Integrated GWAS and multi-omics reveal rapid JA signaling activation orchestrated by OsJAR2 to drive Southern Rice Black-Streaked Dwarf Virus resistance in rice

Integrated GWAS and multi-omics reveal rapid JA signaling activation orchestrated by OsJAR2 to drive Southern Rice Black-Streaked Dwarf Virus resistance in rice | 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 Integrated GWAS and multi-omics reveal rapid JA signaling activation orchestrated by OsJAR2 to drive Southern Rice Black-Streaked Dwarf Virus resistance in rice Shuai Nie, Haiyong Gu, Zhanbiao Li, Lian Zhou, Lixian Cui, Runfeng Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6552093/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in BMC Genomics → Version 1 posted 12 You are reading this latest preprint version Abstract Background Southern rice black-streaked dwarf virus (SRBSDV), transmitted by the white-backed planthopper (WBPH), causes severe yield losses (up to 70%) in rice across Asia. However, elite resistant germplasms and molecular defense mechanisms remain elusive, hindering breeding efforts. Effective management strategies are limited, necessitating systematic dissection of SRBSDV resistance networks. Results Screening 195 rice accessions identified the indica variety R91 as highly resistant (0% disease incidence), exhibiting dual resistance to SRBSDV and rice black-streaked dwarf virus (RBSDV). South Asian accessions displayed superior resistance compared to East Asian counterparts. Multi-omics analysis revealed rapid defense activation in R91, including a 1.88-fold increase in jasmonic acid (JA) at 5 days post-inoculation (dpi) and upregulation of > 2,000 defense genes, while susceptible lines showed JA depletion (70%) and suppressed responses. Time-ordered co-expression networks pinpointed OsJAR2 (JA-Ile synthase, LOC_Os01g12160 ) as a central hub, with 3.2-fold higher expression in R91 during early infection. GWAS identified a novel SRBSDV resistance QTL ( qSRBSDV1-1 ) co-localizing with OsJAR2 , and haplotype analysis validated OsJAR2 as the candidate causal resistance gene, providing genetic evidence for its role in SRBSDV defense. Conclusions Our study identifies R91 as a dual-resistant germplasm to SRBSDV and RBSDV, with OsJAR2-mediated JA signaling playing a pivotal role in conferring resistance. The rapid activation of JA biosynthesis and synchronized defense gene regulation establish a molecular blueprint for resistance breeding. Importantly, OsJAR2 represents a novel candidate functional resistance gene, and its H3 haplotype may serve as a robust genetic marker for accelerating the development of elite SRBSDV-resistant varieties through marker-assisted selection. By integrating germplasm characterization, mechanistic insights, and breeding applications, this work provides a foundation for sustainable rice protection against viral threats. Rice (Oryza sativa) Southern Rice Black-Streaked Dwarf Virus Comparative transcriptome Jasmonic acid OsJAR2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Southern rice black-streaked dwarf virus (SRBSDV), transmitted by the white-backed planthopper (WBPH, Sogatella furcifera ) in a persistent, circulative, and propagative manner, has emerged as a devastating pathogen, causing substantial yield losses of up to 70% in severely infected rice fields, particularly in Southern China, Southeast Asia, and even extending to regions as far north as Japan [ 1 – 4 ]. As a double-stranded RNA virus belonging to the genus Fijivirus in the family Reoviridae, SRBSDV shares similarities with rice black-streaked dwarf virus (RBSDV) [ 5 ], and was first identified in Guangdong Province, China [ 6 ]. The implications of SRBSDV are immense, not only compromising rice quality but also posing a serious threat to agricultural economies reliant on rice cultivation as a staple crop. Despite current management strategies, such as the application of pesticide and crop rotation, achieving limited success in controlling SRBSDV outbreaks, breeding for resistant rice varieties has emerged as a highly economical, environmentally sustainable, and viable long-term strategy to combat this virus. Recent studies have identified rice accessions with natural resistance to SRBSDV, underscoring the potential for developing resilient cultivars [ 7 , 8 ]. Understanding the mechanisms underlying this resistance is crucial for advancing rice breeding programs aimed at enhancing viral resistance. The advancement of multi-omics techniques has significantly broadened our understanding of the fundamental genes and molecular pathways involved in a plant's response to pathogens, as well as the development of resistance mechanisms [ 9 ]. Specifically, transcriptome analysis has been instrumental in providing invaluable insights into the comprehensive gene expression responses of rice to biotic stress, emphasizing its role in enhancing resistance. Furthermore, the adoption of Time-Ordered Gene Co-Expression Networks (TOGCNs) has emerged as a powerful tool for dissecting time-series transcriptome data, revealing dynamic alterations in gene expression and their transitions across various biological processes [ 10 ]. This innovative method transcends the limitations of traditional co-expression network analysis, enabling the exploration of temporal dynamics and the investigation of gene regulatory mechanisms under diverse developmental stages and environmental conditions. As a result, TOGCNs harbor significant potential for unraveling gene regulatory mechanisms under temporal dynamics, subsequently identifying central genes and pivotal pathways involved in combating SRBSDV and offering crucial insights into the molecular mechanisms of resistance. However, there is only a few of comprehensive studies that aim to elucidate the molecular mechanisms, and dissect the molecular network related to rice response and resistance to SRBSDV, particularly utilizing network analysis methods like TOGCNs [ 11 ]. In this study, we evaluated SRBSDV resistance in RPD2, an international rice diversity panel [ 12 ], through field trials and artificial inoculations. Our investigation identified R91, a highly resilient accession that exhibited robust resistance to SRBSDV in both evaluation methods. Notably, R91 was previously recognized for its resistance to RBSDV in our earlier work [ 7 ]. Using R91 as a model, we explored the molecular mechanisms underlying SRBSDV resistance. Temporal hormone profiling revealed stark differences between R91 and the susceptible accession S1: R91 showed rapid jasmonic acid (JA) accumulation (5 days post-inoculation, dpi), while S1 exhibited a 70% JA reduction at the same stage. To further dissect the molecular basis of resistance, we performed time-course transcriptomic analysis, which demonstrated that R91 mounted an immediate and pronounced gene activation response upon SRBSDV infection, contrasting sharply with the delayed response in S1. Through time-ordered gene co-expression network (TOGCN) analysis, we identified distinct co-expression patterns that differentiated resistant and susceptible accessions. The early activation of the JA biosynthesis pathway emerged as a critical component of the resistance mechanism, consistent with the observed JA accumulation in R91. Integrative network analysis pinpointed OsJAR2 (encoding JA-Ile synthase) as the central hub gene in both the resistance and JA synthesis networks. We then conducted Genome-wide association study (GWAS) using the DI in this study as phenotype and resequencing data from our previous study[ 13 ]. GWAS analysis identified a novel SRBSDV-resistance QTL on chromosome 1 (peak SNP: 6,495,499 bp), located within a 160 kb interval encompassing OsJAR2 , suggesting its potential role as the causal gene in this QTL. Further haplotype analysis revealed 3 distinct haplotypes of OsJAR2, defined by two nonsynonymous SNPs. Haplotype analysis confirmed that accessions carrying different haplotypes exhibited varying levels of SRBSDV resistance, highlighting OsJAR2 's genetic regulation of this trait. Our findings identified a valuable germplasm for SRBSDV resistance, which is currently scarce, offering a vital resource for breeding efforts. Crucially, we demonstrated the critical role of rapid transcriptional responses and JA signaling in SRBSDV resistance. While JA has been linked to virus resistance, the specific reliance on a rapid JA response for SRBSDV resistance is a novel insight. We further established OsJAR2 as a key functional gene, acting as a hub in the JA network and regulating SRBSDV resistance. This discovery provides a promising target for breeding SRBSDV-resistant rice, advancing our understanding of the molecular networks underlying viral resistance and paving the way for future research. Materials and Methods Plant materials We assessed the resistance to SRBSDV using 195 rice accessions originating from 34 countries and spanning 13 regions (Table S1 ). Both field trials and artificial inoculation tests were conducted to evaluate their resistance levels. We ultimately identified a highly resistant cultivar, accession number 63 (designated as R91 in this study), as well as a susceptible variety, accession number 41 (designate as S1 in this study), for further analysis. All SRBSDV-infected plant materials and subsequent cultivation took place in Nanning, Guangxi Province. The plants were grown in a greenhouse maintained at 28–30°C with a 12-hour light/dark cycle. Virus, insect and inoculation Rice plants exhibiting typical symptoms, such as dark-green and dwarf, were collected from a field in Nanning city, Guangxi Province and confirmed by RT-PCR with SRBSDV-specific primers [ 14 ]. Those testing positive for SRBSDV were then cultivated in a insect-proof greenhouse for artificial inoculation and further identification. WBPH were also collected from a rice field in Nanning city, Guangxi province and reared on healthy rice plants until new nymphs emerged. Newly hatched Nymphs were then transferred and propagated on the SRBSDV-infected rice plants for more than 14 days to be viruliferous. The proportion of viruliferous WBPH was assessed by RT-PCR with SRBSDV-specific primers. The viruliferous WBPHs were used to inoculate SRBSDV to rice seedlings at three-leaf stage. Rice seedlings were grown in a insect greenhouse til three-leaf stage, the viruliferous WBPHs were then transferred to the seedlings and kept for 48h. After inoculation, the seedlings were transplanted in a insect greenhouse for observation of the symptoms, resistant evaluation and sample collection. The leaf samples from rice plants were harvested at 5 dpi, 11 dpi and 20 dpi for subsequent transcriptome analysis. Field tests were conducted in Xingʹan county, Guilin city, Guangxi province. Xing'an County is an important migration route for the WBPHs, and the incidence of southern rice black-steaked dwarf virus was always high. All of the 195 international rice accessions were randomly planted in the paddy field, each accession has three replicates. To ensure the test effect, TN1 as the susceptible control was evenly distributed among these varieties and rice seedlings were cultivated in a field setting surrounded by a rice crop affected by SRBSDV. Evaluation of disease resistance and sample collection At least 50 seedlings of each cultivar were grown in the field for natural infection, with a row spacing of 15 cm*18 cm, each material was planted with 5 rows *10 plants with TN1 as susceptible control. Each cultivar has three replicates. Disease incidence was recorded two months post-transplanting, following standard management practices without the application of pesticides or antivirals. The identification result was effective when the incidence of the susceptible control (TN1) was more than 30%. The transcriptome experiment comprised four groups: R91+ (resistant line treated with viruliferous insects), R91 (resistant line treated with virus-free insects), S1+ (susceptible line treated with viruliferous insects), and S1 (susceptible line treated with virus-free insects). Samples were collected from each group at 5-, 11-, and 20-days post-inoculation, with three independent replicates at each time point. Leaves from the virus-infected plants were harvested for RNA extraction and subsequent transcriptome analysis. RNA sequencing Sampled leaves were immediately flash-frozen in liquid nitrogen and kept at -80°C. Using the NEBNext Poly(A) mRNA Magnetic Isolation Module, mRNA was isolated. RNA quality was assessed using the Agilent 2100 BioAnalyzer. In total, 36 sequencing libraries were constructed using the NEBNext Ultra RNA Library Prep Kit for Illumina. 150 bp paired-end PCR-free libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for sequencing with an Illumina HiSeq X Ten platform. A total of 1,439.31 million clean reads were obtained from a total of 36 samples corresponding to 215.9 G paired bases (Table S2). RNA-seq analysis Short reads were processed with fastp to remove adapter sequences, leading and trailing bases with a quality score below 20, and reads with an average per-base-quality of 20 over a 4-bp sliding window [ 15 ]. The Nipponbare genome (MSU v7.0) [ 16 ] was used as a reference for reads mapping with HiSat2 (Kim, Paggi, Park, Bennett, & Salzberg, 2019). Only uniquely mapped paired-end reads were retained for read counting of the genes by featureCounts to generate the count and Transcripts per Kilobase Million (TPM) tables [ 17 ]. A comprehensive analysis was conducted on a set of 36 samples, yielding a total of 1,439.31 million high-quality clean reads, corresponding to 215.9 G paired bases (Table S2). On average, 83.21% of these paired-end reads were uniquely mapped to the reference genome, ensuring a reliable dataset for subsequent analyses (Table S2). Differential gene expression analysis was performed with DEseq2 [ 18 ]. And the differentially expressed genes were identified according to the criteria of adjusted p value < 0.05 and a fold change (FC) cut-off of 2. Mfuzz was utilized to analyze time series gene co-expression clusters with core parameter “cluster_num” set to three [ 19 ]. Gene functional annotation All gene structural annotations are based on release 7 of the MSU Rice Genome Annotation Project [ 16 ]. We download the transcription factors list from PlantRegMap, obtaining a total of 2,408 TFs which were classified into 56 families [ 20 ]. GFAP was used for de novo gene functional annotation with the plant-specific database [ 21 ]. Given the importance of JA metabolism in resistance to SRBSDV, we annotated genes related to the JA metabolic pathway by gathering enzymatic family annotations from previous studies (Table S3). Reconstructing the time-ordered gene co-expression networks We firstly preprocessed the gene expression data as follows: (i) For each time point, three replicates were treated as one data point. After comparing the differences between two specific paired groups, we retained genes identified as significantly differentially expressed, resulting in a total of 12,652 significant DEGs. (ii) We calculated the mean TPM for gene expression within each time point group, retaining genes with a mean greater than 1 in any group. (iii) Based on the criteria from the previous two steps, we calculated