Transcriptome profiles of leaves and roots of Brassica napus L. in response to antimony stress

Transcriptome profiles of leaves and roots of Brassica napus L. in response to antimony stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptome profiles of leaves and roots of Brassica napus L. in response to antimony stress Xianjun Liu, Liang You, Wencong Yu, Yuhui Yuan, Wei Zhang, Mingli Yan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4850929/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Antimony (Sb), a non-essential heavy metal, exerts severe toxic effects on the growth and development of plants. This study investigated the response of Brassica napus to 75 mg/L Sb(III) stress under hydroponic conditions, focusing on Sb accumulation, physiological indexes, and transcriptome sequencing. Sb accumulation in six B. napus varieties ranged from 199.73 to 561.42 mg/kg. Enzymatic activities (SOD, POD, CAT) and MDA content showed initial increases followed by declines under varying Sb treatments. Transcriptomic analysis identified 8,802 genes in root tissues and 13,612 genes in leaf tissues responsive to Sb stress, predominantly involved in oxidative stress responses, glutathione metabolism, plant hormone signaling, ABC transporters, and MAPK pathways. Upregulation of antioxidant-related genes like GPX2, APX2, PER34, and GSTU4 in root tissues correlated with physiological index changes, while photosynthesis-related genes were largely downregulated in leaf tissues. This study provides crucial insights into B. napus's response mechanisms to Sb stress and highlights its potential for phytoremediation efforts. Biological sciences/Plant sciences/Plant stress responses Biological sciences/Genetics/Sequencing/Rna sequencing Brassica napus L. Sb stress Physiological indexes Transcriptome sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction As urban industrialization progresses, the exploitation of mineral resources, along with the accumulation and smelting of heavy metals, has led to the widespread distribution of heavy metals and their compounds in the atmosphere, water bodies, and soil. Heavy metals such as cadmium (Cd), lead (Pb), chromium (Cr), antimony (Sb), among others, have inflicted significant damage on the natural environment, resulting in severe pollution 1,2 Antimony Sb is a crucial strategic resource with extensive applications in semiconductors, batteries, flame retardants, ceramics, weapons, and pharmaceutical materials 3-5 . Regrettably, inadequate handling practices have led to varying levels of Sb contamination in soil, adversely affecting the plant growth and development. Sb exists in four oxidation states (-III, 0, III, and V) in the natural environment. Among these, inorganic Sb exhibits higher toxicity compared to its organic counterpart, and its toxicity varies depending on its oxidation states. Specifically, Sb(III) is ten times more toxic than that Sb(V) 2,6,7 . In recent years, research into the mechanisms of plant uptake and toxicity related to the heavy metal Sb has emerged a significant field. Studies on the sunflower and maize have revealed varing levels of tolerance to Sb toxicity among different crops, with maize showing greater susceptibility to Sb in soil 8 . Research findings demonstrate that Sb effectively inhibits root elongation and germination in rice, significantly impacting rice productivity 9 . Further studies have indicated that Sb(III) at a concentration of 20 mg/L can generate superoxide anion free radicals (·O2 − ), thereby impairing the integrity of root cell membranes. Conversely, Sb(V) promotes shoot biomass accumulation and enhances the uptake of essential elements 10 . Under heavy metal stresses, the activity of antioxidant enzymes such as peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH), serves as critical indicators of the plant's antioxidative defense system. When the concentration of Sb in soil exceeds 50 mg/kg, the activities of POD and SOD in maize plants are significantly inhibited, while CAT activity exhibits a positive correlation with soil Sb concentration 11 . Brassica napus L., commonly known as rapeseed, is a globally significant oilseed crop extensively utilized in soil phytoremediation studies due to its rapid growth rate, substantial shoot biomass, and remarkable capacity to adsorb heavy metals 12-15 . Additionally, RNA sequencing (RNA-seq) technology has been widely employed to investigate the molecular response mechanisms of plants under heavy metal toxicity stress. These studies have explored the effects of various metals, including Mn 16 , Cu 1 7 , Pb 18 , and Cd 19,20 , in diverse plant species such as Schima superba , Citrus grandis , Raphanus sativus L., Brassica oleracea L. (Broccoli). However, research on the response of B. napus to Sb-induced stress is currently limited, and the molecular mechanisms underlying its response to Sb stress remain unclear. In this study, different varieties of B. napus were exposed to hydroponic solutions with varying Sb concentrations. The varietiy exhibiting the highest Sb accumulation in its shoot was chosen for further investigation, specifically focusing on the physiological indicators of its roots and leaves. Following that, RNA-seq was employed to compare differentially expressed genes (DEGs) associated with the response to Sb toxicity in both roots and leaves. This study lays the groundwork for future research into the pathways and functional genes involved in the response to Sb toxicity. Results Sb accumulation in different B. napus varieties In this study, the accumulation level Sb in entire plants of B. napus was used as a crucial indicator to evaluate its phytoremediation potential. The findings showed an increasing trend in Sb accumulation in the plants of six B. napus varieties as the Sb concentration increased. Eventually, the Sb concentration in plants reached a peak and subsequently declined (Fig. 1). Specifically, among the varieties XZY787, XZY512, ZYZ28, and QY18H, the highest levels of Sb accumulation were observed at an Sb concentration of 75 mg/L, with values of 325.63, 561.42, 257.40, and 199.73 mg/kg, respectively. FY789 exhibited its highest Sb accumulation of 222.29 mg/kg at an Sb concentration of 50 mg/L, while HYZ50 reached its maximum Sb content of 204.35 mg/kg at a concentration of 25 mg/L. Remarkably, XZY512 exhibited the highest Sb content of 561.42 mg/kg at an Sb concentration of 75 mg/L among the six varieties, making it the focal variety selected for further research endeavors. Variations in physiological indexes To assess the impact of heavy metals on plants, toxicity to plant growth is frequently evaluated by examining enzyme activities and metabolite levels, such as SOD, POD, CAT, and MDA 21,22 . Therefore, this study measured the activities and contents of these enzymes or metabolites in both root and leaf tissues of B. napus XZY512 under varying Sb concentration treatments. Compared to the controls, root tissue exhibited an upregulation in the activities or contents of SOD (Fig. 2A), POD (Fig. 2B), CAT (Fig. 2C), and MDA (Fig. 2D) in response to increasing Sb concentrations, particularly at low concentration. Peak values of these indexes were observed at 50 or 75 mg/L Sb, followed by a subsequent decline. Additionally, the patterns of SOD and POD activity changes in leaf and root tissues varied inconsistently under different Sb treatments (Fig. 2A-B). However, CAT activity in leaf tissues exhibited a declining trend under high-concentration Sb solutions (Fig. 2C), while MDA content showed a notable decrease (Fig. 2D). These results indicate that Sb stress at varying concentrations significantly impacts the growth of B. napus, with various physiological indices displaying a declining trajectory after reaching their peak values. This suggests a potential link to the adaptability of B. napus to Sb stress or the occurrence of physiological damage induced by Sb toxicity. RNA sequencing and identification of differentially expressed genes (DEGs) To investigate the molecular mechanisms underlying the impact of Sb stress on B. napus , we conducted RNA-seq analysis on the roots and leaves treated with concentrations of 0 mg/L (control) and 75 mg/L Sb (treatment). The samples were designated as root control (RCK), root treatment (RTr), leaf control (LCK), and leaf treatment (LTr). Following high-throughput sequencing of various different plant samples, raw data were obtained from a total of 12 samples. These data were uploaded to the NCBI SRA database, where they were assigned the following accession numbers: SRR29906309 (RCK1), SRR29906308 (RCK2), SRR29906305 (RCK3), SRR29906304 (RTr1), SRR29906303 (RTr2), SRR29906302 (RTr3), SRR29906301 (LCK1), SRR29906300 (LCK2), SRR29906299 (LCK3), SRR29906298 (LTr1), SRR29906307 (LTr2), and SRR29906306 (LTr3). Overall, we obtained 80.58 Gb of clean bases, with all samples yielding 5.97 Gb of clean data and exhibiting a Q30 value surpassing 91.56%. All of the XZY512 leaf samples showed alignment rates higher than 93.25% when aligned against the ZS11 reference genome. However, sample RTr3 in the roots showed a far lower alignment rate of only 7.64% (Table S1). To protect the integrity