Transcriptomic and metabolomic response of Methodist line peppers to BBWV2 infection in Qinghai Province, China

Transcriptomic and metabolomic response of Methodist line peppers to BBWV2 infection in Qinghai Province, China | 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 Transcriptomic and metabolomic response of Methodist line peppers to BBWV2 infection in Qinghai Province, China SHU Qin, YAN Jiahui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6927090/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2026 Read the published version in BMC Plant Biology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Pepper ( Capsicum annuum L.) is an important economic crop in Qinghai Province, China. In recent years, Broad bean wilt virus 2 (BBWV2) has severely affected the production of the local Methodist line pepper, leading to significant yield and quality losses. To elucidate the molecular and physiological mechanisms underlying the pepper's response to BBWV2 infection, we conducted a comprehensive analysis combining transcriptomics and metabolomics. Results Physiological measurements showed that BBWV2 infection significantly increased soluble sugar, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA) contents in pepper leaves. Transcriptome analysis revealed a large number of differentially expressed genes (DEGs), mainly enriched in defense responses, MAPK signaling pathway, and phenylpropanoid metabolism. Metabolomic profiling identified substantial changes in the accumulation of lipids, organic acids, and secondary metabolites. Joint analysis indicated that BBWV2 infection triggered a coordinated regulation between gene expression and metabolite profiles, particularly enhancing the biosynthesis of various secondary metabolites such as flavonoids and lignans. Conclusion These findings provide valuable insights into the defense mechanisms of Methodist line pepper against BBWV2 and offer a theoretical foundation for future breeding of virus-resistant cultivars. Capsicum annuum L. Broad bean wilt virus 2 transcriptomics metabolomics secondary metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Background Pepper ( Capsicum annuum L.) is one of the most widely cultivated economic crops worldwide, and its yield and quality directly affect agricultural productivity and market supply. In Qinghai Province, China, pepper is a high-value vegetable with strong development potential. It plays a significant role in promoting agricultural industrialization and increasing farmers’ income. However, with the expansion of pepper cultivation in the region, the incidence of viral diseases has steadily increased. In 2017, Li et al. [ 1 ] reported the occurrence of pepper mild mottle virus (PMMoV) in greenhouse-grown peppers in Haidong, Qinghai. Later, Wu et al. [ 2 ] detected tomato spotted wilt virus (TSWV) in peppers cultivated in Xining in 2020.. The Xunhua pepper landrace, a prominent local variety in eastern Qinghai, is characterized by high yield and economic benefit. Nevertheless, recent years have seen growing challenges from plant diseases and pests. In 2020, Luo et al. [ 3 ] from our laboratory identified severe infections of broad bean wilt virus 2 (BBWV2) in Xunhua pepper, resulting in significant yield loss and quality degradation, ultimately threatening the sustainability of the local pepper industry. BBWV2 is a member of the Fabavirus genus within the Secoviridae family [ 4 ] . This virus has an exceptionally broad host range, infecting important crop families such as Fabaceae [ 5 ] , Solanaceae [ 6 ] , and Brassicaceae [ 7 ] . With the advancement of high-throughput sequencing technologies, additional host species have been identified, including Mirabilis jalapa [ 8 ] , Perilla frutescens [ 9 ] , and Commelina communis [ 10 ] . BBWV2 exhibits diverse transmission modes, contributing to its high epidemic potential in agricultural systems. Despite its expanding host spectrum, the pathogenic mechanisms of BBWV2 in Xunhua pepper remain poorly understood. To investigate the host–virus interaction at the molecular level, we employed integrative transcriptomic and metabolomic approaches. Transcriptomics provides insights into gene regulatory networks activated in response to viral infection [ 11 ] , while metabolomics reveals dynamic metabolic shifts associated with stress responses [ 12 ] . Combined omics strategies have been successfully applied to uncover complex regulatory mechanisms in plants. For example, Zong et al. [ 13 ] elucidated the role of MYB transcription factors in anthocyanin biosynthesis in tobacco using multi-omics analyses. Lei et al. [ 14 ] studied the effects of nitrogen levels and leaf retention on tobacco molecular traits, and Shen et al. [ 15 ] identified adaptive mechanisms of a chive variety to high-altitude conditions through integrative analysis. In this study, we aimed to systematically investigate the molecular and physiological responses of Xunhua pepper to BBWV2 infection using transcriptome and metabolome profiling. The results provide new insights into the defense mechanisms of pepper against BBWV2 and lay a theoretical foundation for disease control and the breeding of resistant cultivars. Results Physiological and biochemical responses of pepper to BBWV2 infection To investigate the physiological impact of BBWV2 infection in Xunhua pepper, we quantified soluble sugar content, peroxidase (POD) activity, alanine aminotransferase (ALT) activity, and jasmonic acid (JA) levels in leaf samples collected at 1, 3, 5, 7, 9, 11, and 15 days post-inoculation (dpi), with three biological replicates per time point. The results revealed dynamic changes in all four indicators. Soluble sugar content (Fig. 1A) in infected plants showed a continuous increase, peaking at 9 dpi, after which it stabilized and was no longer significantly different from the control at 15 dpi. POD(Fig. 1B) activity exhibited a similar increasing trend, reaching a modest peak at 11 dpi, followed by a slight decline; no significant change was observed in the control group. ALT(Fig. 1C) activity increased gradually in the early stages of infection, reaching a maximum at 11 dpi—59.04% higher than that of the control—before declining thereafter. JA (Fig. 1D) content in infected leaves increased sharply in the early phase, peaked at 9 dpi with a 42.98% increase relative to controls, and subsequently decreased. In contrast, JA levels in the control plants remained relatively constant throughout the experiment. Collectively, these findings suggest that BBWV2 infection induces significant physiological and biochemical alterations in Xunhua pepper, including enhanced sugar accumulation and activation of antioxidant and hormone-related responses, which may play key roles in the plant's stress adaptation and defense mechanisms. Transcriptome Analysis Quality assessment of RNA-Seq data To ensure the reliability of transcriptome data, raw reads were subjected to quality control procedures including adapter trimming and removal of low-quality sequences. On average, 47.88 million raw reads and 46.74 million clean reads were obtained per sample, with an average of 6.86 G clean bases. All samples showed Q30 values exceeding 95% and an average GC content of 42.70%, indicating high-quality sequencing data with no apparent GC bias (Table 2 ). Table 2 Results of quality preprocessing of sequencing data Sample RawReads(M) CleanReads(M) CleanBases(G) Q30(%) GC(%) CK1 49.16 47.89 7.02 97.18 43.6 CK2 48.81 47.65 7 97.1 43.68 CK3 48.24 47.1 6.91 97.03 43.47 C3_1 48.31 47.24 6.94 96.74 43.03 C3_2 48.99 47.86 7.03 97.65 43.05 C3_3 48.72 47.62 7 97.05 42.9 C9_1 47.25 46.1 6.77 97.04 42.59 C9_2 48.88 47.27 6.9 97.22 41.72 C9_3 48.49 47.35 6.95 97.34 41.71 C11_1 43.21 42.18 6.19 97.47 42.46 C11_2 47.86 46.84 6.89 96.88 42.05 C11_3 46.68 45.72 6.73 97.28 42.1 平均 47.88 46.74 6.86 Note: CK is the control group, and C3, C9, and C11 refer to the treatment groups at 3, 9, and 11 days post-inoculation, respectively. Differential gene expression analysis Differentially expressed genes (DEGs) were identified by pairwise comparison between BBWV2-infected samples at 3, 9, and 11 days post-inoculation (dpi) and their respective controls. In total, 11,159 DEGs were detected, including 5667 (C3-vs-CK), 8149 (C9-vs-CK), and 8193 (C11-vs-CK). Among these, the number of up-regulated genes was 2946, 3914, and 3689, respectively, while down-regulated genes totaled 2721, 4235, and 4504. A total of 3373 DEGs were shared across all three comparisons, involving genes associated with disease resistance, secondary metabolism, signal transduction, and several unannotated loci (Fig. 2A, Fig. 2B). GO and KEGG enrichment analysis Gene Ontology (GO) enrichment analysis revealed that up-regulated DEGs were significantly enriched in biological processes (BP) such as defense responses, cytokinin metabolism, and ethylene biosynthesis. In the cellular component (CC) category, enriched terms included plasma membrane, mitochondrial membrane, and membrane-related complexes, indicating cellular remodeling under viral stress. Molecular functions (MF) such as transmembrane transport, heme binding, and chitin binding were prominently enriched (Fig. 3 A). KEGG pathway enrichment showed that the MAPK signaling pathway—plant (cann04016) was the most significantly enriched across all three time points, followed by pathways related to phenylpropanoid biosynthesis, peroxisome function, and plant–pathogen interaction (Fig. 3 B). These pathways suggest a coordinated activation of immune responses, oxidative stress adaptation, and secondary metabolism in response to BBWV2 infection. Based on KEGG mapping, 26 up-regulated DEGs were identified within the MAPK signaling pathway—plant. These included pathogenesis-related proteins (e.g., PR1, PR6), ethylene receptors (ETR1, ETR2), MAP kinase kinases (e.g., MAPKKK18, SAPK3), and oxidative burst-related genes such as RBOHE. The up-regulation of these genes suggests that MAPK cascades play a crucial role in BBWV2-triggered immune responses, involving pathogen recognition, hormonal signaling, and defense activation (Table 3 ). Table 3 Genes with significant changes in theMAPK signaling pathway-plant pathways Metabolism ID Enzyme name MAPK signaling pathway-plant (cann04016) LOC107839239 1-aminocyclopropane-1-carboxylic acid synthase 2 (ACS2) LOC107840074 Calmodulin (CaM) LOC107840155 Basic pathogenesis-related protein 1 (PR1) LOC107840204 Serine/threonine protein kinase (OXI1) LOC107840225 Basic 30 kDa endochitinase (CHI9) LOC107842907 Pathogenesis-related leaf protein 6 (PR1B1) LOC107852024 Ethylene receptor (ETR1) LOC107853533、LOC107877224 Mitogen-activated protein kinase kinase kinase 18 (MAPKKK18) LOC107855207 EIEIN3-binding F-box protein 2 (EBF2) LOC107855301 Protein phosphatase 2C 51 (PP2C51) LOC107859246 Putative LRR receptor-like serine/threonine-protein kinase At3g47570 LOC107859251 Predicted receptor-like protein kinase At3g47110 LOC107859801、LOC107859802 Acidic endochitinase pcht28 LOC107859803 Acidic 27 kDa endochitinase (CHI17) LOC107859806 Basic endochitinase (CHI14) LOC107860003 Mitogen-activated protein kinase 7 (MPK7) LOC107864513 1-aminocyclopropane-1-carboxylic acid synthase 6 (ACS6) LOC107864521 1-aminocyclopropane-1-carboxylic acid synthase 1 (ACS1) LOC107864583 Mitogen-activated protein kinase homolog (MMK2) LOC107868209、LOC107868254、LOC107868265、LOC107868390、LOC107870158、LOC107877593、LOC107877942 Receptor kinase-like protein (Xa21) LOC107868580 Serine/threonine protein kinase (SAPK3) LOC107869344 LRR