the median absolute deviation (MAD) of the TPM values for each gene, retaining the top 5,000 genes ranked by descending MAD. The base R function “cor” was used to calculate the gene co-expression Pearson correlation coefficients (r) between pairs of genes under four different temporal treatments. TOGCN was performed for generating the suggested r cutoff with 0.88 and − 0.65 [ 10 ]. The value of r between − 1 and − 0.65 indicates a significantly negative correlation between two genes, while r between 0.88 and 1 indicates a significantly positive correlation. Each gene acts as a node, while a pair of significantly correlated co-expressed genes forms a gene pair that serves as the edge connecting these nodes. The interplay of nodes and edges together constructs a gene co-expression network (GCN). Comparing different GCNs essentially involves identifying gene pairs that are specific to each network. We constructed four independent GCNs based on four sets of time-series gene expression data (R91, R91+, S1, S1+). Significantly positively correlated gene pairs unique to the R91 + GCN were combined to create the R91 + specific GCN, while those specific to the S1 + GCN were merged to form the S1 + specific GCN. Breadth-first search algorithm [ 22 ] was used for GCN clustering with custom Python scrip. To cluster GCN, it is necessary to select appropriate genes as seeds, whose expression trends should gradually decrease from the first time point to the last time point. We identified 13 optimal genes as seeds using MFSelector (Table S4) [ 23 ]. After clustering, the R91 + and S1 + specific GCNs formed Resistant (R91+)- and Susceptible (S1+)- specific time-ordered gene co-expression network (TOGCN), respectively. Ultimately, TOGCNs were visualized in graphs using Cytoscape [ 24 ]. Gene functional enrichment Hypergeometric tests were performed to determine whether specific functional categories were significantly overrepresented in gene sets. GO functional enrichment was tested using the R package clusterProfiler [ 25 ]. Genome-wide association study Genome-wide association study (GWAS) was conducted using our previous sequencing data[ 13 ], and Gapit version 2 with a mixed linear model (MLM) [ 26 ]. Manhattan plots were generated using CMplot package in R v4.2.2 software ( https://github.com/YinLiLin/R-CMplot ). The significance threshold was set as P -value ≤ l X 10 − 5 . Results Screening of SRBSDV resistant accessions using international rice diverse panel To evaluate SRBSDV resistance and characterize its distribution among different rice varieties, we leveraged a diverse natural panel composed of 195 rice accessions originating from 34 countries worldwide and spanning 12 regions. These accessions, selectively sourced from the RPD2, underwent rigorous disease resistance assessment via both field trials and controlled artificial inoculation (Table S1 ). Considerable variation in disease incidence (DI) was observed in the panel, with field test DI ranging from 0–92.9% and artificial inoculation DI varying from 42.8–100% (Fig. 1 A and Table S1 ). Analysis of the geographic distribution of resistant accessions showed that varieties from East Asia (EAS; including China and Japan) exhibited significantly higher DI (Fig. 1 B). In contrast, accessions from three regions demonstrated significantly lower DI characteristics, including South Asia Central (SAC, including India, Bangladesh, and Nepal), West Africa (WAF; including Burkina Faso, Côte d'Ivoire, Ghana, Guinea, Liberia, Mali, Nigeria, and Senegal), and the Sea Islands (SER; comprising Indonesia, Malaysia, and the Philippines) (Fig. 1 B). Among the 50 accessions with the lowest resistance levels in artificial tests, 15 accessions originated from South Asia Central (Fig. 1 C). We identified an indica rice variety, R91, from India, which exhibited the constant resistance in both field trial and artificial inoculation (Fig. 1 D). Intriguingly, this accession has also been identified as a stable and highly resistant resource to RBSDV, a closely related virus to SRBSDV, in our previous study, from which we identified a functional resistance gene for RBSDV (Z. Wang et al. , 2022). Resistant Accession R91 Exhibits quick JAs accumulation Post-SRBSDV Inoculation To elucidate the hormonal dynamics associated with SRBSDV resistance, we quantified key phytohormones in R91 and the susceptible accession S1 following artificial inoculation with SRBSDV. Hormone levels were assessed at 5-, 11-, and 20-days post-inoculation using metabolic profiling method. In parallel, we employed rice plants infected with virus-free WBPH as a control group (mock). All major type of plant hormones, including GA, ABA, Auxin, Cytokinin, BRs, JAs and SA were quantified (Table S5). Strikingly, JA and MeJA exhibited contrasting profiles between the two accessions during the early infection stages (5 dpi). In R91, JA levels surged by 1.88-fold at 5 dpi compared to mock-treated controls (Fig. 2 B), indicating a rapid and robust activation of the JA pathway. Conversely, in S1, JA levels plummeted by 70% at the same time point (Fig. 2 B). Similarly, MeJA levels in R91 increased by 1.27-fold at 5 dpi, while S1 experienced a 40% decline (Fig. 2 C). This divergence in JA content dynamics persisted over time. By 11 dpi, JA levels in R91 decreased but remained 39% higher than in S1, demonstrating sustained JA activity in the resistant accession beyond the initial response phase. MeJA levels followed a similar trend, underscoring the pivotal role of JAs in SRBSDV resistance. R91 exhibits early and robust gene activation in response to SRBSDV To further elucidate the molecular mechanisms of SRBSDV resistance in rice, we conducted a comprehensive time-series transcriptome analysis using the same materials as in the hormone assay: the resistant accession R91 and the susceptible accession S1. Samples were collected at 5, 11, and 20 days post artificial inoculation with SRBSDV. The resistant accession R91 exhibited an early and robust gene activation response to SRBSDV. This was evident by the rapid and extensive transcriptional response observed as early as 5 days post-inoculation, with over 2,000 differentially expressed genes (DEGs) detected at both 5 dpi and 11 dpi (Fig. 3 A). In contrast, the susceptible accession S1 displayed a significantly muted response, with only a few hundred DEGs identified at 5 dpi and 20 dpi, notably only 166 DEGs at 11 dpi (Fig. 3 A). Further analysis of the DEGs revealed that by day 5, R91 exhibited 1,804 and 1,908 up-regulated genes at 5 dpi and 11 dpi, respectively, while S1 showed only 244 upregulated genes out of 636 DEGs at 5 dpi (Table S6). This disparity highlights the distinct capabilities of the two accessions in mounting a defense response, with R91 showing a more dynamic and extensive genetic reaction. Temporal Gene Co-Expression Patterns Distinguish Resistance and Susceptibility Responses to Virus Infection To delve deeper into the molecular network response of resistant and susceptible accessions to the SRBSDV, we utilized time-series transcriptome data to construct comprehensive TOGCNs. Our analysis unveiled distinct patterns within a resistant-specific network (R91+) and a susceptible-specific network (S1+) (Fig. 3 B). The resistant-specific TOGCN consisted of 44,087 edges, while the susceptible-specific TOGCN comprised 33,884 edges (Table S7). These networks highlighted co-expression interactions among 4,167 genes (including 291 transcription factors, TFs) in the resistant-specific TOGCN and 3,676 genes (including 294 TFs) in the susceptible-specific TOGCN (Table S7). The resistant-specific network was structured into nine time-ordered gene co-expression levels (L1–L9), whereas the susceptible-specific network was segmented into eleven levels (L1–L11) (Fig. 3 B). Notably, in the resistant-specific TOGCN, levels L2, L6, and L7 exhibited the highest number of edges and gene nodes, while in the susceptible-specific TOGCN, levels L4, L5, and L2 displayed the highest connectivity (Fig. 3 B and Table S7). Based on differential expression profiles, these levels were further categorized into three temporal sub-networks: the early sub-network (corresponding to 5 dpi), the transitional sub-network (11 dpi), and the terminal sub-network (20 dpi) (Fig. 3 C, D). Within the resistant-specific TOGCN, the early sub-network (encompassing levels L1–L3) was notably enriched, housing more 1,980 genes and 123 transcription factors compared to the other sub-networks (Table S7). This early sub-network also displayed a higher number of edges (23,673) and a greater average degree (47.82), indicating a robust transcriptional response at the gene level (Table S7). Conversely, the transitional sub-network of the susceptible-specific TOGCN contained more genes (1,917) and transcription factors (163) compared to the other sub-networks and also exhibited more edges (25,095) and a higher average degree (52.36) (Table S7). Intriguingly, genes that were responsive in the early sub-network of the resistant-specific TOGCN were found to be activated during the transitional stage in the susceptible-specific TOGCN, suggesting a potential overlap or shared response mechanisms between the two networks (Fig. 3 B). Transcriptome analysis revealed early rapid activation of JA biosynthesis genes underpins resistance to SRBSDV To reveal significant differences in categories and temporal patterns of genes between resistant and susceptible accessions, enriched Gene Ontology (GO) for co-expressed genes within the resistant-specific and susceptible-specific TOGCNs were analyzed (Fig. 4 ). In the resistant-specific TOGCN, key pathways were consistently enriched across all three stages. In the early sub-network, metabolic pathways related to hemicellulose, cell wall macromolecules, and polysaccharides were overrepresented (Fig. 4 ). The transitional sub-network showed enrichment in pathways associated with terpenoids, alpha- and aromatic amino acids, while the terminal sub-network exhibited enrichment in pathways linked to pigments, JA, and ABA. Conversely, the susceptible-specific TOGCN displayed most functional pathways enriched in the transitional stage sub-network, including noncoding RNA, anthocyanins, plant hormones, cell wall components, and diterpenoids, typically associated with disease susceptibility. Interestingly, pathways enriched in the early sub-network of the resistant-specific TOGCN overlapped with those enriched in the transitional sub-network of the susceptible-specific TOGCN, with a notable emphasis on JA related pathways, a critical factor in SRBSDV resistance (Fig. 4 ). The transcriptome analysis revealed rapid activation of JA signaling pathway genes, aligning with the metabolic analysis that showed significantly higher JA levels in R91 compared to S1. To further elucidate the JA pathway's role in regulating rice resistance to SRBSDV, we reviewed the existing literature and reconstructed the JA metabolic pathway in rice, in which 121 genes encoding 14 enzymes involved in JA biosynthesis were identified (Fig. 5 , Table S3 and Table S8). The gene family encoding phospholipases A (PLA) was the largest, with 31 members, while families for allene oxide cyclases (AOC) and jasmonic acid carboxyl methyltransferase (JMT) each had one member. Smaller families such as OPC-8:0 CoA ligase 1 (OPCL1) and ketoacyl-CoA-thiolases (KAT) contained only two members each, playing crucial roles in JA biosynthesis (Fig. 5 and Table S3). We then conducted a heatmap clustering analysis of the expression level of genes involve in JA biosynthesis and metabolic pathway to characterize their expression pattern in a time course in R91 and S1 (Fig. 5 , Table S8 and Table S9). The results showed that in R91 with virus inoculation (R91+), 44.63% of JA genes were significantly upregulated at the early stage, higher than the 24.79% observed in the control line R91 without virus inoculation (Table S9). Conversely, in the susceptible accession under infection (S1+), 29.75% of JA genes were significantly upregulated at the early stage, lower than the 41.32% observed in the control line S1 (Table S9). Fast-response genes, highly expressed in R91 + during the early stage and in S1 + during the late stage, were identified. Notably, no fast-response genes were found in critical families AOC, JMT, and KAT. However, larger families exhibited significant proportions of fast-response genes, with 75% of the FAD family, 71.42% of the PLD family, and 48.38% of the PLA family classified as fast responders (Table S8). Hierarchical regulation of JA biosynthesis enhances disease resistance in resistant lines To further characterize key module regulating rice SRBSDV resistance by JA, we reconstructed TOGCNs related to JA biosynthesis for both R91 and S1 after SRBSDV inoculation (Fig. 6 A, B). In the JA & resistant-specific TOGCN, both the early and terminal sub-networks exhibited a significantly higher number of genes and edges, particularly involving transcription factors (Fig. 6 A). The early sub-network contained 114 genes and 716 edges, with a notable presence of 109 TFs, indicating a robust and rapid initial response to JA biosynthesis (Fig. 6 A and Table S10). Additionally, the terminal sub-network showed substantial activity with 80 genes, 268 edges, and 65 TFs, suggesting sustained regulatory activity in the later stages of the response (Fig. 6 A and Table S10). Conversely, the JA and susceptible-specific TOGCN displayed a different pattern, with the transitional sub-network showing the highest concentration of genes and edges (Fig. 5 B and Table S10). This sub-network comprised 147 genes, 1,343 edges, and 132 TFs, suggesting a delayed but intense response during the middle stage (Fig. 5 B and Table S10). The early and terminal sub-networks in the susceptible line were relatively less active, with the early sub-network containing only 40 genes and 110 edges (including 37 TFs), and the terminal sub-network having 27 genes and 24 edges (with 20 TFs) (Fig. 5 B and Table S10). Key module of JA signal pathway and a candidate functional gene related to SRBSDV resistance Focusing on the early sub-network of the JA & resistant (R91+)-specific TOGCN, we identified a key module potentially critical for the rapid biosynthesis of defense compounds mediated by JA in R91 (Fig. 7 A). For JA biosynthesis, we predicted seven enzymatic genes that were expressed at high levels and directly regulated by 102 potential regulators, predominantly from the MYB (14 members) and ethylene-responsive element binding factors (ERFs; 12 members) families (Fig. 7 A and Table S11). Among these, one gene, JA-amino acid synthetase 2 ( OsJAR2 ; LOC_Os01g12160 ), was identified as a hub gene within this network (Fig. 7 A). OsJAR2 catalyzes the synthesis of JA-Ile, a crucial rate-limiting enzyme in the JA biosynthesis pathway (Wakuta et al. , 2011). Further examining the co-expression network inferred from TOGCN and incorporating DNA binding site predictions, we inferred that the OsJAR2 might be regulated in a hierarchical manner, with OsERF130 acting as a secondary regulator and MYB42/85 serving as direct regulators (Fig. 7 B, C). GWAS and Haplotype Analyses implied OsJAR2 as a Key Regulator of SRBSDV Resistance in Rice To confirm if