of subsequent analyses, the RTr3 sample was removed, and the analysis proceeded using the remaining 11 samples. With the RTr3 sample removed, the analysis continued with the 11 remaining samples to protect the integrity of the other studies. Applying a filtering threshold of P-value 1, a total of 8,802 (4,274 up-regulated and 4,528 down-regulated) and 13,612 (6,653 up-regulated and 6,959 down-regulated) DEGs were identified in the comparisons RCK_vs_RTr and LCK_vs_LTr, respectively (Fig. 3A). Notably, 2,218 DEGs were shared between root and leaf tissues, comprising 1,498 up-regulated genes and 720 down-regulated genes (Fig. 3B). GO enrichment and KEGG analysis of DEGs in root and leaf tissues To deeper understand the function of DEGs between the control and treatment groups, we performed GO enrichment analysis on DEGs derived from RCK_vs_RTr, LCK_vs_LTr, and (RCK_vs_RTr)_vs_(LCK_vs_LTr). We then performed statistical classification of the relevant GO terms. These DEGs primarily participate in functions such as cell, cell part, organelle, binding, catalytic activity, cellular process, metabolic process, and response to stimulus (Fig. S1). The comparison of up-regulated and downregulated gene counts between RCK_vs_RTr and LCK_vs_LTr showed comparability. However, in the (RCK_vs_RTr)_vs_(LCK_vs_LTr) comparison, there was a notably higher count of up-regulated genes among the shared DEGs pool. GO enrichment and KEGG annotation analysis of DEGs among RCK_vs_RTr and LCK_vs_LTr revealed distinct preferences. Specifically, DEGs in RCK_vs_RTr were significantly enriched in GO terms such as response to oxidative stress (GO:0006979), response to toxic substance (GO:0009636), iron ion binding (GO:0005506), and glutathione transferase activity (GO:0004364) (Fig. 4A). On the other hand, the DEGs in LCK_vs_LTr were predominantly enriched in GO terms including chloroplast thylakoid (GO:0009534), photosystem II (GO:0009523), response to abscisic acid (GO:0009737), and chlorophyll binding (GO:0016168) (Fig. 4C). KEGG annotation findings indicated that Sb stress treatment led to significant enrichment of DEGs in root tissues, particularly in pathways such as Glutathione metabolism (map00480), ABC transporters (map02010), MAPK signaling pathway-plant (map04016), and Plant hormone signal transduction (map04075) (Fig. 4B). In contrast, DEGs in the leaves showed enrichment in pathways such as carbon fixation in photosynthetic organisms (map00710), photosynthesis (map00195), and oxidative phosphorylation (map00190) (Fig. 4D). The top 25 enriched GO terms and KEGG pathways in (RCK_vs_RTr)_vs_(LCK_vs_LTr) included significant terms like response to toxic substance (GO:0009636), response to oxidative stress (GO:0006979), vacuolar membrane (GO:0005774), and glutathione binding (GO:0043295). Additionally, important metabolic pathways comprised glutathione metabolism (map00480), protein processing in endoplasmic reticulum (map04141), and ABC transporters (map02010) (Fig. 4E-F). Based on the aforementioned findings, we propose that there are significant disparities in DEGs between the plant treated with 75 m g/L Sb and the control groups. Moreover, the observed DEGs exhibit a notable preference for root and leaf tissues. The toxic effects of Sb may primarily influence gene expression variations related to oxidative stress, toxic stress, and glutathione metabolism in root tissues. In leaves, the affected genes are mainly associated with photosynthesis. Discussion on the involvement of DEGs in response to Sb stress During cellular metabolism, cell organelles produce a amount of reactive oxygen species (ROS), which are effectively regulated by the antioxidant system to maintain a dynamic equilibrium 23 . Nonetheless, excessive exposure to heavy metals triggers a substantial increase in ROS production, disrupting the homeostasis of the antioxidant system. As a result, there is a significant accumulation of ROS within the organism, causing enduring damage to cellular structures, proteins, and other essential components 24 . This study revealed that under Sb stress conditions, a predominant subset of genes (58/64) associated with the response to oxidant stress (GO:0006979) exhibited notable up-regulation in the root and leaf tissues of B. napus (Table S2). Notably, this set of genes included Glutathion peroxidase GPX2 , GPX6 , Ascorbate peroxidase APX2 , 17.6 kDa class I heat shock protein HSP17.6B , Peroxidase PER34 , PER54 , as well as Zinc finger protein ZAT1 (Fig. 5A, Table S3). In our study, we also observed significant upregulation of numerous Glutathione transferase genes, such as GSTF3 , GSTU4 , and GSTU5 (Fig. 5B, Table S3), in response to Sb toxicity. This finding suggests that GSH may play a vital role in mitigating Sb toxicity and countering oxidative damage. Furthermore, our study revealed distinct gene responses to Sb stress, including Auxin-related genes ( IAA3 , IAA7 , IAA17 , IAA26 , and ARF5) , ABC transporters ( ABCB4 , ABCB11 , and ABCC9) , as well as components of the MAPK signaling pathway ( WRKY29 , MEKK1 , and RBOHB) (Fig. 5C-E, Table S3). Remarkably, Sb treatment led to a significant enrichment of DEGs associated with the Photosynthesis in leaves. Among the 122 genes implicated in Photosynthesis (map00195), the majority (119 genes) exhibited down-regulation (Fig. 5F, Table S4). This compelling evidence indicates that the primary toxic effect of heavy metal Sb on the leaves of B. napus is characterized by a significant impact on the molecular mechanisms of photosynthesis. Validation of RNA-Seq by qRT-PCR To validate the RNA-seq data results, nine genes were selected for qRT-PCR verification. These genes comprised transcription factor genes WRKY28 (BnaC07G0420700ZS), WRKY75 (BnaC09G0529900ZS), and BHLH10 (BnaA08G0218600ZS), auxin-related genes IAA17 (BnaA08G0316300ZS) and AIR12 (BnaA03G0308600ZS), as well as metal transport or binding protein genes CML27 (BnaA06G0124500ZS) and HMA2 (BnaA02G0384700ZS) (Table S5). The findings indicate a strong positive correlation between the qRT-PCR and RNA-seq data, with correlation coefficients (R 2 ) of 0.9499 and 0.9045 for DEGs in root and leaf tissues, respectively (Fig. 6A-B). This high degree of correlation provides robust validation for the reliability of the RNA-seq data. Discussion Variations in Sb uptake and physiological indices in B. napus under Sb stress Sb is a toxic heavy metal that can adversely impact plant growth and development. This study conducted experiments with varying Sb concentrations to investigate the variations in Sb accumulation among different B. napus varieties. Our preliminary findings indicated that Sb accumulation in B. napus followed a dose-dependent relationship, with higher Sb concentration resulting in increased uptake. Similar observations have been reported in Brassica juncea , where arsenic accumulation demonstrated a positive correlation with rising arsenic concentrations 22 . These results suggest that B. napus exhibits a toxicological response to Sb similar to that of arsenic, a clan element, . MDA, a product of lipid peroxidation, signifies reduced free radical scavenging capability in plants as its levels rise. The antioxidant enzyme system, including SOD, POD, CAT, and other enzymes, plays a crucial role in eliminating free radicals and ROS, thus protecting cells from damage 25 . Therefore, the MDA content and activities of SOD, POD, CAT partially reflect the extent of membrane lipid peroxidation and the plant's response to stress. In this study, the above physiological indices in B. napus root tissues exhibited an increased trend with rising Sb concentration, reaching a peak and then declining. In leaf tissues, SOD and POD activities remained stable whereas CAT activity and MDA content were significantly affected by high-level Sb concentration (Fig. 2). Consequently, Sb-induced stress can lead to cellular damage in B. napus root tissues, causing substantial physiological alterations (Fig. 7). Effects of Sb stress on oxidative stress and glutathione metabolism genes In response to Sb-induced oxidative stress, plants often release specific antioxidant enzymes and small molecule antioxidants to against the toxicity of Sb 26-28 . This investigation discovered that antioxidant genes, including both enzymes and non-enzymes such as Glutathione peroxidase GPX2 and GPX6 , Ascorbate peroxidase APX2 , and Peroxidase PER 34 and PER54 , were up-regulated and actively participate in mitigating Sb-induced oxidative stress. GPX have been reported as a potential biomarker of cytotoxicity, while APX and PER may be involved in the elimination of harmful H 2 O 2 within cellular organelles 22 . Hence, the upregulation of genes associated with oxidative enzyme activity, along with increased activities of antioxidant enzymes (SOD, POD, and CAT), confirmed the pronounced toxicity of a 75 mg/L Sb concentration on B. napus root tissues (Fig. 7). Moreover, the role of small molecule antioxidants like GSH in mitigating Sb-induced oxidative stress cannot be overlooked. GSH, a precursor of phytochelatins and an antioxidant, plays a crucial role in heavy metal detoxification 29 . Research on B. juncea arsenic tolerance reveals