receptor-like serine/threonine-protein kinase (EFR) LOC107873245 Ethylene receptor 2 (ETR2) LOC107875521 Putative protein phosphatase 2C 24 (PP2C24) LOC107877834 Respiratory burst oxidase homolog E (RBOHE) LOC107879918 Pathogenesis-related protein 1A (PR1A) Metabolomic Analysis Principal component analysis (PCA) Principal component analysis (PCA) was employed to assess global metabolomic differences across the four sampling time points. As shown in Fig. 4 , PCA clearly separated the groups along the first two principal components. PC1 explained 50.7% of the total variance and PC2 accounted for 16.9%, with a cumulative contribution of 67.6%. The tight clustering within groups and clear separation between groups indicate significant temporal variation in metabolic profiles in response to BBWV2 infection. Identification of differentially accumulated metabolites (DAMs) Differential metabolites were identified using volcano plot analysis (p < 0.05, fold change ≥ 2 or ≤ 0.5). In the C3-vs-CK comparison (Fig. 5 A), 537 metabolites were identified, including 488 up-regulated and 49 down-regulated. For C9-vs-CK (Fig. 5 B), 1098 metabolites were identified, with 896 up-regulated and 202 down-regulated. In C11-vs-CK (Fig. 5 C), 1177 metabolites were detected, of which 918 were up-regulated and 259 were down-regulated. Across all comparisons, the number of up-regulated metabolites exceeded down-regulated ones, suggesting metabolic activation under BBWV2 stress. Venn diagram analysis revealed that 839 differential metabolites were shared across the three comparisons, while 364, 167, and 186 were unique to C3-vs-CK, C9-vs-CK, and C11-vs-CK, respectively. These metabolites included lipids, organic acids, heterocyclic compounds, and phenylpropanoids. Ten metabolites were significantly up-regulated (p < 0.01) across all time points, including methotrexate-D9 and cyclosulfamuron, which were annotated in the KEGG database (Table 4 ). Table 4 Very significant level (P ≤ 0.01) Differential metabolites Number Metabolites Norm KEGG 1 2'-GMP (2'-guanosine monophosphate) log2FC 2 Dihydropiperlonguminine log2FC 3 Trimethoprim-D9 log2FC C01965 4 (2R,3R,4R)-3, 4, 5-trihydroxy-1-oxopent-2-enyl-2-amino-3-thiopropanoic acid log2FC 5 Pyroxsulam log2FC C18852 6 Dibutyryl cyclic 3', 5'-cyclic adenosine monophosphate (dibutyryl-cAMP) log2FC 7 Nα-Cinnamoyl-L-histidine log2FC 8 Temafloxacin log2FC 9 Aristolochic acid B log2FC 10 TPPU (1-Trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl)urea) log2FC Pathway enrichment analysis of differential metabolites KEGG pathway enrichment analysis (p < 0.05) revealed that DAMs were significantly enriched in 18, 20, and 20 pathways in C3-vs-CK(Fig. 6 A), C9-vs-CK(Fig. 6 B), and C11-vs-CK(Fig. 6 C), respectively. Six pathways were consistently enriched across all comparisons, including lysine biosynthesis, arginine biosynthesis, glycerophospholipid metabolism, alpha-linolenic acid metabolism, flavonoid biosynthesis, and biosynthesis of various plant secondary metabolites. The latter pathway was most significantly enriched in all three comparisons, indicating its key role in plant defense against BBWV2 through antiviral and antioxidant mechanisms. Dynamics of plant secondary metabolite biosynthesis Further analysis of the KEGG pathway "biosynthesis of various plant secondary metabolites" revealed five metabolites associated with BBWV2 response(Table 5 ). Among them, four compounds—pellitorine, medicarpin, hordenine B, and 7-demethoxycurcumin—were consistently up-regulated, while N1-trans-feruloylbutylamine was down-regulated. The overall increase in secondary metabolite biosynthesis reflects an enhanced defense state in infected pepper leaves. Table 5 Differential metabolites in the biosynthesis of various plant secondary metabolites KEGG id Metabolites log2FoldChange (C3-vs-CK) log2FoldChange (C9-vs-CK) log2FoldChange (C11-vs-CK) Mode of expression C01864 Piperlongumine 2.7045303 2.9266147 2.4213894 Up C05158 Melilotoside 1.9774879 2.1613523 2.5195974 Up C08308 Hordenine B 3.0061817 3.2390607 4.7876966 Up C18083 7-Demethylpiperlonguminine 4.0961131 4.9138803 5.343856 Up C18325 N1-trans-Feruloylbutylamine 1.7235795 -2.6094813 -1.7633178 Down Integrated analysis of transcriptome and metabolome Global gene–metabolite correlation To explore the relationship between gene expression and metabolite accumulation under BBWV2 stress, correlation distribution density plots were generated for C3, C9, and C11 versus CK (Fig. 7 ). Strong positive (r > 0.7) and negative (r < − 0.7) correlations were observed, indicating both synergistic and antagonistic regulation. At C3(Fig. 7 A), positively correlated genes and metabolites dominated, suggesting coordinated activation. By C9(Fig. 7 B), negative correlations increased, reflecting possible metabolic imbalances. At C11(Fig. 7 C), both positive and negative correlations intensified and balanced, implying complex transcriptional and metabolic reprogramming. Joint KEGG enrichment analysis KEGG enrichment was performed at both transcript and metabolite levels for each comparison. In C3-vs-CK(Fig. 8 A), glycerophospholipid metabolism and secondary metabolite biosynthesis were significantly enriched, with higher transcript-level significance. In C9-vs-CK(Fig. 8 B), biosynthesis of various plant secondary metabolites was significantly enriched at both levels, indicating coordinated transcriptional and metabolic regulation. In contrast, C11-vs-CK(Fig. 8 C)showed moderate enrichment, with stronger gene-level than metabolite-level responses, implying transcriptional changes not yet translated into metabolite accumulation. Biosynthesis of secondary metabolites under viral stress Targeted pathway analysis revealed distinct temporal dynamics of secondary metabolism. In C3(Fig. 9A), lignan and flavonoid biosynthesis were activated, accompanied by increased levels of feruloyl-CoA and angelol. At C9(Fig. 9B), flavonoids such as coumarate and angelol peaked, while cannabinoid and alkaloid biosynthesis declined. At C11(Fig. 9C), scopoletin and feruloyl-CoA remained elevated, but crocin and picrocrocin decreased, suggesting metabolic resource reallocation. Persistent up-regulation of key defense compounds across time points highlights their potential roles in antiviral resistance. RT-qPCR Validation To validate the reliability of the transcriptomic profiles, quantitative real-time PCR (qRT-PCR) was performed on a subset of differentially expressed genes. Fifteen genes, including MTB , RBCS , RD21B , GILT , CAT , ACO , RCA , PSBR , SBE1 , HSC-2 , UBI11 , PER42 , AP1 , petC , and TSJT1 , were randomly selected for expression validation (Fig. 10 ). Relative expression levels were quantified based on both fragments per kilobase of transcript per million mapped reads (FPKM) from RNA-seq and qRT-PCR assays. A dual Y-axis bar-line plot was generated with time points post-inoculation as the X-axis. As illustrated in Fig. 10 , the expression patterns derived from qRT-PCR were highly concordant with the RNA-seq data, confirming the robustness and accuracy of the transcriptome analysis. Discussion In this study, we measured changes in four physiological and biochemical indicators—soluble sugars, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA)—in pepper leaves under BBWV2 infection. The results revealed that the contents of soluble sugars, POD, ALT, and JA were all significantly elevated compared to the control, with ALT and JA exhibiting a dynamic pattern of initial increase followed by a decrease. These findings suggest that BBWV2 infection induces complex physiological and biochemical adjustments in pepper leaves, reflecting a multilayered stress response and intricate plant–virus interactions. Virus infection disrupts normal plant metabolism, thereby affecting growth and development [ 18 ] . Measurement of physiological and biochemical parameters enables assessment of the metabolic disturbances caused by viral stress. Soluble sugars not only serve as energy sources but also act as signaling molecules that activate defense genes and secondary metabolite biosynthesis, thus limiting viral spread. Previous studies have shown that viral infections, such as ZYMV in watermelon [ 19 ] , and infections in lily [ 20 ] and cassava [ 21 ] , similarly increase soluble sugar levels. Furthermore, virus infection inhibits photosynthesis and alters carbohydrate metabolism, resulting in elevated soluble sugar content [ 22 ] . In rice, blast disease infection significantly increases soluble sugar accumulation [ 23 ] . Excessive accumulation of reactive oxygen species (ROS) is a common phenomenon under biotic stress. POD plays a critical role in mitigating oxidative damage by catalyzing the decomposition of hydrogen peroxide (H₂O₂) [ 24 ] . In our study, POD activity progressively increased following BBWV2 infection, suggesting an activated antioxidant defense mechanism. This finding is consistent with previous studies reporting increased POD activity under pathogen attack [ 25 – 28 ] . ALT, a key enzyme in amino acid metabolism, exhibited increased activity under BBWV2 stress, likely reflecting enhanced amino acid turnover and membrane damage associated with virus-induced metabolic disturbances [ 29 – 31 ] . Regarding JA, an essential signaling molecule in plant defense responses, its content rose sharply during early infection stages and gradually declined later. This dynamic is consistent with activation of systemic acquired resistance and restoration of normal physiological functions [ 32 – 34 ] . Based on the physiological findings, transcriptomic and metabolomic analyses were conducted at 1, 3, 9, and 11 days post-inoculation. Transcriptome analysis identified significant differential gene expression involving defense-related genes, secondary metabolism genes, and signal transduction components. GO enrichment indicated that upregulated genes were associated with defense responses, membrane structure, and energy/material transport. These results are consistent with previous findings that pathogen infection triggers the synthesis of lignin, phytoalexins, and other secondary metabolites through innate immune responses [ 35 – 39 ] , and that plant immune systems regulate various signaling pathways to rapidly respond to pathogen attacks [ 40 , 41 ] . KEGG pathway analysis revealed that the MAPK signaling pathway was significantly enriched across all comparison groups, highlighting its pivotal role in pepper defense against BBWV2. Core MAPK components such as MPK3, MPK6, and MPK4 regulate multiple disease resistance processes, including ethylene and phytoalexin biosynthesis, disease resistance gene expression, secondary metabolite production, and stomatal immunity [ 42 , 43 ] . In our study, key upregulated genes within the MAPK pathway included PR1, PR6, and ethylene biosynthesis-related genes, suggesting activation of MAPK-mediated immune signaling in response to BBWV2 infection. Metabolomic profiling identified 839 differential metabolites common to the three comparison groups, mainly including lipids, organic acids, heterocyclic compounds, and phenolic compounds. Ten significantly enriched metabolites were consistently upregulated, among which methoxybenzylpyrimidine-D9 and sulfosulfuron were functionally annotated. Top differential metabolites such as aristolochic acid B and 2'-GMP were highly expressed across all time points, suggesting a role in stress mitigation or antiviral defense. KEGG enrichment of metabolomic data demonstrated significant activation of secondary metabolite biosynthesis