Flavonoid metabolism plays an important role in response to Pb stress in maize at seedling stage

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Although some progress has been made in understanding the damage caused by Pb stress and plant response strategies, the regulatory mechanisms and resistance genes involved in the response to lead stress in crops are largely unknown. In this study, the response mechanism of maize to Pb stress, the expression of Pb tolerance genes, physiological and biochemical indexes, the transcriptome, and the metabolome under different concentrations of Pb stress were combined for comprehensive analysis. As a result, the antioxidant system was significantly inhibited under Pb stress, especially under relatively high Pb concentrations. Transcriptome analysis revealed 3559 co-DEGs under the four Pb concentration treatments, which were enriched mainly in the GO terms related to DNA-binding transcription factor activity, response to stress, response to reactive oxygen species, cell death, the plasma membrane and root epidermal cell differentiation. Metabolome analysis revealed 72 and 107 DEMs under T500 and T2000, respectively, and 36 co-DEMs. KEGG analysis of the DEMs and DEGs revealed a common metabolic pathway, namely, flavonoid biosynthesis. An association study between the flavonoid biosynthesis-related DEMs and DEGs revealed 20 genes associated with flavonoid-related metabolites, including 3 for genistin and 17 for calycosin. This study not only provides genetic resources for the genetic improvement of maize Pb tolerance but also enriches the theoretical basis of the maize Pb stress response. Maize Pb stress transcriptome metabolome flavonoid biosynthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Maize ( Zea mays L.) is a crucial crop that can serve multiple purposes, such as food, fodder, feed, and as an industrial raw material. With the development of industry, corn yields are also increasing; however, new crises and challenges have emerged, such as environmental pollution and climate change[ 1 , 2 ]. Maize is exposed to many pollutants, which are abundant in the environment and enter plants through soil [ 3 ] or the atmosphere [ 4 ], where they affect the production of crops. Among pollutants, Pb is one of the most toxic and the most abundant [ 5 ]. On the one hand, Pb in agricultural soils can result in changes in soil microorganisms and their activities and soil fertility [ 6 ]; on the other hand, Pb can be absorbed and enriched by plants and can pass through the food chain to humans [ 7 ], resulting in damage to nerves, kidneys, blood, bones, and immune and reproductive systems[ 2 ]. Additionally, 1.5% of all soil samples in China are contaminated with Pb ( www.mee.gov.cn ). Therefore, deciphering the mechanisms of Pb absorption, enrichment, and regulation in maize is crucial. Plants that take up Pb through absorption at root surfaces in soil accumulate in different plant tissues [ 8 ], which depends on H + /ATPase pump activity, which aids in maintaining its gradient potential in rhizoderm cells [ 9 ]. Most plant species have adopted a mitigation strategy to accumulate approximately 95% of the Pb absorbed through roots, while only a minute proportion is translocated to shoots and leaves, as reported for V. unguiculata [ 10 ], Nicotiana tabacum [ 11 ], Lathyrus sativus [ 12 ], and Zea mays [ 13 ]. The accumulation of Pb inhibits the germination and growth of plants [ 14 ]. Pb stress induces deterioration of seed germination, as reported in many plants, such as Brassica juncea [ 15 ], Phaseolus vulgaris [ 16 ], and Zea mays [ 17 ]. When plants are exposed to Pb stress, the development of roots and aerial parts is attenuated, especially in roots, probably due to increased Pb accumulation [ 18 , 19 ]. Previous studies have shown that the reasons for the retardation of seed germination and plant growth under Pb are as follows: inhibited enzyme activity [ 20 ], disruption of plant water status [ 21 ], disruption of nutrient uptake [ 22 ], photosynthesis inhibition [ 23 ], disruption of cell division [ 24 ], oxidative stress [ 25 ], and lipid peroxidation [ 26 ]. To survive in soils, plants adopt several mechanisms to mitigate Pb stress, including cell wall adsorption and obstruction [ 27 ], excretion of metal ions into extracellular spaces [ 28 ], cellular sequestration [ 28 ], an increase in nonenzymatic antioxidants [ 29 ], and an increase in metal-binding ligands [ 30 ]. Although the Pb stress response has been studied extensively in plants, the mechanism and key genes involved in the Pb response in maize are largely unknown. Plants tolerate abiotic stresses through a cascade of complex biological processes involving a series of molecular and physio-biochemical changes [ 31 , 32 ]. Traditional phenotypic identification is macroscopic, a comprehensive reflection of various changes, and cannot accurately reflect the internal mechanism of tolerance to abiotic stresses; therefore, physio-biochemical analysis, transcriptome analysis, and metabolome analysis are widely used for identifying abiotic stress in plants [ 22 , 33 ]. A multiomics approach involving differential profiling of the transcriptome and metabolome in Vitis quinquangularis in response to aluminum (Al) stress revealed that the phenylalanine metabolic pathway could play a crucial role in alleviating Al stress in Vitis quinquangularis [ 34 ]. Similarly, integrated omics analysis confirmed that rice leaves respond to high saline‒alkali stress by engaging ABC transporters, dicarboxylate metabolism, glyoxylate, amino acid biosynthesis, and glutathione metabolism [ 35 ]. Similarly, salt stress could be tolerated by alleviating phenylpropanoid biosynthesis, starch and sucrose metabolism, plant hormone signal transduction and alpha-linolenic acid pathways in Beta vulgaris [ 36 ]. In this study, to reveal the response mechanism to Pb stress in maize, we performed phenotypic identification, physiological and biochemical analyses, transcriptome analysis, and nontarget metabolome analysis under a series of Pb concentration gradients in the elite inbred line HCL624. Our findings will reveal the response mechanism of maize to lead stress from different perspectives and levels, and identify important metabolic pathways and key candidate genes, which will not only provide genetic resources for the genetic improvement of maize Pb tolerance but also enriches the theoretical basis of the maize Pb stress response. 2. Materials and methods 2.1 Plant material and treatments An inbred line ‘HCL624’ of maize was used to exploit the Pb stress response in maize at the seedling stage. Seeds of HCL624 were sown in quartz sand under optimum growth conditions (16 ± 8 h day to night) at a temperature of 25/22°C. Seedlings grown to the two-leaf stage were transplanted into Hoagland solution for 3 days for adaptable growth. Then, the plants were divided into 5 groups for different degrees of Pb stress, namely, 0 mg/L, 500 g/L, 1000 g/L, 2000 g/L, and 3000 g/L Pb(NO 3 ) 2 . Two days later, they were sampled for physiological characteristics, transcriptome analysis, and metabolome analysis. 2.2 Physiological measurements and Pb content of maize Whole-plant-dried samples were collected and analyzed, and various biochemical parameters, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and hydrogen peroxide (H 2 O 2 ), as well as malondialdehyde (MDA) content, were determined according to the instructions of the reagent kit (Nanjing, Jiancheng Bioengineering Institute, China). Similarly, reactive oxygen species (ROS) levels were determined using enzyme-linked immunosorbent assays (ELISAs) according to the manufacturer’s instructions (Shanghai Yuanju Biotechnology Center, Shanghai, China). To quantify Pb 2+ uptake and absorption in plant tissues, the roots and shoots of plants under different Pb gradient stress treatments were harvested (in triplicate, n = 3), dried at 65°C and then digested by treatment with HNO 3 . Thereafter, Pb 2+ uptake was quantified by ICP–OES 5110 VDV (Agilent Instruments Inc. State of California, USA). 2.3 Transcriptomic sequencing and data analysis Total RNA was extracted from the roots of HCL624 plants subjected to different Pb stress treatments using TRIzol reagent (Thermo Fisher Scientific) and then treated with an RNeasy Mini Kit (Qiagen) to harvest higher-quality total RNA. Three biological replicates were performed for transcriptome analysis. The prepared RNA libraries were loaded on the Illumina NovaSeq TM 6000 platform by LC Biotechnology Co., Ltd. (Hangzhou, China). Adapter sequences were trimmed, and poor-quality reads were filtered out using FastQC (v0.10.1, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) and RSeQC Toolkit (v4.0.0, http://code.google.com/p/rseqc/ ) to obtain clean read data. The obtained clean reads were aligned to the maize B73 genome 26 (RefGen_v4, www.maizesequence.org ) and the transcriptome using HISAT2 (v2.2.1, http://ccb.jhu.edu/software/hisat2/index.shtml ). Gene count quantification and normalization were performed using TopHat2 and Cufflinks, and digital gene expression values were determined in terms of fragments per kilobase million (FPKM) values. Thereafter, the DESeq2 ( http://bioconductor.org/packages/stats/bioc/DESeq2/ ) /edgeR package ( http://bioconductor.org/packages/stats/bioc/edgeR/ ) was used with filter fold change ( log2 FC ≥ 1 and q ≤ 0.05) to determine the set of genes differentially regulated in Pb-treated maize seedlings. To further explore the functional aspects of the identified set of DEGs, Gene Ontology (GO) terms were determined, and enrichment analysis (GSEA) was subsequently performed according to a previous study [ 37 ]. The p value was determined based on false discovery rate (FDR) correction with 0.05 as the threshold. Finally, GO terms with a false discovery rate (FDR) ≤ 0.05 were selected for subsequent analysis. Pathway enrichment was performed through the KEGG (Kyoto Encyclopedia of Genes and Genomes) online resource as described in previous studies [ 38 , 39 ]. The adjusted P value ( P.adj ) value was determined based on FDR correction (FDR ≤ 0.05). 2.4. Sample extraction and measurements for metabolomic analysis To prepare for metabolic profiling, the collected samples were homogenized and thawed on ice, and 20 µL of each sample was dissolved in 120 µL of precooled 50% methanol buffer. The prepared mixture was vortexed for 1 min and then incubated for 10 min to ensure maximum metabolite extraction. These mixtures were then kept at -20°C overnight. To prepare for metabolite quantification, the mixtures were centrifuged at 4000 × g for 20 min, and the resulting supernatant was transferred to a semiskirted 96-well plate (with 5 technical repeats, n = 5). Thereafter, the samples were subjected to LC‐MS analysis. To ensure the quality of the procedures, a pooled quality control (QC) sample containing 10 µL of the reaction mixture was also prepared. 