significant upregulation of genes associated with glutamate cysteine ligase, glutathione synthase, and glutathione S-transferase 22 . The upregulation of glutathione-related genes in response to Sb stress, particularly those encoding for glutathione S-transferase (GSTs), indicates an enhanced detoxification mechanism at play. GSTs facilitate the conjugation of GSH to a wide array of electrophilic compounds, thus neutralizing the toxicity of Sb and other reactive intermediates produced during oxidative stress.Additionally, the pathway analysis of glutathione metabolism (map00480) highlights the substantial role of these genes in the overall stress response mechanism. This suggests that the modulation of GSH levels and the expression of GSTs are pivotal strategies employed by plants to counteract Sb-induced cytotoxicity. Furthermore, the interconnectedness of the antioxidant network, comprising enzymes like GPX, APX, and PER, along with GSH, underscores a comprehensive defensive system that plants utilize to mitigate the detrimental effects of Sb. Auxin, MAPK signaling, and ABC transporters respond to Sb stress The exogenous application of plant hormones such as auxin (IAA), brassinosteroids (BR), salicylic acid (SA), and gibberellins (GA), have been shown to alleviate the adverse impacts of heavy metals on plants 30 . Studies have indicated that auxin homeostasis induces defense responses to Cd stress by regulating auxin biosynthesis, catabolism, transport, and signal transduction 20,31 . High levels Cd can inhibit plant growth and reduce IAA content, with genes involved in IAA synthesis being suppressed under Cd stress 20 . In this study, the down-regulation of auxin-related genes such as IAA7 , IAA17 , and IAA26 under Sb stress suggests that negative Sb may negatively affect IAA levels. Additionally, gibberellin-responsive regulator RGL1 , ethylene response sensor ERS2 , abscisic acid receptor PYL1 , PYL6 , PYL8 and PYR 1 , along with other plant hormones (Table S4), were implicated in regulating the response to Sb stress. MAPK signaling cascades have been extensively studied in plants and shown to participate in stress adaptation, cell death, defense responses, ethylene signal transduction, and maintenance of ROS responses 22,32 . Following Sb treatment, DEGs associated with the MAPK signaling pathway and ABC transporters were significantly enriched in root tissues. These genes include WRKY transcription factors ( WRKY29 and WRKY33 ), mitogen-activated protein kinase kinase kinase ( MEKK1 ), respiratory burst oxidase homolog proteins ( RBOHB and RBOHF ), and metal transporters such as ABCB4 , ABCB11 , ABCC9 , and ATMRP11 (Table S3, Fig. 7). The presence of these DEGs in root tissues indicates that the response of B. napus to Sb stress involves a complex regulatory network, with these DEGs potentially playing key roles in the defense response against Sb toxicity. Effects of Sb stress on photosynthetic system Furthermore, the detrimental effects of Sb(III) on the photosynthetic machinery were evident from the significant downregulation of genes involved in photosystem II (GO:0009768). The downregulation of 47 out of 48 DEGs in this category suggests a substantial disruption in the photosynthetic electron transport chain. This disruption is likely to impair the overall efficiency of photosynthesis, leading to decreased energy production and reduced growth and development in plants. Previous studies have shown that the heavy metal Sb has a negative impact on plant photosynthesis. High concentrations of Sb(III) are found to significantly reduce PS II (Fv/Fm and PIABS) in maize leaf 11 , as well as cause a significant decrease in leaf pigment contents (chlorophyll a, b, carotenoid), net photosynthetic rate (Pn), stomatal conductance (Gs), evaporation rate (E), PSII maximum photochemical efficiency (Fv/Fm), and PSII electron transfer quantum yield rate (ΦPSII) of Acorus calamus 33 . Given these findings, it is crucial to explore the molecular mechanisms underlying Sb-induced photosynthetic inhibition. Investigating the expression and functionality of key photosynthetic proteins, as well as the potential protective roles of antioxidant systems, could provide deeper insights into how plants cope with Sb stress at the molecular level. Additionally, assessing the impact of Sb on other photosynthetic components such as light-harvesting complexes and carbon fixation enzymes will be beneficial. Future research should also focus on evaluating specific photosynthetic indices, including the rate of photosynthetic electron transport, photochemical or non-photochemical quenching, to comprehensively understand the extent of Sb-induced photosynthetic impairment. Such studies would not only elucidate the mechanisms of Sb toxicity but also aid in developing strategies to enhance plant tolerance to heavy metal stress, thereby contributing to agricultural sustainability in Sb-contaminated areas. Conclusion Different B. napus varieties exhibited varying responses to Sb accumulation in plants, with variety XZY512 demonstrating the highest Sb accumulation, indicating its potential for phytoremediation. Analysis of physiological indexes such as SOD, POD, CAT, and MDA in the root and leaf tissues of XZY512 revealed an upward trend in the root tissue with increasing Sb concentration, peaking, and then declining. In leaf tissues, CAT and MDA significantly decreased under Sb stress. Transcriptomic analysis identified DEGs in B. napus root tissues under Sb stress, associated with oxidative stress response, glutathione metabolism, plant hormone signaling, ABC transporters, and MAPK signaling pathway. Notably, photosynthesis-related genes were significantly down-regulated in leaf tissues. This study investigated the effects of heavy metal Sb on B. napus through Sb accumulation, physiological indexes, and transcriptome sequencing, providing initial insights into the biological processes and gene networks in B. napus under Sb stress. Materials And Methods Plant material and growth conditions The commercial varieties used in this study, namely Xiangzayou 787(XZY787), Fengyou 789(FY789), Xiangzayou 512(XZY512), Huazayou 50(HYZ50), Zhongzayou 28(ZYZ28), and Qinyou 18 Hao(QY18H), were provided by the Oliseed Molecular Breeding Team at Hunan Agricultural University. The hydroponic system employed was based on Thakur et al. 22 with specific modifications. Seeds were disinfected using a 50% sodium hypochlorite (v/v) solution for 2 minutes,followed by rinsed with distilled water. Subsequently, the seeds were placed in perlite trays soaked with 1/2 Hoagland's solution. Plants were cultivated in a phytotron for 35 to 40 days until they reached the 5-leaf stage, under a 12-hours light cycle, with a relative humidity of 50-60% and a temperature of 24±2°C. They were then transferred to hydroponic tanks containing a 1/2 Hoagland's solutions. After a 72-hours incubation, the plants were treated with Sb(III) solutions (C 4 H 4 KO 7 Sb·(1/2)H 2 O blending) at concentrations of 0, 10, 25, 50, 75, and 100 mg/L. Each variety and concentration were replicated three times. Following the 72-hour treatment, samples were collected, rapidly frozen with liquid nitrogen, and stored in an ultra-low temperature freezer. Determination of Sb accumulation and physiological indices To assess the Sb accumulation, the entire plants both the control and Sb-treated groups were subjected to a drying process. Initially, they were placed in a drying oven set at 105°C for 30 minutes, followed by furher drying at 75 °C until a constant weight were achieved. The sample processing procedure followed the method described by Liao et al. 34 After sample digestion, the Sb accumulation was quantified using ICP-MS (PerkinElmer NexlON®2000, USA). To investigate the impact of varying Sb concentrations on the roots and leaves of B. napus , assay kits from Nanjing Jiancheng Bioengineering Institute were used to measure the activity of antioxidant enzymes. Specifically, the following assay kits were utilized: superoxide dismutase (SOD) assay kit (A001-3, WST-1), peroxidase (POD) assay kit (A084-3, colorimetric method), catalase (CAT) assay kit (A007-1, visible light method), and malondialdehyde (MDA) assay kit (A003-1, TBA method) to measure physiological indexes of the respective B. napus tissues. Measurements of Sb accumulation and physiological indices were conducted with at least three biological replicates. RNA extract, library preparation and transcriptome sequencing Based on the Sb accumulation results, a total of 12 samples (Three replicates each for roots and leaves) from XZY512, following a 72-hour treatment with 0 mg/L and 75 mg/L Sb concentrations, were selected for transcriptome sequencing. This analysis was comducted by Genepioneer Biotechnologies Company in Nanjing, China. Total RNA was extracted using the RNAprep Pure Plant Plus Kit (Tiangen, Beijing, China), following the manufacturer's protocol. The quality of the extracted RNA was evaluated using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), Qubit® RNA Assay Kit in Qubit® 2.0 Fluorometer (Life Technologies, CA, USA), and Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA) prior to