Skip to main content
NCBI home page
Molecular Plant Pathology logoLink to Molecular Plant Pathology
. 2026 Sep 25;27(9):e70349. doi: 10.1111/mpp.70349

Suppression of ZmJAZ12 Expression Modulates Jasmonic Acid and Salicylic Acid Signalling and Enhances Maize Resistance to Southern Corn Rust

Jiarui Han 1,2,3, Qi Wang 1,3, Xiaopeng Sun 4, Zhijia Yu 1,2,3, Bingyang Li 1,3, Ziyu Wang 1,3, Chuang Zhang 1,3, Weilin Wu 2, Guan‐Feng Wang 5, Gengshen Chen 6,✉, Xiangguo Liu 1,2,3,✉
PMCID: PMC13615170  PMID: 42791599

ABSTRACT

Southern corn rust (SCR), caused by the biotrophic fungus Puccinia polysora, severely threatens maize yield worldwide. In this study, we identified ZmJAZ12, a member of the JASMONATE ZIM‐domain (JAZ) protein family, as a negative regulator of SCR resistance in maize. Genome‐wide association study (GWAS) and expression analyses revealed that the expression level of ZmJAZ12 was negatively correlated with SCR resistance, and the resistant haplotypes exhibited significantly lower transcript abundance of ZmJAZ12. The ZmJAZ12 overexpression (OE) lines showed enhanced susceptibility to SCR, while ZmJAZ12 knockout mutants generated via CRISPR/Cas9‐mediated gene editing were more resistant to SCR. Transcriptomic profiling revealed that downregulation of key genes in the salicylic acid (SA) and jasmonic acid (JA) pathways occurred in the OE lines. ZmJAZ12 interacted with ZmMYC5, a core transcription factor in the JA signalling cascade, forming a regulatory module that regulates hormonal signalling and immune responses. Collectively, these findings reveal that ZmJAZ12 acts as a susceptibility gene, providing evidence for the JA/SA signalling crosstalk mechanism in plant immunity. This study provides a potential genetic target for enhancing disease resistance in maize breeding.

Keywords: jasmonate‐salicylate pathway, JAZ‐MYC signalling, maize ( Zea mays ), southern corn rust, susceptibility gene

We identified ZmJAZ12 as a key susceptibility regulator that suppresses JA/SA‐mediated defence through its interaction with ZmMYC5, thereby uncovering a hormonal crosstalk mechanism governing maize resistance to southern corn rust.

graphic file with name MPP-27-e70349-g005.webp

1. Introduction

Maize is one of the world's major cultivated crops, a crucial source of food, fuel and feed, and is essential for global food security. Southern corn rust (SCR), caused by the fungus Puccinia polysora, is one of the most destructive foliar diseases of maize worldwide. As an obligate biotrophic fungus, P. polysora extracts nutrients from living host cells through specialized structures called haustoria. Infection can disrupt photosynthesis and compromise crop yield, resulting in substantial yield loss exceeding 50% during severe epidemics under favourable conditions (Cheaib and Killiny 2025; Li, Cheng, et al. 2023). This disease is particularly prevalent in tropical and subtropical regions, where favourable temperature and humidity conditions promote the pathogen's growth (Pavgi 1972; Chang et al. 2025). Given that climatic conditions favourable for disease outbreaks are projected to intensify, SCR continues to pose a significant challenge to global maize production (Debnath et al. 2019; Halvorson et al. 2021).

Plant immunity against biotrophic pathogens is governed by a multilayered defence network that integrates recognition of pathogen‐associated molecular patterns (PAMPs), activation of defence‐related signalling cascades and subsequent transcriptional reprogramming (Hou et al. 2019; Ali et al. 2024). In maize, while several dominant resistance (R) genes against SCR have been identified to confer race‐specific immunity (Wu et al. 2015; Wang et al. 2020; Lv et al. 2021; Deng et al. 2022; Chen et al. 2022), quantitative resistance involving reactive oxygen species (ROS) accumulation, hormone signalling and transcriptional reprogramming plays an equally critical role in broad‐spectrum defence (Wang et al. 2019, 2025; Mu et al. 2022; Liu et al. 2025). Within this immune network, crosstalk between jasmonic acid (JA) and salicylic acid (SA) signalling pathways is a hallmark of plant defence regulation: Generally, SA signalling is associated with defence against biotrophic and hemibiotrophic pathogens, whereas JA signalling is primarily linked to resistance against necrotrophic pathogens and herbivores (Glazebrook 2005). While JA is classically associated with defence against necrotrophic pathogens, emerging evidence highlights its complex but crucial role in fine‐tuning basal immunity against diverse pathogen lifestyles through intricate crosstalk with SA signalling.

A dynamic signalling network between SA and JA has been widely reported. In Arabidopsis thaliana , SA‐induced NPR1 not only initiates SA‐responsive gene expression but also acts as a potent repressor of JA‐responsive gene expression (Nomoto et al. 2021). In strawberries, for example, HSP17.4 activates SA‐mediated defence while suppressing JA signalling, enhancing resistance to Colletotrichum spp. (Fang et al. 2021). This antagonistic relationship represents one facet of signalling crosstalk, while synergistic interactions between JA and SA pathways have been observed in various species, including maize and poplar, where non‐canonical crosstalk enhances resistance to biotrophic pathogens (Yamada et al. 2012; Tamaoki et al. 2013; Liu et al. 2016; Guerreiro et al. 2016; Jiao et al. 2022; Wang et al. 2019). Increasing evidence suggests that the coordination between JA and SA signalling is mediated by multiple transcriptional regulators, among which MYC transcription factors function as central hubs integrating hormone signalling and immune responses (Luo et al. 2023; Song et al. 2022; Li et al. 2024), enabling fine‐tuned regulation of immune responses (Mu et al. 2025). Extensive evidence indicates that crosstalk between JA and SA pathways provides plants with the flexibility to fine‐tune immune responses under diverse biotic stresses (Hou and Tsuda 2022).

More broadly, JA signalling regulates extensive transcriptional reprogramming and specialized metabolic responses through complex transcriptional networks (Kazan and Manners 2008; De Geyter et al. 2012). Jasmonate ZIM‐domain (JAZ) proteins are widely recognized as central components of JA signalling and plant hormone‐mediated stress responses (Waadt et al. 2022; Zhao et al. 2024). Mechanistically, JAZ proteins repress JA signalling by interacting with MYC transcription factors and preventing MYC‐dependent transcriptional activation of JA‐responsive genes (Chini et al. 2007; Thines et al. 2007; Sheard et al. 2010; Major et al. 2017). Upon JA‐Ile accumulation, JAZ proteins are recruited by the SCFCOI1 ubiquitin ligase complex and degraded through the 26S proteasome pathway, thereby releasing MYC transcription factors to activate downstream immune responses (Chini et al. 2007; Thines et al. 2007; Sheard et al. 2010; Major et al. 2017). In contrast, under low JA levels, JAZ repressors form inhibitory complexes with MYCs, suppressing their transcriptional activity and blocking defence signalling (Fu et al. 2020; Chen et al. 2021).

MYC transcription factors have been recognized as important transcriptional hubs that integrate JA signalling with plant growth, stress responses and specialized metabolism (Luo et al. 2023; Song et al. 2022; Li et al. 2024). Functional studies have demonstrated that JAZ‐mediated regulatory modules participate in disease resistance in multiple plant species. In rice, OsJAZ8 is involved in JA‐induced resistance to bacterial blight by modulating JA‐responsive defence genes (Yamada et al. 2012), whereas OsJAZ13 negatively regulates JA signalling and activates hypersensitive cell death responses (Feng et al. 2020). In Arabidopsis, bacterial effectors such as HopZ1a and HopX1 directly target JAZ repressors through distinct mechanisms, thereby activating J signalling and promoting pathogen infection (Jiang et al. 2013; Gimenez‐Ibanez et al. 2014). These findings indicate that JAZ proteins are not merely passive repressors of JA signalling but important immune regulators that connect hormone perception, MYC‐mediated transcriptional regulation and pathogen‐induced defence modulation (Johnson et al. 2023).

Besides regulating MYC activity, JAZ‐associated regulatory networks can also intersect with other transcriptional regulators, including WRKY proteins, thereby linking JA signalling with broader immune responses (Chen et al. 2021; Johnson et al. 2023). Such regulatory modules enable plants to fine‐tune immune responses according to pathogen lifestyles while minimizing growth penalties (Major et al. 2017; Li et al. 2024). However, despite the well‐established roles of JAZ–MYC regulatory modules in plant immunity, whether a similar mechanism regulates maize responses to P. polysora remains largely unknown. In particular, the biological function of ZmJAZ12 and its potential interaction with MYC transcription factors during P. polysora infection have not been experimentally characterized.

In this study, we investigated the functional role of ZmJAZ12 in maize immunity to P. polysora. By combining genome‐wide association study (GWAS), transgenic approaches and molecular assays, we demonstrate that ZmJAZ12 acts as a susceptibility factor that modulates JA‐ and SA‐related defence responses, potentially through interaction with ZmMYC5 and modulation of JA–SA signalling. Our findings provide new insights into the molecular basis of P. polysora susceptibility in maize and highlight the potential of targeting JAZ–MYC regulatory modules as a strategy for improving SCR resistance in maize.