pathways under BBWV2 infection. These secondary metabolites are involved in antiviral defense, ROS scavenging, and enhancing disease resistance. Integrated transcriptomic and metabolomic analysis confirmed consistent enrichment of secondary metabolism pathways, reinforcing the role of secondary metabolites in plant adaptation to viral stress. Specifically, alkaloid, phenylpropanoid, and flavonoid biosynthesis pathways were significantly affected. Alkaloid biosynthesis was downregulated, while phenylpropanoid and flavonoid biosynthesis were enhanced. Key metabolites such as angelol, feruloyl-CoA, and (+)-coumarate accumulated, driven by upregulation of phenylpropanoid pathway genes. These findings suggest that BBWV2 infection modulates metabolic networks, repressing alkaloid production while promoting phenylpropanoid- and flavonoid-derived compounds to enhance antiviral defenses. Secondary metabolites are crucial for plant adaptation, environmental resistance, and survival [ 44 – 46 ] . In this study, BBWV2 infection induced notable shifts in the biosynthesis of alkaloids, phenylpropanoids, and flavonoids in Methodist line pepper. Alkaloids, with broad antiviral activities [ 47 ] , and phenylpropanoid-derived flavonoids, important for stress responses [ 48 – 50 ] , were differentially regulated, highlighting the metabolic plasticity of peppers under viral stress. In conclusion, both transcriptomic and metabolomic analyses revealed distinct molecular and metabolic reprogramming in Methodist line pepper following BBWV2 infection, providing valuable insights into the plant’s antiviral defense mechanisms and offering a theoretical basis for breeding virus-resistant cultivars. Conclusion In this study, we performed a comprehensive investigation of the physiological, transcriptomic, and metabolomic responses of Methodist line pepper to BBWV2 infection. Our results demonstrated that BBWV2 induces significant physiological changes, including elevated levels of soluble sugar, POD, ALT, and JA. Transcriptome profiling revealed that differentially expressed genes were enriched in defense-related pathways such as MAPK signaling and phenylpropanoid biosynthesis. Concurrently, metabolomic analysis identified widespread accumulation of lipids and secondary metabolites, especially flavonoids and lignans. The integrative analysis of gene expression and metabolite abundance highlighted a strong coordination between transcriptional regulation and metabolic reprogramming during viral infection. These findings provide novel insights into the molecular defense mechanisms of pepper against BBWV2 and lay a theoretical foundation for breeding virus-resistant cultivars. Materials and Methods Plant materials and virus inoculation Pepper ( Capsicum spp .) Seeds of the Xunhua County chili variety in Qinghai Province were purchased from local farmers. The seeds are conventionally cultivated local varieties that are artificially grown, not collected in the wild. Seeds were soaked in sterile water for 12 hours, then sown in nutrient soil (50 pots total) and grown under controlled conditions in a growth chamber (28 ± 2°C, 70 ± 5% RH, 16 h light/8 h dark). Seedlings at the 4–6 leaf stage were used for inoculation. Agrobacterium-mediated inoculation was performed following previously described protocols [ 16 , 17 ] . Twenty-five plants were infiltrated with BBWV2-containing constructs, and the remaining plants received an empty vector as negative controls. Leaf samples were collected at 1, 3, 5, 7, 9, 11, 13, and 15 days post-inoculation (dpi). Each time point included three biological replicates. Collected tissues were flash-frozen in liquid nitrogen and stored at − 80°C for further analyses. Physiological and biochemical measurements Soluble sugar, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA) contents were determined using assay kits (Boxbio, Beijing, China) following the manufacturer’s instructions. Each treatment included three biological and three technical replicates to ensure reproducibility and accuracy. RNA extraction, library construction, and transcriptome sequencing Transcriptomic data have been uploaded to the NCBI Sequence Read Archive under accession number PRJNA1290684. Total RNA was extracted from leaves at 1, 3, 9, and 11 dpi using the TRIzol reagent (Invitrogen), and RNA quality was assessed with a NanoDrop 2000 spectrophotometer and Agilent 2100 Bioanalyzer. Libraries were constructed using the VAHTS Universal V5 RNA-Seq Library Prep Kit and sequenced on the Illumina NovaSeq 6000 platform (OE Biotech Co., Ltd., Shanghai, China). Low-quality reads were filtered using fastp , and clean reads were mapped to the reference Capsicum genome (GCF_000710875.1) with HISAT2 . Gene expression levels were normalized to FPKM values. Raw read counts were obtained with HTSeq-count , and differentially expressed genes (DEGs) were identified using DESeq2 with thresholds of FDR ≤ 0.05 and |log2 fold change| ≥ 2. Functional enrichment and clustering analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the hypergeometric test based on annotations from the GO database ( http://geneontology.org/ ) and KEGG database ( http://www.genome.jp/kegg/ ). Enriched terms with p-values < 0.05 were considered statistically significant. Quantitative real-time PCR (qRT-PCR) validation Selected DEGs were validated using qRT-PCR. Primers were designed using PrimerQuest Tool (IDT, USA)(Table 1 ). qRT-PCR was conducted using SYBR Green Master Mix (TIANGEN, China), and reactions were run on a Bio-Rad CFX96 system. Actin7 was used as the reference gene. Relative gene expression was calculated using the 2^−ΔΔCT method. Each reaction included three biological and three technical replicates. Metabolomic profiling and data analysis Metabolomic data have been uploaded to the National Genomics Data Center OMIX platform under accession number OMIX010952-01.Frozen pepper leaves were extracted with 50% methanol buffer. Supernatants were transferred to 96-well plates for LC-MS/MS analysis. Pooled quality control (QC) samples were prepared from equal volumes of all extracts. Analyses were performed using a Waters ACQUITY UPLC I-Class Plus system coupled to a Thermo Q Exactive HF mass spectrometer. Chromatographic separation was achieved on an ACQUITY UPLC T3 column, and data were acquired in both positive and negative ionization modes with a mass range of 70–1050 m/z and resolution of 60,000. Raw LC-MS data were processed using Progenesis QI v3.0 for peak picking, alignment, and normalization. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were used to assess metabolic differences. Differential metabolites were identified through combined univariate and multivariate statistical analyses. Functional annotation and pathway enrichment were conducted using KEGG, Reactome, and GSEA databases. Cluster visualization was performed with TBtools. Table 1 Primers used for qRT-PCR validation Gene name Primer sequence MTB F: ATGGAAGTCTTCAGCGTCTA R: GACCGTCTTGACTGCTCTGA RBCS F: CGTAACGAAGTAAATGGTCGT R: TAACGATCTCCAGCAGGTCTA RD21B F: TGGCTCAGGATTGTGAAGGT R: ACTCCATCGTGGAACTCCAG GILT F: CGGATGATCTCGTAGCTCAAAT R: CATTTCCTCTCAGGCGACTT CAT1 F: CGGATGATCTCGTAGCTCAAAT R: CATTTCCTCTCAGGCGACTT CAT2 F: GTGTCTTCTCCTATGCCGATAC R: AATCCCTCATGGTGGTTGTT CAT3 F: CAGGCAAGACAGGTTTGTTAAG R: GTTCTGACCTGAGACCAGTAAG ACO F: CGACGGTAATGTGTACCCAAAG R: GAAGCACAGGTCGAAGGTATTC RCA2 F: GACAGATTTCTTCGGTGCTTTG R: CAGCCTCTTTCCGATCTTCTC PSBR F: TGGTGGGTTCTCTAAACAATGA R: CCATCGACGTTAGCTCCATAC SBE1 F: GTCAAAGCTTCCAGAGCTAGT R: CTCCTGGCTTCATTTGGTATCT HSC-2 F: TCACAGTGTGCTTCGACATT R: GAGAGTCTGCCCTTGTCATTAG UBI11 F: CAGAAGGAATCAACCCTCCATC R: GTCAATGGTGTCAGAACTCTCC PER42 F: AAAGGAGGCTAGCAGAAGTG R: CCACCACCAACAGGCTAATA AP1 F: TGCAGTGGTCTGTATCAAGAAG R: AGCCAAGGTTGCAGTTGTA petC F: CGACAAGACTCTAGCGACATAC R: GATCCATGGCAAGGACATAGAA TSJT1 F: TGCTGCAGATGGGTCTTTAG R: TAAGAAACCTGGAGGAAATGGG Actin7 F: CCACCATGTTCCCTGGTATTG R: TCCAGACACTGTACTTCCTCTC Abbreviations BBWV2 Broad bean wilt virus 2 POD Peroxidase ALT Alanine aminotransferase JA jasmonic acid DEGs Differentially expressed genes KEGG Kyoto encyclopedia of genes and genomes GO Gene ontology BP Biological processes CC Cellular component MF Molecular functions PCA Principal component analysis DAMs Differentially accumulated metabolites FDR False discovery rate FPKM Fragments per kilobase million LC-MS/MS Liquid chromatograph mass spectrometer/ Mass spectrometer ACQUITY UPLC Ultra performance liquid chromatography PLS-DA Partial least squares discriminant analysis HCA Hierarchical clustering analysis GSEA Gene set enrichment analysis. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are available in the NCBI Sequence Read Archive (PRJNA1290684) (https://submit.ncbi.nlm.nih.gov/subs/sra/SUB15455641) and the National Genomics Data Center OMIX repository (OMIX010952) (https://ngdc.cncb.ac.cn/omix/releaseList). Competing Interests The authors declare that they have no competing interests. Author's Information First author: Shu Qin, female, master's degree, research focus: plant pathology. E-mail: [email protected] Corresponding author: Yan Jiahui, female, associate researcher, research focus: plant pathology. E-mail: [email protected] Funding This study was supported by the Qinghai Provincial Science and Technology Department International Cooperation Special Project (2022-HZ-806).The funders had no role in the experimental design, data collection and analysis or writing the manuscript. Acknowledgements The authors thank the Plant Protection Research Institute of the Qinghai Academy of Agricultural and Forestry Sciences for providing the laboratory facilities, and Ouyi Bio for providing the data analysis cloud platform. References Li TF, Wu SH, Zhao WH, Ji YH, Guo QY. Molecular detection of Pepper mild mottle virus in facility-grown peppers in Qinghai Province[J]. Jiangsu Agricultural Sci. 2017;33(4):958–60. Wu SH, Tu LQ, Xian WR, Yan JH, Gao LN, Ji Y, Tao XR, Zhou YJ, Guo QY, Ji YH. Detection and identification of Tomato spotted wilt virus in peppers from Qinghai Province[J]. J Hortic. 2020;47(7):1391–400. Luo HL, Yuan L, Weng H, Yan JH, Guo QY, Wang WQ, Ma XM. 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Crystal structure of histidinol phosphate aminotransferase (HisC) from Escherichia coli,and its covalent complex with pyridoxal-5′-phosphate and lhistidinolphosphate[J]. J Mol Biol. 2001;311(4):761–76. Blee E. Impact of phyto-oxylipins in plant defense[J]. Trends Plant Sci. 2002;7(7):315–22. Yue Y, Liang QL, Wei LX, He H, Meng XP, Jiang YL, Lin R, Chen YE. Effects of mixed infection of alfalfa mosaic virus and white clover mosaic virus on the levels of five hormones in Nicotiana benthamiana[J]. Grassl Sci. 2021;38(11):2255–65. Mei CS, Qi M, Sheng GY, Yang YN. Inducible overexpression of a rice allene oxide synthase gene increases the endogenous jasmonic acid level,PR gene expression,and host resistance to fungal infection[J]. Mol Plant Microbe Interact. 2006;19(10):1127–37. Deyoung BJ, Innes RW. Plant NBS-LRR proteins in pathogen sensing and host defense[J]. Nat Immunol. 2006;7(12):1243–9. Zhang QP. Molecular mechanism analysis of resistance and susceptibility to Sclerotinia stem rot in Brassica napus based on RNA-seq technology[D]. Hunan Changsha, Hunan Agricultural University; 2014. Van Loon LC, Rep M, Pieterse CM. Significance of inducible defense-related proteins in infected plants[J]. Phytopathology. 