library preparation. Sequencing libraries were generated using NEB Next® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) according to the manufacturer’s instructions. The libraries were sequenced on an Illumina Nova seq6000 platform, resulting in 150 bp paired-end reads. Clean reads were mapped to the reference genome sequence of B. napus (ZS11) ( https://www.ncbi.nlm.nih.gov/assembly/GCF_000686985.2/ ) utilizing the default parameters of the HISAT v2.1.0 software Kim et al. 35 The Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) was employed as a quantification metric for transcripts or gene expression levels. Comparative transcriptome analysis and gene functional annotation DEGs among different treatment groups were identified using the DESeq R package (Love et al., 2014) 36 with a significance threshold of P-value 1. Subsequently, venn diagram and heatmap were generated to visualize the expression patterns of DEGs across various experimental conditions. Furthermore, Gene Ontology (GO) enrichment analysis (http://www.geeontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (http://www.kegg.jp) were conducted to screen DEGs significantly enriched in GO terms and metabolic pathways, respectively, at a significance level of P < 0.05. qRT-PCR validation Total RNA (1 μg) was reverse-transcribed using the PrimeScript™ RT reagent kit with gDNA Eraser (Takara, Japan). Quantitative real-time (qRT-PCR) was performed using SYBR Green II (SYBR® Premix Ex Taq™ Kit, Takara) on a CFX96 real-time system (BIO-RAD, USA). The qRT-PCR protocol included an initial denaturation step at 95 °C for 30 s, followed by 40 cycles of amplification at 95 °C for 5 s and extension at 60 °C for 30 s. To validate the RNA-Seq findings, nine DEGs showing significant differences among various treatments were selected for qRT-PCR analysis. Gene-specific qRT-PCR primers (Table 1) were designed using Primer-Premier 3.0 software. BnActin7 , described by Chen et al. 37 , served as an internal control for normalizing gene expression levels. The relative expression of each DEGs was evaluated by the 2 −ΔΔCt method. Each gene and sample were analyzed with three biological replicates and three technical replicates. Table 1 qRT-PCR primers used in the study. Gene ID Name of primers Sequence of forward primers (5’-3’) Sequence of reverse primers(5’-3’) BnaA06G0124500ZS q0124500 CTTCGATCTGTACGATCAGGAC CCTCGAAATTAACATTCCCGTC BnaC05G0211300ZS q0211300 GTCAAACGCTACTCTTTCTTCC GCCAAATTGTTTTGTGGCTCAA BnaA08G0218600ZS q0218600 TATGAAGCTTCCTCTGTCCATC GTTGTTGCTGTTGTTCTCATCT BnaA03G0308600ZS q0308600 GTACAACATCAGCAGCTACAAC AAATCACGATCAGATTACCGGG BnaA08G0316300ZS q0316300 GCAAGAGTAGAGCTTGAAGTTG CCACAATTAACACTCATCAATGTG BnaA02G0384700ZS q0384700 AGCCTTTATACGTCATCGAGAG CGTAGAGCACATGAAACCAAAT BnaC07G0420700ZS q0420700 CCAATTCCCTTCATCCTTCTCT GAAGGAGTAGGAAGATGGATCG BnaC07G0494200ZS q0494200 GTTTTTGGTTTCGTTCTTTCGC TCGATCAGAGGAACTTCTGATG BnaC09G0529900ZS q0529900 CGAGAAGCAGGGATTTGAAAAA CATCCTCCATATGTGCACCTAT Declarations Acknowledgments This research was supported by Hunan Provincial Natural Science Foundation of China (2023JJ50083, 2023JJ50475), the National Natural Science Foundation of China (32371589), Research Foundation of Education Bureau of Hunan Province, China (23B0809, 22B0844) and the construct program of plant protection applied characteristic discipline in Hunan Province. Author contributions Xianjun Liu: Writing—original draft, Writing—review and editing, Resources. Liang You: Writing—review and editing, Methodology. Wencong Yu: Methodology, Data curation, Formal analysis. Yuhui Yuan: Data curation, Formal analysis. Wei Zhang: Material provision. Mingli Yan: Data curation. Yu Zheng: Data curation. Renyan Duan: Writing—review and editing. Guiyuan Meng: Writing—review and editing. Yong Chen:Writing—review and editing. Zhongsong Liu: Writing—review and editing, Fund support. Guohong Xiang: Writing—review and editing, Fund support. Competing interests The authors declare no competing interests. Data availability The RNA-seq data from the 12 samples used in this study have been uploaded to the NCBI SRA database. The accession numbers are as follows: SRR29906309 (RCK1), SRR29906308 (RCK2), SRR29906305 (RCK3), SRR29906304 (RTr1), SRR29906303 (RTr2), SRR29906302 (RTr3), SRR29906301 (LCK1), SRR29906300 (LCK2), SRR29906299 (LCK3), SRR29906298 (LTr1), SRR29906307 (LTr2), and SRR29906306 (LTr3). RCK (1, 2, 3), RTr (1, 2, 3), LCK (1, 2, 3), and LTr (1, 2, 3) represent three biological replicates for the control and treated samples of roots and leaves, respectively. References Naja, G. M. & Volesky, B. Toxicity and Sources of Pb, Cd, Hg, Cr, As, and Radionuclides in the Environment. (Kluwer Academic Publishers, 2017). Zhang, Y., O'Loughlin, E. J. & Kwon, M. J. Antimony redox processes in the environment: A critical review of associated oxidants and reductants. J. Hazard. Mater. 431 , 128607, doi:10.1016/j.jhazmat.2022.128607 (2022). Ahmad, M. et al. 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Alleviation of metal stress in rape seedlings ( Brassica napus L.) using the antimony-resistant plant growth-promoting rhizobacteria Cupriavidus sp. S-8-2. Sci. Total. Environ. 858 , 159955, doi:10.1016/j.scitotenv.2022.159955 (2023). Liaquat, F. et al. Reprisal of Schima superba to Mn stress and exploration of its defense mechanism through transcriptomic analysis. Front. Plant Sci. 13 , 1022686, doi:10.3389/fpls.2022.1022686 (2022). Ren, Q.-Q. et al. Physiological and molecular adaptations of Citrus grandis roots to long-term copper excess revealed by physiology, metabolome and transcriptome. Environ. Exp. Bot. 203 , 105049, doi:10.1016/j.envexpbot.2022.105049 (2022). Wang, Y. et al. Transcriptome profiling of radish ( Raphanus sativus L.) root and identification of genes involved in response to Lead (Pb) stress with next generation sequencing. PLoS One. 8 , e66539, doi:10.1371/journal.pone.0066539 (2013). Han, M. et al. 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Tolerance to drought and salt stress in plants: Unraveling the signaling networks. Front. Plant Sci. 5 , 151, doi:10.3389/fpls.2014.00151 (2014). Zhou, X., Sun, C., Zhu, P. & Liu, F. Effects of antimony stress on photosynthesis and growth of acorus calamus. Front. Plant Sci. 9 , 579, doi:10.3389/fpls.2018.00579 (2018). Liao, G. et al. Efficiency evaluation for remediating paddy soil contaminated with cadmium and arsenic using water management, variety screening and foliage dressing technologies. J. Environ. Manage. 170 , 116-122, doi:10.1016/j.jenvman.2016.01.008 (2016). Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37 , 907-915, doi:10.1038/s41587-019-0201-4 (2019). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550, doi:10.1186/s13059-014-0550-8 (2014). Chen, X., Truksa, M., Shah, S. & Weselake, R. J. A survey of quantitative real-time polymerase chain reaction internal reference genes for expression studies in Brassica napus . Anal. Biochem. 405 , 138-140, doi:10.1016/j.ab.2010.05.032 (2010). Additional Declarations No competing interests reported. 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01:55:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4850929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4850929/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88521-3","type":"published","date":"2025-03-19T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64092552,"identity":"d9f4fdce-556f-4b67-9c8c-a13427cbd094","added_by":"auto","created_at":"2024-09-06 15:12:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329685,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in Sb accumulation of different varieties of\u003cem\u003e B. napus\u003c/em\u003e. Different lowercase letters represent significant differences at \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 level using one-way ANOVA and Tukey’ s correction.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/7fc24dac219a7c0ba64757a1.png"},{"id":64092758,"identity":"78766947-6be7-47cc-8579-c20ef17d62d0","added_by":"auto","created_at":"2024-09-06 15:20:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552250,"visible":true,"origin":"","legend":"\u003cp\u003eDetection of (A) SOD, (B) POD, (C) CAT, and (D) MDA in root and leaf tissues of XZY512. Different lowercase letters represent significant differences at \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e level using one-way ANOVA and Tukey’ s correction.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/569140b8d8646607ff10ae8a.png"},{"id":64092556,"identity":"ad040ec4-503a-4402-bf7b-5f26beda0072","added_by":"auto","created_at":"2024-09-06 15:12:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":420130,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of histograms (A) and Venn diagram (B) of DEGs in different tissues. RCK, RTr, LCK, and LTr represent control and treatment samples of roots and leaves, respectively.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/492f20d7def4a123bfbb6560.png"},{"id":64092554,"identity":"e8ac3a96-3d6b-4ece-b13d-c0d6e249fb74","added_by":"auto","created_at":"2024-09-06 15:12:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1038180,"visible":true,"origin":"","legend":"\u003cp\u003eTop 25 GO terms and KEGG pathways enrichment analysis of DEGs in RCK_vs_RTr (A, B), LCK_vs_LTr (C, D), and (RCK_vs_RTr)_vs_(LCK_vs_LTr) (E, F). RCK, RTr, LCK, and LTr represent control and treatment samples of roots and leaves, respectively.