2. Results

2.1. Association of ZmJAZ12 Genetic Variation With Resistance to P. polysora

To dissect the genetic basis underlying natural variation in SCR resistance, we performed a GWAS focused on genomic regions containing JAZ transcriptional repressor family genes in an association mapping panel of 513 diverse maize inbred lines (Figure 1a, Table 1). A candidate association signal was detected at peak SNP_16783498 (SNP‐5, p = 5.6e−5, MLM) (Figure S1), within which three JAZ family genes were found. Transcriptome profiling revealed that two genes (GRMZM5G838098 and GRMZM2G445634) were active upon pathogen infection (Figure S2). This peak SNP localizes to the promoter region of GRMZM5G838098 in this cluster (Table S1). Collectively, these results support GRMZM5G838098 (zim27, ZmJAZ12) as the primary candidate gene underlying the detected association signal. In the promoter region, three SNPs (SNP‐251, SNP‐36 and SNP‐5) with p < 0.001 were selected for haplotype analysis. Lines carrying the GCC haplotype were significantly more susceptible to P. polysora compared to those harbouring the CGC, CCG and CGG haplotypes within the NSS subpopulation (18/109, p = 0.004, Figure 1b). Expression profiling further revealed that susceptible haplotype GCC exhibited markedly high ZmJAZ12 transcript abundance, and statistical analysis confirmed a strong negative correlation between ZmJAZ12 expression and P. polysora resistance (Hap GCC vs. Hap CGC, CCG, CGG, n = 18/109, p < 0.0001, Figure 1c). Simultaneously, similar results were observed in the TST subpopulation, where lines harbouring the susceptible GCC haplotype exhibited significantly elevated expression levels and correspondingly high SCR scores (Figure S3). Therefore, these results suggest that polymorphisms in the ZmJAZ12 promoter region regulate its expression level, thereby affecting P. polysora resistance. Tissue‐ and stage‐specific expression profiling showed that ZmJAZ12 was expressed in multiple organs but displayed relatively higher expression in certain vegetative tissues and developing reproductive organs (Figure S4). Taken together, these findings demonstrate that natural variation in ZmJAZ12 is strongly associated with P. polysora resistance.

FIGURE 1.

FIGURE 1

Open in a new tab

Natural variations in ZmJAZ12 were significantly associated with resistance to Puccinia polysora (southern corn rust, SCR) in maize. (a) ZmJAZ12‐based association mapping and pairwise linkage disequilibrium (LD) analysis. The y‐axis shows –log10(p) values, and the x‐axis displays the SNP position relative to the start codon ATG and gene structure is indicated below the x‐axis. Three significantly associated promoter SNPs at −251, −36 and −5 bp (relative to the ATG) are highlighted in red. The lower panel shows the LD pattern (r 2) among SNPs in this region. (b) Box plot of SCR scores for lines with different haplotypes defined by three SNPs, with the corresponding allele combinations shown below. Different letters above the boxes indicate statistically significant differences among haplotypes (p < 0.05). (c) ZmJAZ12 transcript abundance (transcripts per million [TPM]) of lines with different haplotypes. Different letters indicate significant differences in expression (p < 0.05).

TABLE 1.

Genome‐wide association study (GWAS) results of the ZmJAZ gene family for resistance to Puccinia polysora (southern corn rust [SCR] score) in association mapping panel.

Gene ID Name Chr. SNP number MLM (p < 0.01) MLM (p < 0.001)
GRMZM2G343157 zim26 1 50 0 0
GRMZM5G838098 zim27 (ZmJAZ12) 1 100 11 3
GRMZM2G445634 zim16 1 51 1 0
GRMZM2G110131 zim22 1 6 0 0
GRMZM2G117513 zim24 1 70 0 0
GRMZM2G024680 zim21 1 126 0 0
GRMZM2G145412 zim18 1 25 0 0
GRMZM2G145458 zim3 1 14 0 0
GRMZM2G382794 zim19 1 0 0 0
GRMZM2G086920 zim32 2 114 0 0
GRMZM2G145407 zim33 2 22 0 0
GRMZM2G171830 zim8 2 3 0 0
GRMZM2G114681 zim15 2 90 0 0
GRMZM2G005954 zim13 2 64 0 0
GRMZM2G101769 zim12 2 42 0 0
GRMZM2G327263 zim17 3 6 0 0
GRMZM2G122160 zim11 4 11 0 0
GRMZM2G151519 zim35 4 46 0 0
GRMZM2G022514 zim46 4 80 0 0
GRMZM2G065896 zim2 5 74 0 0
GRMZM2G063632 zim7 5 2 0 0
GRMZM2G064775 zim29 5 1 0 0
AC197764.4_FG003 zim30 5 18 0 0
GRMZM2G173596 zim10 5 22 1 0
GRMZM2G058479 zim36 5 25 1 0
GRMZM2G303161 zim37 5 1 0 0
AC196795.4_FG009 zim9 6 0 0 0
GRMZM2G338829 zim9 6 0 0 0
GRMZM2G080509 zim20 6 47 0 0
GRMZM2G314145 zim25 6 7 0 0
GRMZM2G370797 zim38 7 5 0 0
GRMZM2G126507 zim1 7 100 2 0
GRMZM2G116614 zim28 7 40 0 0
GRMZM2G066020 zim31 7 73 0 0
GRMZM2G089736 zim23 7 12 0 0
GRMZM2G054689 zim5 8 61 0 0
GRMZM2G036351 zim4 9 55 0 0
GRMZM2G036349 zim6 9 58 0 0
GRMZM2G036288 zim14 9 6 0 0
GRMZM2G442458 zim40 10 0 0 0
GRMZM2G143402 zim34 10 43 0 0
Open in a new tab

2.2. Functional Characterization of ZmJAZ12 in Transgenic Maize Lines

To functionally validate the role of ZmJAZ12 in P. polysora resistance, we generated three independent overexpression (OE) lines and two knockout (KO) lines using CRISPR/Cas9 (Figure 2a). The KO lines carried large‐frame deletions within the first and second exons, specifically a 377‐bp deletion in KO‐1 and a 390‐bp deletion in KO‐2 (Figure 2b). Reverse transcription‐quantitative PCR (RT‐qPCR) analysis confirmed elevated transcript accumulation in all OE lines, with interline variability in expression levels (Figure 2c). Field trials demonstrated that all three independent OE lines (OE‐1, OE‐2 and OE‐3) consistently displayed a statistically significant increase in lesion area relative to wild‐type controls (n = 39/33, p = 0.004, n = 20/25, p = 2.3e−09, n = 30/30, p = 0.0067, Figure 2d,f). To systematically assess the phenotypic impact and eliminate potential selection bias, we further evaluated all successfully generated ZmJAZ12 overexpression events across multiple field trials (Figure S5). Because individual events exhibited varying degrees of phenotypic shifts under natural conditions, we pooled the positive transgenic plants (OE+) and their corresponding negative segregants (OE−) into two comparative groups for each environment. In Henan, the pooled analysis revealed a highly significant increase in disease severity in the combined OE+ group compared to the OE− group (pooled p = 3.0e−06). In Hainan, a consistent trend of increased disease severity was similarly observed in the combined OE+ group (pooled p = 0.0716). These pooled data robustly support that ZmJAZ12 overaccumulation promotes P. polysora susceptibility. Conversely, both KO lines exhibited markedly reduced lesion areas and improved resistance (n = 17/21, p = 0.0254, n = 16/22, p = 0.0002, Figure 2e,g). These data demonstrate that ZmJAZ12 acts as a negative regulator of P. polysora resistance in maize, with elevated expression leading to increased susceptibility.

FIGURE 2.

FIGURE 2

Open in a new tab

Functional characterization of ZmJAZ12‐mediated resistance to Puccinia polysora (southern corn rust, SCR) in maize. (a) Schematic representation of constructs used for ZmJAZ12 overexpression (OE) and CRISPR/Cas9‐mediated knockout (KO). (b) Gene structure of ZmJAZ12 showing the deleted regions in KO‐1 (377 bp) and KO‐2 (390 bp) mutants, confirmed by sequencing. (c) Relative ZmJAZ12 expression levels in three OE lines compared with control plants. (d, e) Representative phenotypes of OE and KO lines after P. polysora infection under field conditions. (f, g) Comparison of SCR scores among control, OE and KO lines. Disease severity was evaluated at 2–3 weeks after pollen shed under natural infection pressure using a 1–9 scale. In the box plots, OE lines and their corresponding non‐OE controls are labelled as OE‐(+) and OE‐(−), respectively, while KO lines and their non‐edited controls are labelled as KO‐(+) and KO‐(−), respectively. Numbers above the x‐axis indicate the sample size for each group. Statistical significance was assessed using Student's t‐test.

2.3. ZmJAZ12 Suppresses JA‐ and SA‐Mediated Defence Responses

To investigate whether ZmJAZ12 alters the basal immune status prior to pathogen challenge, RNA‐seq analysis was performed on uninfected seedlings of three independent OE lines (OE‐1, OE‐2 and OE‐3) and their wild‐type controls, allowing us to capture basal transcriptomic shifts. A total of 282 differentially expressed genes (DEGs) (|log2FC| > 1, false discovery rate [FDR] < 0.05) were identified, among which 136 (48.2%) were upregulated and 146 (51.8%) were downregulated, indicating a slight bias toward transcriptional repression (Figures S6 and S7). Enrichment analysis revealed significant over‐representation of genes involved in defence‐related pathways such as plant hormone signalling, plant–pathogen interactions, and the MAPK signalling cascade (Figure 3a), indicating that ZmJAZ12 primarily modulates immune and signalling networks.

