jpg"},{"id":39110159,"identity":"2a16e993-75ea-421f-8258-d50ed95794e8","added_by":"auto","created_at":"2023-06-26 18:43:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":226201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCmWRKY6\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e confers resistance to black spot disease in chrysanthemum ‘Jinba’.\u003c/strong\u003e (a) The \u003cem\u003eA. alternata\u003c/em\u003e infection phenotypes in inoculated leaves of WT, OX-CmWRKY6 and RNAi-CmWRKY6 plants, respectively. (b) Disease severity was determined by measuring the lesion area (mm\u003csup\u003e2\u003c/sup\u003e) 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).\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/87e9de4e835d5fa2ca9de3f8.jpg"},{"id":39112925,"identity":"715b314e-e320-45b6-b39a-1df122a8931f","added_by":"auto","created_at":"2023-06-26 18:59:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":203301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothetical model of the jasmonate-induced defense mechanism. \u003c/strong\u003eJA signaling activates endogenous Ca\u003csup\u003e2+\u003c/sup\u003e, ROS, MAPK, and TF signaling transduction and the accumulation of anti-fungal molecules, which enhances resistance to \u003cem\u003eA. alternate\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/2e51b2ad34661dee75aff127.jpg"},{"id":43640462,"identity":"8363a616-a56b-4879-8f0d-90354ba67130","added_by":"auto","created_at":"2023-09-25 15:06:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1300023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/7a44bc07-ee23-49db-8e6b-14eee123fdac.pdf"},{"id":39110173,"identity":"146fecec-ed1e-4da4-8285-cb583eb82887","added_by":"auto","created_at":"2023-06-26 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18:43:42","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11016,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/eb974f2336bac6ec4355c368.xlsx"},{"id":39111219,"identity":"26e11a3a-7f0c-4744-bcc9-9becdc83e948","added_by":"auto","created_at":"2023-06-26 18:51:42","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":16890,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3046091/v1/f760dbe9edd81c6eb2b85693.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum","fulltext":[{"header":"Background","content":"\u003cp\u003ePlants 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 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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 Ca\u003csup\u003e2+\u003c/sup\u003e and the production of ROS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 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) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous research has shown that SA is a major hormone against biotrophic and hemi-biotrophic pathogens, which rely on living plant tissue for nutrients [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast, the JA/ET pathway is critical for plant defense against necrotrophic diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eJA biosynthesis begins with the release of α-linolenic acid from membrane lipids in the chloroplast [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. 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 \u003cem\u003ede novo\u003c/em\u003e 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 SCF\u003csup\u003eCOI1\u003c/sup\u003e-JAZ [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. At present, many studies have reported that exogenous feeding or external stimuli, can induce endogenous JA synthesis and signal transduction to activate JA signaling [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Generally, many defense secondary metabolites, such as phenylpropanoids, flavonoids, and phytoalexins, which serve as signal molecules in plant\u0026ndash;pathogen interactions, have anti-fungal or anti-oxidant characteristics [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have explored the mechanism of host\u0026ndash;pathogen interactions for \u003cem\u003eAlternaria alternata\u003c/em\u003e. For example, in \u003cem\u003eNicotiana attenuate\u003c/em\u003e, JA signaling regulates the biosynthesis and accumulation of phytoalexin scopoletin through MYC2, and activated abscise acid (ABA) signaling promotes stomatal closure to enhance resistance to \u003cem\u003eA. alternata\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The latest evidence suggests that NaWRKY3 is an important factor in scopoletin synthesis, which transcriptionally regulates Rboh-mediated stomatal closure [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In apple (\u003cem\u003eMalus domestica\u003c/em\u003e Borkh.), ET, JA, and SA signaling and pathogen-induced release of elicitors from the cell wall also contribute to \u003cem\u003eA. alternata\u003c/em\u003e resistance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In chrysanthemum, the cross-talk between hormone and Ca\u003csup\u003e2+\u003c/sup\u003e signal transduction pathways is the most effective defense response against \u003cem\u003eA. alternata\u003c/em\u003e infection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the transgenic silencing of the Mildew Resistance Locus O gene (\u003cem\u003eCmMLO17\u003c/em\u003e) in chrysanthemum regulates ABA and Ca\u003csup\u003e2+\u003c/sup\u003e signaling pathways, resulting in reduced susceptibility to \u003cem\u003eA. alternata\u003c/em\u003e infection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eC. morifolium\u003c/em\u003e 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. \u003cem\u003eA. alternata\u003c/em\u003e 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 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and once established, it can spread, which 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 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsing transcriptomic analysis, we reported that \u003cem\u003eA. alternata\u003c/em\u003e activates the transcription of JA biosynthesis and signaling genes in chrysanthemum [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the molecular mechanism associated with JA-induced defense responses against \u003cem\u003eA. alternata\u003c/em\u003e in chrysanthemum is largely unknown. Here, we revealed that JA pre-treatment of the chrysanthemum 'Jinba' significantly reduced its susceptibility to \u003cem\u003eA. alternata\u003c/em\u003e infection. By keeping track of the large-scale metabolomic and transcriptomic changes in the leaves after JA pre-treatment and \u003cem\u003eA. alternata\u003c/em\u003e infection, we pinpointed potential defense response underpinning mechanisms. We also indicated that JA treatment has a little effect on gene expression but does induce regulatory genes associated with the defense response such as receptor kinases, Ca\u003csup\u003e2+\u003c/sup\u003e regulation, TFs, and phytohormones. When leaves were pretreated with JA before infection with \u003cem\u003eA. alternata\u003c/em\u003e, we noticed transcriptional reprogramming of genes related to the cell wall, resistance proteins, and Ca\u003csup\u003e2+\u003c/sup\u003e, 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 JA pre-treatment and \u003cem\u003eA. alternata\u003c/em\u003e 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 \u0026lsquo;Jinba\u0026rsquo;. Our research presents insight into the overall mechanism of JA signaling that drives defense responses against \u003cem\u003eA. alternata\u003c/em\u003e in chrysanthemum, which will aid the screening of relevant candidate anti-fungal elicitors that could serve as ecologically friendly disease control agents.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eJA-treated chrysanthemum leaves reduced\u003c/b\u003e \u003cb\u003eA. alternata\u003c/b\u003e \u003cb\u003esusceptibility\u003c/b\u003e \u003c/p\u003e \u003cp\u003eJAs has been shown to reduce the susceptibility to necrotrophic pathogen infection [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To test whether methyl jasmonate (MeJA) treatment of chrysanthemum leaves could reduce susceptibility to \u003cem\u003eA. alternata\u003c/em\u003e, we exogenously sprayed whole \u003cem\u003eC. morifolium\u003c/em\u003e 'Jinba' plants with MeJA before leaves were inoculated with \u003cem\u003eA. alternata\u003c/em\u003e. After 48 h post-inoculation (hpi), the leaves treated with 100 \u0026micro;M MeJA displayed significantly lower decay in comparison to the control; however, the sensitivity increased with the higher MeJA concentrations (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S1). These results indicated that exogenous 100 \u0026micro;M MeJA induced \u003cem\u003eA. alternata\u003c/em\u003e resistance in Chrysanthemum 'Jinba'.\u003c/p\u003e \u003cp\u003e \u003cb\u003eJA treatment of chrysanthemum caused transcriptional reprogramming following\u003c/b\u003e \u003cb\u003eA. alternata\u003c/b\u003e \u003cb\u003einfection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further explore the function of JA signaling in \u003cem\u003eA. alternata\u003c/em\u003e defense responses at the transcriptome level, we utilized RNA-seq analysis of \u003cem\u003eC. morifolium\u003c/em\u003e plants sprayed with 100 \u0026micro;M MeJA or deionized water as a mock with or without \u003cem\u003eA. alternata\u003c/em\u003e inoculation. Approximately 90% of the reads for each sample were mapped to the reference chrysanthemum genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.21655364.v2\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.21655364.v2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and the biological replicates for each treatment showed good correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d), which were self-activated genes independent of JA signaling after inoculation. In contrast, JA pre-treatment prevented this change in gene expression following 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 \u003cem\u003eA. alternata\u003c/em\u003e. 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 \u003cem\u003eA. alternata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe differentially expressed genes, including those upregulated by JA 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 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The JA 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. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea). In JA vs. MOCK group, up-regulated regulatory genes involved in protein modification and degradation, receptor kinases, Ca\u003csup\u003e2+\u003c/sup\u003e regulation, TFs, and phytohormones (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). 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. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea). 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. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ea). In contrast, in the JA 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. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ec). It is worth noting that almost all regulatory pathways, such as receptor kinases, Ca\u003csup\u003e2+\u003c/sup\u003e regulation, TFs, and phytohormones, in plants pre-treated with JA before infection were positive regulation compared to controls. These results showed that \u003cem\u003eA. alternata\u003c/em\u003e infection caused significant changes in metabolic pathways, whereas pre-treatment with JA prevented this reaction and maintained the expression of defense-related regulatory genes.\u003c/p\u003e \u003cp\u003eWe 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 \u003cem\u003eA. alternata\u003c/em\u003e. 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, 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 (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). 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. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb). These results indicated that JA could regulate the complex biological pathways of chrysanthemum inoculated with \u003cem\u003eA. alternata\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe genes in the major enrichment pathways were primarily involved in MAP kinases, Ca\u003csup\u003e2+\u003c/sup\u003e signaling, ROS regulation, JA and ET signaling, and phenylpropanoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). 