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/f1ad8b24c72c9091aacd2ea1.png"},{"id":64092555,"identity":"bb2a34f6-17c0-49b2-863d-81df19170bf9","added_by":"auto","created_at":"2024-09-06 15:12:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":871412,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps of DEGs involved in various biological functions or signaling pathways, including Response to oxidative stress (A), glutathione transferase activity (B), auxin responsive (C), ABC transporters (D), MAPK signaling pathway (E), and chlorophyll a-b binding protein (F).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/d366ef57aec3613ee6af3699.png"},{"id":64092760,"identity":"533524f0-8f7a-4f07-9808-60f8ac5b9eee","added_by":"auto","created_at":"2024-09-06 15:20:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":196054,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of RNA-seq data through qRT-PCR (A) and analysis of its correlation (B).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/1e2ac44f41249a6bb189abe0.png"},{"id":64092559,"identity":"eb67ccf0-24ff-41b3-b4dd-2e6ab1d90f7b","added_by":"auto","created_at":"2024-09-06 15:12:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":499216,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the response to Sb in the leaves and roots of \u003cem\u003eB. napus.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/d6e7b27842ccd3498e17782d.png"},{"id":79120459,"identity":"af240a58-23cf-4b7f-b6d1-6f794b4672e2","added_by":"auto","created_at":"2025-03-24 16:08:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4424781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/49038966-e08b-4dec-89fe-c24108393dbb.pdf"},{"id":64092759,"identity":"c173d24a-8550-482c-97c9-f8e939da7536","added_by":"auto","created_at":"2024-09-06 15:20:15","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":879810,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/0e7f1d3537354514bfef8e7e.tif"},{"id":64092557,"identity":"e46def76-dc0b-440b-9e28-944771452e2e","added_by":"auto","created_at":"2024-09-06 15:12:16","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3188142,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/ffff09cbac44ec84eac64cc1.tif"},{"id":64092561,"identity":"73fd88e3-824f-4c18-93ca-5ced203de8cf","added_by":"auto","created_at":"2024-09-06 15:12:16","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":52087,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtable15.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4850929/v1/8d00b710719da0ac92ce0810.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome profiles of leaves and roots of Brassica napus L. in response to antimony stress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs urban industrialization progresses, the exploitation of mineral resources, along with the accumulation and smelting of heavy metals, has\u0026nbsp;led to the widespread distribution of heavy metals and their compounds in the atmosphere, water bodies, and soil.\u0026nbsp;Heavy metals\u0026nbsp;such as\u0026nbsp;cadmium (Cd), lead (Pb), chromium (Cr), antimony (Sb),\u0026nbsp;among\u0026nbsp;others,\u0026nbsp;have inflicted significant damage on the natural environment, resulting in severe pollution\u003csup\u003e1,2\u003c/sup\u003e Antimony Sb is a crucial strategic resource with extensive applications in semiconductors, batteries, flame retardants, ceramics, weapons, and pharmaceutical materials\u003csup\u003e3-5\u003c/sup\u003e. Regrettably, inadequate handling practices have led to varying levels of Sb contamination in soil, adversely affecting the plant growth and development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSb exists in four oxidation states (-III, 0, III, and V) in the natural environment. Among these, inorganic Sb exhibits higher toxicity compared to its organic counterpart, and its toxicity varies depending on its oxidation states. Specifically, Sb(III) is ten times more toxic than that Sb(V)\u003csup\u003e2,6,7\u003c/sup\u003e.\u0026nbsp;In recent years, research into the mechanisms of plant uptake and toxicity related to the heavy metal Sb has emerged a significant field. Studies on the sunflower and maize have revealed varing levels of tolerance to Sb toxicity among different crops, with maize showing greater susceptibility to Sb in soil\u003csup\u003e8\u003c/sup\u003e . Research findings demonstrate that Sb effectively inhibits root elongation and germination in rice, significantly impacting rice productivity\u003csup\u003e9\u003c/sup\u003e. Further studies have indicated that Sb(III) at a concentration of 20 mg/L can generate superoxide anion free radicals (·O2\u003csup\u003e\u0026minus;\u003c/sup\u003e), thereby impairing the integrity of root cell membranes. Conversely, Sb(V) promotes shoot biomass accumulation and enhances the uptake of essential elements\u003csup\u003e10\u003c/sup\u003e. Under heavy metal stresses, the activity of antioxidant enzymes such as peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH), serves as critical indicators of the plant\u0026apos;s antioxidative defense system. When the concentration of Sb in soil exceeds 50 mg/kg, the activities of POD and SOD in maize plants are significantly inhibited, while CAT activity exhibits a positive correlation with soil Sb concentration\u003csup\u003e11\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBrassica napus\u003c/em\u003e L., commonly known as rapeseed, is a globally significant oilseed crop extensively utilized in soil phytoremediation studies due to its rapid growth rate, substantial shoot biomass, and remarkable capacity to adsorb heavy metals\u003csup\u003e12-15\u003c/sup\u003e. Additionally, RNA sequencing (RNA-seq) technology has been widely employed to investigate the molecular response mechanisms of plants under heavy metal toxicity stress. These studies have explored the effects of various metals, including Mn\u003csup\u003e16\u003c/sup\u003e, Cu\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e, Pb\u003csup\u003e18\u003c/sup\u003e, and Cd\u003csup\u003e19,20\u003c/sup\u003e, in diverse plant species such as \u003cem\u003eSchima superba\u003c/em\u003e, \u003cem\u003eCitrus grandis\u003c/em\u003e, \u003cem\u003eRaphanus sativus\u003c/em\u003e L., \u003cem\u003eBrassica oleracea\u003c/em\u003e L. (Broccoli). However, research on the response of \u003cem\u003eB. napus\u003c/em\u003e to Sb-induced stress is currently limited,\u0026nbsp;and the molecular mechanisms underlying its response to Sb stress remain unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, different varieties of \u003cem\u003eB. napus\u003c/em\u003e were exposed to hydroponic solutions with varying Sb concentrations. The varietiy exhibiting the highest Sb accumulation in its shoot was chosen for further investigation, specifically focusing on the physiological indicators of its roots and leaves. Following that, RNA-seq was employed to compare differentially expressed genes (DEGs) associated with the response to Sb toxicity in both roots and leaves. This study lays the groundwork for future research into the pathways and functional genes involved in the response to Sb toxicity.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSb accumulation in different\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eB. napus\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;varieties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the accumulation level Sb in entire plants of B. napus was used as a crucial indicator to evaluate its phytoremediation potential. The findings showed an increasing trend in Sb accumulation in the plants of six B. napus varieties as the Sb concentration increased. Eventually, the Sb concentration in plants reached a peak and subsequently declined (Fig. 1). Specifically, among the varieties XZY787, XZY512, ZYZ28, and QY18H, the highest levels of Sb accumulation were observed at an Sb concentration of 75 mg/L, with values of 325.63, 561.42, 257.40, and 199.73 mg/kg, respectively. FY789 exhibited its highest Sb accumulation of 222.29 mg/kg at an Sb concentration of 50 mg/L, while HYZ50 reached its maximum Sb content of 204.35 mg/kg at a concentration of 25 mg/L. Remarkably, XZY512 exhibited the highest Sb content of 561.42 mg/kg at an Sb concentration of 75 mg/L among the six varieties, making it the focal variety selected for further research endeavors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariations in physiological indexes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the impact of heavy metals on plants, toxicity to plant growth is frequently evaluated by examining enzyme activities and metabolite levels, such as SOD, POD, CAT, and MDA\u003csup\u003e21,22\u003c/sup\u003e. Therefore, this study measured the activities and contents of these enzymes or metabolites in both root and leaf tissues of B. napus XZY512 under varying Sb concentration treatments. Compared