FIGURE 3.

FIGURE 3

Open in a new tab

Transcriptomic profiling of ZmJAZ12 overexpression (OE) lines reveals altered jasmonic acid (JA)/salicylic acid (SA) signalling. (a) Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) between OE and control plants. Bars show the number of genes in each significantly enriched GO term related to hormone regulation and biotic/abiotic stress responses, and are coloured according to –log10(p). (b) Heatmap shows expression patterns of representative genes involved in the JA pathway, the SA pathway and disease resistance‐related genes in control and OE lines. (c–f) Reverse transcription‐quantitative PCR (RT‐qPCR) validation of JA‐related genes AOS3, AOS4, zim28 and ABCG14 in control (CK) and OE plants. (g–j) RT‐qPCR analysis of SA‐related genes PRP7, PRP14, PRP10 and the disease‐resistance gene cta1 in CK and OE plants. In (c–j), data represent mean ± SD (n = 3 biological replicates). Statistical significance was assessed using Student's t‐test.

We next focused on DEGs associated with JA and SA pathways. Consistent with the canonical repressor function of JAZ proteins, multiple components of the JA biosynthetic and signalling cascade were downregulated in OE plants, including AOS3, AOS4, LOX5 and bHLH14 (Figure 3b, Table S2, Figure S8). The heatmap reveals a general transcriptional perturbation and rewiring of the defensive network in the OE background, highlighting that specific key functional nodes are targeted for repression. Consistent with the RNA‐seq results, targeted RT‐qPCR analysis validated a widespread repressive trend. While the basal expression of defence‐related genes in uninfected wild‐type plants exhibited characteristic biological fluctuation, the overexpression of ZmJAZ12 consistently maintained the transcript abundance of JA biosynthetic genes AOS3 and AOS4 at a stable, minimal basal level (Figure 3c,d), suggesting reduced transcriptional potential for JA biosynthesis. Concomitantly, the JA‐pathway repressor zim28 was significantly induced (p < 0.05), whereas ABCG14 was significantly downregulated (p < 0.05) (Figure 3e,f), further reinforcing a transcriptional environment unfavourable to JA‐mediated defence activation.

A parallel repressive trend was observed in the SA pathway. Visualization of the entire SA cascade similarly revealed a distinct transcriptional perturbation of the SA network in the OE lines (Figure S9). Similar to the JA pathway, the basal expression of specific defence‐responsive nodes such as PRP7, PRP10 and PRP14 was consistently restricted to a minimal level in OE plants, counteracting the broad biological fluctuations observed in wild‐type controls (Figure 3g–i). Furthermore, the disease‐resistance gene cta1 was significantly downregulated (p < 0.05, Figure 3j), as validated by RT‐qPCR.

Of particular interest, the methylesterase 3 (SABP2‐like) gene—an established integrator of JA/SA crosstalk—was also suppressed, suggesting that it may be a regulatory node in JA/SA crosstalk. These data suggest that ZmJAZ12 may repress both JA‐ and SA‐mediated immunity, with several SA‐responsive genes being particularly affected by ZmJAZ12 accumulation.

Beyond the JA/SA‐related genes, a number of genes associated with other hormone signalling pathways also exhibited altered expression (Figure 3b, Table S3), suggesting that ZmJAZ12 may influence immune outcomes through wider hormonal crosstalk. In parallel, numerous canonical defence genes—such as chitinases, zeamatin, thaumatin‐like PR proteins and several receptor‐like kinases—were consistently downregulated. These genes span protease‐mediated immunity, osmotic stress adaptation and pattern‐recognition functions, indicating a systematic dampening of basal defence capacity in the OE background.

To assess the biological relevance of these transcriptional patterns, we compared our dataset with published transcriptomes from the SCR‐resistant inbred lines L119A and Qi319. In both genetic backgrounds, inoculation with P. polysora triggered strong induction of AOS3, AOS4, PRP10 and PRP14, reflecting robust activation of JA/SA‐mediated immunity in resistant plants. In contrast, these same genes were uniformly suppressed in ZmJAZ12 OE plants, suggesting that elevated ZmJAZ12 expression may counteract pathogen‐induced immune activation (Figure 4, Table 2).

FIGURE 4.

FIGURE 4

Open in a new tab

Expression dynamics of jasmonic acid (JA)/salicylic acid (SA) pathway‐related genes in resistant and susceptible maize lines after Puccinia polysora inoculation. Expression levels (fragments per kilobase of transcript per million mapped reads [FPKM]) of seven genes—ZmMYC5, AOS3, AOS4, zim28, PRP7, PRP14 and PRP10—were analysed in the resistant line Qi319 and the susceptible line 8112 at 0 h and 48 h after inoculation. R0 and R48 represent the resistant line at 0 h and 48 h; S0 and S48 represent the susceptible line at 0 h and 48 h. Error bars denote standard deviation (SD, n = 3). Different lowercase letters above the bars indicate statistically significant differences according to one‐way ANOVA (p < 0.05).

TABLE 2.

The list of partially differentially expressed genes in maize resistant line L119A after inoculation and non‐inoculation with Puccinia polysora.

Gene name Gene ID (RefGen_v3) Gene ID (RefGen_v4) Log2Fold Change FDR Regulated
cta1 GRMZM2G051943 Zm00001d003190 1.43 4.74e−06 Up
Methylesterase 3 GRMZM2G440003 Zm00001d012603 1.98 1.3e−11 Up
sip1 GRMZM2G374971 Zm00001d021901 2.55 6.69e−46 Up
ZmJAZ12 GRMZM5G838098 Zm00001d027900 1.15 1.64e−07 Up
PRP10‐1 GRMZM2G112538 Zm00001d028814 1.03 2.6e−07 Up
PRP10‐2 GRMZM2G112538 Zm00001d028815 1.09 2.54e−08 Up
PRP14 GRMZM2G304442 Zm00001d029558 1.99 9.6e−08 Up
AOS4 GRMZM2G072653 Zm00001d034184 1.51 2.7e−06 Up
AOS3 GRMZM2G376661 Zm00001d034186 1.51 2.02e−E‐08 Up
Blight‐associated protein p12 GRMZM2G099092 Zm00001d036539 1.95 2.5e−14 Up
Open in a new tab

Expression dynamics of key regulators mirrored this divergence: ZmJAZ12 was induced after infection in resistant line Qi319 but reduced in susceptible line 8112, whereas the JA activator ZmMYC5 displayed the opposite pattern (Figure 4, Figure S2). Together, these observations indicate that ZmJAZ12 overaccumulation disrupts the transcriptional programmes normally mobilised during resistance, supporting a model in which ZmJAZ12 functions as a susceptibility factor by attenuating JA–SA signalling outputs critical for defence.

2.4. Interaction of ZmJAZ12 With ZmMYC5 in JA Signalling

The JAZ–MYC module is a conserved regulatory complex in JA signalling. JAZ repressors regulate downstream defence responses by binding to the MYC transcription factor. To determine whether ZmJAZ12 functions through this module, we investigated its interaction with five ZmMYC genes (maize MYC transcription factors involved in JA‐mediated defence). Yeast two‐hybrid (Y2H) assays revealed that ZmJAZ12 specifically interacted with ZmMYC5, as indicated by the formation of blue colonies on selective medium (Figure 5a). Luciferase complementation imaging (LCI) in Nicotiana benthamiana leaves revealed a strong luminescence signal in cells co‐expressing ZmJAZ12 and ZmMYC5, confirming their physical association (Figure 5b). Co‐immunoprecipitation (Co‐IP) experiments further validated this interaction, with ZmMYC5‐FLAG consistently detected in the complex precipitated with GFP‐tag magnetic beads (Figure 5c). Collectively, these data suggest that ZmJAZ12 acts as a negative regulator of JA‐mediated defence responses in maize and physically interacts with ZmMYC5; this interaction likely contributes to reduced induction of some immune‐related genes during pathogen challenge.

FIGURE 5.

FIGURE 5

Open in a new tab

ZmJAZ12 physically interacts with ZmMYC5 in the jasmonic acid (JA) signalling pathway. (a) Yeast two‐hybrid (Y2H) assays showing specific interaction between ZmJAZ12 and ZmMYC5. Yeast colonies turning blue on SD/−His/−Trp/−Ura+X‐α‐Gal plates indicate interactions. EV, empty vector. (b) Luciferase complementation imaging (LCI) assays in Nicotiana benthamiana leaves showing reconstituted fluorescence in cells co‐expressing ZmJAZ12‐nLUC and ZmMYC5‐cLUC. LUC, luciferase. (c) Co‐immunoprecipitation (Co‐IP) assay confirming the physical interaction between ZmJAZ12‐GFP and ZmMYC5‐FLAG in N. benthamiana. Protein molecular weight markers (kDa) are indicated.

3. Discussion

This study identified ZmJAZ12 as a negative regulator of SCR resistance in maize. ZmJAZ12 is associated with the suppression of JA/SA‐mediated immune responses and interacts with ZmMYC5, which together suggest a possible contribution to increased disease susceptibility. These results provide novel insights into the crosstalk between JA and SA signalling pathways in maize, and position ZmJAZ12 as a promising target for improving SCR resistance through genetic manipulation.