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. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). This analysis suggested that these genes might contribute to resistance to \u003cem\u003eA. alternata\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe metabolomics results also verified that downstream metabolic changes were involved in JA biosynthesis, lignin biosynthesis, and oxidative stress processes. Exogenous JA treatment in plant leaves led to the accumulation of numerous derived phenolics, phenylpropanoids, and flavonoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this study, JA 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 JA pre-treating the leaves led to an absorption of JA and subsequent activation of downstream metabolites. The primary impact of JA 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, JA pre-treatment increased or further promoted the accumulation of these metabolites (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and S5), suggesting that fungal infection can negatively affect the antioxidant system of plants, whereas JA pre-treatment prevented the decline in these metabolites and maintained intracellular ROS homeostasis. In JA-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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, S5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of TFs involved in JA treatment and\u003c/b\u003e \u003cb\u003eA. alternata\u003c/b\u003e \u003cb\u003einfection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTFs 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 \u003cem\u003eA. alternata\u003c/em\u003e defense [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Of the 6680 common genes, 425, including 87 WRKYs and 104 AP2/EREBPs, encoded TFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). We identified 122 TFs specifically in the JA-I group, including 14 WRKYs and 13 AP2/EREBPs (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, S8). 145 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.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eValidation of differential gene expression using reverse transcription quantitative PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTo 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 \u003cem\u003eA. alternata\u003c/em\u003e 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 JA-treatment. Similar upregulation or downregulation expression patterns were seen in the qRT-PCR and RNA-seq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating that our transcriptome data was reliable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOverexpression of\u003c/b\u003e \u003cb\u003eCmWRKY6\u003c/b\u003e \u003cb\u003econfers chrysanthemum resistance to black spot disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further verify the reliability of the results, we choose WRKY6 (evm.TU.scaffold_9505.8), which was induced by \u003cem\u003eA. alternata\u003c/em\u003e, to generate overexpressed and silenced (RNA interference [RNAi]) CmWRKY6 chrysanthemum plants [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Inoculation assays demonstrated that compared with wild-type (WT) \u0026lsquo;Jinba\u0026rsquo;, 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.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). 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 \u003cem\u003eA. alternata\u003c/em\u003e resistance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eJA enhances resistance to fungal pathogen in plants\u003c/h2\u003e \u003cp\u003eTo 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 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], 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 \u003cem\u003eBotrytis cinerea\u003c/em\u003e in petunia, Arabidopsis, tomato leaves, and chrysanthemum flowers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Exogenous JA also induced resistance to fungal pathogen in potato [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and rose leaves [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Here, we showed that JA pre-treatment decreased the susceptibility of chrysanthemum leaves to \u003cem\u003eA. alternata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 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 \u003cem\u003eA. alternata\u003c/em\u003e infection with JA signaling are not completely clear, especially in chrysanthemum plants. Therefore, we monitored the dynamics of metabolites and transcriptomes in leaves after JA pre-treatment and \u003cem\u003eA. alternata\u003c/em\u003e 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 \u003cem\u003eA. alternata\u003c/em\u003e infection in chrysanthemum.\u003c/p\u003e \u003cp\u003e \u003cb\u003eJA enhances the secondary metabolism in chrysanthemum after\u003c/b\u003e \u003cb\u003eA. alternata\u003c/b\u003e \u003cb\u003einfection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSimilar to a previous study, JA 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, S2b). Inducing the expression of genes associated with secondary metabolism (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea) further increased the concentrations of secondary metabolites [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], specifically phenylpropanoids and flavonoids, including shikimic acid, caffeic acid, 3,5-dicaffeoylquinic acid, syringin, quercetin, and taxifolin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), which have anti-fungal and anti-oxidative activity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The JA pre-treatment prevented the reduction in the levels of the volatiles, such as phenylacetaldehyde and eugenol in the JA-I group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This could be as a result of the enhanced carbon flow towards the formation of eugenol in JA-pretreated leaves following infection, consistent with the release of volatiles being associated with pathogen resistance [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, JA pre-treatment resulted in transcriptional reprogramming of a group of genes involved in the cell wall, lipids, Ca\u003csup\u003e2+\u003c/sup\u003e signaling