to the controls, root tissue exhibited an upregulation in the activities or contents of SOD (Fig. 2A), POD (Fig. 2B), CAT (Fig. 2C), and MDA (Fig. 2D) in response to increasing Sb concentrations, particularly at low concentration. Peak values of these indexes were observed at 50 or 75 mg/L Sb, followed by a subsequent decline. Additionally, the patterns of SOD and POD activity changes in leaf and root tissues varied inconsistently under different Sb treatments (Fig. 2A-B). However, CAT activity in leaf tissues exhibited a declining trend under high-concentration Sb solutions (Fig. 2C), while MDA content showed a notable decrease (Fig. 2D). These results indicate that Sb stress at varying concentrations significantly impacts the growth of B. napus, with various physiological indices displaying a declining trajectory after reaching their peak values. This suggests a potential link to the adaptability of B. napus to Sb stress or the occurrence of physiological damage induced by Sb toxicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequencing and identification of differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the molecular mechanisms underlying the impact of Sb stress on\u0026nbsp;\u003cem\u003eB. napus\u003c/em\u003e, we conducted RNA-seq analysis on the roots and leaves treated with concentrations of 0 mg/L (control) and 75 mg/L Sb (treatment).\u0026nbsp;The samples were designated as root control (RCK), root treatment (RTr), leaf control (LCK), and leaf treatment (LTr). Following\u0026nbsp;high-throughput sequencing of various different plant samples, raw data were obtained from a total of 12 samples. These data were uploaded to the NCBI SRA database, where they were assigned the following accession numbers: SRR29906309 (RCK1), SRR29906308 (RCK2), SRR29906305 (RCK3), SRR29906304 (RTr1), SRR29906303 (RTr2), SRR29906302 (RTr3), SRR29906301 (LCK1), SRR29906300 (LCK2), SRR29906299 (LCK3), SRR29906298 (LTr1), SRR29906307 (LTr2), and SRR29906306 (LTr3). Overall, we obtained 80.58 Gb of clean bases, with all samples yielding 5.97 Gb of clean data and exhibiting a Q30 value surpassing 91.56%. All of the XZY512 leaf samples showed alignment rates higher than 93.25% when aligned against the ZS11 reference genome. However, sample RTr3 in the roots showed a far lower alignment rate of only 7.64% (Table S1). To protect the integrity of subsequent analyses, the RTr3 sample was removed, and the analysis proceeded using the remaining 11 samples. With the RTr3 sample removed, the analysis continued with the 11 remaining samples to protect the integrity of the other studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApplying a filtering threshold of P-value \u0026lt; 0.05 and |log2(fold-change, FC)| \u0026gt; 1, a total of 8,802 (4,274 up-regulated and 4,528 down-regulated) and 13,612 (6,653 up-regulated and 6,959 down-regulated) DEGs were identified in the comparisons RCK_vs_RTr and LCK_vs_LTr, respectively (Fig. 3A). Notably, 2,218 DEGs were shared between root and leaf tissues, comprising 1,498 up-regulated genes and 720 down-regulated genes (Fig. 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO enrichment and KEGG analysis of DEGs in root and leaf tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo deeper understand the function of DEGs between the control and treatment groups, we performed GO enrichment analysis on DEGs derived from RCK_vs_RTr, LCK_vs_LTr, and (RCK_vs_RTr)_vs_(LCK_vs_LTr). We then performed statistical classification of the relevant GO terms. These DEGs primarily participate in functions such as cell, cell part, organelle, binding, catalytic activity, cellular process, metabolic process, and response to stimulus (Fig.\u0026nbsp;S1). The comparison of up-regulated and downregulated gene counts between RCK_vs_RTr and LCK_vs_LTr showed comparability. However, in the (RCK_vs_RTr)_vs_(LCK_vs_LTr) comparison, there was a notably higher count of up-regulated genes among the shared DEGs pool.\u003c/p\u003e\n\u003cp\u003eGO enrichment and KEGG annotation analysis of DEGs among RCK_vs_RTr and LCK_vs_LTr revealed distinct preferences. Specifically, DEGs in RCK_vs_RTr were significantly enriched in GO terms such as response to oxidative stress (GO:0006979), response to toxic substance (GO:0009636), iron ion binding (GO:0005506), and glutathione transferase activity (GO:0004364) (Fig.\u0026nbsp;4A). On the other hand, the DEGs in LCK_vs_LTr were predominantly enriched in GO terms including chloroplast thylakoid (GO:0009534), photosystem II (GO:0009523), response to abscisic acid (GO:0009737), and chlorophyll binding (GO:0016168) (Fig.\u0026nbsp;4C). KEGG annotation findings indicated that Sb stress treatment led to significant enrichment of DEGs in root tissues, particularly in pathways such as Glutathione metabolism (map00480), ABC transporters (map02010), MAPK signaling pathway-plant (map04016), and Plant hormone signal transduction (map04075) (Fig.\u0026nbsp;4B). In contrast, DEGs in the leaves showed enrichment in pathways such as carbon fixation in photosynthetic organisms (map00710), photosynthesis (map00195), and oxidative phosphorylation (map00190) (Fig.\u0026nbsp;4D). The top 25 enriched GO terms and KEGG pathways in (RCK_vs_RTr)_vs_(LCK_vs_LTr) included significant terms like response to toxic substance (GO:0009636), response to oxidative stress (GO:0006979), vacuolar membrane (GO:0005774), and glutathione binding (GO:0043295). Additionally, important metabolic pathways comprised glutathione metabolism (map00480), protein processing in endoplasmic reticulum (map04141), and ABC transporters (map02010) (Fig.\u0026nbsp;4E-F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned findings, we propose that there are significant disparities in DEGs between the plant treated with 75 m g/L Sb and the control groups. Moreover, the observed DEGs exhibit a notable preference for root and leaf tissues. The toxic effects of Sb may primarily influence gene expression variations related to oxidative stress, toxic stress, and glutathione metabolism in root tissues. In leaves, the affected genes are mainly associated with photosynthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion on the involvement of DEGs in response to Sb stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring cellular metabolism, cell organelles produce a amount of reactive oxygen species (ROS), which are effectively regulated by the antioxidant system to maintain a dynamic equilibrium\u003csup\u003e23\u003c/sup\u003e. Nonetheless, excessive exposure to heavy metals triggers a substantial increase in ROS production, disrupting the homeostasis of the antioxidant system. As a result, there is a significant\u0026nbsp;accumulation of ROS within the organism, causing enduring damage to cellular structures, proteins, and other essential components\u003csup\u003e24\u003c/sup\u003e. This study revealed that under Sb stress conditions, a predominant subset of genes (58/64) associated with the response to oxidant stress (GO:0006979) exhibited notable up-regulation in the root and leaf tissues of \u003cem\u003eB. napus\u003c/em\u003e (Table S2). Notably, this set of genes included Glutathion peroxidase \u003cem\u003eGPX2\u003c/em\u003e, \u003cem\u003eGPX6\u003c/em\u003e, Ascorbate peroxidase \u003cem\u003eAPX2\u003c/em\u003e, 17.6 kDa class I heat shock protein \u003cem\u003eHSP17.6B\u003c/em\u003e, Peroxidase \u003cem\u003ePER34\u003c/em\u003e, \u003cem\u003ePER54\u003c/em\u003e, as well as Zinc finger protein \u003cem\u003eZAT1\u0026nbsp;\u003c/em\u003e(Fig.\u0026nbsp;5A, Table S3). In our study, we also observed significant upregulation of numerous Glutathione transferase genes, such as \u003cem\u003eGSTF3\u003c/em\u003e, \u003cem\u003eGSTU4\u003c/em\u003e, and \u003cem\u003eGSTU5\u0026nbsp;\u003c/em\u003e(Fig.\u0026nbsp;5B,\u0026nbsp;Table S3), in response to Sb toxicity. This finding suggests that GSH may play a vital role in mitigating Sb toxicity and countering oxidative damage.\u003c/p\u003e\n\u003cp\u003eFurthermore, our study revealed distinct gene responses to Sb stress, including Auxin-related genes (\u003cem\u003eIAA3\u003c/em\u003e, \u003cem\u003eIAA7\u003c/em\u003e, \u003cem\u003eIAA17\u003c/em\u003e,\u003cem\u003e\u0026nbsp;IAA26\u003c/em\u003e, and \u003cem\u003eARF5)\u003c/em\u003e, ABC transporters (\u003cem\u003eABCB4\u003c/em\u003e, \u003cem\u003eABCB11\u003c/em\u003e, and \u003cem\u003eABCC9)\u003c/em\u003e, as well as components of the MAPK signaling pathway (\u003cem\u003eWRKY29\u003c/em\u003e, \u003cem\u003eMEKK1\u003c/em\u003e, and \u003cem\u003eRBOHB)\u0026nbsp;\u003c/em\u003e(Fig.