While several resistance genes against fungal pathogens have been identified in maize, the number of genes with defined molecular functions remains limited (Chen et al. 2022; Wang et al. 2024). Since the mid‐20th century, numerous SCR resistance (R) genes have been identified, including Rpp1 to Rpp11 (Storey and Ryland 1954; Ullstrup 1965). In recent years, additional dominant resistance loci such as RppS (Wu et al. 2015), RppM (Wang et al. 2020) and RppCML496 (Lv et al. 2021) have been characterized. Some R genes have corresponding avirulence (Avr) partners in the pathogen, including the RppC– AvrRppC and RppK– AvrRppK gene‐for‐gene pairs (Deng et al. 2022; Chen et al. 2022; Sun et al. 2026). Among these, Rpp9 has been widely deployed in US hybrid maize breeding for over three decades. However, these traditional dominant R genes typically confer strictly race‐specific resistance dependent on effector recognition. Consequently, the rapid evolution of P. polysora isolates poses a major challenge to these R genes for the development of durable resistance (Dolezal et al. 2009; Wang et al. 2024). In contrast, susceptibility genes, which facilitate successful pathogen infection through manipulating basal host processes, are considered essential for compatible plant–pathogen interactions. Consequently, mutations of these susceptibility genes could lead to durable, recessively inherited and non‐race‐specific broad‐spectrum resistance in plants (Koseoglou et al. 2022), a concept that aligns well with the long‐sought goal of achieving general resistance against SCR in maize breeding (Brewbaker et al. 2011). Our disease evaluations were conducted under severe natural field conditions, exposing the plants to naturally occurring P. polysora populations rather than a single purified isolate. Mechanistically, previous studies have shown that diverse pathogen effectors, such as HopZ1a and HopX1, can target host JAZ proteins through biochemical modification or proteolytic cleavage to alter their stability and manipulate host immunity (Jiang et al. 2013; Gimenez‐Ibanez et al. 2014). This pathogenic strategy exploits conserved immune regulatory hubs rather than relying solely on strict receptor–ligand‐specific recognition. Therefore, we hypothesize that ZmJAZ12 loss‐of‐function may partially bypass traditional race‐specific constraints and provide a potential route toward more durable SCR resistance under mixed natural infection pressure. Editing susceptibility‐related regulatory genes may therefore serve as a complementary strategy to traditional R gene‐mediated resistance breeding.

Unlike major resistance genes, ZmJAZ12 functions as a minor‐effect locus, and knocking out this gene partially enhances SCR resistance. Such quantitative traits are commonly observed for transcriptional regulators of plant immunity, whose perturbation often results in partial rather than qualitative resistance because they modulate downstream defence networks instead of directly mediating pathogen recognition (Eulgem and Somssich 2007; Tsuda and Somssich 2015).

ZmJAZ12, as a key repressor in the JA pathway, enhances susceptibility to P. polysora by inhibiting both JA and SA signalling. While it may not conform to the classical definition of an S gene, its role as a ‘regulatory‐type susceptibility factor’ aligns with current understanding of plant–pathogen interactions. Recent studies suggest that loss‐of‐function mutations in susceptibility genes may provide durable, broad‐spectrum resistance (Guo et al. 2022; Li, Cheng, et al. 2023; Li, Gu, et al. 2023). In support of this regulatory role, several genes associated with canonical JA and SA pathways were significantly repressed in the ZmJAZ12 OE transcriptome analysis. JA biosynthetic enzymes such as LOX‐, AOS‐, AOC‐ and OPR‐like genes, together with key signalling components including TIFY/JAZ‐domain proteins and MYC‐like bHLH transcription factors, exhibited reduced expression, consistent with the established model in which JAZ proteins suppress JA‐dependent immunity by inhibiting MYC activity (Zhu et al. 2024; Christensen et al. 2015). Likewise, several hallmark components of the SA pathway—including TGA transcription factors and PR (pathogenesis‐related) genes—were downregulated, aligning with the requirement of NPR‐TGA‐PR signalling for SA‐mediated defence (Shimizu et al. 2022; Jia et al. 2023). The simultaneous suppression of JA‐ and SA‐associated defence genes fits well with the well‐established crosstalk and antagonistic balance between JA and SA pathways during pathogen infection (Kumar et al. 2025). Although the ABC transporter family is generally involved in the transport of various plant hormones, including abscisic acid and cytokinin, in this study, we classified ABC transporter G14 as part of the JA signalling pathway, focusing on its role in JA‐mediated immune responses (Zhao et al. 2023; Aryal et al. 2023; An et al. 2024; Zhou et al. 2024; Tao et al. 2024). Specifically, in the context of P. polysora infection, the successful activation of JA and SA signalling cascades probably restricts fungal colonization by driving the accumulation of antifungal pathogenesis‐related (PR) proteins (such as chitinases and zeamatin identified in our dataset) and specialized secondary metabolites that directly target biotrophic haustorial structures (Roberts and Selitrennikoff 1990; Garnica et al. 2014). These transcriptional patterns further support that ZmJAZ12 functions at the intersection of JA/SA hormonal networks. Because MYC transcription factors participate in both JA signalling and JA/SA crosstalk, repression of MYC activity by ZmJAZ12 is expected to indirectly attenuate SA‐responsive defence outputs. Mechanistically, MYC transcription factors are known to serve as master switches that can modulate SA pathways—either antagonistically or synergistically—by regulating downstream targets such as NAC transcription factors or modifying the activity of SA‐binding proteins (e.g., SABP2) (Zheng et al. 2012; Hou and Tsuda 2022). Therefore, the physical interaction between ZmJAZ12 and ZmMYC5 provides a potential molecular bridge that may explain the concurrent downregulation of SA‐mediated defence. Given its role in regulating hormone‐mediated immunity, the loss of function of ZmJAZ12 may represent a promising strategy for enhancing durable and broad‐spectrum resistance in maize.

The transcriptomic profiling of ZmJAZ12 in this study used transgenic lines driven by the strong constitutive PZmUBI promoter. While this approach successfully amplified the downstream regulatory effects of ZmJAZ12 and revealed defence‐related pathways affected by sustained ZmJAZ12 accumulation, it may not fully recapitulate the spatiotemporal expression dynamics of ZmJAZ12 under its native regulatory context. To address this limitation, we compared our OE transcriptome data with two independent public transcriptome datasets from maize lines with different SCR resistance backgrounds, L119A and Qi319. Unlike PZmUBI‐driven OE materials, these datasets reflect gene expression patterns under native genetic backgrounds and endogenous regulatory conditions. Notably, several JA/SA‐associated genes suppressed in ZmJAZ12 OE lines were induced during P. polysora responses in resistant maize lines, supporting the biological relevance of the ZmJAZ12‐associated transcriptional rewiring. Thus, although the OE transcriptome should be interpreted as a functional perturbation dataset rather than a direct substitute for the native transcriptome profile, its comparison with naturally regulated maize backgrounds supports our conclusion that ZmJAZ12 modulates JA/SA‐related defence networks. Nevertheless, the enhanced SCR resistance observed in the KO mutants provides genetic support that targeting the JAZ–MYC regulatory module may represent a potential strategy for improving SCR resistance.

It is noteworthy that the expression dynamics of ZmJAZ12 differed significantly between uninfected basal states and post‐infection stress responses. Our GWAS and uninfected panel data demonstrated that a high basal transcript abundance of ZmJAZ12 represses basal immunity, rendering the plant more susceptible to SCR. However, transcriptomic data from infected natural inbred lines (Figure 4) revealed a seemingly paradoxical induction of ZmJAZ12 alongside JA/SA activation in the resistant line (Qi319). This phenomenon is consistent with a classic negative feedback loop, a well‐documented regulatory mechanism in plant immunity (Chini et al. 2007; Thines et al. 2007). Upon successful pathogen recognition, resistant plants mount a strong immune response characterized by activation of JA‐ and SA‐associated defence pathways. To prevent runaway autoimmune responses and subsequent tissue damage, plants rapidly upregulate transcriptional repressors like JAZ proteins to eventually dampen the immune signalling and restore cellular homeostasis (Major et al. 2017). Similar homeostatic strategies to suppress excessive hypersensitive response (HR) have been documented in other maize immune pathways (Tang et al. 2026). Thus, the co‐induction of ZmJAZ12 and JA/SA pathway genes during active defence reflects the simultaneous engagement of the immune system's activation and suppression mechanisms (Campos et al. 2014). Ultimately, this finely tuned feedback mechanism exemplifies the classical ‘growth–defence trade‐off’ paradigm. Because mounting a massive immune response incurs severe fitness costs, the timely induction of ZmJAZ12 allows the plant to safely disengage the defence machinery, thereby reallocating limited metabolic resources back to normal growth and reproductive processes (Huot et al. 2014; Guo et al. 2018). In contrast, in susceptible lines, this coordinated activation and feedback regulation may be compromised.

From an application perspective, the selected transformation recipient line used in this study presents a technical limitation for phenotypic evaluation. Because the recipient material is inherently highly susceptible to SCR, it exhibited extreme baseline disease symptoms under natural infection. In such a highly susceptible background, overexpressing a susceptibility gene makes it challenging to observe an even more extreme susceptible phenotype due to a severe baseline saturation effect, which adds difficulty to accurate phenotypic assessment. To evaluate the exact phenotypic impact, we analysed the five independent OE events across different environments by pooling the positive transgenic plants (OE+) and their corresponding negative segregants (OE−) into two comparative groups. By comparing the combined OE+ versus OE− groups, our results clearly demonstrated that elevated ZmJAZ12 expression promotes SCR susceptibility. To overcome this current limitation, our future research plan is to overexpress ZmJAZ12 in resistant genetic backgrounds to further validate its gene function.