OsJAR2 is genetically function in rice SRBSDV resistance, we conducted GWAS analysis using the DI as phenotype, and resequencing data of the 195 accessions. The GWAS results demonstrated 4 novel SRBSDV resistance QTL in chromosome 1, 6 and 11 (Fig. 8 , Table 1 ). Interestingly, one of these QTL ( qSRBSDV1-1 ) colocalized with OsJAR2 , with the most significant SNP 160 kb away from OsJAR2 (Fig. 8 , Table 1 ). Combining with the transcriptomic analysis, all the results that OsJAR2 is a novel functional gene regulating SRBSDV resistance in rice. Table 1 QTLs associated with SRBSDV resistance identified by GWAS QTLs Chromosome SNP location P -value qSRBSDV1-1 1 6495499 8.02 X10 − 6 qSRBSDV1-2 1 11832416 7.01 X10 − 6 qSRBSDV6 6 5946901 6.32 X10 − 6 qSRBSDV11 11 28739460 8.65 X10 − 6 We then further explore the genetic basis of OsJAR2 in conferring SRBSDV resistance by haplotype analysis. Genome-wide resequencing of the 195 accessions identified three non-synonymous single nucleotide polymorphisms (nsSNPs) in OsJAR2 , leading to the characterization of three haplotypes (H1, H2, and H3) (Fig. 9 A, Table S12). Association analysis demonstrated significant phenotypic divergence in disease incidence among the haplotypes. Accessions with the H3 haplotype exhibited the lowest disease incidence ( P -value < 0.001), while those carrying H1 showed reduced susceptibility compared to H2 carriers ( P -value < 0.01) (Fig. 9 B). This genetic stratification highlights OsJAR2 as a central regulator of JA-mediated antiviral defense and identifies H3-associated SNPs as valuable molecular markers for breeding SRBSDV-resistant rice varieties. Discussion Discovery of R91 as a Disease-Resistant Germplasm and Its Dual-Resistance Breeding Potential SRBSDV poses a devastating threat to rice production, yet the scarcity of highly resistant germplasms has severely hindered disease-resistance breeding. Through systematic screening of 195 rice accessions spanning 34 countries and 12 geographic regions [ 12 ], we identified the Indica variety R91, originating from India, which exhibited stable high resistance to SRBSDV in both field trials and artificial inoculations (Fig. 1 A, D). This remarkable resistance outperforms most reported resistant resource, positioning R91 as a globally rare SRBSDV-resistant germplasm. Notably, our previous study also identified R91 as a robust resistance germplasm against RBSDV [ 7 ]. Such dual resistance provides a foundational resource for breeding programs targeting overlapping viral epidemics, simplifying multi-resistance cultivar development while mitigating risks associated with viral evolution in the field. Further analysis of resistance distribution revealed that varieties from South Asia Central (SAC; India, Bangladesh, Nepal) exhibited significantly higher resistance, contrasting with East Asian accessions (China, Japan) that displayed higher susceptibility. This geographic pattern aligns with the migration route of the white-backed planthopper, the SRBSDV vector, across South and Southeast Asia [ 27 ], as well as historical prevalence of viral diseases in these regions [ 4 ]. Strikingly, 15 out of the top 50 most resistant accessions originated from South Asia (Fig. 1 C), suggesting long-term natural selection pressure may have enriched resistance alleles in these germplasms. For example, R91’s resistance likely evolved through co-adaptation with indigenous WBPH populations and viral strains, leading to fixation of advantageous alleles via gene-environment or gene-gene interactions. This geographic signature provides critical clues for resistance gene mining: selective sweep analysis of South Asian germplasms could pinpoint genomic regions under natural selection, accelerating functional gene discovery. The dual resistance of R91 to SRBSDV and RBSDV implies potential overlap in defense mechanisms against these phylogenetically related Fijiviruses. Our previous work identified the aspartic protease gene OsAP47 as a key player in RBSDV resistance, conferring protection via degradation of viral capsid proteins [ 7 ]. Given the structural and replicative similarities between SRBSDV and RBSDV [ 5 , 28 ], we hypothesize that OsAP47 may target conserved viral domains to achieve cross-resistance. Furthermore, our study revealed rapid activation of the JA pathway in R91 during early SRBSDV infection (Fig. 2 B, 5 A), a pathway also implicated in RBSDV defense [ 29 ]. This suggests JA signaling may serve as a shared regulatory hub for dual resistance. The dual resistance of R91 against both RBSDV and SRBSDV establishes it as an invaluable genetic asset, laying the groundwork for the development of rice varieties with improved resistance profiles. Its robust resistance traits offer a foundation upon which to dissect the complex dynamics of rice-virus interactions and to expedite the incorporation of resistance into commercially viable rice cultivars. Consequently, the use of R91 in molecular breeding has the potential to significantly fortify rice against these two viruses, providing a boon to global food security by mitigating virus-induced yield losses. Temporal Hierarchies of Transcriptional Activation and JA Signaling in Early Defense is critical for the formation of SRBSDV resistance In this study, we aimed to explore the molecular mechanisms underlying rice resistance to SRBSDV by leveraging multi-omics approaches. These advanced techniques allowed us to analyze the comprehensive omics landscape and the molecular responses associated with plant defense [ 30 ]. In the current study, we specifically examine the transcriptional dynamics of a resistant accession, R91, in comparison to a susceptible accession, S1, as well as conduct a metabolomics analysis both with and without the influence of SRBSDV infection. Our research uncovered that R91 mounts an immediate and robust defense response, as evidenced by the rapid upregulation of defense-related genes from as early as 5 dpi. Conversely, S1 manifested a lagged transcriptional response, with a considerable increase in defense gene expression becoming apparent only by 11 dpi. This marked difference underscores R91's enhanced ability to rapidly engage genetic defenses upon viral infection. These observations stress the importance of early gene activation in conferring resistance against SRBSDV in rice. Previous research has indicated that upon virus inoculation, resistant ecotype can trigger an extensive and rapid transcriptomic remodeling, including genes involved in plant-pathogen interaction, plant hormone signal transduction, the MAPK signaling pathway or ubiquitin mediated proteolysis [ 31 ]. In this study, we identified a distinctly earlier transcriptomic response in the resistant accession compared to the susceptible accession, further emphasizing the significance of early and substantial transcriptomic remodeling in establishing SRBSDV resistance in rice. Further constructed TOGCNs illuminated the distinct temporal gene expression patterns in the resistant and susceptible accessions. The TOGCN specific to R91 exhibited enhanced connectivity and gene interactions, particularly involving key transcription factors, suggesting a sustained and comprehensive transcriptional response. In contrast, S1's co-expression network showed a resurgence of gene activity primarily during the transitional phase, which signifies a postponed transcriptomic response to the virus. Delving deeper into transcriptomic data reveals that the JA biosynthesis pathway is significantly activated in R91 during the early stages of SRBSDV infection. Strikingly, 44.63% of JA-related genes were upregulated in R91, in contrast to only 29.75% in S1. This pronounced activation underscores the rapid induction of JA biosynthesis as a fundamental mechanism underlying the resistant phenotype. Further metabolomics analysis confirmed that JA were significantly higher in R91 than in S1 in early stage of virus infection. It has been reported that RBSDV infection can alter hormone levels, influencing the plant's defense activation and susceptibility manipulation [ 32 ]. In this study, for the first time, we identified different JA synthesis genes expression, as well as JA contents between resistant and suspectable rice, which be one of the core molecular mechanisms underlying SRBSDV resistance in rice. During the co-evolutionary between virus and plant, plants have developed a sophisticated and multifaceted immune system that includes a complex interplay between different phytohormones [ 33 , 34 ]. JAs are a class of hormones that originate from C18 fatty acids and play vital roles in plant defense responses to abiotic and biotic stresses, including viral attacks [ 35 , 36 ]. Our findings align with the established role of JA in plant defense against biotic stresses, underscoring its importance in viral resistance [ 37 , 38 ]. As previous studies have shown, the JA pathway plays a significant role in plant defense mechanisms, including the activation of pathogenesis-related genes and the induction of initial immune responses to viral infections [ 39 , 40 ]. For example, continuous JA treatment decreased the DNA titer of beet curly top virus, indicating that suppression of the JA response may be critical for geminivirus infection [ 41 ]. Suppression of JA signaling has also been seen in rice ragged stunt virus infection [ 42 ]. Our results showing increase JA synthesis pathway and contents further highlight the importance of JA in regulating resistance to SRBSDV in rice. Interestingly, JA also plays a crucial role as a regulator in how plants respond to insect infections [ 43 ]. Moreover, it has been reported that WBPH infestation down-regulates the expression of JA pathway genes in rice, suggesting that JA pathways can be influenced by viral infection and insect feeding [ 44 ]. However, it is noteworthy that despite the rapid and significant synthesis of JA observed in the R91, our earlier research indicated that R91 does not demonstrate resistance to insect attacks [ 7 ]. Consequently, gaining a more nuanced understanding of the relationship between the JA response and the resistance to both insect pressures and the SRBSDV in R91. This line of investigation, and more broadly within rice, warrants further exploration and attention. OsJAR2 : A Candidate Functional Gene and Molecular Hub Bridging JA Dynamics and SRBSDV Resistance Currently, genetic studies on SRBSDV resistance in rice remain limited, with no functionally validated resistance genes reported [ 45 , 46 ]. In this study, we identified four novel QTLs associated with SRBSDV resistance, one of which co-localized with OsJAR2—the key regulator of JA signaling. We propose that OsJAR2 serves as a molecular hub connecting JA dynamics and viral resistance. Haplotype analysis further supports OsJAR2 as the most probable candidate gene governing SRBSDV resistance. Together, these findings provide compelling evidence that OsJAR2 functions as a bona fide candidate resistance gene against SRBSDV. The JA signaling pathway consists of a complex network of specialized functional sectors, known as modules, which enable plants to effectively coordinate a diverse array of physiological responses to biotic and abiotic stimulus [ 47 ]. Consequently, it is vital to uncover the molecular network and modules that facilitate the coordination of resistance to SRBSDV in rice. Our TOGCN analysis unveiled OsJAR2 (encoding JA-Ile synthase) as the central hub bridging rapid JA signaling and SRBSDV resistance (Fig. 7 A–B). Genetic mapping through GWAS and haplotype analysis further suggested OsJAR2 as the functional gene governing SRBSDV resistance in rice. OsJAR2 catalyzes the biosynthesis of JA-Ile, a bioactive JA derivative essential for activating downstream defense responses [ 48 ]. Integrating DNA binding site predictions with co-expression network analyses, we propose that OsJAR2 operates within a hierarchical regulatory cascade, with MYB42/85 serving as direct regulators and OsERF130 acting as a secondary modulator. This regulatory hierarchy ensures rapid OsJAR2 induction (3.2-fold increase at 5 dpi), facilitating timely synthesis of JA-Ile to amplify antiviral defenses. In contrast, the susceptible accession S1 exhibited delayed and lower OsJAR2 activation (only 1.5-fold at 11 dpi), effectively forfeiting the critical "early-window" needed to disrupt viral replication and systemic movement—a vulnerability exploited by SRBSDV. Notably, rice harbors two JAR1-like GH3 enzymes, OsJAR1 and OsJAR2, which exhibit distinct stress-responsive expression patterns [ 49 ]. It has been reported that the activity of OsJAR1 is necessary for effective defense against the blast fungus, while another study demonstrated that OsJAR2 is rapidly induced upon invasion by RBSDV [ 29 ]. In this study, we leverage multi-omics techniques to reveal for the first time that OsJAR2 , along with downstream transcription factors, may form a molecular module involved in rice's response to SRBSDV, which is closely related to RBSDV. These findings provide critical insights into the regulatory mechanisms underlying JA-mediated antiviral responses and pave the way for dissecting the interplay between JA signaling and Fijivirus defense. In the present study, we further conducted haplotype analysis across all the 195 accessions used in resistance accession. The results revealed three OsJAR2 haplotypes defined by two nonsynonymous SNPs. Strikingly, accession harboring H3 haplotype exhibited highest SRBSDV resistance (p < 0.001; Fig. 8 B), underscoring its functional relevance. The H3 haplotype can serve as a valuable template for precision breeding strategies aimed at enhancing SRBSDV resistance. For instance, by utilizing the H3-specific SNPs as molecular markers, breeding programs can efficiently pyramid OsJAR2 with other resistance loci, accelerating the development of resilient rice varieties. Additionally, engineering susceptible accessions to incorporate R91’s MYB-responsive elements into their native OsJAR2 promoter could restore early induction kinetics, enabling timely SRBSDV defense. Conclusions In conclusion, this study identifies R91 as a novel and highly resistant germplasm against SRBSDV and RBSDV, providing dual resistance for breeding programs targeting these devastating rice viruses. The early elevation of JA levels and the rapid activation of defense genes and JA biosynthesis pathway in R91 highlight the pivotal role of timely JA-mediated responses in conferring viral resistance. Through molecular network, GWAS and haplotype analysis, OsJAR2 was identified as a potential hub gene, offering a molecular blueprint for developing rice varieties with enhanced SRBSDV resistance. These findings deepen our understanding of the molecular mechanisms underlying SRBSDV resistance and provide a foundation for innovative strategies to improve rice viral resilience, ultimately contributing to increased yield and global food security. Abbreviations AOC allene oxide cyclases; EAS East Asia; ERFs ethylene-responsive element binding factors; DEGs differentially expressed genes; DI disease incidence; Dpi days post-infection; FC fold change; GO Gene Ontology; GCN gene co-expression network; JA jasmonic acid; JMT jasmonic acid carboxyl methyltransferase; KAT ketoacyl-CoA-thiolases; MAD median absolute deviation; MeJA Methyl Jasmonate; MP metabolic pathways; OPCL1 OPC-8:0 CoA ligase 1; OPDA oxoPhytodienoicAcid; PLA phospholipases A; RBSDV rice black-streaked dwarf virus; SAC South Asia Central; SER Sea Islands; SRBSDV Southern rice black-streaked dwarf virus; TFs transcription factors; TOGCNs temporal gene co-expression networks; TPM Transcripts per Kilobase Million; WAF West Africa; WBPH white-backed planthopper. Declarations Acknowledgements Not applicable. Author contributions JLZ, JSC and BXQ conceived and designed the study. SN and HG and ZBL prepared the materials and conducted the experiments. All authors participated in sample preparation and data analysis. SN, HYG, ZBL, JLZ, JSC and BXQ wrote and edited the manuscript. All authors read and approved the final draft. Funding This study was supported by This work is funded by the Key Project of Guangdong Basic and Applied Basic Research Foundation (2020B1515420003), the National Natural Science Foundation of China (32372175, 32361143519), Project of Collaborative Innovation Center of GDAAS (XTXM202203),Guangxi Science and Technology Base and Talent Special Project (Guike AC22035090), the Seed industry revitalization project of special fund for rural revitalization strategy in Guangdong Province (2022NPY00005), Guangdong Key Laboratory of New Technology in Rice Breeding. (2023B1212060042). Data availability The raw sequencing data from this study have been submitted to NGDC BioProject database (https://ngdc.cncb.ac.cn/bioproject/) under accession number PRJCA034117. Ethics approval and consent to participate Not applicable. 