\u0026nbsp;5C-E,\u0026nbsp;Table S3). Remarkably, Sb treatment led to a significant\u0026nbsp;enrichment of DEGs associated with the Photosynthesis in leaves. Among the 122 genes implicated in Photosynthesis (map00195), the majority (119 genes) exhibited down-regulation\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig. 5F, Table S4). This compelling evidence indicates that the primary toxic effect of heavy metal Sb on the leaves of \u003cem\u003eB. napus\u003c/em\u003e is characterized by a significant impact on the molecular mechanisms of photosynthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of RNA-Seq by qRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the RNA-seq data results, nine genes were selected for qRT-PCR verification. These genes comprised transcription factor genes\u0026nbsp;WRKY28\u0026nbsp;(BnaC07G0420700ZS),\u0026nbsp;WRKY75\u0026nbsp;(BnaC09G0529900ZS), and\u0026nbsp;BHLH10\u0026nbsp;(BnaA08G0218600ZS), auxin-related genes\u0026nbsp;IAA17\u0026nbsp;(BnaA08G0316300ZS) and\u0026nbsp;AIR12\u0026nbsp;(BnaA03G0308600ZS), as well as metal transport or binding protein genes\u0026nbsp;CML27\u0026nbsp;(BnaA06G0124500ZS) and\u0026nbsp;HMA2\u0026nbsp;(BnaA02G0384700ZS) (Table S5). The findings indicate a strong positive correlation between the qRT-PCR and RNA-seq data, with correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e) of 0.9499 and 0.9045 for DEGs in root and leaf tissues, respectively (Fig. 6A-B). This high degree of correlation provides robust validation for the reliability of the RNA-seq data.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eVariations in Sb uptake and physiological indices in \u003cem\u003eB. napus\u003c/em\u003e under Sb stress\u003c/p\u003e\n\u003cp\u003eSb is a toxic heavy metal that can adversely impact plant growth and development. This study conducted experiments with varying Sb concentrations to investigate the variations in Sb accumulation among different \u003cem\u003eB. napus\u003c/em\u003e varieties. Our preliminary findings indicated that Sb accumulation in \u003cem\u003eB. napus\u003c/em\u003e followed a dose-dependent relationship, with higher Sb concentration resulting in increased uptake. Similar observations have been reported in \u003cem\u003eBrassica juncea\u003c/em\u003e, where arsenic accumulation demonstrated a positive correlation with rising arsenic concentrations\u003csup\u003e22\u003c/sup\u003e. These results suggest that \u003cem\u003eB. napus\u003c/em\u003e exhibits a toxicological response to Sb similar to that of arsenic, a clan element, .\u003c/p\u003e\n\u003cp\u003eMDA, a product of lipid peroxidation, signifies reduced free radical scavenging capability in plants as its levels rise. The antioxidant enzyme system, including SOD, POD, CAT, and other enzymes, plays a crucial role in eliminating free radicals and ROS, thus protecting cells from damage \u003csup\u003e25\u003c/sup\u003e. Therefore, the MDA content and activities of SOD, POD, CAT partially reflect the extent of membrane lipid peroxidation and the plant\u0026apos;s response to stress. In this study, the above physiological indices in \u003cem\u003eB. napus\u003c/em\u003e root tissues exhibited an increased trend with rising Sb concentration, reaching a peak and then declining. In leaf tissues, SOD and POD activities remained stable whereas CAT activity and MDA content were significantly affected by high-level Sb concentration (Fig. 2). Consequently, Sb-induced stress can lead to cellular damage in \u003cem\u003eB. napus\u003c/em\u003e root tissues, causing substantial physiological alterations (Fig. 7).\u003c/p\u003e\n\u003cp\u003eEffects of Sb stress on oxidative stress and glutathione metabolism genes\u003c/p\u003e\n\u003cp\u003eIn response to Sb-induced oxidative stress, plants often release specific antioxidant enzymes and small molecule antioxidants to against the toxicity of Sb\u003csup\u003e26-28\u003c/sup\u003e. This investigation discovered that antioxidant genes, including both enzymes and non-enzymes such as Glutathione peroxidase \u003cem\u003eGPX2\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGPX6\u003c/em\u003e, Ascorbate peroxidase \u003cem\u003eAPX2\u003c/em\u003e, and Peroxidase \u003cem\u003ePER\u003c/em\u003e\u003cem\u003e34\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePER54\u003c/em\u003e, were up-regulated and actively participate in mitigating\u0026nbsp;Sb-induced oxidative stress. GPX have been reported as a potential biomarker of cytotoxicity, while APX and PER may be involved in the elimination of harmful H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e within cellular organelles\u003csup\u003e22\u003c/sup\u003e. Hence, the upregulation of genes associated with oxidative enzyme activity, along with increased activities of antioxidant enzymes (SOD, POD, and CAT), confirmed the pronounced toxicity of a 75 mg/L Sb concentration on \u003cem\u003eB. napus\u003c/em\u003e root tissues (Fig. 7).\u003c/p\u003e\n\u003cp\u003eMoreover, the role of small molecule antioxidants like GSH in mitigating Sb-induced oxidative stress cannot be overlooked. GSH, a precursor of phytochelatins and an antioxidant, plays a crucial role in heavy metal detoxification\u003csup\u003e29\u003c/sup\u003e. Research on \u003cem\u003eB. juncea\u003c/em\u003e arsenic tolerance reveals significant upregulation of genes associated with glutamate cysteine ligase, glutathione synthase, and glutathione S-transferase\u003csup\u003e22\u003c/sup\u003e. The upregulation of glutathione-related genes in response to Sb stress, particularly those encoding for glutathione S-transferase (GSTs), indicates an enhanced detoxification mechanism at play. GSTs facilitate the conjugation of GSH to a wide array of electrophilic compounds, thus neutralizing the toxicity of Sb and other reactive intermediates produced during oxidative stress.Additionally, the pathway analysis of glutathione metabolism (map00480) highlights the substantial role of these genes in the overall stress response mechanism. This suggests that the modulation of GSH levels and the expression of GSTs are pivotal strategies employed by plants to counteract Sb-induced cytotoxicity. Furthermore, the interconnectedness of the antioxidant network, comprising enzymes like GPX, APX, and PER, along with GSH, underscores a comprehensive defensive system that plants utilize to mitigate the detrimental effects of Sb.\u003c/p\u003e\n\u003cp\u003eAuxin, MAPK signaling, and ABC transporters respond to Sb stress\u003c/p\u003e\n\u003cp\u003eThe exogenous application of plant hormones such as auxin (IAA), brassinosteroids (BR), salicylic acid (SA), and gibberellins (GA), have been shown to alleviate the adverse\u0026nbsp;impacts of heavy metals on plants\u003csup\u003e30\u003c/sup\u003e. Studies have indicated that auxin homeostasis induces defense responses to Cd stress by regulating auxin biosynthesis, catabolism, transport, and signal transduction\u003csup\u003e20,31\u003c/sup\u003e. High levels Cd can inhibit plant growth and reduce IAA content, with genes involved in IAA synthesis being suppressed under Cd stress\u003csup\u003e20\u003c/sup\u003e. In this study, the down-regulation of auxin-related genes such as \u003cem\u003eIAA7\u003c/em\u003e, \u003cem\u003eIAA17\u003c/em\u003e, and IAA26 under Sb stress suggests that negative Sb may negatively affect IAA levels. Additionally, gibberellin-responsive regulator \u003cem\u003eRGL1\u003c/em\u003e, ethylene response sensor \u003cem\u003eERS2\u003c/em\u003e, abscisic acid receptor \u003cem\u003ePYL1\u003c/em\u003e, \u003cem\u003ePYL6\u003c/em\u003e, \u003cem\u003ePYL8\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePYR\u003c/em\u003e\u003cem\u003e1\u003c/em\u003e, along with other plant hormones (Table S4), were implicated in regulating the response to Sb stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAPK signaling cascades have been extensively studied in plants and shown to participate in stress adaptation, cell death, defense responses, ethylene signal transduction, and maintenance of \u0026nbsp; ROS responses\u003csup\u003e22,32\u003c/sup\u003e. Following Sb treatment,\u0026nbsp;DEGs associated with the MAPK signaling pathway and ABC transporters\u0026nbsp;were significantly enriched in root tissues. These genes include WRKY transcription factors (\u003cem\u003eWRKY29\u0026nbsp;\u003c/em\u003eand \u003cem\u003eWRKY33\u003c/em\u003e), mitogen-activated protein kinase kinase kinase (\u003cem\u003eMEKK1\u003c/em\u003e), respiratory burst oxidase homolog proteins (\u003cem\u003eRBOHB\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRBOHF\u003c/em\u003e), and metal transporters such as \u003cem\u003eABCB4\u003c/em\u003e, \u003cem\u003eABCB11\u003c/em\u003e, \u003cem\u003eABCC9\u003c/em\u003e, and \u003cem\u003eATMRP11\u0026nbsp;\u003c/em\u003e(Table S3, Fig. 7). The presence of these DEGs in root tissues indicates that the response of \u003cem\u003eB. napus\u003c/em\u003e to Sb stress involves a complex regulatory network, with these DEGs potentially playing key roles in the defense response against Sb toxicity.\u003c/p\u003e\n\u003cp\u003eEffects of Sb stress on photosynthetic system\u003c/p\u003e\n\u003cp\u003eFurthermore, the detrimental effects of Sb(III) on the photosynthetic machinery were evident from the significant downregulation of genes involved in photosystem II (GO:0009768). The downregulation of 47 out of 48 DEGs in this category suggests a substantial disruption in the photosynthetic electron transport chain. This disruption is likely to impair the overall efficiency of photosynthesis, leading to decreased energy production and reduced growth and development in plants.