2.5 LC‒MS analysis A TripleTOF 5600 Plus mass spectrometer (SCIEX, Warrington, UK) was used to analyze all the control and treated samples. Metabolites in these samples were analyzed and separated via chromatography using an ultra-performance liquid chromatography (UPLC) system (SCIEX, UK). Metabolite separation was performed in reversed-phase using an ACQUITY UPLC T3 column (100 mm × 2.1 mm, 1.8 µm, Waters, UK). During metabolite separation, two different solvents were used for the mobile phase. Two solvents with specific formulations were used (Solvent A: water, 0.1% formic acid; Solvent B: acetonitrile, 0.1% formic acid). The gradient elution conditions (flow rate of 0.4 ml/min: 5% solvent B for 0-0.5 min; 5-100% solvent B for 0.5-7 min; 100% solvent B for 7–8 min; 100-5% solvent B for 8-8.1 min; and 5% solvent B for 8.1–10 min) maintained at 35°C were used to promote maximum metabolite separation. The different metabolites were eluted based on their physiochemical properties and detected through a specified column fitted in a TripleTOF 5600 Plus system under specific conditions (curtin gas pressure = 30 psi, gas1 pressure = 60 psi, gas2 pressure = 60 psi, and interface heater = 650°C). Since the TipleTOF 5600 Plus system was run in both ion modes, the spray floating voltage was adjusted accordingly (for positive ions = 5 kV, for negative ions = -4.5 kV). TOF mass spectrometer data (60-1200 Da) were collected. Repeated scans of the built-in metabolite library were performed with customized settings (total cycle time = 0.56 s, pulse frequency = 11 kHz and a multichannel TDC detector at 40 GHz, dynamic exclusion = 4 s). To maintain quality throughout the whole data acquisition duration, mass accuracy calibrations were performed after every 20 measurements, while a quality check (QC) sample was also analyzed every 10 samples to evaluate the stability of the LC‒MS. 2.6 Metabolomic data processing The platform-generated metabolite profiling data from LC‒MS were preanalyzed with XCMS software ( https://sciex.com/products/software/xcms-plus-software ) to generate the raw data points. These data points were first transferred to the mzXML format. Further data processing involved R environment-compatible packages such as XCMS, CAMERA and metaX ( http://metax.genomics.cn/ ). Each metabolite was identified based on critical data attributes relevant to retention time, peak area and m/z. The intensity of each peak was determined by analyzing a three-dimensional matrix in which peak attributes (retention time-m/z pairs) were arbitrarily assigned. This information was processed for all samples, and variable names were retrieved. The information generated was cross-matched, and annotation profiles were generated for all detected metabolites using public databases such as KEGG ( http://www.genome.jp/kegg/ ) MetaCyc ( https://metacyc.org/smarttables ) and HMDB ( http://hmdb.ca ). To further improve the peak quality attributes, the metaX tool was used to retrieve missing peaks based on the k‐nearest neighbor algorithm. Irrelevant differences observed in metabolomics datasets could lead to false detection and thus deterioration of the overall analysis quality. Orthogonal least squares-discriminant analysis (OPLS-DA) can aid in determining variance while removing irrelevant differences. Through the OPLS-DA model, variable importance in projection (VIP) can be identified from different sets of treatments. QC data based on the order of injection and signal intensity over a specific time drift were used to fit the QC-based robust LOESS signal. To ensure quality and data stability, QC samples with a standard deviation ≥ 30% were removed. Data normalization was performed using QC-robust spline batch correction for the QC samples, while for the remaining samples, normalization was performed using a probabilistic quotient algorithm. The significance of pairwise comparisons was determined using Student’s t‐tests, and the P value calculated was adjusted using FDR (Benjamini–Hochberg) correction. Differentially regulated metabolites in response to Pb stress were identified using fold change (FC ≥ 1.5 or FC ≤ 1.5/1) along with a VIP ≥ 1. In addition, a supervised PLS-DA was performed (VIP cutoff = 1.0) to detect group-specific differentially regulated metabolites 2.7 Spearman’s correlation coefficient for correlation analysis between gene expression and metabolic abundance To reveal the association between key genes and target metabolites, we used Spearman’s correlation coefficient for correlation analysis between gene expression and metabolic abundance. Finally, hub genes were further filtered based on absolute correlation (> 0.8) and adjusted P value (< 0.01). 3. Results 3.1 Physiological characteristics of maize under Pb stress Abiotic stress disturbed the overall metabolic balance of ROS scavengers, leading to oxidative stress. Therefore, physiological measurements of maize under Pb stress were performed. The results demonstrated that with increasing Pb uptake, the metabolic activity of antioxidants, such as SOD, POD and CAT, tended to decrease (Fig. 1 A-C), which suggested that the activity of antioxidant enzymes was suppressed by Pb. Moreover, the net accumulation of H 2 O 2 and MDA significantly increased under Pb stress (Fig. 1 D-E), while there were no differences between the different Pb contents. The Pb content in roots and shoots under different degrees of Pb stress was also detected, which showed that the Pb content significantly increased in both roots and shoots with increasing Pb stress. Additionally, 15-fold (3000 mg/L) to 280-fold (500 mg/L) greater Pb uptake in roots than in shoots (Fig. 1 F-G). According to the results, with increasing Pb stress, the absorption and transport of Pb increased. 3.2. Transcriptomic analysis of maize roots under different degrees of Pb stress To elucidate the response mechanism to Pb stress, RNA-seq analysis of roots at 2 days of 4 degrees of Pb stress (500 mg/L Pd (NO 3 ) 2 , 1000 mg/L Pd (NO 3 ) 2 , 2000 mg/L Pd (NO 3 ) 2 , 3000 mg/L Pd (NO 3 ) 2 and the control (0 mg/L Pd (NO 3 ) 2 )) was performed. The raw reads were obtained and trimmed to remove adapter sites, followed by quality control filtering to obtain high-quality clean read data. The average percentage of clean reads was 94.9%, the average Q20 was 99.0%, and the average Q30 was 95.5%. The average GC content of these clean read data was 50.5% (Table S1 ), which suggested that the cleaned read data could be used for further analysis. Principal component analysis (PCA) represents the degree of variability among the samples and treatments. The PCA plot showed that variability among the control and treatment groups could be related to PC2, while PC1 corroborated the degree of variability among the treatment groups (Fig. 2 A). Different sets of DEGs were identified under 4 levels of Pb stress in maize. In total, 5836, 8484, 19074 and 9038 DEGs were identified in maize under 500 mg/L, 1000 mg/L, 2000 mg/L, and 3000 mg/L Pd(NO 3 ) 2 , respectively. Among them, 3625, 3951, 13778, and 5115 genes were downregulated, and 2211, 4533, 5296 and 3923 genes were upregulated (Fig. 2 B). Additionally, to identify genes that were coupregulated or downregulated under 4 degrees of Pb stress, Venn diagram analysis was performed; 1762 genes were assigned as codownregulated genes (Fig. 2 C), and 1797 genes were assigned as coupregulated genes (Fig. 2 D). To further explore the functional aspects of DEGs involved in the genetic regulation of the Pb stress response in maize, GO enrichment analysis of the co-upregulated or downregulated genes was performed. Under Pb stress, DNA-binding transcription factor activity, metal cluster binding, ubiquitin-like protein transferase activity, response to water deficit stress, response to salicylic acid, response to ROS, metal ion transport, cell death and other GO terms were significantly enriched in the downregulated genes (Fig. 3 A), while peroxidase activity, cellulose synthase (UDP-forming) activity, plasma membrane, cell wall, root epidermal cell differentiation, 2-alkenal reductase [NAD(P)+] activity, calcium ion transport, and other GO terms were significantly enriched in the upregulated genes (Fig. 3 B). 3.3. Metabolic analysis of maize roots under different degrees of Pb stress To further reveal the mechanism underlying the metabolic response of maize to Pb stress, metabolome-based profiling was used to identify the differentially regulated metabolites. To predict a representative model, OPLS-DA was used to create a relationship interaction model among the metabolite regulation and the sample category (M-CK, M-500, M-2000). The metabolite peak regions from the Pb-treated and control groups were adjusted, and OPLS-DA was subsequently performed (Fig. 4 A). As a result, the samples from the same treatment were clearly clustered, which suggested that there was good repeatability between biological replicates and strong specificity between treatments. Based on the VIP score (VIP ≥ 1), Student’s t test statistic ( P ≤ 0.05) and degree of shift in abundance upon Cd exposure (ratio ≥ 1.5 or ≤ 1.5/1), a group of metabolites were identified for their contribution to sample segregation between the CK and Pb treatments. As a result, 72 DEMs and 107 DEMs were identified from T-500 and T-2000, respectively. Among the DEMs, 36 were detected in both groups. There were 11 upregulated DEMs and 23 downregulated DEMs (Fig. 4 B-C). According to the taxonomical characterization of the metabolites, the 36 metabolites were divided into 8 groups: benzenoids (4), lipids and lipid-like molecules (7), nucleosides (2), organic oxygen compounds (3), organic acids and derivatives (4), organoheterocyclic compounds (6), phenylpropanoids and polyketides (2), and unknown metabolites (6) (Fig. 4 D). Among them, the metabolites of nucleosides, organic acids and derivatives were downregulated, while the metabolites of phenylpropanoids and polyketides were upregulated. Six of the metabolites of lipids and lipid-like molecules were downregulated, while only one was upregulated. The number of downregulated metabolites of benzenoids and unknown compounds was the same as that of upregulated metabolites, while the number of downregulated metabolites of organic oxygen compounds and organoheterocyclic compounds was twice that of upregulated metabolites (Fig. 4 D). 