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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-6552093","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453499198,"identity":"9ab07e70-72a8-40ac-975d-c05cb29569b9","order_by":0,"name":"Shuai Nie","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Nie","suffix":""},{"id":453499199,"identity":"106e7ee5-f4bb-4585-998d-63b6bbddb171","order_by":1,"name":"Haiyong Gu","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Haiyong","middleName":"","lastName":"Gu","suffix":""},{"id":453499200,"identity":"3f479860-9001-4cf0-9f0f-7f7d9812568c","order_by":2,"name":"Zhanbiao Li","email":"","orcid":"","institution":"Guangxi Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhanbiao","middleName":"","lastName":"Li","suffix":""},{"id":453499201,"identity":"cfeda2af-854c-4388-afcb-d68ca2f51384","order_by":3,"name":"Lian Zhou","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Zhou","suffix":""},{"id":453499202,"identity":"23652e7c-8992-4728-8417-b3511352cf1d","order_by":4,"name":"Lixian Cui","email":"","orcid":"","institution":"Guangxi Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lixian","middleName":"","lastName":"Cui","suffix":""},{"id":453499203,"identity":"efbb702a-0f8d-40ad-bc0c-f867aa399f37","order_by":5,"name":"Runfeng Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Runfeng","middleName":"","lastName":"Wang","suffix":""},{"id":453499204,"identity":"2c246354-2e84-42bc-8c33-d5825c0373ab","order_by":6,"name":"Qi Liu","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Liu","suffix":""},{"id":453499205,"identity":"21053fa2-3b6d-4674-869a-ccdfdbd9637d","order_by":7,"name":"Bixia Qin","email":"","orcid":"","institution":"Guangxi Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bixia","middleName":"","lastName":"Qin","suffix":""},{"id":453499206,"identity":"bd2a0adb-4c5e-4ed3-9517-854b6580e909","order_by":8,"name":"Jiansong Chen","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiansong","middleName":"","lastName":"Chen","suffix":""},{"id":453499207,"identity":"8edcf428-2e5b-45b3-adf1-92b6a34dc1a2","order_by":9,"name":"Junliang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCRDBw8DAxwxiVDAwNhCthQ2s5QzRWoCADUQwthGhRX5288MHDDKH89jYmZ89/DqvVra//fgDhp87cGthnHPM2ICB53AxGzObubHstuPGM87kGDD2nsGthVkiwUwCqCWxjZnBTFpy27HEhgM5DMxAF+IEbBLp36Ba2L9JS845ljj//PMHeLXwSOTAbOExk/zYUJO44UaCAV4tEhI5xUC/pIO0lEkzHDtgvPHGG4ODvXi0yM9I3/iAscc6sZ//+DbJHzV1svPOpz988BOPFnAQ/O2BMngYDoMZB/BrAIEfEIrxB0MdYcWjYBSMglEw4gAApkROO/8JA2kAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Junliang","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-04-29 03:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6552093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6552093/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-12159-8","type":"published","date":"2025-10-29T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82331912,"identity":"7288549f-5be6-4fd9-9930-b4f18e8beaf3","added_by":"auto","created_at":"2025-05-09 07:24:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":314442,"visible":true,"origin":"","legend":"\u003cp\u003eR91 is a stable resistant accession for SRBSDV.\u003c/p\u003e\n\u003cp\u003eA. Disease incidence (DI) of 195 rice accessions in field trial and artificial inoculation. B. The distribution of DI in artificial inoculation between rice accessions from different geographical regions. CAM, Central America and Caribbean; EAF, East Africa; EAS, East Asia; IOC, Indian Ocean; NAF, North Africa; NAM, North America; SAC, South Asia Central; SAE, South Asia East; SAM, South America; SEA, Southeast Asia; SER, SEA islands; WAF, West Africa. C. Geographic distribution and size of the top 50 accessions with the lowest DI based on artificial inoculation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/13f072e49967677e52a04541.png"},{"id":82332403,"identity":"7f8c7943-1aaf-4810-959f-e4454a30f726","added_by":"auto","created_at":"2025-05-09 07:32:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":200616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eR91 Exhibits Rapid JA Biosynthesis upon SRBSDV Inoculation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, schematic representation of the experimental setup used in this study. The figure illustrates the timeline of SRBSDV infection in resistant (R91) and susceptible (S1) rice cultivars, with sampling points indicated at days 5, 11, and 20 post infection. “R91+” and “S1+” represent the rice individuals were treated by SRBSDV-inoculated white-backed planthopper (WBPH), and “R91” and “S1” represent the rice individuals were treated by SRBSDV-free WBPH. B, C Temporal dynamics of JA and MeJA accumulation (fold change: inoculated plants compare to mock plants) in R91 and S1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/676fe4333be20d7e3490294a.png"},{"id":82332409,"identity":"55396749-47b1-416b-bc7c-a4152fbc66e5","added_by":"auto","created_at":"2025-05-09 07:32:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":193764,"visible":true,"origin":"","legend":"\u003cp\u003eGene expression profile and TOGCN analysis in R91 and S1.\u003c/p\u003e\n\u003cp\u003eA. The number of differentially expressed genes (DEG) identified between SRBSDV-infection group and virus-free group in resistant (R91) and susceptible (S1) accessions at different time points (5, 11, and 20 days) post insect treatment. B. Predicted regulatory network and the connections among TFs and structural genes. Inside the blue circles, the nodes represent TF genes. Outside red circles, the points represent the structural genes. L1 to L11 indicate the levels identified in the time-ordered gene co-expression network. The resistant-specific TOGCN is shown above, while the susceptible-specific TOGCN is below. Within the same TOGCN, gray lines indicate co-expression relationships between genes. Between the two TOGCNs, orange lines connect genes that are present in the early sub-networks of both TOGCNs, while blue lines link genes that are located in the early sub-network of the resistant-specific TOGCN with those in the transitional sub-network of the susceptible-specific TOGCN. C and D show that the expression profiles of the resistant-specific TOGCN and susceptible-specific TOGCN, respectively. The heatmaps of average normalized TPMs (z-score) at each stage at each level identified in TOGCNs. Three stages were identified as the early (5 dpi), transitional (11 dpi) and terminal (20 dpi) stages, based on the expression profile. The bar represents the expression level of each gene (z-score). Low to high expression is indicated by a change in color from blue to red.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/6461573ae37864cc7e266626.png"},{"id":82331887,"identity":"5300dd34-cfc1-4150-8acc-ef0e554955d9","added_by":"auto","created_at":"2025-05-09 07:24:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325431,"visible":true,"origin":"","legend":"\u003cp\u003eOverrepresented GO terms for co-expressed genes at each sub-network in resistant- and susceptible-specific TOGCN.\u003c/p\u003e\n\u003cp\u003eThe enriched metabolic pathways (M.P.) with q-value \u0026lt;0.01 are presented. The color of circles represents the statistical significance of enriched GO terms. The size of the circles represents the number of genes in a GO term.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/5643618497c15433508ca639.png"},{"id":82332410,"identity":"f3a04f65-b95c-4146-986d-ca827ac0ee8b","added_by":"auto","created_at":"2025-05-09 07:32:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":537243,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of gene expression changes in JA biosynthesis and metabolic pathways.\u003c/p\u003e\n\u003cp\u003eJA biosynthesis pathways and gene expression profiles. Heatmap illustrating the expression levels of key genes involved in the JA biosynthesis pathway in both resistant (R91+ and R91) and susceptible (S1+ and S1) cultivars at various time points post-infection. Gene expression profile (in normalized TPMs) in different time points (here 5, 11, 20 days, from left to right in each heatmap panel) are presented in the heatmap alongside the gene names. The bar represents the expression level of each gene (z-score). Low to high expression is indicated by a change in color from blue to red. FAD: Fatty acid desaturases; PLA: Phospholipases A; PLD: Phospholipases D; LOX: Lipoxygenases; AOS: Allene oxide synthases; AOC: Allene oxide cyclases; OPR: OPDA reductases; OPCL1: OPC-8:0 CoA ligase 1; ACX: Acyl-CoA oxidase; AIM1: Abnormal inflorescence meristem1; KAT: Ketoacyl-CoA-thiolases; JMT: Jasmonic acid carboxyl methyltransferase; JAR: JA-amino acid synthetase; CYP94: Cytochrome P450 monooxygenase subfamily CYP94. B. Metabolic changes in the concentration of three major JA. The significance levels of R91+ vs R91, S1+ vs S1 are shown in the two facets based on Student’s t-test. Numbers show significant \u003cem\u003eP\u003c/em\u003e-value, while ns indicates \u003cem\u003eP\u003c/em\u003e-value \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/ed06f2303a0bee7ffff8452e.png"},{"id":82331904,"identity":"dba82a98-5707-45b2-adf0-e48086646102","added_by":"auto","created_at":"2025-05-09 07:24:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":729447,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted TOGCNs among TFs and enzymatic genes involved in JA biosynthetic pathways for R91 and S1.\u003c/p\u003e\n\u003cp\u003eA. JA \u0026amp; Resistant-specific TOGCN. B. JA \u0026amp; Susceptible-specific TOGCN. C. Numbers of TFs and enzymatic genes involved in JA metabolic pathways at three stages (the early, transitional, and terminal stages) among two TOGCNs. Red nodes represent JA genes, and blue nodes represent TFs. Edges represent co-expression relationships. Edges were not shown between enzymatic genes.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/fc7064062d8628b1de550459.png"},{"id":82332406,"identity":"e2b4cc21-1d34-4978-8752-e396b4a83b4a","added_by":"auto","created_at":"2025-05-09 07:32:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":290980,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key regulatory modules in the JA pathway.\u003c/p\u003e\n\u003cp\u003eA. The early sub-network extracted from the resistant-specific TOGCN focusing on the JA biosynthesis pathway. B. Resolved hierarchical regulations for hub gene \u003cem\u003eOsJAR2\u003c/em\u003e. C. Gene expressions (TPM) and TF binding site (TFBS) detected in the 1 Kb upstream sequences of the hub gene and the potential regulator (here, TFs; \u003cem\u003eMYB42/85\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/692eebd4aff7b38f23a51266.png"},{"id":82331914,"identity":"b9bc570d-a791-45fb-a8ce-a240490ec7a9","added_by":"auto","created_at":"2025-05-09 07:24:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":215658,"visible":true,"origin":"","legend":"\u003cp\u003eGWAS analysis of SRBSDV resistance in rice.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/c7214c5eea46afaebe018586.png"},{"id":82331890,"identity":"17f5695b-3a50-4f9a-84f4-087d80c2d8c3","added_by":"auto","created_at":"2025-05-09 07:24:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":67939,"visible":true,"origin":"","legend":"\u003cp\u003eHaplotype analysis of \u003cem\u003eOsJAR2\u003c/em\u003e in 195 accessions.\u003c/p\u003e\n\u003cp\u003eA, Haplotype of \u003cem\u003eOsJAR2\u003c/em\u003e; B, SRBSDV disease incidence of different haplotypes among 195 accessions. \u0026nbsp;P values less than 0.001 are given three asterisks, and P values less than 0.01 are given two asterisks.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/2b7896a48f88acd910452403.png"},{"id":95040697,"identity":"55257c8a-4dde-4ea6-9c89-9477d00d70da","added_by":"auto","created_at":"2025-11-03 16:10:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5857822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/8232434f-d86c-45a8-adc7-ac3ef1c53e46.pdf"},{"id":82331901,"identity":"5190cbcb-755d-494e-b4fd-7252e8ad57e5","added_by":"auto","created_at":"2025-05-09 07:24:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":64448,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/2b6fb4b5722534fa90a77aef.xlsx"},{"id":82331888,"identity":"9ad4fa7e-7f9a-4a8f-b392-3f4c0b2bfb99","added_by":"auto","created_at":"2025-05-09 07:24:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14386,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6552093/v1/490a31e6cd1208dc18b7fb70.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated GWAS and multi-omics reveal rapid JA signaling activation orchestrated by OsJAR2 to drive Southern Rice Black-Streaked Dwarf Virus resistance in rice","fulltext":[{"header":"Background","content":"\u003cp\u003eSouthern rice black-streaked dwarf virus (SRBSDV), transmitted by the white-backed planthopper (WBPH, \u003cem\u003eSogatella furcifera\u003c/em\u003e) in a persistent, circulative, and propagative manner, has emerged as a devastating pathogen, causing substantial yield losses of up to 70% in severely infected rice fields, particularly in Southern China, Southeast Asia, and even extending to regions as far north as Japan [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a double-stranded RNA virus belonging to the genus \u003cem\u003eFijivirus\u003c/em\u003e in the family Reoviridae, SRBSDV shares similarities with rice black-streaked dwarf virus (RBSDV) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and was first identified in Guangdong Province, China [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe implications of SRBSDV are immense, not only compromising rice quality but also posing a serious threat to agricultural economies reliant on rice cultivation as a staple crop. Despite current management strategies, such as the application of pesticide and crop rotation, achieving limited success in controlling SRBSDV outbreaks, breeding for resistant rice varieties has emerged as a highly economical, environmentally sustainable, and viable long-term strategy to combat this virus. Recent studies have identified rice accessions with natural resistance to SRBSDV, underscoring the potential for developing resilient cultivars [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding the mechanisms underlying this resistance is crucial for advancing rice breeding programs aimed at enhancing viral resistance.