processes, and hormonal signaling processes, which are connected to plant defense responses to pathogen attack (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These results confirmed the JA can regulate specific primary and secondary metabolic processes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe influence of JA pre-treatment, both at the transcriptomic and metabolic levels suggested that the JA priming of plant leaves enabled a rapid immune response after \u003cem\u003eA. alternata\u003c/em\u003e infection through the activation of a diverse set of defense mechanisms. In this study, plants that received the JA pre-treatment showed small transcriptional changes after \u003cem\u003eA. alternata\u003c/em\u003e infection in contrast to the drastic increase in mock-treated leaves after infection (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). This difference may be related to the overall early pathogen defense response induced by exogenously applied JA [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To further explore the mechanism of JA-induced resistance against \u003cem\u003eA. alternata\u003c/em\u003e, 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), reflecting the relationship between JA signaling and \u003cem\u003eA. alternata\u003c/em\u003e. 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eJA enhanced the cell wall integrity maintenance system\u003c/h2\u003e \u003cp\u003eThe 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 Ca\u003csup\u003e2+\u003c/sup\u003e signaling, as ROS can function as Ca\u003csup\u003e2+\u003c/sup\u003e sensors [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. We found the upregulation of Ca\u003csup\u003e2+\u003c/sup\u003e signaling and ROS metabolic genes in the JA and JA-I groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), suggesting the interplay between ROS and Ca\u003csup\u003e2+\u003c/sup\u003e signals contributed to the defensive response. The overexpression of ROS can promote cell injury; therefore, maintaining intracellular ROS homeostasis can promote plant defense response [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Previous reports have provided evidence that JA prevents excess ROS generation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and promotes accumulation of antioxidant enzymes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, S5b).\u003c/p\u003e \u003cp\u003eROS and Ca\u003csup\u003e2+\u003c/sup\u003e signaling also mediate defense mechanisms through cell wall strengthening and re-organization [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], 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 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Different classes of flavonoids and lignin are synthesized via the phenylpropanoid pathway [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Lignin is a structural component of the cell wall and the expression of its biosynthetic pathway genes enhance plant defense [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The expression of genes encoding key enzymes of the phenylpropanoid pathway was significantly upregulated following \u003cem\u003eA. alternata\u003c/em\u003e infection in chrysanthemum (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). 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 \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The downstream lignin synthesis substrate monolignols also accumulated in the JA-treated plants (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003ea) after enzymatic oxidization and the subsequent radical coupling in the cell walls and formed a heterogenous polymer that constitutes lignin [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], suggesting that this might contribute to the stronger physical barrier for plant defense.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eJA enhanced pathogen-induced MAPKs signaling\u003c/h2\u003e \u003cp\u003eMAPK 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 [\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. 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 \u003cem\u003eA. alternata\u003c/em\u003e infection in chrysanthemum caused the induction of MAPKs, MAPKKs, and MAPKKKs signaling events (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). In Arabidopsis, MPK3/MPK6 and their orthologs were proposed to share a subset of defense responses [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] that affect many downstream transduction pathways. For example, MPK3/MPK6 cascade and Ca\u003csup\u003e2+\u003c/sup\u003e signaling pathway crosstalk regulate the biosynthesis of camalexin [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. MPK3/MPK6 regulate ET biosynthesis during pathogen attack [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Additionally, the functions of the MKK4/MKK5\u0026ndash;MPK3/MPK6 module in plant immunity have been identified [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and the search for the MAPKKK(s) upstream of the MKK4/MKK5\u0026ndash;MPK3/MPK6 module has progressed in recent years [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], 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 \u003cem\u003eA. alternata\u003c/em\u003e and were more significantly up-regulated in the JA-I group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This indicated that JA signaling may regulate Ca\u003csup\u003e2+\u003c/sup\u003e 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\u0026ndash;MPK3 cascade to activate immune signaling, which could be related to oxidative signal transduction. Early studies have shown that H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e can activate the specific Arabidopsis MAPKKK, ANP1, to initiate a phosphorylation cascade involving the stress MAPKs, AtMPK3 and AtMPK6 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Recently, MKK4/MKK5 has been reported to regulate plant defense pathways, including ROS production and the synthesis of ET and SA [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], suggesting that ANP1-MKK4/MKK5\u0026ndash;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\u0026ndash;MPK3 