2006;44(44):1–7. Wu XJ. Cloning and identification of candidate genes for bacterial brown spot resistance in maize and transcriptome analysis of NILs[D]. Beijing: China Agricultural University; 2014. Yan JW. Identification and functional analysis of defense-related genes for citrus canker resistance[D]. Hunan Changsha: Hunan Agricultural University; 2014. Jones JDG, Dangl JL. The plant immune system[J]. Nature. 2006;444(7117):323–9. Boller T, Felix G. A renaissance of elicitors: perception of microbe-associated molecular patterns and danger signals by pattern-recognition receptors[J]. Plant Biol. 2009;60(60):379–406. Zhang MM, Su JB, Zhang Y, Xu J, Zhang SQ. Conveying endogenous and exogenous signals: MAPK cascades in plant growth and defense[J]. Curr Opin Plant Biol. 2018;45:1–10. Wang DC, Wei LR, Liu T, Ma JB, Huang KY, Guo HM, Huang YF, Zhang L, Zhao J, Tsuda K, Wang YM. Suppression of ETI by PTI priming to balance plant growth and defense through an MPK3/MPK6-WRKYs-PP2Cs module[J]. Mol Plant. 2023;16(5):903–18. Jiang N, Qin LY, Li L, Miu JH. Effects of environmental stress on secondary metabolites in medicinal plants[J]. Hubei Agricultural Sci. 2012;51(8):1528–32. Gao Y. Effect of water on root growth and accumulation of active ingredients in root-based Chinese medicinal herbs[J]. Mod Res Pract Traditional Chin Med. 2004;12(3):10–5. Niinemets U. Uncovering the hidden facets of drought stress: secondary metabolites make the difference[J]. Tree Physiol. 2015;36(2):129–32. Mohamadi N, Sharififar F, Pournamdari M, Ansari M. A review on biosynthesis, analytical techniques, and pharmacological activities of trigonelline as a plant alka loid[J]. J Diet Supplements. 2018;15(2):207–22. Zeng LT, Yang ZY. Advances in the biosynthesis and stress response of phenylpropanoid/benzene ring-type volatile compounds in tea plants[J]. J Trop Subtropical Bot. 2019;27(5):591–600. Ferreyra MLF, Rius SP, Casati P. Flavonoids: bi osynthesis, biological functions, and biotechnological appli cations[J]. Front Plant Sci. 2012;3:222. Yang WL, Bai ZY, Zou MZ, Wang XR, Xie JK, Zhang FT. Full-length transcriptome sequencing of Cervus elaphus and mining of genes related to the biosynthesis of secondary metabolites[J]. J Jiangxi Normal Univ (Natural Sci Edition). 2023;47(01):99–110. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 15 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6927090","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486516075,"identity":"ef863370-78ee-4272-9f06-9794061c7ff7","order_by":0,"name":"SHU Qin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"SHU","middleName":"","lastName":"Qin","suffix":""},{"id":486516076,"identity":"b703fee1-7940-4b8d-9bb0-bb96ed5922ac","order_by":1,"name":"YAN Jiahui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3Pv2rDMBDH8ROCm45mvZAQv4JDwJMfRiKgrZDRQ6AOBHto/qwplD5Dx44CgSdlzxjvGeqtU2n2BNvZOugz3xd+BxAE/xBGztrmN30ponp1VtmyO3li1PUBjdjB3MVnX3UnE6bZjNCJDzBmWK9lj2GjPBkRGYngk0znCIPyVbUnY2uG75wiik1y0l9jYH/8bE9AVXyJDaGka+IRYn7uSnTBpBwjUrLQheyR8FxOyboYCQ30S6gS9VtuFLJ0rHxFnb9E5f7bNnmqooNYNT/ZcjIot+3JDXrsPAiCILjrD+ZERomv1zo6AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"YAN","middleName":"","lastName":"Jiahui","suffix":""}],"badges":[],"createdAt":"2025-06-19 03:38:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6927090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6927090/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-08040-1","type":"published","date":"2026-02-27T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86954867,"identity":"552bc489-51e5-42ba-9655-0e9f7a8d01fe","added_by":"auto","created_at":"2025-07-17 14:59:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45488,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in soluble sugar, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA) contents in pepper seedlings after BBWV2 inoculation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/123a671436e79817ee631ce0.png"},{"id":86953822,"identity":"acbc5928-6b11-4230-969b-488e221d470d","added_by":"auto","created_at":"2025-07-17 14:51:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36993,"visible":true,"origin":"","legend":"\u003cp\u003eA Number of DEGs in each comparison group\u003c/p\u003e\n\u003cp\u003eB Differently expressed genes statistical chart\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/c1aec53c29852afdb7b66625.png"},{"id":86955394,"identity":"a041da3e-82f0-42ea-abdc-a66c3e999a6b","added_by":"auto","created_at":"2025-07-17 15:07:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119549,"visible":true,"origin":"","legend":"\u003cp\u003eA Significantly enriched plots of down-regulated DEGs GO in different comparison groups\u003c/p\u003e\n\u003cp\u003eB Significantly enriched plots of down-regulated DEGs KEGG in different comparison groups\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/f2a5e8762977983a91e9408b.png"},{"id":86953825,"identity":"3d3b9524-e871-43ff-901f-202822d341b6","added_by":"auto","created_at":"2025-07-17 14:51:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32509,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score plot of samples from four time points\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/4816899969f7ec84c6022938.png"},{"id":86953831,"identity":"e6f3a9f3-895a-477d-8f46-91b8186db078","added_by":"auto","created_at":"2025-07-17 14:51:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71991,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differential metabolites between different comparison groups\u003c/p\u003e\n\u003cp\u003eNote: Red dots represent upregulated metabolites in the experimental group, blue dots represent downregulated metabolites, and gray dots indicate non-significantly changed metabolites. The x-axis shows the log₂(FC) values between the two groups, and the y-axis represents the –log₁₀(p-value).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/82c522c22e668d8f52283fbe.png"},{"id":86953827,"identity":"e760e3a0-643c-4702-8428-d2c6b8b5fde1","added_by":"auto","created_at":"2025-07-17 14:51:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":73296,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG classification of differential metabolites between different comparison groups\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/156baf83986977fa635599f3.png"},{"id":86953834,"identity":"8a6ea401-e2c9-4959-9e22-64f28520c52b","added_by":"auto","created_at":"2025-07-17 14:51:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49482,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation distribution density map between gene expression and metabolite abundance at different time points\u003c/p\u003e\n\u003cp\u003eNote: The X-axis is Pearson correlation coefficient, and the Y-axis is density distribution. A: 3 days after stress ( C3 ); B: 9 days after stress ( C9 ); C: 11 days after stress ( C11 ).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/e6a5d0be849a0346be2f60db.png"},{"id":86954868,"identity":"66e518f5-6e23-4fb7-8bac-47d674cb321b","added_by":"auto","created_at":"2025-07-17 14:59:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":59565,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant comparison of KEGG pathway enrichment\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/05aca2c0e524b44f3bb6bf85.png"},{"id":86953836,"identity":"9ddff5f3-2453-43d6-8b9c-e9727a173170","added_by":"auto","created_at":"2025-07-17 14:51:58","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":88875,"visible":true,"origin":"","legend":"\u003cp\u003eBiosynthesis pathway map of secondary metabolites in different periods\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/94c659337d4201aa0e7b8956.png"},{"id":86955396,"identity":"d89bc035-ee8f-4f91-8f39-a4f76fc73449","added_by":"auto","created_at":"2025-07-17 15:07:58","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":77995,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the qRT-PCR and FPKM expression trend of differential genes\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/875a9b29ffc3bdf84e4635d3.png"},{"id":103766986,"identity":"910962d8-a687-4622-a37c-080b1432327e","added_by":"auto","created_at":"2026-03-02 16:20:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1869245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6927090/v1/ff0632f3-4be5-428a-9d07-007410396585.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic and metabolomic response of Methodist line peppers to BBWV2 infection in Qinghai Province, China","fulltext":[{"header":"Background","content":"\u003cp\u003ePepper (\u003cem\u003eCapsicum annuum\u003c/em\u003e L.) is one of the most widely cultivated economic crops worldwide, and its yield and quality directly affect agricultural productivity and market supply. In Qinghai Province, China, pepper is a high-value vegetable with strong development potential. It plays a significant role in promoting agricultural industrialization and increasing farmers\u0026rsquo; income. However, with the expansion of pepper cultivation in the region, the incidence of viral diseases has steadily increased. In 2017, Li et al.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003ereported the occurrence of pepper mild mottle virus (PMMoV) in greenhouse-grown peppers in Haidong, Qinghai. Later, Wu et al.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003edetected tomato spotted wilt virus (TSWV) in peppers cultivated in Xining in 2020..\u003c/p\u003e\u003cp\u003eThe Xunhua pepper landrace, a prominent local variety in eastern Qinghai, is characterized by high yield and economic benefit. Nevertheless, recent years have seen growing challenges from plant diseases and pests. In 2020, Luo et al.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003efrom our laboratory identified severe infections of broad bean wilt virus 2 (BBWV2) in Xunhua pepper, resulting in significant yield loss and quality degradation, ultimately threatening the sustainability of the local pepper industry.\u003c/p\u003e\u003cp\u003eBBWV2 is a member of the \u003cem\u003eFabavirus\u003c/em\u003e genus within the \u003cem\u003eSecoviridae\u003c/em\u003e family\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. This virus has an exceptionally broad host range, infecting important crop families such as Fabaceae\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, Solanaceae\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and Brassicaceae\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. With the advancement of high-throughput sequencing technologies, additional host species have been identified, including \u003cem\u003eMirabilis jalapa\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003ePerilla frutescens\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, and \u003cem\u003eCommelina communis\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. BBWV2 exhibits diverse transmission modes, contributing to its high epidemic potential in agricultural systems. Despite its expanding host spectrum, the pathogenic mechanisms of BBWV2 in Xunhua pepper remain poorly understood.\u003c/p\u003e\u003cp\u003eTo investigate the host\u0026ndash;virus interaction at the molecular level, we employed integrative transcriptomic and metabolomic approaches. Transcriptomics provides insights into gene regulatory networks activated in response to viral infection\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, while metabolomics reveals dynamic metabolic shifts associated with stress responses\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Combined omics strategies have been successfully applied to uncover complex regulatory mechanisms in plants. For example, Zong et al.