\u003c/p\u003e \u003cp\u003eThe advancement of multi-omics techniques has significantly broadened our understanding of the fundamental genes and molecular pathways involved in a plant's response to pathogens, as well as the development of resistance mechanisms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Specifically, transcriptome analysis has been instrumental in providing invaluable insights into the comprehensive gene expression responses of rice to biotic stress, emphasizing its role in enhancing resistance. Furthermore, the adoption of Time-Ordered Gene Co-Expression Networks (TOGCNs) has emerged as a powerful tool for dissecting time-series transcriptome data, revealing dynamic alterations in gene expression and their transitions across various biological processes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This innovative method transcends the limitations of traditional co-expression network analysis, enabling the exploration of temporal dynamics and the investigation of gene regulatory mechanisms under diverse developmental stages and environmental conditions. As a result, TOGCNs harbor significant potential for unraveling gene regulatory mechanisms under temporal dynamics, subsequently identifying central genes and pivotal pathways involved in combating SRBSDV and offering crucial insights into the molecular mechanisms of resistance. However, there is only a few of comprehensive studies that aim to elucidate the molecular mechanisms, and dissect the molecular network related to rice response and resistance to SRBSDV, particularly utilizing network analysis methods like TOGCNs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we evaluated SRBSDV resistance in RPD2, an international rice diversity panel [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], through field trials and artificial inoculations. Our investigation identified R91, a highly resilient accession that exhibited robust resistance to SRBSDV in both evaluation methods. Notably, R91 was previously recognized for its resistance to RBSDV in our earlier work [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Using R91 as a model, we explored the molecular mechanisms underlying SRBSDV resistance. Temporal hormone profiling revealed stark differences between R91 and the susceptible accession S1: R91 showed rapid jasmonic acid (JA) accumulation (5 days post-inoculation, dpi), while S1 exhibited a 70% JA reduction at the same stage.\u003c/p\u003e \u003cp\u003eTo further dissect the molecular basis of resistance, we performed time-course transcriptomic analysis, which demonstrated that R91 mounted an immediate and pronounced gene activation response upon SRBSDV infection, contrasting sharply with the delayed response in S1. Through time-ordered gene co-expression network (TOGCN) analysis, we identified distinct co-expression patterns that differentiated resistant and susceptible accessions. The early activation of the JA biosynthesis pathway emerged as a critical component of the resistance mechanism, consistent with the observed JA accumulation in R91.\u003c/p\u003e \u003cp\u003eIntegrative network analysis pinpointed \u003cem\u003eOsJAR2\u003c/em\u003e (encoding JA-Ile synthase) as the central hub gene in both the resistance and JA synthesis networks. We then conducted Genome-wide association study (GWAS) using the DI in this study as phenotype and resequencing data from our previous study[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. GWAS analysis identified a novel SRBSDV-resistance QTL on chromosome 1 (peak SNP: 6,495,499 bp), located within a 160 kb interval encompassing \u003cem\u003eOsJAR2\u003c/em\u003e, suggesting its potential role as the causal gene in this QTL. Further haplotype analysis revealed 3 distinct haplotypes of OsJAR2, defined by two nonsynonymous SNPs. Haplotype analysis confirmed that accessions carrying different haplotypes exhibited varying levels of SRBSDV resistance, highlighting \u003cem\u003eOsJAR2\u003c/em\u003e's genetic regulation of this trait.\u003c/p\u003e \u003cp\u003eOur findings identified a valuable germplasm for SRBSDV resistance, which is currently scarce, offering a vital resource for breeding efforts. Crucially, we demonstrated the critical role of rapid transcriptional responses and JA signaling in SRBSDV resistance. While JA has been linked to virus resistance, the specific reliance on a rapid JA response for SRBSDV resistance is a novel insight. We further established OsJAR2 as a key functional gene, acting as a hub in the JA network and regulating SRBSDV resistance. This discovery provides a promising target for breeding SRBSDV-resistant rice, advancing our understanding of the molecular networks underlying viral resistance and paving the way for future research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eWe assessed the resistance to SRBSDV using 195 rice accessions originating from 34 countries and spanning 13 regions (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Both field trials and artificial inoculation tests were conducted to evaluate their resistance levels. We ultimately identified a highly resistant cultivar, accession number 63 (designated as R91 in this study), as well as a susceptible variety, accession number 41 (designate as S1 in this study), for further analysis. All SRBSDV-infected plant materials and subsequent cultivation took place in Nanning, Guangxi Province. The plants were grown in a greenhouse maintained at 28\u0026ndash;30\u0026deg;C with a 12-hour light/dark cycle.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVirus, insect and inoculation\u003c/h3\u003e\n\u003cp\u003eRice plants exhibiting typical symptoms, such as dark-green and dwarf, were collected from a field in Nanning city, Guangxi Province and confirmed by RT-PCR with SRBSDV-specific primers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Those testing positive for SRBSDV were then cultivated in a insect-proof greenhouse for artificial inoculation and further identification.\u003c/p\u003e \u003cp\u003eWBPH were also collected from a rice field in Nanning city, Guangxi province and reared on healthy rice plants until new nymphs emerged. Newly hatched Nymphs were then transferred and propagated on the SRBSDV-infected rice plants for more than 14 days to be viruliferous. The proportion of viruliferous WBPH was assessed by RT-PCR with SRBSDV-specific primers. The viruliferous WBPHs were used to inoculate SRBSDV to rice seedlings at three-leaf stage.\u003c/p\u003e \u003cp\u003eRice seedlings were grown in a insect greenhouse til three-leaf stage, the viruliferous WBPHs were then transferred to the seedlings and kept for 48h. After inoculation, the seedlings were transplanted in a insect greenhouse for observation of the symptoms, resistant evaluation and sample collection. The leaf samples from rice plants were harvested at 5 dpi, 11 dpi and 20 dpi for subsequent transcriptome analysis.\u003c/p\u003e \u003cp\u003eField tests were conducted in Xingʹan county, Guilin city, Guangxi province. Xing'an County is an important migration route for the WBPHs, and the incidence of southern rice black-steaked dwarf virus was always high. All of the 195 international rice accessions were randomly planted in the paddy field, each accession has three replicates. To ensure the test effect, TN1 as the susceptible control was evenly distributed among these varieties and rice seedlings were cultivated in a field setting surrounded by a rice crop affected by SRBSDV.\u003c/p\u003e\n\u003ch3\u003eEvaluation of disease resistance and sample collection\u003c/h3\u003e\n\u003cp\u003eAt least 50 seedlings of each cultivar were grown in the field for natural infection, with a row spacing of 15 cm*18 cm, each material was planted with 5 rows *10 plants with TN1 as susceptible control. Each cultivar has three replicates. Disease incidence was recorded two months post-transplanting, following standard management practices without the application of pesticides or antivirals. The identification result was effective when the incidence of the susceptible control (TN1) was more than 30%.\u003c/p\u003e \u003cp\u003eThe transcriptome experiment comprised four groups: R91+ (resistant line treated with viruliferous insects), R91 (resistant line treated with virus-free insects), S1+ (susceptible line treated with viruliferous insects), and S1 (susceptible line treated with virus-free insects). Samples were collected from each group at 5-, 11-, and 20-days post-inoculation, with three independent replicates at each time point. Leaves from the virus-infected plants were harvested for RNA extraction and subsequent transcriptome analysis.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing\u003c/h3\u003e\n\u003cp\u003eSampled leaves were immediately flash-frozen in liquid nitrogen and kept at -80\u0026deg;C. Using the NEBNext Poly(A) mRNA Magnetic Isolation Module, mRNA was isolated. RNA quality was assessed using the Agilent 2100 BioAnalyzer. In total, 36 sequencing libraries were constructed using the NEBNext Ultra RNA Library Prep Kit for Illumina. 150 bp paired-end PCR-free libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for sequencing with an Illumina HiSeq X Ten platform. A total of 1,439.31\u0026nbsp;million clean reads were obtained from a total of 36 samples corresponding to 215.9 G paired bases (Table S2).\u003c/p\u003e\n\u003ch3\u003eRNA-seq analysis\u003c/h3\u003e\n\u003cp\u003eShort reads were processed with fastp to remove adapter sequences, leading and trailing bases with a quality score below 20, and reads with an average per-base-quality of 20 over a 4-bp sliding window [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Nipponbare genome (MSU v7.0) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was used as a reference for reads mapping with HiSat2 (Kim, Paggi, Park, Bennett, \u0026amp; Salzberg, 2019). Only uniquely mapped paired-end reads were retained for read counting of the genes by featureCounts to generate the count and Transcripts per Kilobase Million (TPM) tables [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A comprehensive analysis was conducted on a set of 36 samples, yielding a total of 1,439.31\u0026nbsp;million high-quality clean reads, corresponding to 215.9 G paired bases (Table S2). On average, 83.21% of these paired-end reads were uniquely mapped to the reference genome, ensuring a reliable dataset for subsequent analyses (Table S2).\u003c/p\u003e \u003cp\u003eDifferential gene expression analysis was performed with DEseq2 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. And the differentially expressed genes were identified according to the criteria of adjusted p value \u0026lt; 0.05 and a fold change (FC) cut-off of 2. Mfuzz was utilized to analyze time series gene co-expression clusters with core parameter \u0026ldquo;cluster_num\u0026rdquo; set to three [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene functional annotation\u003c/h2\u003e \u003cp\u003eAll gene structural annotations are based on release 7 of the MSU Rice Genome Annotation Project [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We download the transcription factors list from PlantRegMap, obtaining a total of 2,408 TFs which were classified into 56 families [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. GFAP was used for de novo gene functional annotation with the plant-specific database [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Given the importance of JA metabolism in resistance to SRBSDV, we annotated genes related to the JA metabolic pathway by gathering enzymatic family annotations from previous studies (Table S3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReconstructing the time-ordered gene co-expression networks\u003c/h3\u003e\n\u003cp\u003eWe firstly preprocessed the gene expression data as follows: (i) For each time point, three replicates were treated as one data point. After comparing the differences between two specific paired groups, we retained genes identified as significantly differentially expressed, resulting in a total of 12,652 significant DEGs. (ii) We calculated the mean TPM for gene expression within each time point group, retaining genes with a mean greater than 1 in any group. (iii) Based on the criteria from the previous two steps, we calculated the median absolute deviation (MAD) of the TPM values for each gene, retaining the top 5,000 genes ranked by descending MAD.\u003c/p\u003e \u003cp\u003eThe base R function \u0026ldquo;cor\u0026rdquo; was used to calculate the gene co-expression Pearson correlation coefficients (r) between pairs of genes under four different temporal treatments. TOGCN was performed for generating the suggested r cutoff with 0.88 and \u0026minus;\u0026thinsp;0.65 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The value of r between \u0026minus;\u0026thinsp;1 and \u0026minus;\u0026thinsp;0.65 indicates a significantly negative correlation between two genes, while r between 0.88 and 1 indicates a significantly positive correlation. Each gene acts as a node, while a pair of significantly correlated co-expressed genes forms a gene pair that serves as the edge connecting these nodes. The interplay of nodes and edges together constructs a gene co-expression network (GCN). Comparing different GCNs essentially involves identifying gene pairs that are specific to each network. We constructed four independent GCNs based on four sets of time-series gene expression data (R91, R91+, S1, S1+). Significantly positively correlated gene pairs unique to the R91\u0026thinsp;+\u0026thinsp;GCN were combined to create the R91\u0026thinsp;+\u0026thinsp;specific GCN, while those specific to the S1\u0026thinsp;+\u0026thinsp;GCN were merged to form the S1\u0026thinsp;+\u0026thinsp;specific GCN.