cascade to regulate immune responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe role of transcription factors in the JA signaling-induced defense response\u003c/h2\u003e \u003cp\u003eTFs play key roles in coordinating large-scale transcriptional reprogramming that resolve plant immune mechanisms [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Numerous TFs that act as essential players in JA signal transduction have been discovered using forward and reverse genetic methods [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), 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 [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], 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 [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. In the present study, most of the AP2-EREBP TFs induced by JA and \u003cem\u003eA. alternata\u003c/em\u003e infection belonged to ERF (60 members, 57.7%, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Previous studies reported that JA and ET synergistically activate defense signaling against necrotrophic pathogens [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Moreover, WRKY TFs participate in phytohormone-mediated signaling pathways and transcriptional reprogramming associated with plant defense responses like the MAPK signaling cascade [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. NaWRKY3 and NaWRKY6 can regulate the synthesis of JA to mediate pathogen defense responses [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. WRKY33 positively regulates target genes involved in the biosynthesis of the antimicrobial compound camalexin and JA/ET downstream signaling [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], and functions as a key transcriptional regulator required for immunity in Arabidopsis towards \u003cem\u003eBotrytis cinerea\u003c/em\u003e [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. In this study, most of the WRKY TFs that regulate immune responses induced by JA and \u003cem\u003eA. alternata\u003c/em\u003e belonged to WRKY33 (42 members, 48%), followed by WRKY22 (22 members, 25%), and WRKY29 (8 members, 14%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The WRKY33 and WRKY22/29 TFs were significantly upregulated in JA-I compared with those in the MOCK-I group (Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). This is potentially the reason that JA pre-treatment had a stronger induction of resistance genes than the MOCK-I group (Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, 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 \u003cem\u003eA. alternata\u003c/em\u003e in chrysanthemum. Our recent research shows that CmWRKY6 negatively regulates the resistance to \u003cem\u003eFusarium oxysporum\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], 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 \u003cem\u003eF. oxysporum\u003c/em\u003e is a soilborne plant pathogen whose hypha penetrates plant roots rather than invading leaves like \u003cem\u003eA. alternata\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe have presented experimental evidence that JA 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 Ca\u003csup\u003e2+\u003c/sup\u003e 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 JA pre-treatment mediates the transcriptional reprogramming of defense response activation before fungal infection. During pathogen attack, JA pre-treatment probably activates the ANP1-MKK4/MKK5\u0026ndash;MPK3 cascade, ROS and ET signaling events, and induces TFs that mediate gene regulatory networks in response to \u003cem\u003eA. alternata\u003c/em\u003e, 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.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). 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.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003ePlant material and\u003c/b\u003e \u003cb\u003eA. alternata\u003c/b\u003e \u003cb\u003eculture\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe chrysanthemum cultivar \u0026lsquo;Jinba\u0026rsquo; 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 \u0026deg;C and 70% humidity. After the transplants had up to 10 mature leaves, they were used in the experiments.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eA. alternata\u003c/em\u003e strain used for this study was isolated and identified from typical infected leaves of Chrysanthemum \u0026lsquo;Fubaiju\u0026rsquo; at our laboratory. The test strain was moved to potato dextrose agar solid medium, where it was grown for around a week at 28\u0026deg;C. Thereafter, fungal cakes were transferred into 200 mL potato dextrose water liquid medium and grown overnight at 28 \u0026deg;C with shaking at 180 rpm before inoculation assays were conducted.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInoculation with\u003c/b\u003e \u003cb\u003eA. alternata\u003c/b\u003e \u003cb\u003eand JA treatments\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eC. morifolium\u003c/em\u003e leaves were sprayed with distilled water or 100 \u0026micro;M 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 \u003cem\u003eA. alternata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Each inoculation site was about 1 cm in diameter. There were four treatments: 100 \u0026micro;M MeJA pre-treatment (JA group), 100 \u0026micro;M 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 \u0026deg;C and 70% humidity. The lesion area was observed at 48 hpi.