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003eelucidated the role of MYB transcription factors in anthocyanin biosynthesis in tobacco using multi-omics analyses. Lei et al.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003estudied the effects of nitrogen levels and leaf retention on tobacco molecular traits, and Shen et al.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003eidentified adaptive mechanisms of a chive variety to high-altitude conditions through integrative analysis.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to systematically investigate the molecular and physiological responses of Xunhua pepper to BBWV2 infection using transcriptome and metabolome profiling. The results provide new insights into the defense mechanisms of pepper against BBWV2 and lay a theoretical foundation for disease control and the breeding of resistant cultivars.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhysiological and biochemical responses of pepper to BBWV2 infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the physiological impact of BBWV2 infection in \u003cem\u003eXunhua\u003c/em\u003e pepper, we quantified soluble sugar content, peroxidase (POD) activity, alanine aminotransferase (ALT) activity, and jasmonic acid (JA) levels in leaf samples collected at 1, 3, 5, 7, 9, 11, and 15 days post-inoculation (dpi), with three biological replicates per time point.\u003c/p\u003e\n\u003cp\u003eThe results revealed dynamic changes in all four indicators. Soluble sugar content (Fig.\u0026nbsp;1A) in infected plants showed a continuous increase, peaking at 9 dpi, after which it stabilized and was no longer significantly different from the control at 15 dpi. POD(Fig.\u0026nbsp;1B) activity exhibited a similar increasing trend, reaching a modest peak at 11 dpi, followed by a slight decline; no significant change was observed in the control group. ALT(Fig.\u0026nbsp;1C) activity increased gradually in the early stages of infection, reaching a maximum at 11 dpi\u0026mdash;59.04% higher than that of the control\u0026mdash;before declining thereafter. JA (Fig.\u0026nbsp;1D) content in infected leaves increased sharply in the early phase, peaked at 9 dpi with a 42.98% increase relative to controls, and subsequently decreased. In contrast, JA levels in the control plants remained relatively constant throughout the experiment.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings suggest that BBWV2 infection induces significant physiological and biochemical alterations in Xunhua pepper, including enhanced sugar accumulation and activation of antioxidant and hormone-related responses, which may play key roles in the plant\u0026apos;s stress adaptation and defense mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment of RNA-Seq data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the reliability of transcriptome data, raw reads were subjected to quality control procedures including adapter trimming and removal of low-quality sequences. On average, 47.88 million raw reads and 46.74 million clean reads were obtained per sample, with an average of 6.86 G clean bases. All samples showed Q30 values exceeding 95% and an average GC content of 42.70%, indicating high-quality sequencing data with no apparent GC bias (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of quality preprocessing of sequencing data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRawReads(M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCleanReads(M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCleanBases(G)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ30(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGC(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC9_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC9_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC9_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC11_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC11_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC11_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e平均\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: CK is the control group, and C3, C9, and C11 refer to the treatment groups at 3, 9, and 11 days post-inoculation, respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified by pairwise comparison between BBWV2-infected samples at 3, 9, and 11 days post-inoculation (dpi) and their respective controls. In total, 11,159 DEGs were detected, including 5667 (C3-vs-CK), 8149 (C9-vs-CK), and 8193 (C11-vs-CK). Among these, the number of up-regulated genes was 2946, 3914, and 3689, respectively, while down-regulated genes totaled 2721, 4235, and 4504. A total of 3373 DEGs were shared across all three comparisons, involving genes associated with disease resistance, secondary metabolism, signal transduction, and several unannotated loci (Fig. 2A, Fig. 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis revealed that up-regulated DEGs were significantly enriched in biological processes (BP) such as defense responses, cytokinin metabolism, and ethylene biosynthesis. In the cellular component (CC) category, enriched terms included plasma membrane, mitochondrial membrane, and membrane-related complexes, indicating cellular remodeling under viral stress. Molecular functions (MF) such as transmembrane transport, heme binding, and chitin binding were prominently enriched (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment showed that the MAPK signaling pathway\u0026mdash;plant (cann04016) was the most significantly enriched across all three time points, followed by pathways related to phenylpropanoid biosynthesis, peroxisome function, and plant\u0026ndash;pathogen interaction (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). These pathways suggest a coordinated activation of immune responses, oxidative stress adaptation, and secondary metabolism in response to BBWV2 infection.\u003c/p\u003e\n\u003cp\u003eBased on KEGG mapping, 26 up-regulated DEGs were identified within the MAPK signaling pathway\u0026mdash;plant. These included pathogenesis-related proteins (e.g., PR1, PR6), ethylene receptors (ETR1, ETR2), MAP kinase kinases (e.g., MAPKKK18, SAPK3), and oxidative burst-related genes such as RBOHE. The up-regulation of these genes suggests that MAPK cascades play a crucial role in BBWV2-triggered immune responses, involving pathogen recognition, hormonal signaling, and defense activation (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenes with significant changes in theMAPK signaling pathway-plant pathways\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolism\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnzyme name\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"26\"\u003e\n \u003cp\u003eMAPK signaling pathway-plant\u003c/p\u003e\n \u003cp\u003e(cann04016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107839239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-aminocyclopropane-1-carboxylic acid synthase 2 (ACS2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107840074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalmodulin (CaM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107840155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic pathogenesis-related protein 1 (PR1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107840204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine/threonine protein kinase (OXI1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107840225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic 30 kDa endochitinase (CHI9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107842907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenesis-related leaf protein 6 (PR1B1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107852024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthylene receptor (ETR1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107853533、LOC107877224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitogen-activated protein kinase kinase kinase 18 (MAPKKK18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107855207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEIEIN3-binding F-box protein 2 (EBF2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107855301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein phosphatase 2C 51 (PP2C51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107859246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePutative LRR receptor-like serine/threonine-protein kinase At3g47570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107859251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicted receptor-like protein kinase At3g47110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107859801、LOC107859802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcidic endochitinase pcht28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107859803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcidic 27 kDa endochitinase (CHI17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107859806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic endochitinase (CHI14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107860003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitogen-activated protein kinase 7 (MPK7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107864513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-aminocyclopropane-1-carboxylic acid synthase 6 (ACS6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107864521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-aminocyclopropane-1-carboxylic acid synthase 1 (ACS1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107864583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMitogen-activated protein kinase homolog (MMK2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107868209、LOC107868254、LOC107868265、LOC107868390、LOC107870158、LOC107877593、LOC107877942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReceptor kinase-like protein (Xa21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107868580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine/threonine protein kinase (SAPK3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107869344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLRR receptor-like serine/threonine-protein kinase (EFR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107873245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthylene receptor 2 (ETR2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107875521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePutative protein phosphatase 2C 24 (PP2C24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107877834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory burst oxidase homolog E (RBOHE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC107879918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathogenesis-related protein 1A (PR1A)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal component analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(PCA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was employed to assess global metabolomic differences across the four sampling time points. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, PCA clearly separated the groups along the first two principal components. PC1 explained 50.7% of the total variance and PC2 accounted for 16.9%, with a cumulative contribution of 67.6%. The tight clustering within groups and clear separation between groups indicate significant temporal variation in metabolic profiles in response to BBWV2 infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of differentially accumulated metabolites (DAMs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential metabolites were identified using volcano plot analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5). In the C3-vs-CK comparison (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), 537 metabolites were identified, including 488 up-regulated and 49 down-regulated. For C9-vs-CK (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB), 1098 metabolites were identified, with 896 up-regulated and 202 down-regulated. In C11-vs-CK (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC), 1177 metabolites were detected, of which 918 were up-regulated and 259 were down-regulated. Across all comparisons, the number of up-regulated metabolites exceeded down-regulated ones, suggesting metabolic activation under BBWV2 stress.\u003c/p\u003e\n\u003cp\u003eVenn diagram analysis revealed that 839 differential metabolites were shared across the three comparisons, while 364, 167, and 186 were unique to C3-vs-CK, C9-vs-CK, and C11-vs-CK, respectively. These metabolites included lipids, organic acids, heterocyclic compounds, and phenylpropanoids. Ten metabolites were significantly up-regulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) across all time points, including methotrexate-D9 and cyclosulfamuron, which were annotated in the KEGG database (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVery significant level (P\u0026thinsp;\u0026le;\u0026thinsp;0.01) Differential metabolites\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolites\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNorm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026apos;-GMP (2\u0026apos;-guanosine monophosphate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDihydropiperlonguminine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrimethoprim-D9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC01965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2R,3R,4R)-3, 4, 5-trihydroxy-1-oxopent-2-enyl-2-amino-3-thiopropanoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePyroxsulam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDibutyryl cyclic 3\u0026apos;, 5\u0026apos;-cyclic adenosine monophosphate (dibutyryl-cAMP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026alpha;-Cinnamoyl-L-histidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemafloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAristolochic acid B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPPU (1-Trifluoromethoxyphenyl-3-(1-propionylpiperidin-4-yl)urea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis of differential metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) revealed that DAMs were significantly enriched in 18, 20, and 20 pathways in C3-vs-CK(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA), C9-vs-CK(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB), and C11-vs-CK(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC), respectively. Six pathways were consistently enriched across all comparisons, including lysine biosynthesis, arginine biosynthesis, glycerophospholipid metabolism, alpha-linolenic acid metabolism, flavonoid biosynthesis, and biosynthesis of various plant secondary metabolites. The latter pathway was most significantly enriched in all three comparisons, indicating its key role in plant defense against BBWV2 through antiviral and antioxidant mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDynamics of plant secondary metabolite biosynthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther analysis of the KEGG pathway \u0026quot;biosynthesis of various plant secondary metabolites\u0026quot; revealed five metabolites associated with BBWV2 response(Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among them, four compounds\u0026mdash;pellitorine, medicarpin, hordenine B, and 7-demethoxycurcumin\u0026mdash;were consistently up-regulated, while N1-trans-feruloylbutylamine was down-regulated. The overall increase in secondary metabolite biosynthesis reflects an enhanced defense state in infected pepper leaves.\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential metabolites in the biosynthesis of various plant secondary metabolites\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKEGG id\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolites\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog2FoldChange\u003c/p\u003e\n \u003cp\u003e(C3-vs-CK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog2FoldChange\u003c/p\u003e\n \u003cp\u003e(C9-vs-CK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog2FoldChange\u003c/p\u003e\n \u003cp\u003e(C11-vs-CK)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMode of expression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC01864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePiperlongumine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7045303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9266147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4213894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC05158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMelilotoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9774879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1613523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5195974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC08308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHordenine B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0061817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2390607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7876966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7-Demethylpiperlonguminine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0961131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9138803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.343856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1-trans-Feruloylbutylamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7235795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.6094813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.7633178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated analysis of transcriptome and metabolome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal gene\u0026ndash;metabolite correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between gene expression and metabolite accumulation under BBWV2 stress, correlation distribution density plots were generated for C3, C9, and C11 versus CK (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Strong positive (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and negative (r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.7) correlations were observed, indicating both synergistic and antagonistic regulation.\u003c/p\u003e\n\u003cp\u003eAt C3(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA), positively correlated genes and metabolites dominated, suggesting coordinated activation. By C9(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB), negative correlations increased, reflecting possible metabolic imbalances. At C11(Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC), both positive and negative correlations intensified and balanced, implying complex transcriptional and metabolic reprogramming.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoint KEGG enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG enrichment was performed at both transcript and metabolite levels for each comparison. In C3-vs-CK(Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA), glycerophospholipid metabolism and secondary metabolite biosynthesis were significantly enriched, with higher transcript-level significance. In C9-vs-CK(Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB), biosynthesis of various plant secondary metabolites was significantly enriched at both levels, indicating coordinated transcriptional and metabolic regulation. In contrast, C11-vs-CK(Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC)showed moderate enrichment, with stronger gene-level than metabolite-level responses, implying transcriptional changes not yet translated into metabolite accumulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiosynthesis of secondary metabolites under viral stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTargeted pathway analysis revealed distinct temporal dynamics of secondary metabolism. In C3(Fig. 9A), lignan and flavonoid biosynthesis were activated, accompanied by increased levels of feruloyl-CoA and angelol. At C9(Fig. 9B), flavonoids such as coumarate and angelol peaked, while cannabinoid and alkaloid biosynthesis declined. At C11(Fig. 9C), scopoletin and feruloyl-CoA remained elevated, but crocin and picrocrocin decreased, suggesting metabolic resource reallocation. Persistent up-regulation of key defense compounds across time points highlights their potential roles in antiviral resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRT-qPCR Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the reliability of the transcriptomic profiles, quantitative real-time PCR (qRT-PCR) was performed on a subset of differentially expressed genes. Fifteen genes, including \u003cem\u003eMTB\u003c/em\u003e, \u003cem\u003eRBCS\u003c/em\u003e, \u003cem\u003eRD21B\u003c/em\u003e, \u003cem\u003eGILT\u003c/em\u003e, \u003cem\u003eCAT\u003c/em\u003e, \u003cem\u003eACO\u003c/em\u003e, \u003cem\u003eRCA\u003c/em\u003e, \u003cem\u003ePSBR\u003c/em\u003e, \u003cem\u003eSBE1\u003c/em\u003e, \u003cem\u003eHSC-2\u003c/em\u003e, \u003cem\u003eUBI11\u003c/em\u003e, \u003cem\u003ePER42\u003c/em\u003e, \u003cem\u003eAP1\u003c/em\u003e, \u003cem\u003epetC\u003c/em\u003e, and \u003cem\u003eTSJT1\u003c/em\u003e, were randomly selected for expression validation (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Relative expression levels were quantified based on both fragments per kilobase of transcript per million mapped reads (FPKM) from RNA-seq and qRT-PCR assays. A dual Y-axis bar-line plot was generated with time points post-inoculation as the X-axis. As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, the expression patterns derived from qRT-PCR were highly concordant with the RNA-seq data, confirming the robustness and accuracy of the transcriptome analysis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we measured changes in four physiological and biochemical indicators\u0026mdash;soluble sugars, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA)\u0026mdash;in pepper leaves under BBWV2 infection. The results revealed that the contents of soluble sugars, POD, ALT, and JA were all significantly elevated compared to the control, with ALT and JA exhibiting a dynamic pattern of initial increase followed by a decrease. These findings suggest that BBWV2 infection induces complex physiological and biochemical adjustments in pepper leaves, reflecting a multilayered stress response and intricate plant\u0026ndash;virus interactions.