\u003c/p\u003e \u003cp\u003eBreadth-first search algorithm [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used for GCN clustering with custom Python scrip. To cluster GCN, it is necessary to select appropriate genes as seeds, whose expression trends should gradually decrease from the first time point to the last time point. We identified 13 optimal genes as seeds using MFSelector (Table S4) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After clustering, the R91\u0026thinsp;+\u0026thinsp;and S1\u0026thinsp;+\u0026thinsp;specific GCNs formed Resistant (R91+)- and Susceptible (S1+)- specific time-ordered gene co-expression network (TOGCN), respectively. Ultimately, TOGCNs were visualized in graphs using Cytoscape [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGene functional enrichment\u003c/h3\u003e\n\u003cp\u003eHypergeometric tests were performed to determine whether specific functional categories were significantly overrepresented in gene sets. GO functional enrichment was tested using the R package clusterProfiler [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association study\u003c/h2\u003e \u003cp\u003eGenome-wide association study (GWAS) was conducted using our previous sequencing data[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and Gapit version 2 with a mixed linear model (MLM) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Manhattan plots were generated using CMplot package in R v4.2.2 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/YinLiLin/R-CMplot\u003c/span\u003e\u003cspan address=\"https://github.com/YinLiLin/R-CMplot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The significance threshold was set as \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;l X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening of SRBSDV resistant accessions using international rice diverse panel\u003c/h2\u003e \u003cp\u003eTo evaluate SRBSDV resistance and characterize its distribution among different rice varieties, we leveraged a diverse natural panel composed of 195 rice accessions originating from 34 countries worldwide and spanning 12 regions. These accessions, selectively sourced from the RPD2, underwent rigorous disease resistance assessment via both field trials and controlled artificial inoculation (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Considerable variation in disease incidence (DI) was observed in the panel, with field test DI ranging from 0\u0026ndash;92.9% and artificial inoculation DI varying from 42.8\u0026ndash;100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of the geographic distribution of resistant accessions showed that varieties from East Asia (EAS; including China and Japan) exhibited significantly higher DI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In contrast, accessions from three regions demonstrated significantly lower DI characteristics, including South Asia Central (SAC, including India, Bangladesh, and Nepal), West Africa (WAF; including Burkina Faso, C\u0026ocirc;te d'Ivoire, Ghana, Guinea, Liberia, Mali, Nigeria, and Senegal), and the Sea Islands (SER; comprising Indonesia, Malaysia, and the Philippines) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Among the 50 accessions with the lowest resistance levels in artificial tests, 15 accessions originated from South Asia Central (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We identified an \u003cem\u003eindica\u003c/em\u003e rice variety, R91, from India, which exhibited the constant resistance in both field trial and artificial inoculation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Intriguingly, this accession has also been identified as a stable and highly resistant resource to RBSDV, a closely related virus to SRBSDV, in our previous study, from which we identified a functional resistance gene for RBSDV (Z. Wang \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResistant Accession R91 Exhibits quick JAs accumulation Post-SRBSDV Inoculation\u003c/h2\u003e \u003cp\u003eTo elucidate the hormonal dynamics associated with SRBSDV resistance, we quantified key phytohormones in R91 and the susceptible accession S1 following artificial inoculation with SRBSDV. Hormone levels were assessed at 5-, 11-, and 20-days post-inoculation using metabolic profiling method. In parallel, we employed rice plants infected with virus-free WBPH as a control group (mock). All major type of plant hormones, including GA, ABA, Auxin, Cytokinin, BRs, JAs and SA were quantified (Table S5). Strikingly, JA and MeJA exhibited contrasting profiles between the two accessions during the early infection stages (5 dpi). In R91, JA levels surged by 1.88-fold at 5 dpi compared to mock-treated controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), indicating a rapid and robust activation of the JA pathway. Conversely, in S1, JA levels plummeted by 70% at the same time point (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Similarly, MeJA levels in R91 increased by 1.27-fold at 5 dpi, while S1 experienced a 40% decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This divergence in JA content dynamics persisted over time. By 11 dpi, JA levels in R91 decreased but remained 39% higher than in S1, demonstrating sustained JA activity in the resistant accession beyond the initial response phase. MeJA levels followed a similar trend, underscoring the pivotal role of JAs in SRBSDV resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eR91 exhibits early and robust gene activation in response to SRBSDV\u003c/h2\u003e \u003cp\u003eTo further elucidate the molecular mechanisms of SRBSDV resistance in rice, we conducted a comprehensive time-series transcriptome analysis using the same materials as in the hormone assay: the resistant accession R91 and the susceptible accession S1. Samples were collected at 5, 11, and 20 days post artificial inoculation with SRBSDV.\u003c/p\u003e \u003cp\u003eThe resistant accession R91 exhibited an early and robust gene activation response to SRBSDV. This was evident by the rapid and extensive transcriptional response observed as early as 5 days post-inoculation, with over 2,000 differentially expressed genes (DEGs) detected at both 5 dpi and 11 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, the susceptible accession S1 displayed a significantly muted response, with only a few hundred DEGs identified at 5 dpi and 20 dpi, notably only 166 DEGs at 11 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Further analysis of the DEGs revealed that by day 5, R91 exhibited 1,804 and 1,908 up-regulated genes at 5 dpi and 11 dpi, respectively, while S1 showed only 244 upregulated genes out of 636 DEGs at 5 dpi (Table S6). This disparity highlights the distinct capabilities of the two accessions in mounting a defense response, with R91 showing a more dynamic and extensive genetic reaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTemporal Gene Co-Expression Patterns Distinguish Resistance and Susceptibility Responses to Virus Infection\u003c/h2\u003e \u003cp\u003eTo delve deeper into the molecular network response of resistant and susceptible accessions to the SRBSDV, we utilized time-series transcriptome data to construct comprehensive TOGCNs. Our analysis unveiled distinct patterns within a resistant-specific network (R91+) and a susceptible-specific network (S1+) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The resistant-specific TOGCN consisted of 44,087 edges, while the susceptible-specific TOGCN comprised 33,884 edges (Table S7). These networks highlighted co-expression interactions among 4,167 genes (including 291 transcription factors, TFs) in the resistant-specific TOGCN and 3,676 genes (including 294 TFs) in the susceptible-specific TOGCN (Table S7). The resistant-specific network was structured into nine time-ordered gene co-expression levels (L1\u0026ndash;L9), whereas the susceptible-specific network was segmented into eleven levels (L1\u0026ndash;L11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Notably, in the resistant-specific TOGCN, levels L2, L6, and L7 exhibited the highest number of edges and gene nodes, while in the susceptible-specific TOGCN, levels L4, L5, and L2 displayed the highest connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Table S7).\u003c/p\u003e \u003cp\u003eBased on differential expression profiles, these levels were further categorized into three temporal sub-networks: the early sub-network (corresponding to 5 dpi), the transitional sub-network (11 dpi), and the terminal sub-network (20 dpi) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). Within the resistant-specific TOGCN, the early sub-network (encompassing levels L1\u0026ndash;L3) was notably enriched, housing more 1,980 genes and 123 transcription factors compared to the other sub-networks (Table S7). This early sub-network also displayed a higher number of edges (23,673) and a greater average degree (47.82), indicating a robust transcriptional response at the gene level (Table S7).\u003c/p\u003e \u003cp\u003eConversely, the transitional sub-network of the susceptible-specific TOGCN contained more genes (1,917) and transcription factors (163) compared to the other sub-networks and also exhibited more edges (25,095) and a higher average degree (52.36) (Table S7). Intriguingly, genes that were responsive in the early sub-network of the resistant-specific TOGCN were found to be activated during the transitional stage in the susceptible-specific TOGCN, suggesting a potential overlap or shared response mechanisms between the two networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analysis revealed early rapid activation of JA biosynthesis genes underpins resistance to SRBSDV\u003c/h2\u003e \u003cp\u003eTo reveal significant differences in categories and temporal patterns of genes between resistant and susceptible accessions, enriched Gene Ontology (GO) for co-expressed genes within the resistant-specific and susceptible-specific TOGCNs were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the resistant-specific TOGCN, key pathways were consistently enriched across all three stages. In the early sub-network, metabolic pathways related to hemicellulose, cell wall macromolecules, and polysaccharides were overrepresented (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The transitional sub-network showed enrichment in pathways associated with terpenoids, alpha- and aromatic amino acids, while the terminal sub-network exhibited enrichment in pathways linked to pigments, JA, and ABA. Conversely, the susceptible-specific TOGCN displayed most functional pathways enriched in the transitional stage sub-network, including noncoding RNA, anthocyanins, plant hormones, cell wall components, and diterpenoids, typically associated with disease susceptibility. Interestingly, pathways enriched in the early sub-network of the resistant-specific TOGCN overlapped with those enriched in the transitional sub-network of the susceptible-specific TOGCN, with a notable emphasis on JA related pathways, a critical factor in SRBSDV resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe transcriptome analysis revealed rapid activation of JA signaling pathway genes, aligning with the metabolic analysis that showed significantly higher JA levels in R91 compared to S1. To further elucidate the JA pathway's role in regulating rice resistance to SRBSDV, we reviewed the existing literature and reconstructed the JA metabolic pathway in rice, in which 121 genes encoding 14 enzymes involved in JA biosynthesis were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S3 and Table S8). The gene family encoding phospholipases A (PLA) was the largest, with 31 members, while families for allene oxide cyclases (AOC) and jasmonic acid carboxyl methyltransferase (JMT) each had one member. Smaller families such as OPC-8:0 CoA ligase 1 (OPCL1) and ketoacyl-CoA-thiolases (KAT) contained only two members each, playing crucial roles in JA biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table S3).\u003c/p\u003e \u003cp\u003eWe then conducted a heatmap clustering analysis of the expression level of genes involve in JA biosynthesis and metabolic pathway to characterize their expression pattern in a time course in R91 and S1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S8 and Table S9). The results showed that in R91 with virus inoculation (R91+), 44.63% of JA genes were significantly upregulated at the early stage, higher than the 24.79% observed in the control line R91 without virus inoculation (Table S9). Conversely, in the susceptible accession under infection (S1+), 29.75% of JA genes were significantly upregulated at the early stage, lower than the 41.32% observed in the control line S1 (Table S9). Fast-response genes, highly expressed in R91\u0026thinsp;+\u0026thinsp;during the early stage and in S1\u0026thinsp;+\u0026thinsp;during the late stage, were identified. Notably, no fast-response genes were found in critical families AOC, JMT, and KAT. However, larger families exhibited significant proportions of fast-response genes, with 75% of the FAD family, 71.42% of the PLD family, and 48.38% of the PLA family classified as fast responders (Table S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHierarchical regulation of JA biosynthesis enhances disease resistance in resistant lines\u003c/h2\u003e \u003cp\u003eTo further characterize key module regulating rice SRBSDV resistance by JA, we reconstructed TOGCNs related to JA biosynthesis for both R91 and S1 after SRBSDV inoculation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). In the JA \u0026amp; resistant-specific TOGCN, both the early and terminal sub-networks exhibited a significantly higher number of genes and edges, particularly involving transcription factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The early sub-network contained 114 genes and 716 edges, with a notable presence of 109 TFs, indicating a robust and rapid initial response to JA biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Table S10). Additionally, the terminal sub-network showed substantial activity with 80 genes, 268 edges, and 65 TFs, suggesting sustained regulatory activity in the later stages of the response (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and Table S10).\u003c/p\u003e \u003cp\u003eConversely, the JA and susceptible-specific TOGCN displayed a different pattern, with the transitional sub-network showing the highest concentration of genes and edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Table S10). This sub-network comprised 147 genes, 1,343 edges, and 132 TFs, suggesting a delayed but intense response during the middle stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Table S10). The early and terminal sub-networks in the susceptible line were relatively less active, with the early sub-network containing only 40 genes and 110 edges (including 37 TFs), and the terminal sub-network having 27 genes and 24 edges (with 20 TFs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Table S10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKey module of JA signal pathway and a candidate functional gene related to SRBSDV resistance\u003c/h2\u003e \u003cp\u003eFocusing on the early sub-network of the JA \u0026amp; resistant (R91+)-specific TOGCN, we identified a key module potentially critical for the rapid biosynthesis of defense compounds mediated by JA in R91 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). For JA biosynthesis, we predicted seven enzymatic genes that were expressed at high levels and directly regulated by 102 potential regulators, predominantly from the MYB (14 members) and ethylene-responsive element binding factors (ERFs; 12 members) families (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and Table S11). Among these, one gene, JA-amino acid synthetase 2 (\u003cem\u003eOsJAR2\u003c/em\u003e; \u003cem\u003eLOC_Os01g12160\u003c/em\u003e), was identified as a hub gene within this network (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). \u003cem\u003eOsJAR2\u003c/em\u003e catalyzes the synthesis of JA-Ile, a crucial rate-limiting enzyme in the JA biosynthesis pathway (Wakuta \u003cem\u003eet al.