3.4 Association analysis of transcriptomic and metabolomic expression levels and biological pathways under Pb stress in maize Furthermore, to explore the response pathway to Pb stress, KEGG enrichment analysis of common DEGs and DEMs was performed. The common DEGs among the different Pb concentrations were significantly mapped to 19 distinct biological pathways, including those involved in the biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, brassinosteroid biosynthesis, pentose and glucuronate interconversions, terpenoid and polyketide metabolism, flavonoid biosynthesis, and starch and sucrose metabolism (Fig. 5 , Table S2). As the number of common DEMs was low, the KEGG analyses of the DEMs were performed separately for T500 and T2000, and the results showed that 19 distinct pathways were enriched in both treatments, including biosynthesis of plant secondary metabolites, glyoxylate and dicarboxylate metabolism, arginine biosynthesis, flavonoid biosynthesis, beta-alanine metabolism, nitrogen metabolism, glycolysis/gluconeogenesis, biosynthesis of plant hormones, alanine, aspartate and glutamate metabolism, taurine and hypotaurine metabolism, and biosynthesis of alkaloids derived from histidine and purine (Fig. 6 , Table S3, S4). Finally, both DEGs and DEMs were enriched in the flavonoid biosynthesis KEGG pathway (Fig. 5 , 6 ). There were 2 shared candidate metabolites of flavonoid biosynthesis in T500 and T2000, namely, calycosin and genistin, and 27 shared DEGs. To better understand the contributions of calycosin and genistin to the Pb stress response, the abundances of these two metabolites were determined from the metabolome, and the results showed that both metabolomes significantly increased under Pb stress (Fig. 7 A). To further identify interactions between the DEGs and DEMs that exhibited the same KEGG pathways, association analysis was subsequently performed between the DEGs and the DEMs. There were 18 genes associated with calycosin (12 positive, 6 negative), 3 genes associated with genistin (1 positive, 2 negative), and one gene negatively associated with both metabolites. The correlation coefficient ranged from 0.80 to 0.95 (Fig. 7 B Table S5). Additionally, the expression of the associated genes was analyzed, among which 7 genes were downregulated and 13 genes were upregulated under Pb stress (Fig. 7 C). In summary, the 20 candidate genes related to the flavonoid synthesis is the 4. Discussion 4.1 Pb stress inhibited maize root development by inhibiting the root cell wall, cell membrane and DNA synthesis Plants are rooted in the soil and can absorb the nutrients and water they need directly from the soil via their roots. Soil is not only the source of nutrients for roots but also the living environment of roots, which can cause stress to plant growth and development, including drought stress, salt stress, waterlogging stress, and heavy metal stress [ 40 – 43 ]. As roots are in the soil, they are always the first part to be exposed and respond to stress from the soil [ 44 ]. Pb stress is one of the most common heavy metal stresses in soil and is increasing with the development of industry [ 45 ]. Previous studies have shown that root development, including lateral root formation and root morphological parameters, including length, surface area, volume, bushiness, and biomass, is inhibited by Pb [ 46 , 47 ]. Root cell viability was attenuated, leading to induced cell death under Pb stress [ 48 ]. In our study, the Pb uptake in the roots and shoots tended to increase in response to the different Pb treatments, while the Pb uptake in the roots was relatively greater than that in the shoots, which is consistent with previous studies. The transcriptome analysis revealed that the downregulated genes were enriched in the root development-, plasma membrane-, cell division-, and cell wall-related pathways, and the upregulated genes were enriched in the cell death- and response to stress-related pathways. Additionally, among the 36 metabolites shared between T500 and T2000, six of the lipid and lipid-like molecule metabolites, two of the nucleoside metabolites, and two of the benzenoid metabolites were downregulated, which is consistent with the transcriptome results. According to the results, root development was strongly inhibited by Pb stress, which was mainly due to the inhibition of the synthesis of the cell wall, plasma membrane, and nucleic acid and the acceleration of cell death. 4.2. Flavonoid metabolism plays an important role in the plant response to abiotic stress Flavonoids are a group of secondary metabolites that are mainly composed of compounds with a C6-C3-C6 framework and are common in plants [ 49 ]. To date, more than 8000 flavonoids have been identified in plants [ 50 ]. Flavonoids play critical roles in the stress response by maintaining redox homeostasis through several mechanisms, including 1) attenuation of singlet oxygen; 2) attenuation of enzyme activity involved in ROS production; 3) chelation of transition metal ions; 4) quenching of free radical cascades produced during lipid peroxidation metabolism; and 5) recycling of other antioxidants [ 50 – 52 ]. In our study, Pb stress caused an increase in ROS and destruction of the cell membrane of maize plants. To survive, plants initiate a series of physiological and biochemical responses, including the accumulation of flavonoids. 4.3 This study provides genetic resources for innovation of Pb-tolerant maize germplasm Crop Pb tolerance is a complex quantitative trait that is regulated through multiple processes, such as selective metal uptake, metal binding to the root surface, binding to the cell wall, and the induction of antioxidants [ 5 ]. Previous studies have identified several Pb resistance genes in plants. Pb-sensitive 1 (PSE1) , which encodes an NC domain protein localized in the cytoplasm, was cloned in Arabidopsis, and glutathione-dependent PC synthesis is mainly responsible for mitigating Pb tolerance [ 53 ]. APX1 encodes a cytosolic ascorbate peroxidase and negatively regulates Pb resistance by controlling the expression of ATP-binding cassette (ABC)-type transporters[ 54 ]. ZjDjB1 encodes a DnaJ protein in Z. japonica , and its overexpression in Arabidopsis can enhance Pb tolerance mainly by maintaining catalase activity and increasing the expression level of ATM3 [ 55 ]. ZmHIPP , encoding a heavy metal-associated isoprenylated plant protein in maize, was cloned by using combined linkage mapping and transcriptome analysis; this protein is involved in Pb deposition in the cell walls but is restricted from reaching intracellular organelles, thus attenuating Pb toxicity in maize [ 56 ]. ZmAKINβγ1 , encoding an AKINbetagamma-1 protein kinase, which was identified through GWAS (genome-wide association study) and transcriptomics data sets, was found to negatively regulate maize Pb tolerance by maintaining Pb accumulation in maize [ 57 ]. In our study, we identified 20 candidate genes related to the Pb stress response and accumulation of flavonoids by combining transcriptomic and metabolomic data. Among the candidate genes, Zm00001d038763 and Zm00001d021577 encode UDP-glycosyltransferase proteins, both of which were upregulated under Pb stress. Recently, a study reported that UDP-glycosyltransferases are involved in mitigating abiotic stresses in plants [ 58 , 59 ]. Zm00001d047424 encodes a flavonoid 3'-monooxygenase that has been reported to be involved in regulating oxidative and salt stress tolerance [ 60 ]. 5 Conclusion In summary, Pb can accumulate in maize, especially in roots, where it suppresses the antioxidant system, root development, cell division, cell wall synthesis, and plasma membrane synthesis-related pathways and promotes responses to stress, cell death, and ABA response-related pathways. More importantly, flavonoids (genistin and calycosin) were shown to be involved in the response of maize to Pb stress, and 20 related genes were identified. This study not only provides genetic resources for the genetic improvement of maize Pb tolerance but also enriches the theoretical basis of the maize Pb stress response. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data Availability The raw RNA-seq data have been uploaded to NCBI Sequence Read Archive (SRA) data-base (BioProject ID: PRJNA1113349). Competing interests The authors declare that they have no competing interests. Funding This study was supported by the National Natural Science Foundation of China (32301772); the Key Scientific and Technological Research Project in Henan Province (222102110050) Authors' contributions Z. H., and Y. Z. designed the overall experiment. Z. H., Y. Z., X. Z., B. W., Y. G., and Z. G. conducted the data processing for transcriptomics and metabolomics. Z. H., and Y. Z. wrote the main manuscript, while X. Z. and Z. H. reviewed and revised the paper. 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International journal of molecular sciences 2023, 24 (4). Cui J, Li J, Dai C, Li L: Transcriptome and Metabolome Analyses Revealed the Response Mechanism of Sugar Beet to Salt Stress of Different Durations . International journal of molecular sciences 2022, 23 (17). Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al : Gene ontology: tool for the unification of biology. The Gene Ontology Consortium . Nature genetics 2000, 25 (1):25-29. Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes . Nucleic Acids Res 2000, 28 (1):27-30. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data . Bioinformatics (Oxford, England) 2010, 26 (1):139-140. Narayanan M, Ma Y: Mitigation of heavy metal stress in the soil through optimized interaction between plants and microbes . J Environ Manage 2023, 345 :118732. Zhou H, Shi H, Yang Y, Feng X, Chen X, Xiao F, Lin H, Guo Y: Insights into plant salt stress signaling and tolerance . J Genet Genomics 2024, 51 (1):16-34. Chaves MM, Flexas J, Pinheiro C: Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell . Ann Bot 2009, 103 (4):551-560. Geng S, Lin Z, Xie S, Xiao J, Wang H, Zhao X, Zhou Y, Duan L: Ethylene enhanced waterlogging tolerance by changing root architecture and inducing aerenchyma formation in maize seedlings . J Plant Physiol 2023, 287 :154042. Chai YN, Schachtman DP: Root exudates impact plant performance under abiotic stress . Trends Plant Sci 2022, 27 (1):80-91. Dutta S, Gorain B, Choudhury H, Roychoudhury S, Sengupta P: Environmental and occupational exposure of metals and female reproductive health . Environ Sci Pollut Res Int 2022, 29 (41):62067-62092. Huang L, Chen D, Zhang H, Song Y, Chen H, Tang M: Funneliformis mosseae Enhances Root Development and Pb Phytostabilization in Robinia pseudoacacia in Pb-Contaminated Soil . Front Microbiol 2019, 10 :2591. Hou F, Liu K, Zhang N, Zou C, Yuan G, Gao S, Zhang M, Pan G, Ma L, Shen Y: Association mapping uncovers maize ZmbZIP107 regulating root system architecture and lead absorption under lead stress . Frontiers in plant science 2022, 13 :1015151. Huang TL, Huang HJ: ROS and CDPK-like kinase-mediated activation of MAP kinase in rice roots exposed to lead . Chemosphere 2008, 71 (7):1377-1385. Winkel-Shirley B: Flavonoid biosynthesis. A colorful model for genetics, biochemistry, cell biology, and biotechnology . Plant physiology 2001, 126 (2):485-493. Chen YY, Lu HQ, Jiang KX, Wang YR, Wang YP, Jiang JJ: The Flavonoid Biosynthesis and Regulation in Brassica napus: A Review . International journal of molecular sciences 2022, 24 (1). Lepiniec L, Debeaujon I, Routaboul JM, Baudry A, Pourcel L, Nesi N, Caboche M: Genetics and biochemistry of seed flavonoids . Annual review of plant biology 2006, 57 :405-430. Mierziak J, Kostyn K, Kulma A: Flavonoids as important molecules of plant interactions with the environment . Molecules 2014, 19 (10):16240-16265. Fan T, Yang L, Wu X, Ni J, Jiang H, Zhang Q, Fang L, Sheng Y, Ren Y, Cao S: The PSE1 gene modulates lead tolerance in Arabidopsis . Journal of experimental botany 2016, 67 (15):4685-4695. Jiang L, Wang W, Chen Z, Gao Q, Xu Q, Cao H: A role for APX1 gene in lead tolerance in Arabidopsis thaliana . Plant science : an international journal of experimental plant biology 2017, 256 :94-102. Chen S, Qiu G: Overexpression of Zostera japonica J protein gene ZjDjB1 in Arabidopsis enhanced the tolerance to lead stress . Mol Biol Rep 2023, 50 (6):5117-5124. Ma L, An R, Jiang L, Zhang C, Li Z, Zou C, Yang C, Pan G, Lübberstedt T, Shen Y: Effects of ZmHIPP on lead tolerance in maize seedlings: Novel ideas for soil bioremediation . J Hazard Mater 2022, 430 :128457. Li Z, Jiang L, Wang C, Liu P, Ma L, Zou C, Pan G, Shen Y: Combined genome-wide association study and gene co-expression network analysis identified ZmAKINβγ1 involved in lead tolerance and accumulation in maize seedlings . Int J Biol Macromol 2023, 226 :1374-1386. Ouyang L, Liu Y, Yao R, He D, Yan L, Chen Y, Huai D, Wang Z, Yu B, Kang Y et al : Genome-wide analysis of UDP-glycosyltransferase gene family and identification of a flavonoid 7-O-UGT (AhUGT75A) enhancing abiotic stress in peanut (Arachis hypogaea L.) . BMC plant biology 2023, 23 (1):626. Li P, Yang X, Wang H, Pan T, Wang Y, Xu Y, Xu C, Yang Z: Genetic control of root plasticity in response to salt stress in maize . TAG Theoretical and applied genetics Theoretische und angewandte Genetik 2021, 134 (5):1475-1492. Liu H, Liu S, Wang H, Chen K, Zhang P: The flavonoid 3'-hydroxylase gene from the Antarctic moss Pohlia nutans is involved in regulating oxidative and salt stress tolerance . Biotechnol Appl Biochem 2022, 69 (2):676-686. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.xlsx Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 14 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviews received at journal 13 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviewers invited by journal 04 Jun, 2024 Editor invited by journal 04 Jun, 2024 Editor assigned by journal 04 Jun, 2024 Submission checks completed at journal 04 Jun, 2024 First submitted to journal 03 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4519159","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314344381,"identity":"43b944d1-d165-4a8b-85fc-0480328699d2","order_by":0,"name":"Zanping Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACfvbGxgcfDNjkDKACjA2EtEj2HD5sOKOAzxis5QAxWgxupKVJc3yQS9xAgpYcA2kGA7P07exnjD9/YLCR3XCA+dkDvA4788bAuMAgLXdnT46ZxAGGNOMNB9jMDfBp4TueY5A8w+BY7oYDOWZAhx1O3HCAh00Cr8sO5Bgc5jH4n25w/o3xhwMM/wlrETiRltjMY8CWAPIU0GEHCGsBBTLjDAM2ww03npVJnDFINp55mM0MrxZgVLb/+PCHTd7gfPLmDxUVdrJ9x5uf4fcLKgAFFTMJ6kfBKBgFo2AUYAcAG/ZRwSeWpwIAAAAASUVORK5CYII=","orcid":"","institution":"College of Agronomy, Henan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zanping","middleName":"","lastName":"Han","suffix":""},{"id":314344382,"identity":"ad2738d0-f526-4b26-a84f-14febed5a487","order_by":1,"name":"Yan Zheng","email":"","orcid":"","institution":"College of Agronomy, Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zheng","suffix":""},{"id":314344383,"identity":"c4c6c92a-a606-4884-99da-b28ec2dd3978","order_by":2,"name":"Xiaoxiang Zhang","email":"","orcid":"","institution":"School of Agriculture, Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiang","middleName":"","lastName":"Zhang","suffix":""},{"id":314344384,"identity":"5ac9dd27-9242-4932-9f80-8b457320a0a0","order_by":3,"name":"Bin Wang","email":"","orcid":"","institution":"College of Agronomy, Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":314344385,"identity":"7be4a64b-8de6-4103-b043-29e777726c12","order_by":4,"name":"Yiyang Guo","email":"","orcid":"","institution":"College of Agronomy, Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yiyang","middleName":"","lastName":"Guo","suffix":""},{"id":314344386,"identity":"b0d31e78-d05a-42b9-9ed1-3f7147117866","order_by":5,"name":"Zhongrong Guan","email":"","orcid":"","institution":"Chongqing Yudongnan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhongrong","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2024-06-03 04:12:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4519159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4519159/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05455-0","type":"published","date":"2024-07-30T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58569080,"identity":"53917682-a7bf-431b-a31f-480355a1387a","added_by":"auto","created_at":"2024-06-18 10:39:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95321,"visible":true,"origin":"","legend":"\u003cp\u003ePhysiological response of maize plantsunder Pb stress. (A–C) SOD (A), CAT (B) and POD (C) activities under different concentrations of Pb stress in maize. (D–E) MDA (D) and H2O2 (E) contents of maize under Pb stress. (F-G) Pb content under Pb stress in maize. Mean comparisons denoted using similar letters were not found to be significant according to \u003cem\u003eDuncan’s\u003c/em\u003e multiple range test (\u003cem\u003eP\u003c/em\u003e \u003cu\u003e\u0026lt; \u003c/u\u003e0.05).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/b3bb5ef563d80998425700fe.png"},{"id":58569562,"identity":"6e367431-6a37-49fd-8ead-7ec9e182c284","added_by":"auto","created_at":"2024-06-18 10:47:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157317,"visible":true,"origin":"","legend":"\u003cp\u003ePCA and analysis of differentially expressed genes (DEGs) in the transcriptome of plants subjected to Pb stress along different gradients. (A) PCA of RNA-seq samples. (B) Graphical representation of DEGs; ‘up’ denotes upregulated genes, and ‘down’ denotes downregulated genes. (C) Venn diagram of downregulated and (D) upregulated genes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/d3c5946607654cf2be0930f4.png"},{"id":58569079,"identity":"6f09de6d-039d-4e04-bc6f-6eaad121252c","added_by":"auto","created_at":"2024-06-18 10:39:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":370425,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of co-DEGs under 4 degrees of Pb stress in maize. (A) GO enrichment analysis of co-upregulatedgenes. (B) GO enrichment analysis of co-`downregulated genes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/0953824c069e6e19200f4875.png"},{"id":58569560,"identity":"18ca30a5-0cf3-4fb7-9e57-e0f292da687e","added_by":"auto","created_at":"2024-06-18 10:47:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":931325,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic profiling in response to Pb stress in maize. (A) PLSDA of treatment samples. (B) Venn diagram showingthe DEMs shared between M-500 and M-2000. (C) Heatmapof shared DEMs in the rootsof maize under different Pb stresses. (D) Statistical analysis of the shared DEMs. ‘Up’ representsthe upregulated metabolites, while ‘down’ represents the downregulated metabolites.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/fb83b0dff695a4d4431bfce7.png"},{"id":58570304,"identity":"48067e4d-750e-44a9-9424-3e2dbff2969f","added_by":"auto","created_at":"2024-06-18 10:55:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":331515,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of common DEGs under four Pb treatments.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/86ea2b58cfa946512bf94290.png"},{"id":58569085,"identity":"a6b8bd0d-3d18-4bbd-8b38-83d332ed03b5","added_by":"auto","created_at":"2024-06-18 10:39:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":552965,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis of the shared DEMs. A and B: KEGG analysis of DEMs under T500 (A) and T2000 (B).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/9bd909107332654d1f7d90ee.png"},{"id":58569086,"identity":"879543e1-cb50-44f5-81ee-84ade1a9eb12","added_by":"auto","created_at":"2024-06-18 10:39:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":748539,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of flavonoid biosynthesis-related metabolites and genes. A, The abundance of calycosin and genistin under different Pb treatments. B, Correlation analysis between DEMs and DEGs related to flavonoid biosynthesis. The solid line denotes a significant positive correlation, the dashed line denotes asignificant negative correlation, and the thickness of the line denotes the magnitude of the correlation. C, Expression heatmap of the DEGs associated with calycosinand genistin.