Collectively, our results position ZmJAZ12 as a key susceptibility factor in maize, functioning through the repression of hormone‐mediated defence and direct interaction with transcriptional activators such as ZmMYC5 (Figure 6). In canonical JA signalling, JA‐Ile promotes the rapid degradation of JAZ proteins via the SCFCOI1 complex, liberating MYCs to activate defence (Chini et al. 2007; Thines et al. 2007; Sheard et al. 2010; Wasternack and Song 2017). We hypothesize that the constitutive accumulation of ZmJAZ12 in our OE lines stabilizes this inhibitory complex, preventing ZmMYC5 release even under infection pressure. Future biochemical assays on ZmJAZ12 degradation dynamics during P. polysora infection will help experimentally validate this mechanism. To overcome the traditional growth–defence trade‐off, precise editing of ZmJAZ12 could uncouple its endogenous regulatory function from pathogen manipulation. Similar targeted strategies have been successfully employed in other crops to confer broad‐spectrum resistance without severe developmental penalties (Yamada et al. 2012; Li et al. 2024), highlighting ZmJAZ12 as a promising genetic target for breeding durable SCR resistance in maize.

FIGURE 6.

FIGURE 6

Open in a new tab

Schematic model of the role of ZmJAZ12‐mediated susceptibility to Puccinia polysora (southern corn rust, SCR) in maize through jasmonic acid (JA)–salicylic acid (SA) interactions. When maize is infected with P. polysora, both the JA and SA signalling pathways are activated, and crosstalk exists between the two pathways. ZmJAZ12 was verified to interact with ZmMYC5, a key transcription factor in the JA pathway, using yeast two‐hybrid (Y2H), luciferase complementation imaging (LCI), and co‐immunoprecipitation (Co‐IP) assays. ZmJAZ12 overexpression (OE) lines transcriptionally downregulate the expression of genes involved in the JA pathway (e.g., AOS3/AOS4) and SA pathway genes (e.g., PRP7/PRP10). In ZmJAZ12 OE lines, transcriptional suppression of these genes disrupts hormonal crosstalk, leading to enhanced susceptibility to SCR. Red lines and green arrows indicate activation and suppression, respectively.

4. Experimental Procedures

4.1. GWAS

A total of 513 diverse inbred lines, representing tropical, subtropical and temperate germplasms, were collected to construct a large maize association panel and were genotyped with 1.25 million SNP markers. Detailed information on the population and genotyping has been previously reported (Yang et al. 2011, 2014; Fu et al. 2013; Liu et al. 2017). This panel was planted from 2011 to 2016 in eight environments using a randomized complete block design. The panel lines were planted with one repeat in 2011, 2012, 2015 and 2016, and three repeats in 2013 at Sanya in Hainan Province, two repeats in 2011, 2012 at Nanning in Guangxi Province, and two repeats in 2014 at Xinxiang in Henan Province. At those locations, SCR scores were evaluated during the third or fourth week after pollination under natural conditions by using a 1–9 scale. The best linear unbiased predictor (BLUP) values for each line were calculated, and the BLUP value was applied to subsequent analyses. Detailed information on phenotyping data collection for resistance to SCR has been described previously (Chen et al. 2022). GWAS was conducted in TASSEL v. 5.0 software with a mixed linear model considering population structure (Q) and kinship matrix (K) (Yu et al. 2006). The effects among haplotypes were assessed for statistical significance using Student's t‐test.

4.2. Transcriptome Data of the Association Mapping Panel

To examine natural variation in gene expression among diverse maize germplasms, we additionally incorporated public leaf transcriptome datasets from the maize association panel (Sun et al. 2023). The RNA‐seq data for these inbred lines were obtained from the Genome Sequence Archive (GSA) under accession number CRA003871 (https://ngdc.cncb.ac.cn/gsa/browse/CRA003871).

4.3. Gene Expression Analysis

Gene expression of ZmJAZ12 was analysed based on FPKM values from the qTeller database (https://qteller.maizegdb.org/), examining its distribution across various maize tissues (e.g., leaves, nodes, anthers, roots and seeds) and developmental stages (e.g., 18 and 30 days post‐pollination, 7 days after germination) (Stelpflug et al. 2016).

4.4. Construction and Functional Validation of Transgenic Lines

The CDS sequence of ZmJAZ12 was downloaded from the MaizeGDB database (https://www.maizegdb.org) and optimized for codons and synthesized by GeneScript Biotechnology. A pXG071‐ZmJAZ12 recombinant vector was constructed and introduced into the Hi‐II maize line via Agrobacterium tumefaciens‐mediated transformation. The transgenic lines were backcrossed into the 4CV inbred line, resulting in eight independent OE lines (OE‐1 to OE‐8). Using the E‐CRISPR website (http://cbi.hzau.edu.cn/cgi‐bin/CRISPR), two sgRNAs targeting ZmJAZ12 were designed and gene editing vectors were constructed, with the KN5585 line as the recipient. Two distinct KO mutants were obtained. After four or more generations of backcrossing and self‐pollination, stable transgenic and gene‐edited lines, free of Cas9 protein, were confirmed using specific primers (Table S13). All transformations were performed by Wimi Biotechnology's gene transformation platform.

The transgenic plants (OE lines, KO lines and their corresponding wild‐type controls) were grown under natural field conditions at two locations in Hainan Province (Ledong and Yacheng) and one location in Henan Province (Yongcheng) for P. polysora susceptibility testing. To ensure statistical rigour for evaluating quantitative disease traits, the field trials were laid out in a randomized complete block design (RCBD) with three biological replicates. Each plot consisted of a single 3‐m‐long row with a row spacing of 0.67 m, corresponding to a standard planting density of approximately 45,000 plants/ha. Disease severity was evaluated at 2–3 weeks after pollen shed, when natural infection pressure reached its peak. SCR scores were assessed strictly following the previously established 1–9 disease rating scale (Chen et al. 2022), where ‘1’ represented no disease symptoms or only a HR, and ‘9’ represented complete leaf infection and death. Data distribution and variability were presented with sample points and error bars. Statistical significance between transgenic and control groups was determined using a two‐tailed Student's t‐test (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

4.5. Validation of RNA‐Seq Candidate Gene Expression by RT‐qPCR

ZmJAZ12 OE lines were grown in a growth chamber under a 14 h/10 h light/dark cycle at 25°C, 60% humidity, and a light intensity of 300 μmol m−2 s−1. When the plants reached the three‐leaf stage, PCR‐based screening was performed using specific primers to identify transgenic plants. Leaves and roots from the transgenic plants were collected, with three biological replicates. Total RNA was extracted, reverse transcribed into cDNA, and the expression of ZmJAZ12 was quantified by qPCR. The list of primers used for RT‐qPCR is shown in Table S4. Each measurement was repeated at least three times with consistent results.

4.6. Transcriptome Analysis

Uninfected seedlings of three independent ZmJAZ12 OE lines (OE‐1, OE‐2 and OE‐3) and their corresponding controls were grown on standard medium until the three‐leaf stage, at which point leaf samples were collected and pooled. RNA‐seq was performed by Beijing Biomarker Technologies Co. Ltd., with differential gene expression screening and functional enrichment analysis (GO term enrichment) conducted using their bioinformatics platform.

Library construction followed the Illumina RNA‐seq protocol, and sequencing was carried out on the HiSeq 6000 platform. Raw data were filtered to obtain clean reads. Gene expression was normalized using FPKM, and DESeq2 was used to identify DEGs with |log2FC| > 1 and FDR < 0.05. DEGs were subjected to GO classification analysis.

DEGs were obtained from RNA‐seq analysis with FPKM expression values and functional annotations. To identify genes associated with JA and SA signalling pathways, annotation information including GO, KEGG, KEGG pathway, Swiss‐Prot and NR was merged. Genes were then screened using keyword matching in R (v. 4.3.1) with predefined lists of JA‐related keywords (jasmonic acid, JAZ, MYC2, COI1, LOX, lipoxygenase, OPR and jasmonate) and SA‐related keywords (salicylic acid, NPR1, PR1, systemic acquired resistance, ICS1, EDS1 and salicylate). Genes annotated with both JA and SA keywords were identified as intersection genes. All data processing and filtering were performed in R.

Mu et al. (2022) used transcriptome analysis to explore the potential mechanisms of SCR resistance differences between the resistant line L119A and the susceptible line Lx9801. We identified 10 disease‐resistance‐related genes overlapping with our RNA‐seq data (including ZmJAZ12) and analysed their expression changes before and after P. polysora inoculation in L119A.

Additionally, transcriptome data from the NCBI public database (PRJNA983086; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA983086/) on SCR score were analysed. We extracted and analysed expression data of resistant (Qi319) and susceptible (8112) maize lines before and after P. polysora inoculation (Wang et al. 2025). Ten disease resistance‐related genes (zim26, zim16, ZmJAZ12, AOS3, AOS4, zim28, ZmMYC5, PRP7, PRP14 and PRP10) were visualized in histograms to show gene expression changes in Qi319 and 8112, highlighting differences between resistant and susceptible lines.