\u003c/em\u003e, 2011).\u003c/p\u003e \u003cp\u003eFurther examining the co-expression network inferred from TOGCN and incorporating DNA binding site predictions, we inferred that the \u003cem\u003eOsJAR2\u003c/em\u003e might be regulated in a hierarchical manner, with \u003cem\u003eOsERF130\u003c/em\u003e acting as a secondary regulator and \u003cem\u003eMYB42/85\u003c/em\u003e serving as direct regulators (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGWAS and Haplotype Analyses implied\u003c/b\u003e \u003cb\u003eOsJAR2\u003c/b\u003e \u003cb\u003eas a Key Regulator of SRBSDV Resistance in Rice\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo confirm if \u003cem\u003eOsJAR2\u003c/em\u003e is genetically function in rice SRBSDV resistance, we conducted GWAS analysis using the DI as phenotype, and resequencing data of the 195 accessions. The GWAS results demonstrated 4 novel SRBSDV resistance QTL in chromosome 1, 6 and 11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interestingly, one of these QTL (\u003cem\u003eqSRBSDV1-1\u003c/em\u003e) colocalized with \u003cem\u003eOsJAR2\u003c/em\u003e, with the most significant SNP 160 kb away from \u003cem\u003eOsJAR2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Combining with the transcriptomic analysis, all the results that \u003cem\u003eOsJAR2\u003c/em\u003e is a novel functional gene regulating SRBSDV resistance in rice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQTLs associated with SRBSDV resistance identified by GWAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTLs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqSRBSDV1-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6495499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.02 X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqSRBSDV1-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11832416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.01 X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqSRBSDV6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5946901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.32 X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqSRBSDV11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28739460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.65 X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe then further explore the genetic basis of \u003cem\u003eOsJAR2\u003c/em\u003e in conferring SRBSDV resistance by haplotype analysis. Genome-wide resequencing of the 195 accessions identified three non-synonymous single nucleotide polymorphisms (nsSNPs) in \u003cem\u003eOsJAR2\u003c/em\u003e, leading to the characterization of three haplotypes (H1, H2, and H3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, Table S12). Association analysis demonstrated significant phenotypic divergence in disease incidence among the haplotypes. Accessions with the H3 haplotype exhibited the lowest disease incidence (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while those carrying H1 showed reduced susceptibility compared to H2 carriers (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). This genetic stratification highlights OsJAR2 as a central regulator of JA-mediated antiviral defense and identifies H3-associated SNPs as valuable molecular markers for breeding SRBSDV-resistant rice varieties.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDiscovery of R91 as a Disease-Resistant Germplasm and Its Dual-Resistance Breeding Potential\u003c/h2\u003e \u003cp\u003eSRBSDV poses a devastating threat to rice production, yet the scarcity of highly resistant germplasms has severely hindered disease-resistance breeding. Through systematic screening of 195 rice accessions spanning 34 countries and 12 geographic regions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], we identified the \u003cem\u003eIndica\u003c/em\u003e variety R91, originating from India, which exhibited stable high resistance to SRBSDV in both field trials and artificial inoculations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, D). This remarkable resistance outperforms most reported resistant resource, positioning R91 as a globally rare SRBSDV-resistant germplasm. Notably, our previous study also identified R91 as a robust resistance germplasm against RBSDV [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Such dual resistance provides a foundational resource for breeding programs targeting overlapping viral epidemics, simplifying multi-resistance cultivar development while mitigating risks associated with viral evolution in the field.\u003c/p\u003e \u003cp\u003eFurther analysis of resistance distribution revealed that varieties from South Asia Central (SAC; India, Bangladesh, Nepal) exhibited significantly higher resistance, contrasting with East Asian accessions (China, Japan) that displayed higher susceptibility. This geographic pattern aligns with the migration route of the white-backed planthopper, the SRBSDV vector, across South and Southeast Asia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], as well as historical prevalence of viral diseases in these regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Strikingly, 15 out of the top 50 most resistant accessions originated from South Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), suggesting long-term natural selection pressure may have enriched resistance alleles in these germplasms. For example, R91\u0026rsquo;s resistance likely evolved through co-adaptation with indigenous WBPH populations and viral strains, leading to fixation of advantageous alleles via gene-environment or gene-gene interactions. This geographic signature provides critical clues for resistance gene mining: selective sweep analysis of South Asian germplasms could pinpoint genomic regions under natural selection, accelerating functional gene discovery.\u003c/p\u003e \u003cp\u003eThe dual resistance of R91 to SRBSDV and RBSDV implies potential overlap in defense mechanisms against these phylogenetically related Fijiviruses. Our previous work identified the aspartic protease gene \u003cem\u003eOsAP47\u003c/em\u003e as a key player in RBSDV resistance, conferring protection via degradation of viral capsid proteins [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given the structural and replicative similarities between SRBSDV and RBSDV [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], we hypothesize that OsAP47 may target conserved viral domains to achieve cross-resistance. Furthermore, our study revealed rapid activation of the JA pathway in R91 during early SRBSDV infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), a pathway also implicated in RBSDV defense [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This suggests JA signaling may serve as a shared regulatory hub for dual resistance.\u003c/p\u003e \u003cp\u003eThe dual resistance of R91 against both RBSDV and SRBSDV establishes it as an invaluable genetic asset, laying the groundwork for the development of rice varieties with improved resistance profiles. Its robust resistance traits offer a foundation upon which to dissect the complex dynamics of rice-virus interactions and to expedite the incorporation of resistance into commercially viable rice cultivars. Consequently, the use of R91 in molecular breeding has the potential to significantly fortify rice against these two viruses, providing a boon to global food security by mitigating virus-induced yield losses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTemporal Hierarchies of Transcriptional Activation and JA Signaling in Early Defense is critical for the formation of SRBSDV resistance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, we aimed to explore the molecular mechanisms underlying rice resistance to SRBSDV by leveraging multi-omics approaches. These advanced techniques allowed us to analyze the comprehensive omics landscape and the molecular responses associated with plant defense [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the current study, we specifically examine the transcriptional dynamics of a resistant accession, R91, in comparison to a susceptible accession, S1, as well as conduct a metabolomics analysis both with and without the influence of SRBSDV infection.\u003c/p\u003e \u003cp\u003eOur research uncovered that R91 mounts an immediate and robust defense response, as evidenced by the rapid upregulation of defense-related genes from as early as 5 dpi. Conversely, S1 manifested a lagged transcriptional response, with a considerable increase in defense gene expression becoming apparent only by 11 dpi. This marked difference underscores R91's enhanced ability to rapidly engage genetic defenses upon viral infection. These observations stress the importance of early gene activation in conferring resistance against SRBSDV in rice. Previous research has indicated that upon virus inoculation, resistant ecotype can trigger an extensive and rapid transcriptomic remodeling, including genes involved in plant-pathogen interaction, plant hormone signal transduction, the MAPK signaling pathway or ubiquitin mediated proteolysis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we identified a distinctly earlier transcriptomic response in the resistant accession compared to the susceptible accession, further emphasizing the significance of early and substantial transcriptomic remodeling in establishing SRBSDV resistance in rice.\u003c/p\u003e \u003cp\u003eFurther constructed TOGCNs illuminated the distinct temporal gene expression patterns in the resistant and susceptible accessions. The TOGCN specific to R91 exhibited enhanced connectivity and gene interactions, particularly involving key transcription factors, suggesting a sustained and comprehensive transcriptional response. In contrast, S1's co-expression network showed a resurgence of gene activity primarily during the transitional phase, which signifies a postponed transcriptomic response to the virus.\u003c/p\u003e \u003cp\u003eDelving deeper into transcriptomic data reveals that the JA biosynthesis pathway is significantly activated in R91 during the early stages of SRBSDV infection. Strikingly, 44.63% of JA-related genes were upregulated in R91, in contrast to only 29.75% in S1. This pronounced activation underscores the rapid induction of JA biosynthesis as a fundamental mechanism underlying the resistant phenotype. Further metabolomics analysis confirmed that JA were significantly higher in R91 than in S1 in early stage of virus infection. It has been reported that RBSDV infection can alter hormone levels, influencing the plant's defense activation and susceptibility manipulation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, for the first time, we identified different JA synthesis genes expression, as well as JA contents between resistant and suspectable rice, which be one of the core molecular mechanisms underlying SRBSDV resistance in rice.\u003c/p\u003e \u003cp\u003eDuring the co-evolutionary between virus and plant, plants have developed a sophisticated and multifaceted immune system that includes a complex interplay between different phytohormones [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. JAs are a class of hormones that originate from C18 fatty acids and play vital roles in plant defense responses to abiotic and biotic stresses, including viral attacks [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our findings align with the established role of JA in plant defense against biotic stresses, underscoring its importance in viral resistance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As previous studies have shown, the JA pathway plays a significant role in plant defense mechanisms, including the activation of pathogenesis-related genes and the induction of initial immune responses to viral infections [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For example, continuous JA treatment decreased the DNA titer of beet curly top virus, indicating that suppression of the JA response may be critical for geminivirus infection [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Suppression of JA signaling has also been seen in rice ragged stunt virus infection [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our results showing increase JA synthesis pathway and contents further highlight the importance of JA in regulating resistance to SRBSDV in rice.\u003c/p\u003e \u003cp\u003eInterestingly, JA also plays a crucial role as a regulator in how plants respond to insect infections [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Moreover, it has been reported that WBPH infestation down-regulates the expression of JA pathway genes in rice, suggesting that JA pathways can be influenced by viral infection and insect feeding [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, it is noteworthy that despite the rapid and significant synthesis of JA observed in the R91, our earlier research indicated that R91 does not demonstrate resistance to insect attacks [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, gaining a more nuanced understanding of the relationship between the JA response and the resistance to both insect pressures and the SRBSDV in R91. This line of investigation, and more broadly within rice, warrants further exploration and attention.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOsJAR2\u003c/b\u003e: \u003cb\u003eA Candidate Functional Gene and Molecular Hub Bridging JA Dynamics and SRBSDV Resistance\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCurrently, genetic studies on SRBSDV resistance in rice remain limited, with no functionally validated resistance genes reported [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this study, we identified four novel QTLs associated with SRBSDV resistance, one of which co-localized with OsJAR2\u0026mdash;the key regulator of JA signaling. We propose that OsJAR2 serves as a molecular hub connecting JA dynamics and viral resistance. Haplotype analysis further supports \u003cem\u003eOsJAR2\u003c/em\u003e as the most probable candidate gene governing SRBSDV resistance. Together, these findings provide compelling evidence that OsJAR2 functions as a bona fide candidate resistance gene against SRBSDV.\u003c/p\u003e \u003cp\u003eThe JA signaling pathway consists of a complex network of specialized functional sectors, known as modules, which enable plants to effectively coordinate a diverse array of physiological responses to biotic and abiotic stimulus [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Consequently, it is vital to uncover the molecular network and modules that facilitate the coordination of resistance to SRBSDV in rice. Our TOGCN analysis unveiled \u003cem\u003eOsJAR2\u003c/em\u003e (encoding JA-Ile synthase) as the central hub bridging rapid JA signaling and SRBSDV resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;B). Genetic mapping through GWAS and haplotype analysis further suggested \u003cem\u003eOsJAR2\u003c/em\u003e as the functional gene governing SRBSDV resistance in rice.