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/b67ddbe607ad2ce406378bd7.png"},{"id":61793378,"identity":"835ee068-c950-4c31-a6e2-c6f506c922f5","added_by":"auto","created_at":"2024-08-05 16:11:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5607110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/5d44aafd-a382-41d3-beab-5fccbf5d384f.pdf"},{"id":58569083,"identity":"f5808055-4fb8-466f-8115-941288132cb5","added_by":"auto","created_at":"2024-06-18 10:39:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48159,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4519159/v1/bc4417181b779f399e8f27f1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Flavonoid metabolism plays an important role in response to Pb stress in maize at seedling stage","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is a crucial crop that can serve multiple purposes, such as food, fodder, feed, and as an industrial raw material. With the development of industry, corn yields are also increasing; however, new crises and challenges have emerged, such as environmental pollution and climate change[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Maize is exposed to many pollutants, which are abundant in the environment and enter plants through soil [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] or the atmosphere [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], where they affect the production of crops. Among pollutants, Pb is one of the most toxic and the most abundant [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. On the one hand, Pb in agricultural soils can result in changes in soil microorganisms and their activities and soil fertility [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]; on the other hand, Pb can be absorbed and enriched by plants and can pass through the food chain to humans [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], resulting in damage to nerves, kidneys, blood, bones, and immune and reproductive systems[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, 1.5% of all soil samples in China are contaminated with Pb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.mee.gov.cn\" target=\"_blank\"\u003ewww.mee.gov.cn\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e). Therefore, deciphering the mechanisms of Pb absorption, enrichment, and regulation in maize is crucial.\u003c/p\u003e\n\u003cp\u003ePlants that take up Pb through absorption at root surfaces in soil accumulate in different plant tissues [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], which depends on H\u003csup\u003e+\u003c/sup\u003e/ATPase pump activity, which aids in maintaining its gradient potential in rhizoderm cells [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Most plant species have adopted a mitigation strategy to accumulate approximately 95% of the Pb absorbed through roots, while only a minute proportion is translocated to shoots and leaves, as reported for \u003cem\u003eV. unguiculata\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], \u003cem\u003eNicotiana tabacum\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], \u003cem\u003eLathyrus sativus\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], and \u003cem\u003eZea mays\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The accumulation of Pb inhibits the germination and growth of plants [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Pb stress induces deterioration of seed germination, as reported in many plants, such as \u003cem\u003eBrassica juncea\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], and \u003cem\u003eZea mays\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. When plants are exposed to Pb stress, the development of roots and aerial parts is attenuated, especially in roots, probably due to increased Pb accumulation [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have shown that the reasons for the retardation of seed germination and plant growth under Pb are as follows: inhibited enzyme activity [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], disruption of plant water status [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], disruption of nutrient uptake [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e], photosynthesis inhibition [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], disruption of cell division [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], oxidative stress [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], and lipid peroxidation [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. To survive in soils, plants adopt several mechanisms to mitigate Pb stress, including cell wall adsorption and obstruction [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], excretion of metal ions into extracellular spaces [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], cellular sequestration [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], an increase in nonenzymatic antioxidants [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], and an increase in metal-binding ligands [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although the Pb stress response has been studied extensively in plants, the mechanism and key genes involved in the Pb response in maize are largely unknown.\u003c/p\u003e\n\u003cp\u003ePlants tolerate abiotic stresses through a cascade of complex biological processes involving a series of molecular and physio-biochemical changes [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Traditional phenotypic identification is macroscopic, a comprehensive reflection of various changes, and cannot accurately reflect the internal mechanism of tolerance to abiotic stresses; therefore, physio-biochemical analysis, transcriptome analysis, and metabolome analysis are widely used for identifying abiotic stress in plants [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. A multiomics approach involving differential profiling of the transcriptome and metabolome in \u003cem\u003eVitis quinquangularis\u003c/em\u003e in response to aluminum (Al) stress revealed that the phenylalanine metabolic pathway could play a crucial role in alleviating Al stress in \u003cem\u003eVitis quinquangularis\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, integrated \u003cem\u003eomics\u003c/em\u003e analysis confirmed that rice leaves respond to high saline‒alkali stress by engaging ABC transporters, dicarboxylate metabolism, glyoxylate, amino acid biosynthesis, and glutathione metabolism [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similarly, salt stress could be tolerated by alleviating phenylpropanoid biosynthesis, starch and sucrose metabolism, plant hormone signal transduction and alpha-linolenic acid pathways in \u003cem\u003eBeta vulgaris\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn this study, to reveal the response mechanism to Pb stress in maize, we performed phenotypic identification, physiological and biochemical analyses, transcriptome analysis, and nontarget metabolome analysis under a series of Pb concentration gradients in the elite inbred line HCL624. Our findings will reveal the response mechanism of maize to lead stress from different perspectives and levels, and identify important metabolic pathways and key candidate genes, which will not only provide genetic resources for the genetic improvement of maize Pb tolerance but also enriches the theoretical basis of the maize Pb stress response.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Plant material and treatments\u003c/h2\u003e\n \u003cp\u003eAn inbred line \u0026lsquo;HCL624\u0026rsquo; of maize was used to exploit the Pb stress response in maize at the seedling stage. Seeds of HCL624 were sown in quartz sand under optimum growth conditions (16\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8 h day to night) at a temperature of 25/22\u0026deg;C. Seedlings grown to the two-leaf stage were transplanted into Hoagland solution for 3 days for adaptable growth. Then, the plants were divided into 5 groups for different degrees of Pb stress, namely, 0 mg/L, 500 g/L, 1000 g/L, 2000 g/L, and 3000 g/L Pb(NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e. Two days later, they were sampled for physiological characteristics, transcriptome analysis, and metabolome analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Physiological measurements and Pb content of maize\u003c/h2\u003e\n \u003cp\u003eWhole-plant-dried samples were collected and analyzed, and various biochemical parameters, including superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), as well as malondialdehyde (MDA) content, were determined according to the instructions of the reagent kit (Nanjing, Jiancheng Bioengineering Institute, China). Similarly, reactive oxygen species (ROS) levels were determined using enzyme-linked immunosorbent assays (ELISAs) according to the manufacturer\u0026rsquo;s instructions (Shanghai Yuanju Biotechnology Center, Shanghai, China).\u003c/p\u003e\n \u003cp\u003eTo quantify Pb\u003csup\u003e2+\u003c/sup\u003e uptake and absorption in plant tissues, the roots and shoots of plants under different Pb gradient stress treatments were harvested (in triplicate, n\u0026thinsp;=\u0026thinsp;3), dried at 65\u0026deg;C and then digested by treatment with HNO\u003csub\u003e3\u003c/sub\u003e. Thereafter, Pb\u003csup\u003e2+\u003c/sup\u003e uptake was quantified by ICP\u0026ndash;OES 5110 VDV (Agilent Instruments Inc. State of California, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Transcriptomic sequencing and data analysis\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted from the roots of HCL624 plants subjected to different Pb stress treatments using TRIzol reagent (Thermo Fisher Scientific) and then treated with an RNeasy Mini Kit (Qiagen) to harvest higher-quality total RNA. Three biological replicates were performed for transcriptome analysis. The prepared RNA libraries were loaded on the Illumina NovaSeq TM 6000 platform by LC Biotechnology Co., Ltd. (Hangzhou, China). Adapter sequences were trimmed, and poor-quality reads were filtered out using FastQC (v0.10.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003c/span\u003e) and RSeQC Toolkit (v4.0.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://code.google.com/p/rseqc/\u003c/span\u003e\u003c/span\u003e) to obtain clean read data. The obtained clean reads were aligned to the \u003cem\u003emaize\u003c/em\u003e B73 genome 26 (RefGen_v4, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.maizesequence.org\u003c/span\u003e\u003c/span\u003e) and the transcriptome using HISAT2 (v2.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ccb.jhu.edu/software/hisat2/index.shtml\u003c/span\u003e\u003c/span\u003e). Gene count quantification and normalization were performed using TopHat2 and Cufflinks, and digital gene expression values were determined in terms of fragments per kilobase million (FPKM) values. Thereafter, the DESeq2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioconductor.org/packages/stats/bioc/DESeq2/\u003c/span\u003e\u003c/span\u003e) /edgeR package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioconductor.org/packages/stats/bioc/edgeR/\u003c/span\u003e\u003c/span\u003e) was used with filter fold change (\u003cem\u003elog2\u003c/em\u003eFC\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1 and \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.05) to determine the set of genes differentially regulated in Pb-treated maize seedlings.