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and RNA-seq library construction\u003c/h2\u003e \u003cp\u003eTotal 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\u0026rsquo;s protocol. All 12 libraries were constructed and sequenced using the using an DNBSEQ platform at BGI (Shenzhen, China) to generate sequence reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of RNA-seq datasets\u003c/h2\u003e \u003cp\u003eTo 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) [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] to the chrysanthemum genome [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], matched to reference gene sequences by Bowtie2 [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], and the gene expression level of each sample was determined using RSEM [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Differential expression analysis was performed with DESeq2 [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], using the negative binomial distribution model, the hypothesis test probability (P value) was calculated to identify differences in the gene expression data [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Genes with fold change\u0026thinsp;\u0026ge;\u0026thinsp;2, and adjusted P value (Q value)\u0026thinsp;\u0026le;\u0026thinsp;0.001 were designated as DEGs. Based on the GO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org\u003c/span\u003e\u003cspan address=\"http://geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) notes genes and classifications, the DEGs were functionally classified, and the phyper (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.wikipedia.org/wiki/Hypergeometric_distribution\u003c/span\u003e\u003cspan address=\"https://en.wikipedia.org/wiki/Hypergeometric_distribution\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in the R software was applied for KEGG enrichment analysis, whereas the TermFinder package was utilized for GO enrichment analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metacpan.org/pod/GO::TermFinder\u003c/span\u003e\u003cspan address=\"https://metacpan.org/pod/GO::TermFinder\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The cutoff point for defining candidate genes as significantly enriched was set at Q value\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExtraction and LC-MS/MS profiling of metabolites\u003c/h2\u003e \u003cp\u003eLeaves 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 [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation of RNA-seq data by (RT-qPCR)\u003c/h2\u003e \u003cp\u003e12 DEGs were chosen at random for RT-qPCR. Primers were designed using PRIMER 5 software (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). 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 \u0026micro;L of SYBR\u0026reg; Premix Ex TaqTM II, 1.0 \u0026micro;L of each 10 \u0026micro;M forward and reverse primer, and 2.0 \u0026micro;L of cDNA template were used in each reaction. The following described the reaction conditions: 95\u0026deg;C for 10 min, followed by 40 cycles of 95\u0026deg;C for 15 s, 60\u0026deg;C for 15 s, and 72\u0026deg;C for 20 s. CmEF1α (GenBank: AB548817.1) was used as a reference gene, and the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method was used to calculate each gene's relative expression level [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental 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.\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSZ 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRodriguez PA, Rothballer M, Chowdhury SP, Nussbaumer T, Gutjahr C, Falter-Braun P. Systems Biology of Plant-Microbiome Interactions. Mol Plant. 2019;12(6):804\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa 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.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCouto D, Zipfel C. Regulation of pattern recognition receptor signalling in plants. 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Methods. 2001;25:402\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"JA signaling, Alternaria alternata, Chrysanthemum morifolium, defense responses","lastPublishedDoi":"10.21203/rs.3.rs-3046091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3046091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBlack spot disease caused by the necrotrophic fungus Alternaria spp. is one of the most devastating diseases affecting \u003cem\u003eChrysanthemum morifolium\u003c/em\u003e. There is currently no effective way to prevent chrysanthemum black spot.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe revealed that pre-treatment of chrysanthemum leaves with the plant hormone jasmonate (JA) significantly reduces their susceptibility to \u003cem\u003eAlternaria alternata\u003c/em\u003e. To understand how JA treatment induces resistance, we monitored the dynamics of metabolites and the transcriptome in leaves after JA treatment following \u003cem\u003eA. alternata\u003c/em\u003e infection. JA signaling affected the resistance of plants to pathogens through cell wall modification, Ca\u003csup\u003e2+\u003c/sup\u003e 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.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings indicate that JA 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 \u003cem\u003eA. alternata\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Jasmonate signaling drives defense responses against Alternaria alternata in chrysanthemum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-26 18:43:35","doi":"10.21203/rs.3.rs-3046091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-09T08:59:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-01T07:01:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b584197c-1756-47eb-80b5-22a4fc9bc061","date":"2023-07-27T12:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-27T12:38:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-24T12:15:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-19T07:58:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-19T07:56:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2023-06-10T08:44:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0020a486-5b97-49f8-9899-97d8f5fba8e2","owner":[],"postedDate":"June 26th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-25T15:03:31+00:00","versionOfRecord":{"articleIdentity":"rs-3046091","link":"https://doi.org/10.1186/s12864-023-09671-0","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2023-09-19 15:00:41","publishedOnDateReadable":"September 19th, 2023"},"versionCreatedAt":"2023-06-26 18:43:35","video":"","vorDoi":"10.1186/s12864-023-09671-0","vorDoiUrl":"https://doi.org/10.1186/s12864-023-09671-0","workflowStages":[]},"version":"v1","identity":"rs-3046091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3046091","identity":"rs-3046091","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}