4.7. Protein Interaction Assays

The coding sequences (CDS) of ZmJAZ12 and five ZmMYC genes were amplified from the maize inbred line B73 and cloned into different expression vectors for different analyses: pEG202/LexA and pJG4‐5/B42‐AD for Y2H, p1300‐nLUC and p1300‐cLUC for LCI and pBinGFP2 and pCAMBIA35S‐4XMyc‐CMV‐3XFLAG for Co‐IP. Y2H analysis was performed according to the protocols of Wu et al. (2001) and Braybrooke et al. (2003). Interactions were detected by culturing co‐transformed yeast strains in SD/−His/−Trp/−Ura medium supplemented with X‐α‐Gal. In LCI analysis, protein interactions were detected by co‐expressing ZmJAZ12 and ZmMYC5 in Nicotiana benthamiana leaves, with luminescence used as an indicator of interaction. Co‐IP analysis was performed in a N. benthamiana transient expression system, co‐expressing ZmJAZ12‐GFP and ZmMYC5‐FLAG, along with corresponding controls (GFP vector + ZmMYC5‐FLAG). Protein complexes were immunoprecipitated using an anti‐FLAG antibody and detected by immunoblotting using an anti‐MYC antibody.

Author Contributions

Jiarui Han: visualization, writing – original draft, investigation, data curation, validation. Qi Wang: conceptualization, writing – original draft, methodology, data curation. Xiaopeng Sun: visualization, conceptualization, validation. Zhijia Yu: investigation, data curation, validation. Bingyang Li: investigation, writing – review and editing. Ziyu Wang: investigation, writing – review and editing. Chuang Zhang: writing – review and editing, funding acquisition. Weilin Wu: investigation, writing – review and editing. Guan‐Feng Wang: validation, formal analysis, writing – review and editing. Gengshen Chen: methodology, investigation, writing – review and editing, conceptualization, supervision. Xiangguo Liu: methodology, conceptualization, formal analysis, supervision, resources, project administration, writing – review and editing.

Funding

This research was supported by National Natural Science Foundation of China (No. 32171928, U23A20186); Jilin Science and Technology Development Program (20240602032RC); Jilin Agricultural Science and Technology Innovation Project (CXGC2024ZD001); Jilin Province Development and Reform Commission‐Project for Improving the Independent Innovation Capacity of Major Grain Crops (2024C002); Jilin Agricultural Science and Technology Innovation Project (CXGC2023RCG006).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Regional correlation analysis of ZmJAZ12.

MPP-27-e70349-s010.docx (256.8KB, docx)

Figure S2: Expression patterns of three JAZ family genes located near the SNP_16783498 association peak during Puccinia polysora infection.

MPP-27-e70349-s007.docx (164.5KB, docx)

Figure S3: Haplotype and ZmJAZ12 expression levels in TST subgroups.

MPP-27-e70349-s012.docx (299.4KB, docx)

Figure S4: Spatiotemporal expression patterns of ZmJAZ12 in maize.

MPP-27-e70349-s005.docx (284.7KB, docx)

Figure S5: Disease severity of ZmJAZ12 overexpression events evaluated across different field trials.

MPP-27-e70349-s009.docx (90.4KB, docx)

Figure S6: Histogram of differential gene count in ZmJAZ12 OE RNA‐Seq.

MPP-27-e70349-s008.docx (80.4KB, docx)

Figure S7: Visualization of overall differential gene expression.

MPP-27-e70349-s013.docx (234.3KB, docx)

Figure S8: Global expression profiling of jasmonic acid (JA) pathway genes in ZmJAZ12 overexpression lines.

MPP-27-e70349-s003.docx (1.6MB, docx)

Figure S9: Global expression profiling of salicylic acid (SA) pathway genes in ZmJAZ12 overexpression lines.

MPP-27-e70349-s002.docx (1.6MB, docx)

Table S1: Genome‐wide association study (GWAS) location information.

MPP-27-e70349-s004.xlsx (10.3KB, xlsx)

Table S2: Jasmonic acid (JA)/salicylic acid (SA) pathway‐related differentially expressed genes.

MPP-27-e70349-s006.xlsx (18.3KB, xlsx)

Table S3: Disease resistance‐related differential genes.

MPP-27-e70349-s011.xlsx (38.8KB, xlsx)

Table S4: List of primers and corresponding sequences.

MPP-27-e70349-s001.docx (15.9KB, docx)

Contributor Information

Gengshen Chen, Email: gengshencgs@126.com.

Xiangguo Liu, Email: lxgyyj@jaas.com.cn.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. Raw RNA‐seq data used in this study have been deposited at the NCBI SRA database under accession numbers PRJNA1190191 and PRJNA983086 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1190191; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA983086/).