\u003c/p\u003e\n \u003cp\u003eTo further explore the functional aspects of the identified set of DEGs, Gene Ontology (GO) terms were determined, and enrichment analysis (GSEA) was subsequently performed according to a previous study [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The \u003cem\u003ep\u003c/em\u003e value was determined based on false discovery rate (FDR) correction with 0.05 as the threshold. Finally, GO terms with a false discovery rate (FDR)\u0026thinsp;\u0026le;\u0026thinsp;0.05 were selected for subsequent analysis. Pathway enrichment was performed through the KEGG (Kyoto Encyclopedia of Genes and Genomes) online resource as described in previous studies [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. The adjusted P value (\u003cem\u003eP.adj\u003c/em\u003e) value was determined based on FDR correction (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Sample extraction and measurements for metabolomic analysis\u003c/h2\u003e\n \u003cp\u003eTo prepare for metabolic profiling, the collected samples were homogenized and thawed on ice, and 20 \u0026micro;L of each sample was dissolved in 120 \u0026micro;L of precooled 50% methanol buffer. The prepared mixture was vortexed for 1 min and then incubated for 10 min to ensure maximum metabolite extraction. These mixtures were then kept at -20\u0026deg;C overnight. To prepare for metabolite quantification, the mixtures were centrifuged at 4000 \u0026times; g for 20 min, and the resulting supernatant was transferred to a semiskirted 96-well plate (with 5 technical repeats, n\u0026thinsp;=\u0026thinsp;5). Thereafter, the samples were subjected to LC‐MS analysis. To ensure the quality of the procedures, a pooled quality control (QC) sample containing 10 \u0026micro;L of the reaction mixture was also prepared.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 LC‒MS analysis\u003c/h2\u003e\n \u003cp\u003eA TripleTOF 5600 Plus mass spectrometer (SCIEX, Warrington, UK) was used to analyze all the control and treated samples. Metabolites in these samples were analyzed and separated via chromatography using an ultra-performance liquid chromatography (UPLC) system (SCIEX, UK). Metabolite separation was performed in reversed-phase using an ACQUITY UPLC T3 column (100 mm \u0026times; 2.1 mm, 1.8 \u0026micro;m, Waters, UK). During metabolite separation, two different solvents were used for the mobile phase. Two solvents with specific formulations were used (Solvent A: water, 0.1% formic acid; Solvent B: acetonitrile, 0.1% formic acid). The gradient elution conditions (flow rate of 0.4 ml/min: 5% solvent B for 0-0.5 min; 5-100% solvent B for 0.5-7 min; 100% solvent B for 7\u0026ndash;8 min; 100-5% solvent B for 8-8.1 min; and 5% solvent B for 8.1\u0026ndash;10 min) maintained at 35\u0026deg;C were used to promote maximum metabolite separation.\u003c/p\u003e\n \u003cp\u003eThe different metabolites were eluted based on their physiochemical properties and detected through a specified column fitted in a TripleTOF 5600 Plus system under specific conditions (curtin gas pressure\u0026thinsp;=\u0026thinsp;30 psi, gas1 pressure\u0026thinsp;=\u0026thinsp;60 psi, gas2 pressure\u0026thinsp;=\u0026thinsp;60 psi, and interface heater\u0026thinsp;=\u0026thinsp;650\u0026deg;C). Since the TipleTOF 5600 Plus system was run in both ion modes, the spray floating voltage was adjusted accordingly (for positive ions\u0026thinsp;=\u0026thinsp;5 kV, for negative ions = -4.5 kV). TOF mass spectrometer data (60-1200 Da) were collected. Repeated scans of the built-in metabolite library were performed with customized settings (total cycle time\u0026thinsp;=\u0026thinsp;0.56 s, pulse frequency\u0026thinsp;=\u0026thinsp;11 kHz and a multichannel TDC detector at 40 GHz, dynamic exclusion\u0026thinsp;=\u0026thinsp;4 s). To maintain quality throughout the whole data acquisition duration, mass accuracy calibrations were performed after every 20 measurements, while a quality check (QC) sample was also analyzed every 10 samples to evaluate the stability of the LC‒MS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Metabolomic data processing\u003c/h2\u003e\n \u003cp\u003eThe platform-generated metabolite profiling data from LC‒MS were preanalyzed with XCMS software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sciex.com/products/software/xcms-plus-software\u003c/span\u003e\u003c/span\u003e ) to generate the raw data points. These data points were first transferred to the mzXML format. Further data processing involved R environment-compatible packages such as XCMS, CAMERA and metaX (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metax.genomics.cn/\u003c/span\u003e\u003c/span\u003e). Each metabolite was identified based on critical data attributes relevant to retention time, peak area and m/z. The intensity of each peak was determined by analyzing a three-dimensional matrix in which peak attributes (retention time-m/z pairs) were arbitrarily assigned. This information was processed for all samples, and variable names were retrieved. The information generated was cross-matched, and annotation profiles were generated for all detected metabolites using public databases such as KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003c/span\u003e) MetaCyc (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metacyc.org/smarttables\u003c/span\u003e\u003c/span\u003e) and HMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hmdb.ca\u003c/span\u003e\u003c/span\u003e). To further improve the peak quality attributes, the metaX tool was used to retrieve missing peaks based on the k‐nearest neighbor algorithm.\u003c/p\u003e\n \u003cp\u003eIrrelevant differences observed in metabolomics datasets could lead to false detection and thus deterioration of the overall analysis quality. Orthogonal least squares-discriminant analysis (OPLS-DA) can aid in determining variance while removing irrelevant differences. Through the OPLS-DA model, variable importance in projection (VIP) can be identified from different sets of treatments. QC data based on the order of injection and signal intensity over a specific time drift were used to fit the QC-based robust LOESS signal. To ensure quality and data stability, QC samples with a standard deviation\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;30% were removed. Data normalization was performed using QC-robust spline batch correction for the QC samples, while for the remaining samples, normalization was performed using a probabilistic quotient algorithm. The significance of pairwise comparisons was determined using Student\u0026rsquo;s t‐tests, and the \u003cem\u003eP\u003c/em\u003e value calculated was adjusted using FDR (Benjamini\u0026ndash;Hochberg) correction. Differentially regulated metabolites in response to Pb stress were identified using fold change (FC\u0026thinsp;\u0026ge;\u0026thinsp;1.5 or FC\u0026thinsp;\u0026le;\u0026thinsp;1.5/1) along with a VIP\u0026thinsp;\u0026ge;\u0026thinsp;1. In addition, a supervised PLS-DA was performed (VIP cutoff\u0026thinsp;=\u0026thinsp;1.0) to detect group-specific differentially regulated metabolites\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Spearman\u0026rsquo;s correlation coefficient for correlation analysis between gene expression and metabolic abundance\u003c/h2\u003e\n \u003cp\u003eTo reveal the association between key genes and target metabolites, we used Spearman\u0026rsquo;s correlation coefficient for correlation analysis between gene expression and metabolic abundance. Finally, hub genes were further filtered based on absolute correlation (\u0026gt;\u0026thinsp;0.8) and adjusted P value (\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Physiological characteristics of maize under Pb stress\u003c/h2\u003e\n \u003cp\u003eAbiotic stress disturbed the overall metabolic balance of ROS scavengers, leading to oxidative stress. Therefore, physiological measurements of maize under Pb stress were performed. The results demonstrated that with increasing Pb uptake, the metabolic activity of antioxidants, such as SOD, POD and CAT, tended to decrease (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA-C), which suggested that the activity of antioxidant enzymes was suppressed by Pb. Moreover, the net accumulation of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and MDA significantly increased under Pb stress (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD-E), while there were no differences between the different Pb contents. The Pb content in roots and shoots under different degrees of Pb stress was also detected, which showed that the Pb content significantly increased in both roots and shoots with increasing Pb stress. Additionally, 15-fold (3000 mg/L) to 280-fold (500 mg/L) greater Pb uptake in roots than in shoots (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF-G). According to the results, with increasing Pb stress, the absorption and transport of Pb increased.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Transcriptomic analysis of maize roots under different degrees of Pb stress\u003c/h2\u003e\n \u003cp\u003eTo elucidate the response mechanism to Pb stress, RNA-seq analysis of roots at 2 days of 4 degrees of Pb stress (500 mg/L Pd (NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e, 1000 mg/L Pd (NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e, 2000 mg/L Pd (NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e, 3000 mg/L Pd (NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e and the control (0 mg/L Pd (NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e)) was performed. The raw reads were obtained and trimmed to remove adapter sites, followed by quality control filtering to obtain high-quality clean read data. The average percentage of clean reads was 94.9%, the average Q20 was 99.0%, and the average Q30 was 95.5%. The average GC content of these clean read data was 50.5% (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), which suggested that the cleaned read data could be used for further analysis. Principal component analysis (PCA) represents the degree of variability among the samples and treatments. The PCA plot showed that variability among the control and treatment groups could be related to PC2, while PC1 corroborated the degree of variability among the treatment groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Different sets of DEGs were identified under 4 levels of Pb stress in maize. In total, 5836, 8484, 19074 and 9038 DEGs were identified in maize under 500 mg/L, 1000 mg/L, 2000 mg/L, and 3000 mg/L Pd(NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e, respectively. Among them, 3625, 3951, 13778, and 5115 genes were downregulated, and 2211, 4533, 5296 and 3923 genes were upregulated (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, to identify genes that were coupregulated or downregulated under 4 degrees of Pb stress, Venn diagram analysis was performed; 1762 genes were assigned as codownregulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC), and 1797 genes were