\u003c/p\u003e\u003cp\u003eVirus infection disrupts normal plant metabolism, thereby affecting growth and development\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Measurement of physiological and biochemical parameters enables assessment of the metabolic disturbances caused by viral stress. Soluble sugars not only serve as energy sources but also act as signaling molecules that activate defense genes and secondary metabolite biosynthesis, thus limiting viral spread. Previous studies have shown that viral infections, such as ZYMV in watermelon\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, and infections in lily\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e and cassava\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, similarly increase soluble sugar levels. Furthermore, virus infection inhibits photosynthesis and alters carbohydrate metabolism, resulting in elevated soluble sugar content\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In rice, blast disease infection significantly increases soluble sugar accumulation\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExcessive accumulation of reactive oxygen species (ROS) is a common phenomenon under biotic stress. POD plays a critical role in mitigating oxidative damage by catalyzing the decomposition of hydrogen peroxide (H₂O₂)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In our study, POD activity progressively increased following BBWV2 infection, suggesting an activated antioxidant defense mechanism. This finding is consistent with previous studies reporting increased POD activity under pathogen attack\u003csup\u003e[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eALT, a key enzyme in amino acid metabolism, exhibited increased activity under BBWV2 stress, likely reflecting enhanced amino acid turnover and membrane damage associated with virus-induced metabolic disturbances\u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Regarding JA, an essential signaling molecule in plant defense responses, its content rose sharply during early infection stages and gradually declined later. This dynamic is consistent with activation of systemic acquired resistance and restoration of normal physiological functions\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on the physiological findings, transcriptomic and metabolomic analyses were conducted at 1, 3, 9, and 11 days post-inoculation. Transcriptome analysis identified significant differential gene expression involving defense-related genes, secondary metabolism genes, and signal transduction components. GO enrichment indicated that upregulated genes were associated with defense responses, membrane structure, and energy/material transport. These results are consistent with previous findings that pathogen infection triggers the synthesis of lignin, phytoalexins, and other secondary metabolites through innate immune responses\u003csup\u003e[\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, and that plant immune systems regulate various signaling pathways to rapidly respond to pathogen attacks\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eKEGG pathway analysis revealed that the MAPK signaling pathway was significantly enriched across all comparison groups, highlighting its pivotal role in pepper defense against BBWV2. Core MAPK components such as MPK3, MPK6, and MPK4 regulate multiple disease resistance processes, including ethylene and phytoalexin biosynthesis, disease resistance gene expression, secondary metabolite production, and stomatal immunity\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. In our study, key upregulated genes within the MAPK pathway included PR1, PR6, and ethylene biosynthesis-related genes, suggesting activation of MAPK-mediated immune signaling in response to BBWV2 infection.\u003c/p\u003e\u003cp\u003eMetabolomic profiling identified 839 differential metabolites common to the three comparison groups, mainly including lipids, organic acids, heterocyclic compounds, and phenolic compounds. Ten significantly enriched metabolites were consistently upregulated, among which methoxybenzylpyrimidine-D9 and sulfosulfuron were functionally annotated. Top differential metabolites such as aristolochic acid B and 2'-GMP were highly expressed across all time points, suggesting a role in stress mitigation or antiviral defense.\u003c/p\u003e\u003cp\u003eKEGG enrichment of metabolomic data demonstrated significant activation of secondary metabolite biosynthesis pathways under BBWV2 infection. These secondary metabolites are involved in antiviral defense, ROS scavenging, and enhancing disease resistance. Integrated transcriptomic and metabolomic analysis confirmed consistent enrichment of secondary metabolism pathways, reinforcing the role of secondary metabolites in plant adaptation to viral stress.\u003c/p\u003e\u003cp\u003eSpecifically, alkaloid, phenylpropanoid, and flavonoid biosynthesis pathways were significantly affected. Alkaloid biosynthesis was downregulated, while phenylpropanoid and flavonoid biosynthesis were enhanced. Key metabolites such as angelol, feruloyl-CoA, and (+)-coumarate accumulated, driven by upregulation of phenylpropanoid pathway genes. These findings suggest that BBWV2 infection modulates metabolic networks, repressing alkaloid production while promoting phenylpropanoid- and flavonoid-derived compounds to enhance antiviral defenses.\u003c/p\u003e\u003cp\u003eSecondary metabolites are crucial for plant adaptation, environmental resistance, and survival\u003csup\u003e[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. In this study, BBWV2 infection induced notable shifts in the biosynthesis of alkaloids, phenylpropanoids, and flavonoids in Methodist line pepper. Alkaloids, with broad antiviral activities\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, and phenylpropanoid-derived flavonoids, important for stress responses\u003csup\u003e[\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, were differentially regulated, highlighting the metabolic plasticity of peppers under viral stress.\u003c/p\u003e\u003cp\u003eIn conclusion, both transcriptomic and metabolomic analyses revealed distinct molecular and metabolic reprogramming in Methodist line pepper following BBWV2 infection, providing valuable insights into the plant\u0026rsquo;s antiviral defense mechanisms and offering a theoretical basis for breeding virus-resistant cultivars.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we performed a comprehensive investigation of the physiological, transcriptomic, and metabolomic responses of Methodist line pepper to BBWV2 infection. Our results demonstrated that BBWV2 induces significant physiological changes, including elevated levels of soluble sugar, POD, ALT, and JA. Transcriptome profiling revealed that differentially expressed genes were enriched in defense-related pathways such as MAPK signaling and phenylpropanoid biosynthesis. Concurrently, metabolomic analysis identified widespread accumulation of lipids and secondary metabolites, especially flavonoids and lignans. The integrative analysis of gene expression and metabolite abundance highlighted a strong coordination between transcriptional regulation and metabolic reprogramming during viral infection. These findings provide novel insights into the molecular defense mechanisms of pepper against BBWV2 and lay a theoretical foundation for breeding virus-resistant cultivars.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003ePlant materials and virus inoculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePepper (\u003cem\u003eCapsicum spp\u003c/em\u003e.) Seeds of the Xunhua County chili variety in Qinghai Province were purchased from local farmers. The seeds are conventionally cultivated local varieties that are artificially grown, not collected in the wild. Seeds were soaked in sterile water for 12 hours, then sown in nutrient soil (50 pots total) and grown under controlled conditions in a growth chamber (28\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, 70\u0026thinsp;\u0026plusmn;\u0026thinsp;5% RH, 16 h light/8 h dark). Seedlings at the 4\u0026ndash;6 leaf stage were used for inoculation.\u003c/p\u003e\u003cp\u003eAgrobacterium-mediated inoculation was performed following previously described protocols \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Twenty-five plants were infiltrated with BBWV2-containing constructs, and the remaining plants received an empty vector as negative controls. Leaf samples were collected at 1, 3, 5, 7, 9, 11, 13, and 15 days post-inoculation (dpi). Each time point included three biological replicates. Collected tissues were flash-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for further analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhysiological and biochemical measurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSoluble sugar, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA) contents were determined using assay kits (Boxbio, Beijing, China) following the manufacturer\u0026rsquo;s instructions. Each treatment included three biological and three technical replicates to ensure reproducibility and accuracy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA extraction, library construction, and transcriptome sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTranscriptomic data have been uploaded to the NCBI Sequence Read Archive under accession number PRJNA1290684. Total RNA was extracted from leaves at 1, 3, 9, and 11 dpi using the TRIzol reagent (Invitrogen), and RNA quality was assessed with a NanoDrop 2000 spectrophotometer and Agilent 2100 Bioanalyzer. Libraries were constructed using the VAHTS Universal V5 RNA-Seq Library Prep Kit and sequenced on the Illumina NovaSeq 6000 platform (OE Biotech Co., Ltd., Shanghai, China).\u003c/p\u003e\u003cp\u003eLow-quality reads were filtered using \u003cem\u003efastp\u003c/em\u003e, and clean reads were mapped to the reference \u003cem\u003eCapsicum\u003c/em\u003e genome (GCF_000710875.1) with \u003cem\u003eHISAT2\u003c/em\u003e. Gene expression levels were normalized to FPKM values. Raw read counts were obtained with \u003cem\u003eHTSeq-count\u003c/em\u003e, and differentially expressed genes (DEGs) were identified using \u003cem\u003eDESeq2\u003c/em\u003e with thresholds of FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05 and |log2 fold change| \u0026ge; 2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional enrichment and clustering analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the hypergeometric test based on annotations from the GO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003cspan address=\"http://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Enriched terms with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative real-time PCR (qRT-PCR) validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSelected DEGs were validated using qRT-PCR. Primers were designed using PrimerQuest Tool (IDT, USA)(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). qRT-PCR was conducted using SYBR Green Master Mix (TIANGEN, China), and reactions were run on a Bio-Rad CFX96 system. \u003cem\u003eActin7\u003c/em\u003e was used as the reference gene. Relative gene expression was calculated using the 2^\u0026minus;ΔΔCT method. Each reaction included three biological and three technical replicates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolomic profiling and data analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMetabolomic data have been uploaded to the National Genomics Data Center OMIX platform under accession number OMIX010952-01.Frozen pepper leaves were extracted with 50% methanol buffer. Supernatants were transferred to 96-well plates for LC-MS/MS analysis. Pooled quality control (QC) samples were prepared from equal volumes of all extracts. Analyses were performed using a Waters ACQUITY UPLC I-Class Plus system coupled to a Thermo Q Exactive HF mass spectrometer. Chromatographic separation was achieved on an ACQUITY UPLC T3 column, and data were acquired in both positive and negative ionization modes with a mass range of 70\u0026ndash;1050 m/z and resolution of 60,000.