References

  1. Ali, S. , Tyagi A., and Mir Z. A.. 2024. “Plant Immunity: At the Crossroads of Pathogen Perception and Defense Response.” Plants 13, no. 11: 1434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. An, N. , Huang X., Yang Z., et al. 2024. “Cryo‐EM Structure and Molecular Mechanism of the Jasmonic Acid Transporter ABCG16.” Nature Plants 10, no. 12: 2052–2061. [DOI] [PubMed] [Google Scholar]
  3. Aryal, B. , Xia J., Hu Z., et al. 2023. “An LRR Receptor Kinase Controls ABC Transporter Substrate Preferences During Plant Growth‐Defense Decisions.” Current Biology 33, no. 10: 2008–2023. [DOI] [PubMed] [Google Scholar]
  4. Braybrooke, J. P. , Li J. L., Wu L., Caple F., Benson F. E., and Hickson I. D.. 2003. “Functional Interaction Between the Bloom's Syndrome Helicase and the RAD51 Paralog, RAD51L3 (RAD51D).” Journal of Biological Chemistry 278, no. 48: 48357–48366. [DOI] [PubMed] [Google Scholar]
  5. Brewbaker, J. L. , Kim S. K., So Y. S., et al. 2011. “General Resistance in Maize to Southern Rust (Puccinia polysora Underw.).” Crop Science 51: 1393–1409. [Google Scholar]
  6. Campos, M. L. , Kang J. H., and Howe G. A.. 2014. “Jasmonate‐Triggered Plant Immunity.” Journal of Chemical Ecology 40, no. 7: 657–675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chang, J. , Wei S., Liu Y., Zhao Z., and Shi J.. 2025. “Harnessing Genetic Resistance in Maize and Integrated Rust Management Strategies to Combat Southern Corn Rust.” Journal of Fungi 11, no. 1: 41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cheaib, A. , and Killiny N.. 2025. “Photosynthesis Responses to the Infection With Plant Pathogens.” Molecular Plant–Microbe Interactions 38, no. 1: 9–29. [DOI] [PubMed] [Google Scholar]
  9. Chen, G. , Zhang B., Ding J. Q., et al. 2022. “Cloning Southern Corn Rust Resistant Gene RppK and Its Cognate Gene AvrRppK From Puccinia polysora .” Nature Communications 13: 4392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chen, L. , Zhang L., Xiang S., Chen Y., Zhang H., and Yu D.. 2021. “The Transcription Factor WRKY75 Positively Regulates Jasmonate‐Mediated Plant Defense to Necrotrophic Fungal Pathogens.” Journal of Experimental Botany 72: 1473–1489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chini, A. , Fonseca S. G. D. C., Fernandez G., et al. 2007. “The JAZ Family of Repressors Is the Missing Link in Jasmonate Signalling.” Nature 448, no. 7154: 666–671. [DOI] [PubMed] [Google Scholar]
  12. Christensen, S. A. , Huffaker A., Kaplan F., et al. 2015. “Maize Death Acids, 9‐Lipoxygenase–Derived Cyclopente (a) Nones, Display Activity as Cytotoxic Phytoalexins and Transcriptional Mediators.” Proceedings of the National Academy of Sciences of the United States of America 112: 11407–11412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. De Geyter, N. , Gholami A., Goormachtig S., et al. 2012. “Transcriptional Machineries in Jasmonate‐Elicited Plant Secondary Metabolism.” Trends in Plant Science 17, no. 6: 349–359. [DOI] [PubMed] [Google Scholar]
  14. Debnath, S. , Chhetri S., and Biswas S.. 2019. “Southern Rust Disease of Corn – A Review.” International Journal of Current Microbiology and Applied Sciences 8: 855–862. [Google Scholar]
  15. Deng, C. , Leonard A., Cahill J., et al. 2022. “The RPPC–AvrRppC NLR‐Effector Interaction Mediates the Resistance to Southern Corn Rust in Maize.” Molecular Plant 15: 904–912. [DOI] [PubMed] [Google Scholar]
  16. Dolezal, W. , Tiwari K., Kemerait R., Kichler J., Sapp P., and Pataky J.. 2009. “An Unusual Occurrence of Southern Rust, Caused by Rpp9‐Virulent Puccinia polysora, on Corn in Southwestern Georgia.” Plant Disease 93: 676. [DOI] [PubMed] [Google Scholar]
  17. Eulgem, T. , and Somssich I. E.. 2007. “Networks of WRKY Transcription Factors in Defense Signalling.” Current Opinion in Plant Biology 10, no. 4: 366–371. [DOI] [PubMed] [Google Scholar]
  18. Fang, X. , Chai W., Li S., et al. 2021. “HSP17.4 Mediates Salicylic Acid and Jasmonic Acid Pathways in the Regulation of Resistance to Colletotrichum gloeosporioides in Strawberry.” Molecular Plant Pathology 22: 817–828. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Feng, X. , Zhang L., Wei X., Zhou Y., Dai Y., and Zhu Z.. 2020. “OsJAZ13 Negatively Regulates Jasmonate Signalling and Activates Hypersensitive Cell Death Response in Rice.” International Journal of Molecular Sciences 21: 4379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Fu, J. , Cheng Y., Linghu J., et al. 2013. “RNA Sequencing Reveals the Complex Regulatory Network in the Maize Kernel.” Nature Communications 4: 2832. [DOI] [PubMed] [Google Scholar]
  21. Fu, J. , Liu L., Liu Q., et al. 2020. “ZmMYC2 Exhibits Diverse Functions and Enhances JA Signalling in Transgenic Arabidopsis .” Plant Cell Reports 39: 273–288. [DOI] [PubMed] [Google Scholar]
  22. Garnica, D. P. , Nemri A., Upadhyaya N. M., Rathjen J. P., and Dodds P. N.. 2014. “The Ins and Outs of Rust Haustoria.” PLoS Pathogens 10, no. 9: e1004329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gimenez‐Ibanez, S. , Boter M., Fernández‐Barbero G., Chini A., Rathjen J. P., and Solano R.. 2014. “The Bacterial Effector HopX1 Targets JAZ Transcriptional Repressors to Activate Jasmonate Signalling and Promote Infection in Arabidopsis .” PLoS Biology 12, no. 2: e1001792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Glazebrook, J. 2005. “Contrasting Mechanisms of Defense Against Biotrophic and Necrotrophic Pathogens.” Annual Review of Phytopathology 43: 205–227. [DOI] [PubMed] [Google Scholar]
  25. Guerreiro, A. , Figueiredo J., Sousa Silva M., and Figueiredo A.. 2016. “Linking Jasmonic Acid to Grapevine Resistance Against the Biotrophic Oomycete Plasmopara viticola .” Frontiers in Plant Science 7: 565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Guo, Q. , Yoshida Y., Major I. T., et al. 2018. “JAZ Repressors of Metabolic Defense Promote Growth and Reproductive Fitness in Arabidopsis .” Proceedings of the National Academy of Sciences of the United States of America 115, no. 45: E10768–E10777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Guo, S. , Zhang Y., Li M., et al. 2022. “TaBln1, a Member of the Blufensin Family, Negatively Regulates Wheat Resistance to Stripe Rust by Reducing Ca2+ Influx.” Plant Physiology 189: 1380–1396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Halvorson, J. , Kim Y., Gill U., and Friskop A.. 2021. “First Report of the Southern Corn Rust Pathogen Puccinia polysora on Zea mays in North Dakota.” Canadian Journal of Plant Pathology 43: S352–S357. [Google Scholar]
  29. Hou, S. , Liu Z., Shen H., and Wu D.. 2019. “Damage‐Associated Molecular Pattern‐Triggered Immunity in Plants.” Frontiers in Plant Science 10: 646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hou, S. , and Tsuda K.. 2022. “Salicylic Acid and Jasmonic Acid Crosstalk in Plant Immunity.” Essays in Biochemistry 66: 647–656. [DOI] [PubMed] [Google Scholar]
  31. Huot, B. , Yao J., Montgomery B. L., and He S. Y.. 2014. “Growth–Defense Tradeoffs in Plants: A Balancing Act to Optimize Fitness.” Molecular Plant 7, no. 8: 1267–1287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jia, X. , Wang L., Zhao H., et al. 2023. “The Origin and Evolution of Salicylic Acid Signalling and Biosynthesis in Plants.” Molecular Plant 16: 245–259. [DOI] [PubMed] [Google Scholar]
  33. Jiang, S. , Yao J., Ma K. W., et al. 2013. “Bacterial Effector Activates Jasmonate Signalling by Directly Targeting JAZ Transcriptional Repressors.” PLoS Pathogens 9, no. 10: e1003715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Jiao, L. , Bian L., Luo Z., et al. 2022. “Enhanced Volatile Emissions and Anti‐Herbivore Functions Mediated by the Synergism Between Jasmonic Acid and Salicylic Acid Pathways in Tea Plants.” Horticulture Research 9: uhac144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Johnson, L. Y. D. , Major I. T., Chen Y., et al. 2023. “Diversification of JAZ‐MYC Signalling Function in Immune Metabolism.” New Phytologist 239: 2277–2291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kazan, K. , and Manners J. M.. 2008. “Jasmonate Signalling: Toward an Integrated View.” Plant Physiology 146, no. 4: 1459–1468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Koseoglou, E. , van der Wolf J. M., Visser R. G. F., and Bai Y.. 2022. “Susceptibility Reversed: Modified Plant Susceptibility Genes for Resistance to Bacteria.” Trends in Plant Science 27: 69–79. [DOI] [PubMed] [Google Scholar]
  38. Kumar, P. , Pandey S., and Pati P. K.. 2025. “Interaction Between Pathogenesis‐Related (PR) Proteins and Phytohormone Signalling Pathways in Conferring Disease Tolerance in Plants.” Physiologia Plantarum 177: e70174. [DOI] [PubMed] [Google Scholar]
  39. Li, J. , Cheng D., Guo S., et al. 2023. “Genome‐Wide Association and Genomic Prediction for Resistance to Southern Corn Rust in DH and Testcross Populations.” Frontiers in Plant Science 14: 1109116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Li, Y. J. , Gu J. M., Ma S., et al. 2023. “Genome Editing of the Susceptibility Gene ZmNANMT Confers Multiple Disease Resistance Without Agronomic Penalty in Maize.” Plant Biotechnology Journal 21: 1525–1527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Li, Z. , Huang Y., Shen Z., et al. 2024. “Advances in Functional Studies of Plant MYC Transcription Factors.” Theoretical and Applied Genetics 137: 195. [DOI] [PubMed] [Google Scholar]
  42. Liu, C. , Li X., Meng D., et al. 2017. “A 4‐bp Insertion at ZmPLA1 Encoding a Putative Phospholipase A Generates Haploid Induction in Maize.” Molecular Plant 10: 520–522. [DOI] [PubMed] [Google Scholar]
  43. Liu, J. , Wang K., Wang N., et al. 2025. “Comparative Transcriptome Profiling and Co‐Expression Network Analysis Reveals Important Genes Regulating Maize Response to Southern Corn Rust.” BMC Plant Biology 25: 896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Liu, L. , Sonbol F. M., Huot B., et al. 2016. “Salicylic Acid Receptors Activate Jasmonic Acid Signalling Through a Non‐Canonical Pathway to Promote Effector‐Triggered Immunity.” Nature Communications 7: 13099. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Luo, L. , Wang Y., Qiu L., et al. 2023. “MYC2: A Master Switch for Plant Physiological Processes and Specialized Metabolite Synthesis.” International Journal of Molecular Sciences 24: 3511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lv, M. , Deng C., Li X., et al. 2021. “Identification and Fine‐Mapping of RppCML496, a Major QTL for Resistance to Puccinia polysora in Maize.” Plant Genome 14: e20062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Major, I. T. , Yoshida Y., Campos M. L., et al. 2017. “Regulation of Growth–Defense Balance by the JASMONATE ZIM‐DOMAIN (JAZ)–MYC Transcriptional Module.” New Phytologist 215: 1533–1547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mu, T. , Luo S., Li L., et al. 2025. “A Review of the Interaction Mechanisms Between Jasmonic Acid (JA) and Various Plant Hormones, as Well as the Core Regulatory Role of MYC2.” Plant Science 353: 112407. [DOI] [PubMed] [Google Scholar]
  49. Mu, X. , Dai Z., Guo Z., et al. 2022. “Systematic Dissection of Disease Resistance to Southern Corn Rust by Bulked‐Segregant and Transcriptome Analysis.” Crop Journal 10: 426–435. [Google Scholar]
  50. Nomoto, M. , Skelly M. J., Itaya T., et al. 2021. “Suppression of MYC Transcription Activators by the Immune Cofactor NPR1 Fine‐Tunes Plant Immune Responses.” Cell Reports 37: 110000. [DOI] [PubMed] [Google Scholar]
  51. Pavgi, M. S. 1972. “Morphology and Taxonomy of the Puccinia Species on Corn and Sorghum.” Mycopathologia et Mycologia Applicata 47: 207–220. [Google Scholar]
  52. Roberts, W. K. , and Selitrennikoff C. P.. 1990. “Zeamatin, an Antifungal Protein From Maize With Membrane‐Permeabilizing Activity.” Microbiology 136, no. 9: 1771–1778. [Google Scholar]
  53. Sheard, L. B. , Tan X., Mao H., et al. 2010. “Jasmonate Perception by Inositol‐Phosphate‐Potentiated COI1–JAZ Co‐Receptor.” Nature 468, no. 7322: 400–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Shimizu, K. , Suzuki H., Uemura T., et al. 2022. “Immune Gene Activation by NPR and TGA Transcriptional Regulators in the Model Monocot Brachypodium distachyon .” Plant Journal 110: 470–481. [DOI] [PubMed] [Google Scholar]
  55. Song, C. , Cao Y., Dai J., et al. 2022. “The Multifaceted Roles of MYC2 in Plants: Toward Transcriptional Reprogramming and Stress Tolerance by Jasmonate Signalling.” Frontiers in Plant Science 13: 868874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Stelpflug, S. C. , Sekhon R. S., Vaillancourt B., et al. 2016. “An Expanded Maize Gene Expression Atlas Based on RNA Sequencing and Its Use to Explore Root Development.” Plant Genome 9: 1–12. [DOI] [PubMed] [Google Scholar]
  57. Storey, H. H. , and Ryland A. K.. 1954. “Resistance to the Maize Rust, Puccinia polysora .” Nature 173: 778–779. [Google Scholar]
  58. Sun, J. , Xu Q., Lv M., et al. 2026. “ZmWAKL21 Is a Maize Wall‐Associated Kinase‐Like Protein That Interacts With AvrRppC and Contributes to Southern Corn Rust Resistance.” Cellular & Molecular Biology Letters 31: 104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sun, X. , Xiang Y., Dou N., et al. 2023. “The Role of Transposon Inverted Repeats in Balancing Drought Tolerance and Yield‐Related Traits in Maize.” Nature Biotechnology 41: 120–127. [DOI] [PubMed] [Google Scholar]
  60. Tamaoki, D. , Seo S., Yamada S., et al. 2013. “Jasmonic Acid and Salicylic Acid Activate a Common Defense System in Rice.” Plant Signaling & Behavior 8: e24260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Tang, C. X. , Ma S. J., Ge Q. D., et al. 2026. “The F‐Box Protein ZmFBK1 Facilitates Autophagy‐Mediated Degradation of Maize NLR Protein Rp1‐D21 to Suppress the Hypersensitive Response.” Molecular Plant 19: 784–803. [DOI] [PubMed] [Google Scholar]
  62. Tao, N. , Liu Y., Shi Q., et al. 2024. “ABC Transporter SlABCG23 Regulates Chilling Resistance of Tomato Fruit by Affecting JA Signalling Pathway.” Postharvest Biology and Technology 208: 112662. [Google Scholar]
  63. Thines, B. , Katsir L., Melotto M., et al. 2007. “JAZ Repressor Proteins Are Targets of the SCFCOI1 Complex During Jasmonate Signalling.” Nature 448, no. 7154: 661–665. [DOI] [PubMed] [Google Scholar]
  64. Tsuda, K. , and Somssich I. E.. 2015. “Transcriptional Networks in Plant Immunity.” New Phytologist 206, no. 3: 932–947. [DOI] [PubMed] [Google Scholar]
  65. Ullstrup, A. J. 1965. “Inheritance and Linkage of a Gene Determining Resistance in Maize to an American Race of Puccinia polysora .” Phytopathology 55: 425–428. [Google Scholar]
  66. Waadt, R. , Seller C. A., Hsu P. K., Takahashi Y., Munemasa S., and Schroeder J. I.. 2022. “Plant Hormone Regulation of Abiotic Stress Responses.” Nature Reviews Molecular Cell Biology 23: 680–694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wang, S. , Chen Z., Tian L., et al. 2019. “Comparative Proteomics Combined With Analyses of Transgenic Plants Reveal ZmREM1.3 Mediates Maize Resistance to Southern Corn Rust.” Plant Biotechnology Journal 17: 2153–2168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Wang, S. , Zhang R. Y., Shi Z., et al. 2020. “Identification and Fine Mapping of RppM, a Southern Corn Rust Resistance Gene in Maize.” Frontiers in Plant Science 11: 1057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Wang, S. , Zhang R. Y., Wang R. H., et al. 2024. “Research Progress of Southern Corn Rust and Resistance Breeding.” Scientia Agricultura Sinica 57: 2732–2743. [Google Scholar]
  70. Wang, Y. , Mu C., Li X., et al. 2025. “A Genome‐Wide Association Study and Transcriptome Analysis Reveal the Genetic Basis for the Southern Corn Rust Resistance in Maize.” Journal of Integrative Agriculture 24: 453–466. [Google Scholar]
  71. Wasternack, C. , and Song S.. 2017. “Jasmonates: Biosynthesis, Metabolism, and Signalling by Proteins Activating and Repressing Transcription.” Journal of Experimental Botany 68, no. 6: 1303–1321. [DOI] [PubMed] [Google Scholar]
  72. Wu, L. , Davies S. L., Levitt N. C., and Hickson I. D.. 2001. “Potential Role for the BLM Helicase in Recombinational Repair via a Conserved Interaction With RAD51.” Journal of Biological Chemistry 276: 19375–19381. [DOI] [PubMed] [Google Scholar]
  73. Wu, X. , Li N., Zhao P., He Y., and Wang S.. 2015. “Geographic and Genetic Identification of RppS, a Novel Locus Conferring Broad Resistance to Southern Corn Rust Disease in China.” Euphytica 205: 17–23. [Google Scholar]
  74. Yamada, S. , Kano A., Tamaoki D., et al. 2012. “Involvement of OsJAZ8 in Jasmonate‐Induced Resistance to Bacterial Blight in Rice.” Plant and Cell Physiology 53: 2060–2072. [DOI] [PubMed] [Google Scholar]
  75. Yang, N. , Lu Y., Yang X., et al. 2014. “Genome‐Wide Association Studies Using a New Nonparametric Model Reveal the Genetic Architecture of 17 Agronomic Traits in an Enlarged Maize Association Panel.” PLoS Genetics 10: e1004573. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Yang, X. , Gao S., Xu S., et al. 2011. “Characterization of a Global Germplasm Collection and Its Potential Utilization for Analysis of Complex Quantitative Traits in Maize.” Molecular Breeding 28: 511–526. [Google Scholar]
  77. Yu, J. , Pressoir G., Briggs W. H., et al. 2006. “A Unified Mixed‐Model Method for Association Mapping That Accounts for Multiple Levels of Relatedness.” Nature Genetics 38: 203–208. [DOI] [PubMed] [Google Scholar]
  78. Zhao, J. , Deng X., Qian J., et al. 2023. “ Arabidopsis ABCG14 Forms a Homodimeric Transporter for Multiple Cytokinins and Mediates Long‐Distance Transport of Isopentenyladenine‐Type Cytokinins.” Plant Communications 4: 100468. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Zhao, X. , He Y., Liu Y., Wang Z., and Zhao J.. 2024. “JAZ Proteins: Key Regulators of Plant Growth and Stress Response.” Crop Journal 12: 1505–1516. [Google Scholar]
  80. Zheng, X. Y. , Spivey N. W., Zeng W., et al. 2012. “Coronatine Promotes Pseudomonas syringae Virulence in Plants by Activating a Signalling Cascade That Inhibits Salicylic Acid Accumulation.” Cell Host & Microbe 11, no. 6: 587–596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Zhou, Y. , Wang Y., Zhang D., and Liang J.. 2024. “Endomembrane‐Biased Dimerization of ABCG16 and ABCG25 Transporters Determines Their Substrate Selectivity in ABA‐Regulated Plant Growth and Stress Responses.” Molecular Plant 17: 478–495. [DOI] [PubMed] [Google Scholar]
  82. Zhu, J. , Chen H., Liu L., et al. 2024. “JA‐Mediated MYC2/LOX/AOS Feedback Loop Regulates Osmotic Stress Response in Tea Plant.” Horticultural Plant Journal 10: 931–946. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Regional correlation analysis of ZmJAZ12.