assigned as coupregulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eTo further explore the functional aspects of DEGs involved in the genetic regulation of the Pb stress response in maize, GO enrichment analysis of the co-upregulated or downregulated genes was performed. Under Pb stress, DNA-binding transcription factor activity, metal cluster binding, ubiquitin-like protein transferase activity, response to water deficit stress, response to salicylic acid, response to ROS, metal ion transport, cell death and other GO terms were significantly enriched in the downregulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), while peroxidase activity, cellulose synthase (UDP-forming) activity, plasma membrane, cell wall, root epidermal cell differentiation, 2-alkenal reductase [NAD(P)+] activity, calcium ion transport, and other GO terms were significantly enriched in the upregulated genes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Metabolic analysis of maize roots under different degrees of Pb stress\u003c/h2\u003e\n \u003cp\u003eTo further reveal the mechanism underlying the metabolic response of maize to Pb stress, metabolome-based profiling was used to identify the differentially regulated metabolites. To predict a representative model, OPLS-DA was used to create a relationship interaction model among the metabolite regulation and the sample category (M-CK, M-500, M-2000). The metabolite peak regions from the Pb-treated and control groups were adjusted, and OPLS-DA was subsequently performed (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). As a result, the samples from the same treatment were clearly clustered, which suggested that there was good repeatability between biological replicates and strong specificity between treatments.\u003c/p\u003e\n \u003cp\u003eBased on the VIP score (VIP\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1), Student\u0026rsquo;s \u003cem\u003et test\u003c/em\u003e statistic (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.05) and degree of shift in abundance upon Cd exposure (ratio\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1.5 or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;1.5/1), a group of metabolites were identified for their contribution to sample segregation between the CK and Pb treatments. As a result, 72 DEMs and 107 DEMs were identified from T-500 and T-2000, respectively. Among the DEMs, 36 were detected in both groups. There were 11 upregulated DEMs and 23 downregulated DEMs (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). According to the taxonomical characterization of the metabolites, the 36 metabolites were divided into 8 groups: benzenoids (4), lipids and lipid-like molecules (7), nucleosides (2), organic oxygen compounds (3), organic acids and derivatives (4), organoheterocyclic compounds (6), phenylpropanoids and polyketides (2), and unknown metabolites (6) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Among them, the metabolites of nucleosides, organic acids and derivatives were downregulated, while the metabolites of phenylpropanoids and polyketides were upregulated. Six of the metabolites of lipids and lipid-like molecules were downregulated, while only one was upregulated. The number of downregulated metabolites of benzenoids and unknown compounds was the same as that of upregulated metabolites, while the number of downregulated metabolites of organic oxygen compounds and organoheterocyclic compounds was twice that of upregulated metabolites (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.4 Association analysis of transcriptomic and metabolomic expression levels and biological pathways under Pb stress in maize\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eFurthermore, to explore the response pathway to Pb stress, KEGG enrichment analysis of common DEGs and DEMs was performed. The common DEGs among the different Pb concentrations were significantly mapped to 19 distinct biological pathways, including those involved in the biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, brassinosteroid biosynthesis, pentose and glucuronate interconversions, terpenoid and polyketide metabolism, flavonoid biosynthesis, and starch and sucrose metabolism (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S2).\u003c/p\u003e\n \u003cp\u003eAs the number of common DEMs was low, the KEGG analyses of the DEMs were performed separately for T500 and T2000, and the results showed that 19 distinct pathways were enriched in both treatments, including biosynthesis of plant secondary metabolites, glyoxylate and dicarboxylate metabolism, arginine biosynthesis, flavonoid biosynthesis, beta-alanine metabolism, nitrogen metabolism, glycolysis/gluconeogenesis, biosynthesis of plant hormones, alanine, aspartate and glutamate metabolism, taurine and hypotaurine metabolism, and biosynthesis of alkaloids derived from histidine and purine (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S3, S4). Finally, both DEGs and DEMs were enriched in the flavonoid biosynthesis KEGG pathway (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). There were 2 shared candidate metabolites of flavonoid biosynthesis in T500 and T2000, namely, calycosin and genistin, and 27 shared DEGs.\u003c/p\u003e\n \u003cp\u003eTo better understand the contributions of calycosin and genistin to the Pb stress response, the abundances of these two metabolites were determined from the metabolome, and the results showed that both metabolomes significantly increased under Pb stress (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). To further identify interactions between the DEGs and DEMs that exhibited the same KEGG pathways, association analysis was subsequently performed between the DEGs and the DEMs. There were 18 genes associated with calycosin (12 positive, 6 negative), 3 genes associated with genistin (1 positive, 2 negative), and one gene negatively associated with both metabolites. The correlation coefficient ranged from 0.80 to 0.95 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB Table S5). Additionally, the expression of the associated genes was analyzed, among which 7 genes were downregulated and 13 genes were upregulated under Pb stress (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC). In summary, the 20 candidate genes related to the flavonoid synthesis is the\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cem\u003e4.1 Pb stress inhibited maize root development by inhibiting the root cell wall, cell membrane and DNA synthesis\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePlants are rooted in the soil and can absorb the nutrients and water they need directly from the soil via their roots. Soil is not only the source of nutrients for roots but also the living environment of roots, which can cause stress to plant growth and development, including drought stress, salt stress, waterlogging stress, and heavy metal stress [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As roots are in the soil, they are always the first part to be exposed and respond to stress from the soil [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Pb stress is one of the most common heavy metal stresses in soil and is increasing with the development of industry [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies have shown that root development, including lateral root formation and root morphological parameters, including length, surface area, volume, bushiness, and biomass, is inhibited by Pb [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Root cell viability was attenuated, leading to induced cell death under Pb stress [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our study, the Pb uptake in the roots and shoots tended to increase in response to the different Pb treatments, while the Pb uptake in the roots was relatively greater than that in the shoots, which is consistent with previous studies. The transcriptome analysis revealed that the downregulated genes were enriched in the root development-, plasma membrane-, cell division-, and cell wall-related pathways, and the upregulated genes were enriched in the cell death- and response to stress-related pathways. Additionally, among the 36 metabolites shared between T500 and T2000, six of the lipid and lipid-like molecule metabolites, two of the nucleoside metabolites, and two of the benzenoid metabolites were downregulated, which is consistent with the transcriptome results. According to the results, root development was strongly inhibited by Pb stress, which was mainly due to the inhibition of the synthesis of the cell wall, plasma membrane, and nucleic acid and the acceleration of cell death.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Flavonoid metabolism plays an important role in the plant response to abiotic stress\u003c/h2\u003e \u003cp\u003eFlavonoids are a group of secondary metabolites that are mainly composed of compounds with a C6-C3-C6 framework and are common in plants [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. To date, more than 8000 flavonoids have been identified in plants [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Flavonoids play critical roles in the stress response by maintaining redox homeostasis through several mechanisms, including 1) attenuation of singlet oxygen; 2) attenuation of enzyme activity involved in ROS production; 3) chelation of transition metal ions; 4) quenching of free radical cascades produced during lipid peroxidation metabolism; and 5) recycling of other antioxidants [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In our study, Pb stress caused an increase in ROS and destruction of the cell membrane of maize plants. To survive, plants initiate a series of physiological and biochemical responses, including the accumulation of flavonoids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 This study provides genetic resources for innovation of Pb-tolerant maize germplasm\u003c/h2\u003e \u003cp\u003eCrop Pb tolerance is a complex quantitative trait that is regulated through multiple processes, such as selective metal uptake, metal binding to the root surface, binding to the cell wall, and the induction of antioxidants [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have identified several Pb resistance genes in plants. \u003cem\u003ePb-sensitive 1 (PSE1)\u003c/em\u003e, which encodes an NC domain protein localized in the cytoplasm, was cloned in Arabidopsis, and glutathione-dependent PC synthesis is mainly responsible for mitigating Pb tolerance [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eAPX1\u003c/em\u003e encodes a cytosolic ascorbate peroxidase and negatively regulates Pb