\u003c/p\u003e\u003cp\u003eRaw LC-MS data were processed using Progenesis QI v3.0 for peak picking, alignment, and normalization. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were used to assess metabolic differences. Differential metabolites were identified through combined univariate and multivariate statistical analyses. Functional annotation and pathway enrichment were conducted using KEGG, Reactome, and GSEA databases. Cluster visualization was performed with TBtools.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimers used for qRT-PCR validation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimer sequence\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMTB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: ATGGAAGTCTTCAGCGTCTA\u003c/p\u003e\u003cp\u003eR: GACCGTCTTGACTGCTCTGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CGTAACGAAGTAAATGGTCGT\u003c/p\u003e\u003cp\u003eR: TAACGATCTCCAGCAGGTCTA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRD21B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: TGGCTCAGGATTGTGAAGGT\u003c/p\u003e\u003cp\u003eR: ACTCCATCGTGGAACTCCAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGILT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CGGATGATCTCGTAGCTCAAAT\u003c/p\u003e\u003cp\u003eR: CATTTCCTCTCAGGCGACTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CGGATGATCTCGTAGCTCAAAT\u003c/p\u003e\u003cp\u003eR: CATTTCCTCTCAGGCGACTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GTGTCTTCTCCTATGCCGATAC\u003c/p\u003e\u003cp\u003eR: AATCCCTCATGGTGGTTGTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CAGGCAAGACAGGTTTGTTAAG\u003c/p\u003e\u003cp\u003eR: GTTCTGACCTGAGACCAGTAAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CGACGGTAATGTGTACCCAAAG\u003c/p\u003e\u003cp\u003eR: GAAGCACAGGTCGAAGGTATTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRCA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GACAGATTTCTTCGGTGCTTTG\u003c/p\u003e\u003cp\u003eR: CAGCCTCTTTCCGATCTTCTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSBR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: TGGTGGGTTCTCTAAACAATGA\u003c/p\u003e\u003cp\u003eR: CCATCGACGTTAGCTCCATAC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: GTCAAAGCTTCCAGAGCTAGT\u003c/p\u003e\u003cp\u003eR: CTCCTGGCTTCATTTGGTATCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSC-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: TCACAGTGTGCTTCGACATT\u003c/p\u003e\u003cp\u003eR: GAGAGTCTGCCCTTGTCATTAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUBI11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CAGAAGGAATCAACCCTCCATC\u003c/p\u003e\u003cp\u003eR: GTCAATGGTGTCAGAACTCTCC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePER42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: AAAGGAGGCTAGCAGAAGTG\u003c/p\u003e\u003cp\u003eR: CCACCACCAACAGGCTAATA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: TGCAGTGGTCTGTATCAAGAAG\u003c/p\u003e\u003cp\u003eR: AGCCAAGGTTGCAGTTGTA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epetC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CGACAAGACTCTAGCGACATAC\u003c/p\u003e\u003cp\u003eR: GATCCATGGCAAGGACATAGAA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSJT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: TGCTGCAGATGGGTCTTTAG\u003c/p\u003e\u003cp\u003eR: TAAGAAACCTGGAGGAAATGGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActin7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF: CCACCATGTTCCCTGGTATTG\u003c/p\u003e\u003cp\u003eR: TCCAGACACTGTACTTCCTCTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBBWV2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBroad bean wilt virus 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePeroxidase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlanine aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eJA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ejasmonic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto encyclopedia of genes and genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBiological processes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCellular component\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMolecular functions\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAMs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially accumulated metabolites\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse discovery rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFPKM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFragments per kilobase million\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLC-MS/MS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLiquid chromatograph mass spectrometer/ Mass spectrometer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACQUITY UPLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUltra performance liquid chromatography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLS-DA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePartial least squares discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHierarchical clustering analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene set enrichment analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the NCBI Sequence Read Archive (PRJNA1290684) (https://submit.ncbi.nlm.nih.gov/subs/sra/SUB15455641) and the National Genomics Data Center OMIX repository (OMIX010952) (https://ngdc.cncb.ac.cn/omix/releaseList).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst author: Shu Qin, female, master\u0026apos;s degree, research focus: plant pathology. E-mail:[email protected]\u003c/p\u003e\n\u003cp\u003eCorresponding author: Yan Jiahui, female, associate researcher, research focus: plant pathology. E-mail:[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Qinghai Provincial Science and Technology Department International Cooperation Special Project (2022-HZ-806).The funders had no role in the experimental design, data collection and analysis or writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Plant Protection Research Institute of the Qinghai Academy of Agricultural and Forestry Sciences for providing the laboratory facilities, and Ouyi Bio for providing the data analysis cloud platform.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi TF, Wu SH, Zhao WH, Ji YH, Guo QY. Molecular detection of Pepper mild mottle virus in facility-grown peppers in Qinghai Province[J]. Jiangsu Agricultural Sci. 2017;33(4):958\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu SH, Tu LQ, Xian WR, Yan JH, Gao LN, Ji Y, Tao XR, Zhou YJ, Guo QY, Ji YH. Detection and identification of Tomato spotted wilt virus in peppers from Qinghai Province[J]. J Hortic. 2020;47(7):1391\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo HL, Yuan L, Weng H, Yan JH, Guo QY, Wang WQ, Ma XM. Identification and full-genome sequencing of Broad bean wilt virus 2 isolates from pepper in Qinghai Province[J]. J Hortic. 2023;50(1):161\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKobayashi YO, Kobayashi A, Nakano M, Hagiwara K, Honda Y, Omura T. Analysis of genetic relations between Broad bean wilt virus 1 and Broad bean wilt virus 2[J]. 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J Trop Subtropical Bot. 2019;27(5):591\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreyra MLF, Rius SP, Casati P. Flavonoids: bi osynthesis, biological functions, and biotechnological appli cations[J]. Front Plant Sci. 2012;3:222.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang WL, Bai ZY, Zou MZ, Wang XR, Xie JK, Zhang FT. Full-length transcriptome sequencing of Cervus elaphus and mining of genes related to the biosynthesis of secondary metabolites[J]. J Jiangxi Normal Univ (Natural Sci Edition). 2023;47(01):99\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Capsicum annuum L., Broad bean wilt virus 2, transcriptomics, metabolomics, secondary metabolism","lastPublishedDoi":"10.21203/rs.3.rs-6927090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6927090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePepper (\u003cem\u003eCapsicum annuum\u003c/em\u003e L.) is an important economic crop in Qinghai Province, China. In recent years, Broad bean wilt virus 2 (BBWV2) has severely affected the production of the local Methodist line pepper, leading to significant yield and quality losses. To elucidate the molecular and physiological mechanisms underlying the pepper's response to BBWV2 infection, we conducted a comprehensive analysis combining transcriptomics and metabolomics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePhysiological measurements showed that BBWV2 infection significantly increased soluble sugar, peroxidase (POD), alanine aminotransferase (ALT), and jasmonic acid (JA) contents in pepper leaves. Transcriptome analysis revealed a large number of differentially expressed genes (DEGs), mainly enriched in defense responses, MAPK signaling pathway, and phenylpropanoid metabolism. Metabolomic profiling identified substantial changes in the accumulation of lipids, organic acids, and secondary metabolites. Joint analysis indicated that BBWV2 infection triggered a coordinated regulation between gene expression and metabolite profiles, particularly enhancing the biosynthesis of various secondary metabolites such as flavonoids and lignans.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese findings provide valuable insights into the defense mechanisms of Methodist line pepper against BBWV2 and offer a theoretical foundation for future breeding of virus-resistant cultivars.\u003c/p\u003e","manuscriptTitle":"Transcriptomic and metabolomic response of Methodist line peppers to BBWV2 infection in Qinghai Province, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 14:51:53","doi":"10.21203/rs.3.rs-6927090/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-24T05:38:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T13:14:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T03:26:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205935553764784664206288065524753089764","date":"2025-07-16T19:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205782337610101016849730034797139768358","date":"2025-07-16T16:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51241022267274676680098396281002813930","date":"2025-07-16T15:42:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160851300703305006570081006314391733821","date":"2025-07-16T01:56:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T09:25:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T07:13:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T05:00:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-07-15T04:55:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ab5a80c-5d98-4ef0-96fc-904a394288f7","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:20:30+00:00","versionOfRecord":{"articleIdentity":"rs-6927090","link":"https://doi.org/10.1186/s12870-025-08040-1","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2026-02-27 15:57:50","publishedOnDateReadable":"February 27th, 2026"},"versionCreatedAt":"2025-07-17 14:51:53","video":"","vorDoi":"10.1186/s12870-025-08040-1","vorDoiUrl":"https://doi.org/10.1186/s12870-025-08040-1","workflowStages":[]},"version":"v1","identity":"rs-6927090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6927090","identity":"rs-6927090","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}