MPP-27-e70349-s010.docx (256.8KB, docx)

Figure S2: Expression patterns of three JAZ family genes located near the SNP_16783498 association peak during Puccinia polysora infection.

MPP-27-e70349-s007.docx (164.5KB, docx)

Figure S3: Haplotype and ZmJAZ12 expression levels in TST subgroups.

MPP-27-e70349-s012.docx (299.4KB, docx)

Figure S4: Spatiotemporal expression patterns of ZmJAZ12 in maize.

MPP-27-e70349-s005.docx (284.7KB, docx)

Figure S5: Disease severity of ZmJAZ12 overexpression events evaluated across different field trials.

MPP-27-e70349-s009.docx (90.4KB, docx)

Figure S6: Histogram of differential gene count in ZmJAZ12 OE RNA‐Seq.

MPP-27-e70349-s008.docx (80.4KB, docx)

Figure S7: Visualization of overall differential gene expression.

MPP-27-e70349-s013.docx (234.3KB, docx)

Figure S8: Global expression profiling of jasmonic acid (JA) pathway genes in ZmJAZ12 overexpression lines.

MPP-27-e70349-s003.docx (1.6MB, docx)

Figure S9: Global expression profiling of salicylic acid (SA) pathway genes in ZmJAZ12 overexpression lines.

MPP-27-e70349-s002.docx (1.6MB, docx)

Table S1: Genome‐wide association study (GWAS) location information.

MPP-27-e70349-s004.xlsx (10.3KB, xlsx)

Table S2: Jasmonic acid (JA)/salicylic acid (SA) pathway‐related differentially expressed genes.

MPP-27-e70349-s006.xlsx (18.3KB, xlsx)

Table S3: Disease resistance‐related differential genes.

MPP-27-e70349-s011.xlsx (38.8KB, xlsx)

Table S4: List of primers and corresponding sequences.

MPP-27-e70349-s001.docx (15.9KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. Raw RNA‐seq data used in this study have been deposited at the NCBI SRA database under accession numbers PRJNA1190191 and PRJNA983086 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1190191; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA983086/).

Articles from Molecular Plant Pathology are provided here courtesy of Wiley

RESOURCES