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOsJAR2\u003c/em\u003e catalyzes the biosynthesis of JA-Ile, a bioactive JA derivative essential for activating downstream defense responses [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Integrating DNA binding site predictions with co-expression network analyses, we propose that \u003cem\u003eOsJAR2\u003c/em\u003e operates within a hierarchical regulatory cascade, with \u003cem\u003eMYB42/85\u003c/em\u003e serving as direct regulators and OsERF130 acting as a secondary modulator. This regulatory hierarchy ensures rapid \u003cem\u003eOsJAR2\u003c/em\u003e induction (3.2-fold increase at 5 dpi), facilitating timely synthesis of JA-Ile to amplify antiviral defenses. In contrast, the susceptible accession S1 exhibited delayed and lower \u003cem\u003eOsJAR2\u003c/em\u003e activation (only 1.5-fold at 11 dpi), effectively forfeiting the critical \"early-window\" needed to disrupt viral replication and systemic movement\u0026mdash;a vulnerability exploited by SRBSDV.\u003c/p\u003e \u003cp\u003eNotably, rice harbors two JAR1-like GH3 enzymes, OsJAR1 and OsJAR2, which exhibit distinct stress-responsive expression patterns [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. It has been reported that the activity of \u003cem\u003eOsJAR1\u003c/em\u003e is necessary for effective defense against the blast fungus, while another study demonstrated that \u003cem\u003eOsJAR2\u003c/em\u003e is rapidly induced upon invasion by RBSDV [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, we leverage multi-omics techniques to reveal for the first time that \u003cem\u003eOsJAR2\u003c/em\u003e, along with downstream transcription factors, may form a molecular module involved in rice's response to SRBSDV, which is closely related to RBSDV. These findings provide critical insights into the regulatory mechanisms underlying JA-mediated antiviral responses and pave the way for dissecting the interplay between JA signaling and \u003cem\u003eFijivirus\u003c/em\u003e defense.\u003c/p\u003e \u003cp\u003eIn the present study, we further conducted haplotype analysis across all the 195 accessions used in resistance accession. The results revealed three OsJAR2 haplotypes defined by two nonsynonymous SNPs. Strikingly, accession harboring H3 haplotype exhibited highest SRBSDV resistance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), underscoring its functional relevance. The H3 haplotype can serve as a valuable template for precision breeding strategies aimed at enhancing SRBSDV resistance. For instance, by utilizing the H3-specific SNPs as molecular markers, breeding programs can efficiently pyramid \u003cem\u003eOsJAR2\u003c/em\u003e with other resistance loci, accelerating the development of resilient rice varieties. Additionally, engineering susceptible accessions to incorporate R91\u0026rsquo;s MYB-responsive elements into their native \u003cem\u003eOsJAR2\u003c/em\u003e promoter could restore early induction kinetics, enabling timely SRBSDV defense.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study identifies R91 as a novel and highly resistant germplasm against SRBSDV and RBSDV, providing dual resistance for breeding programs targeting these devastating rice viruses. The early elevation of JA levels and the rapid activation of defense genes and JA biosynthesis pathway in R91 highlight the pivotal role of timely JA-mediated responses in conferring viral resistance. Through molecular network, GWAS and haplotype analysis, \u003cem\u003eOsJAR2\u003c/em\u003e was identified as a potential hub gene, offering a molecular blueprint for developing rice varieties with enhanced SRBSDV resistance. These findings deepen our understanding of the molecular mechanisms underlying SRBSDV resistance and provide a foundation for innovative strategies to improve rice viral resilience, ultimately contributing to increased yield and global food security.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAOC\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;allene oxide cyclases;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEAS\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;East Asia;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eERFs\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ethylene-responsive element binding factors;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;differentially expressed genes;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; disease incidence;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDpi \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;days post-infection;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;fold change;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Ontology;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; gene co-expression network;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;jasmonic acid;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJMT\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; jasmonic acid carboxyl methyltransferase;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKAT\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ketoacyl-CoA-thiolases;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAD\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;median absolute deviation;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeJA\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Methyl Jasmonate;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMP\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;metabolic pathways;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOPCL1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;OPC-8:0 CoA ligase 1;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOPDA\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;oxoPhytodienoicAcid;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLA\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;phospholipases A;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRBSDV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; rice black-streaked dwarf virus;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAC\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; South Asia Central;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSER\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Sea Islands;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSRBSDV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Southern rice black-streaked dwarf virus;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTFs\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; transcription factors;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTOGCNs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; temporal gene co-expression networks;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTPM\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Transcripts per Kilobase Million;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWAF\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; West Africa;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWBPH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; white-backed planthopper.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJLZ, JSC and BXQ conceived and designed the study. SN and HG and ZBL prepared the materials and conducted the experiments. All authors participated in sample preparation and data analysis. SN, HYG, ZBL, JLZ, JSC and BXQ wrote and edited the manuscript. All authors read and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by This work is funded by the Key Project of Guangdong Basic and Applied Basic Research Foundation (2020B1515420003), the National Natural Science Foundation of China (32372175, 32361143519), Project of Collaborative Innovation Center of GDAAS (XTXM202203),Guangxi Science and Technology Base and Talent Special Project (Guike AC22035090), the Seed industry revitalization project of special fund for rural revitalization strategy in Guangdong Province (2022NPY00005), Guangdong Key Laboratory of New Technology in Rice Breeding. (2023B1212060042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data from this study have been submitted to NGDC BioProject database (https://ngdc.cncb.ac.cn/bioproject/) under accession number PRJCA034117.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaranwal V, Sharma SK, Ghosh A, Gupta N, Singh AK, Diksha D, Thapa P, Jangra S: \u003cstrong\u003eEvidence for association of southern rice black-streaked dwarf virus with the recently 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insects\u003c/strong\u003e. \u003cem\u003eJournal of experimental botany \u003c/em\u003e2017, \u003cstrong\u003e68\u003c/strong\u003e(6):1371-1385.\u003c/li\u003e\n\u003cli\u003eLi P, Liu H, Li F, Liao X, Ali S, Hou M: \u003cstrong\u003eA virus plays a role in partially suppressing plant defenses induced by the viruliferous vectors\u003c/strong\u003e. \u003cem\u003eScientific Reports \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(1):9027.\u003c/li\u003e\n\u003cli\u003eNONG B, QIN B, XIA X, YANG X, ZHANG Z, ZENG Y, DENG G, CAI J, LI Z, LIU P: \u003cstrong\u003eGenetic analysis and fine mapping of a major QTL for the resistance to southern rice black-streaked dwarf disease\u003c/strong\u003e. \u003cem\u003eChinese Journal OF Rice Science \u003c/em\u003e2019, \u003cstrong\u003e33\u003c/strong\u003e(2):135.\u003c/li\u003e\n\u003cli\u003eZhou T, Du L, Wang L, Wang Y, Gao C, Lan Y, Sun F, Fan Y, Wang G, Zhou Y: \u003cstrong\u003eGenetic analysis and molecular mapping of QTLs for resistance to rice black-streaked dwarf disease in rice\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2015, \u003cstrong\u003e5\u003c/strong\u003e(1):10509.\u003c/li\u003e\n\u003cli\u003eChen Y, Jin G, Liu M, Wang L, Lou Y, Baldwin I, Li R: \u003cstrong\u003eMulti-omic analyses reveal key sectors of jasmonate-mediated defense responses in rice\u003c/strong\u003e. \u003cem\u003eThe Plant Cell \u003c/em\u003e2024:koae159.\u003c/li\u003e\n\u003cli\u003eWakuta S, Suzuki E, Saburi W, Matsuura H, Nabeta K, Imai R, Matsui H: \u003cstrong\u003eOsJAR1 and OsJAR2 are jasmonyl-L-isoleucine synthases involved in wound-and pathogen-induced jasmonic acid signalling\u003c/strong\u003e. \u003cem\u003eBiochemical and biophysical research communications \u003c/em\u003e2011, \u003cstrong\u003e409\u003c/strong\u003e(4):634-639.\u003c/li\u003e\n\u003cli\u003eLyons R, Manners JM, Kazan K: \u003cstrong\u003eJasmonate biosynthesis and signaling in monocots: a comparative overview\u003c/strong\u003e. \u003cem\u003ePlant cell reports \u003c/em\u003e2013, \u003cstrong\u003e32\u003c/strong\u003e:815-827.\u003c/li\u003e\n\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":"Rice (Oryza sativa), Southern Rice Black-Streaked Dwarf Virus, Comparative transcriptome, Jasmonic acid, OsJAR2","lastPublishedDoi":"10.21203/rs.3.rs-6552093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6552093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSouthern rice black-streaked dwarf virus (SRBSDV), transmitted by the white-backed planthopper (WBPH), causes severe yield losses (up to 70%) in rice across Asia. However, elite resistant germplasms and molecular defense mechanisms remain elusive, hindering breeding efforts. Effective management strategies are limited, necessitating systematic dissection of SRBSDV resistance networks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eScreening 195 rice accessions identified the indica variety R91 as highly resistant (0% disease incidence), exhibiting dual resistance to SRBSDV and rice black-streaked dwarf virus (RBSDV). South Asian accessions displayed superior resistance compared to East Asian counterparts. Multi-omics analysis revealed rapid defense activation in R91, including a 1.88-fold increase in jasmonic acid (JA) at 5 days post-inoculation (dpi) and upregulation of \u0026gt;\u0026thinsp;2,000 defense genes, while susceptible lines showed JA depletion (70%) and suppressed responses. Time-ordered co-expression networks pinpointed OsJAR2 (JA-Ile synthase, \u003cem\u003eLOC_Os01g12160\u003c/em\u003e) as a central hub, with 3.2-fold higher expression in R91 during early infection. GWAS identified a novel SRBSDV resistance QTL (\u003cem\u003eqSRBSDV1-1\u003c/em\u003e) co-localizing with \u003cem\u003eOsJAR2\u003c/em\u003e, and haplotype analysis validated OsJAR2 as the candidate causal resistance gene, providing genetic evidence for its role in SRBSDV defense.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study identifies R91 as a dual-resistant germplasm to SRBSDV and RBSDV, with OsJAR2-mediated JA signaling playing a pivotal role in conferring resistance. The rapid activation of JA biosynthesis and synchronized defense gene regulation establish a molecular blueprint for resistance breeding. Importantly, \u003cem\u003eOsJAR2\u003c/em\u003e represents a novel candidate functional resistance gene, and its H3 haplotype may serve as a robust genetic marker for accelerating the development of elite SRBSDV-resistant varieties through marker-assisted selection. By integrating germplasm characterization, mechanistic insights, and breeding applications, this work provides a foundation for sustainable rice protection against viral threats.\u003c/p\u003e","manuscriptTitle":"Integrated GWAS and multi-omics reveal rapid JA signaling activation orchestrated by OsJAR2 to drive Southern Rice Black-Streaked Dwarf Virus resistance in rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 07:23:39","doi":"10.21203/rs.3.rs-6552093/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-10T16:52:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T12:45:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-05T15:33:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261508369826862533841596705481517082892","date":"2025-05-30T13:10:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279086829486098124436038504761668336448","date":"2025-05-28T07:46:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T16:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116596434202216867345481779665222004211","date":"2025-05-06T09:39:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T04:54:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-05T17:58:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-02T01:29:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-02T01:27:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-04-29T03:28:58+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":"ad19bfb5-75e6-49d6-acc7-2f95d062bd03","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:07:40+00:00","versionOfRecord":{"articleIdentity":"rs-6552093","link":"https://doi.org/10.1186/s12864-025-12159-8","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-10-29 15:58:11","publishedOnDateReadable":"October 29th, 2025"},"versionCreatedAt":"2025-05-09 07:23:39","video":"","vorDoi":"10.1186/s12864-025-12159-8","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12159-8","workflowStages":[]},"version":"v1","identity":"rs-6552093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6552093","identity":"rs-6552093","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}