resistance by controlling the expression of ATP-binding cassette (ABC)-type transporters[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. ZjDjB1 encodes a DnaJ protein in \u003cem\u003eZ. japonica\u003c/em\u003e, and its overexpression in Arabidopsis can enhance Pb tolerance mainly by maintaining catalase activity and increasing the expression level of \u003cem\u003eATM3\u003c/em\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. \u003cem\u003eZmHIPP\u003c/em\u003e, encoding a heavy metal-associated isoprenylated plant protein in maize, was cloned by using combined linkage mapping and transcriptome analysis; this protein is involved in Pb deposition in the cell walls but is restricted from reaching intracellular organelles, thus attenuating Pb toxicity in maize [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. \u003cem\u003eZmAKINβγ1\u003c/em\u003e, encoding an AKINbetagamma-1 protein kinase, which was identified through GWAS (genome-wide association study) and transcriptomics data sets, was found to negatively regulate maize Pb tolerance by maintaining Pb accumulation in maize [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In our study, we identified 20 candidate genes related to the Pb stress response and accumulation of flavonoids by combining transcriptomic and metabolomic data. Among the candidate genes, \u003cem\u003eZm00001d038763\u003c/em\u003e and \u003cem\u003eZm00001d021577\u003c/em\u003e encode UDP-glycosyltransferase proteins, both of which were upregulated under Pb stress. Recently, a study reported that UDP-glycosyltransferases are involved in mitigating abiotic stresses in plants [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. \u003cem\u003eZm00001d047424\u003c/em\u003e encodes a flavonoid 3'-monooxygenase that has been reported to be involved in regulating oxidative and salt stress tolerance [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, Pb can accumulate in maize, especially in roots, where it suppresses the antioxidant system, root development, cell division, cell wall synthesis, and plasma membrane synthesis-related pathways and promotes responses to stress, cell death, and ABA response-related pathways. More importantly, flavonoids (genistin and calycosin) were shown to be involved in the response of maize to Pb stress, and 20 related genes were identified. This study not only provides genetic resources for the genetic improvement of maize Pb tolerance but also enriches the theoretical basis of the maize Pb stress response.\u003c/p\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\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw RNA-seq data have been uploaded to NCBI Sequence Read Archive (SRA) data-base (BioProject ID: PRJNA1113349).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (32301772); the Key Scientific and Technological Research Project in Henan Province (222102110050)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ. H., and Y. Z. designed the overall experiment. Z. H., Y. Z., X. Z., B. W., Y. G., and Z. G. conducted the data processing for transcriptomics and metabolomics. Z. H., and Y. Z. wrote the main manuscript, while X. Z. and Z. H. reviewed and revised the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Lc-Bio Technologies (Hangzhou, China) for their support with sequencing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003evan der Fels-Klerx HJ, Olesen JE, Naustvoll LJ, Friocourt Y, Mengelers MJ, Christensen JH: \u003cstrong\u003eClimate change impacts on natural toxins in food production systems, exemplified by deoxynivalenol in wheat and diarrhetic shellfish toxins\u003c/strong\u003e. \u003cem\u003eFood Addit Contam Part A Chem Anal Control Expo Risk Assess \u003c/em\u003e2012, \u003cstrong\u003e29\u003c/strong\u003e(10):1647-1659.\u003c/li\u003e\n\u003cli\u003eRosas-Castor JM, Guzm\u0026aacute;n-Mar JL, Hern\u0026aacute;ndez-Ram\u0026iacute;rez A, Garza-Gonz\u0026aacute;lez MT, Hinojosa-Reyes L: \u003cstrong\u003eArsenic accumulation in maize crop (Zea mays): a review\u003c/strong\u003e. \u003cem\u003eSci Total Environ \u003c/em\u003e2014, \u003cstrong\u003e488-489\u003c/strong\u003e:176-187.\u003c/li\u003e\n\u003cli\u003eArshad M, Silvestre J, Pinelli E, Kallerhoff J, Kaemmerer M, Tarigo A, Shahid M, Guiresse M, Pradere P, Dumat C: \u003cstrong\u003eA field study of lead phytoextraction by various scented Pelargonium cultivars\u003c/strong\u003e. \u003cem\u003eChemosphere \u003c/em\u003e2008, \u003cstrong\u003e71\u003c/strong\u003e(11):2187-2192.\u003c/li\u003e\n\u003cli\u003eUzu G, Sobanska S, Sarret G, Mu\u0026ntilde;oz M, Dumat C: \u003cstrong\u003eFoliar lead uptake by lettuce exposed to atmospheric fallouts\u003c/strong\u003e. \u003cem\u003eEnviron Sci Technol \u003c/em\u003e2010, \u003cstrong\u003e44\u003c/strong\u003e(3):1036-1042.\u003c/li\u003e\n\u003cli\u003ePourrut B, Shahid M, Dumat C, Winterton P, Pinelli E: \u003cstrong\u003eLead uptake, toxicity, and detoxification in plants\u003c/strong\u003e. \u003cem\u003eRev Environ Contam Toxicol \u003c/em\u003e2011, \u003cstrong\u003e213\u003c/strong\u003e:113-136.\u003c/li\u003e\n\u003cli\u003eLiao M, Chen CL, Zeng LS, Huang CY: \u003cstrong\u003eInfluence of lead acetate on soil microbial biomass and community structure in two different soils with the growth of Chinese cabbage (Brassica chinensis)\u003c/strong\u003e. \u003cem\u003eChemosphere \u003c/em\u003e2007, \u003cstrong\u003e66\u003c/strong\u003e(7):1197-1205.\u003c/li\u003e\n\u003cli\u003eLin C, Wang Y, Hu G, Yu R, Huang H: \u003cstrong\u003eSource apportionment and transfer characteristics of Pb in a soil-rice-human system, Jiulong River Basin, southeast China\u003c/strong\u003e. \u003cem\u003eEnviron Pollut \u003c/em\u003e2023, \u003cstrong\u003e326\u003c/strong\u003e:121489.\u003c/li\u003e\n\u003cli\u003eK\u0026uuml;pper H: \u003cstrong\u003eLead Toxicity in Plants\u003c/strong\u003e. \u003cem\u003eMet Ions Life Sci \u003c/em\u003e2017, \u003cstrong\u003e17\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eSchulz M, Marocco A, Tabaglio V, Macias FA, Molinillo JM: \u003cstrong\u003eBenzoxazinoids in rye allelopathy - from discovery to application in sustainable weed control and organic farming\u003c/strong\u003e. \u003cem\u003eJ Chem Ecol \u003c/em\u003e2013, \u003cstrong\u003e39\u003c/strong\u003e(2):154-174.\u003c/li\u003e\n\u003cli\u003eKopittke PM, Asher CJ, Kopittke RA, Menzies NW: \u003cstrong\u003eToxic effects of Pb2+ on growth of cowpea (Vigna unguiculata)\u003c/strong\u003e. \u003cem\u003eEnviron Pollut \u003c/em\u003e2007, \u003cstrong\u003e150\u003c/strong\u003e(2):280-287.\u003c/li\u003e\n\u003cli\u003eGichner T, Znidar I, Sz\u0026aacute;kov\u0026aacute; 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\u003cstrong\u003e69\u003c/strong\u003e(2):676-686.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"Maize, Pb stress, transcriptome, metabolome, flavonoid biosynthesis","lastPublishedDoi":"10.21203/rs.3.rs-4519159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4519159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePb stress, a toxic abiotic stress, critically affects maize production and food security. Although some progress has been made in understanding the damage caused by Pb stress and plant response strategies, the regulatory mechanisms and resistance genes involved in the response to lead stress in crops are largely unknown. In this study, the response mechanism of maize to Pb stress, the expression of Pb tolerance genes, physiological and biochemical indexes, the transcriptome, and the metabolome under different concentrations of Pb stress were combined for comprehensive analysis. As a result, the antioxidant system was significantly inhibited under Pb stress, especially under relatively high Pb concentrations. Transcriptome analysis revealed 3559 co-DEGs under the four Pb concentration treatments, which were enriched mainly in the GO terms related to DNA-binding transcription factor activity, response to stress, response to reactive oxygen species, cell death, the plasma membrane and root epidermal cell differentiation. Metabolome analysis revealed 72 and 107 DEMs under T500 and T2000, respectively, and 36 co-DEMs. KEGG analysis of the DEMs and DEGs revealed a common metabolic pathway, namely, flavonoid biosynthesis. An association study between the flavonoid biosynthesis-related DEMs and DEGs revealed 20 genes associated with flavonoid-related metabolites, including 3 for genistin and 17 for calycosin. This study not only provides genetic resources for the genetic improvement of maize Pb tolerance but also enriches the theoretical basis of the maize Pb stress response.\u003c/p\u003e","manuscriptTitle":"Flavonoid metabolism plays an important role in response to Pb stress in maize at seedling stage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 10:39:33","doi":"10.21203/rs.3.rs-4519159/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-14T07:16:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-14T02:26:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T13:48:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T05:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234205508666836620594973509156149513987","date":"2024-06-07T00:10:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T14:40:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82499503055896868669071126924237109865","date":"2024-06-06T14:12:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238643310964787662238797590106141670093","date":"2024-06-06T13:37:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82265563098847901032385934828474246892","date":"2024-06-06T08:45:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-05T01:43:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-04T17:41:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-04T17:38:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-04T17:38:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-06-03T04:11:12+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":"cc561710-1d84-403a-bdf1-770ee8a78655","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:00:42+00:00","versionOfRecord":{"articleIdentity":"rs-4519159","link":"https://doi.org/10.1186/s12870-024-05455-0","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-07-30 15:57:08","publishedOnDateReadable":"July 30th, 2024"},"versionCreatedAt":"2024-06-18 10:39:33","video":"","vorDoi":"10.1186/s12870-024-05455-0","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05455-0","workflowStages":[]},"version":"v1","identity":"rs-4519159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4519159","identity":"rs-4519159","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}