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that the heavy metal Sb has a negative impact on plant photosynthesis. High concentrations of Sb(III) are found to significantly reduce PS II (Fv/Fm and PIABS) in maize leaf\u003csup\u003e11\u003c/sup\u003e, as well as cause a significant decrease in leaf pigment contents (chlorophyll a, b, carotenoid), net photosynthetic rate (Pn), stomatal conductance (Gs), evaporation rate (E), PSII maximum photochemical efficiency (Fv/Fm), and PSII electron transfer quantum yield rate (\u0026Phi;PSII) of \u003cem\u003eAcorus calamus\u003c/em\u003e\u003csup\u003e33\u003c/sup\u003e. Given these findings, it is crucial to explore the molecular mechanisms underlying Sb-induced photosynthetic inhibition. Investigating the expression and functionality of key photosynthetic proteins, as well as the potential protective roles of antioxidant systems, could provide deeper insights into how plants cope with Sb stress at the molecular level. Additionally, assessing the impact of Sb on other photosynthetic components such as light-harvesting complexes and carbon fixation enzymes will be beneficial. Future research should also focus on evaluating specific photosynthetic indices, including the rate of photosynthetic electron transport, photochemical or non-photochemical quenching, to comprehensively understand the extent of Sb-induced photosynthetic impairment. Such studies would not only elucidate the mechanisms of Sb toxicity but also aid in developing strategies to enhance plant tolerance to heavy metal stress, thereby contributing to agricultural sustainability in Sb-contaminated areas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDifferent \u003cem\u003eB. napus\u003c/em\u003e varieties exhibited varying responses to Sb accumulation in plants, with variety XZY512 demonstrating the highest Sb accumulation, indicating its potential for phytoremediation. Analysis of physiological indexes such as SOD, POD, CAT, and MDA in the root and leaf tissues of XZY512 revealed an upward trend in the root tissue with increasing Sb concentration, peaking, and then declining. In leaf tissues, CAT and MDA significantly decreased under Sb stress. Transcriptomic analysis identified DEGs in \u003cem\u003eB. napus\u003c/em\u003e root tissues under Sb stress, associated with oxidative stress response, glutathione metabolism, plant hormone signaling, ABC transporters, and MAPK signaling pathway. Notably, photosynthesis-related genes were significantly down-regulated in leaf tissues. This study investigated the effects of heavy metal Sb on \u003cem\u003eB. napus\u003c/em\u003e through Sb accumulation, physiological indexes, and transcriptome sequencing, providing initial insights into the biological processes and gene networks in \u003cem\u003eB. napus\u003c/em\u003e under Sb stress.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003ePlant\u0026nbsp;material and\u0026nbsp;growth\u0026nbsp;conditions\u003c/p\u003e\n\u003cp\u003eThe commercial varieties used in this study, namely Xiangzayou 787(XZY787), Fengyou 789(FY789), Xiangzayou 512(XZY512), Huazayou 50(HYZ50), Zhongzayou 28(ZYZ28), and Qinyou 18 Hao(QY18H), were provided by the Oliseed Molecular Breeding Team at Hunan Agricultural University. The hydroponic system employed was based on\u0026nbsp;Thakur et al.\u003csup\u003e22\u003c/sup\u003e with specific modifications. Seeds were disinfected using a 50% sodium hypochlorite (v/v) solution for 2 minutes,followed by rinsed with distilled water. Subsequently, the seeds were placed in perlite trays soaked with 1/2 Hoagland\u0026apos;s solution. Plants were cultivated in a phytotron for 35 to 40 days until they reached the 5-leaf stage, under a 12-hours light cycle, with a relative humidity of 50-60% and a temperature of 24\u0026plusmn;2\u0026deg;C. They were then transferred to hydroponic tanks containing a 1/2 Hoagland\u0026apos;s solutions. After a 72-hours incubation, the plants were treated with Sb(III) solutions (C\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eKO\u003csub\u003e7\u003c/sub\u003eSb\u0026middot;(1/2)H\u003csub\u003e2\u003c/sub\u003eO blending) at concentrations of 0, 10, 25, 50, 75, and 100 mg/L. Each variety and concentration were replicated three times. Following the 72-hour treatment, samples were collected, rapidly frozen with liquid nitrogen, and stored in an ultra-low temperature freezer.\u003c/p\u003e\n\u003cp\u003eDetermination of Sb accumulation and physiological indices\u003c/p\u003e\n\u003cp\u003eTo assess the Sb accumulation, the entire plants both the control and Sb-treated groups were subjected to a drying process. Initially, they were placed in a drying oven set at 105\u0026deg;C for 30 minutes, followed by furher drying at 75 \u0026deg;C until a constant weight were achieved. The sample processing procedure followed the method described by\u0026nbsp;Liao et al.\u003csup\u003e34\u003c/sup\u003e After sample digestion, the Sb accumulation was quantified using ICP-MS (PerkinElmer NexlON\u0026reg;2000, USA). To investigate the impact of varying Sb concentrations on the roots and leaves of \u003cem\u003eB. napus\u003c/em\u003e, assay kits from Nanjing Jiancheng Bioengineering Institute were used to measure the activity of antioxidant enzymes. Specifically, the following assay kits were utilized: superoxide dismutase (SOD) assay kit (A001-3, WST-1), peroxidase (POD) assay kit (A084-3, colorimetric method), catalase (CAT) assay kit (A007-1, visible light method), and malondialdehyde (MDA) assay kit (A003-1, TBA method) to measure physiological indexes of the respective \u003cem\u003eB. napus\u003c/em\u003e tissues. Measurements of Sb accumulation and physiological indices were conducted with at least three biological replicates.\u003c/p\u003e\n\u003cp\u003eRNA extract, library preparation and transcriptome sequencing\u003c/p\u003e\n\u003cp\u003eBased on the Sb accumulation results, a total of 12 samples (Three replicates each for roots and leaves) from XZY512, following a 72-hour treatment with 0 mg/L and 75 mg/L Sb concentrations, were selected for transcriptome sequencing. This analysis was comducted by Genepioneer Biotechnologies Company in Nanjing, China. Total RNA was extracted using the RNAprep Pure Plant Plus Kit (Tiangen, Beijing, China), following the manufacturer\u0026apos;s protocol. The quality of the extracted RNA was evaluated using the NanoPhotometer\u0026reg; spectrophotometer (IMPLEN, CA, USA), Qubit\u0026reg; RNA Assay Kit in Qubit\u0026reg; 2.0 Fluorometer (Life Technologies, CA, USA), and Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA) prior to library preparation. Sequencing libraries were generated using NEB Next\u0026reg; Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg; (NEB, USA) according to the manufacturer\u0026rsquo;s instructions. The libraries were sequenced on an Illumina Nova seq6000 platform, resulting in 150 bp paired-end reads. Clean reads were mapped to the reference genome sequence of \u003cem\u003eB. napus\u003c/em\u003e (ZS11) (\u003ca href=\"https://www.ncbi.nlm.nih.gov/assembly/GCF_000686985.2/\"\u003ehttps://www.ncbi.nlm.nih.gov/assembly/GCF_000686985.2/\u003c/a\u003e) utilizing the default parameters of the HISAT v2.1.0 software\u0026nbsp;Kim et al.\u003csup\u003e35\u003c/sup\u003e The Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) was employed as a quantification metric for transcripts or gene expression levels.\u003c/p\u003e\n\u003cp\u003eComparative transcriptome analysis and gene functional annotation\u003c/p\u003e\n\u003cp\u003eDEGs among different treatment groups were identified using the DESeq R package\u0026nbsp;(Love et al., 2014)\u003csup\u003e36\u003c/sup\u003e with a significance threshold of P-value \u0026lt; 0.05 and |log2(fold-change, FC)| \u0026gt; 1. Subsequently, venn diagram and heatmap were generated to visualize the expression patterns of DEGs across various experimental conditions. Furthermore, Gene Ontology (GO) enrichment analysis (http://www.geeontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (http://www.kegg.jp) were conducted to screen DEGs significantly enriched in GO terms and metabolic pathways, respectively, at a significance level of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eqRT-PCR validation\u003c/p\u003e\n\u003cp\u003eTotal RNA (1 \u0026mu;g) was reverse-transcribed using the PrimeScript\u0026trade; RT reagent kit with gDNA Eraser (Takara, Japan). Quantitative real-time (qRT-PCR) was performed using SYBR Green II (SYBR\u0026reg; Premix Ex Taq\u0026trade; Kit, Takara) on a CFX96 real-time system (BIO-RAD, USA). The qRT-PCR protocol included an initial denaturation step at 95 \u0026deg;C for 30 s, followed by 40 cycles of amplification at 95 \u0026deg;C for 5 s and extension at 60 \u0026deg;C for 30 s. To validate the RNA-Seq findings, nine DEGs showing significant differences among various treatments were selected for qRT-PCR analysis. Gene-specific qRT-PCR primers (Table\u0026nbsp;1) were designed using Primer-Premier 3.0 software. BnActin7 , described by Chen et al.\u003csup\u003e37\u003c/sup\u003e, served as an internal control for normalizing gene expression levels. The relative expression of each DEGs was evaluated by the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method. Each gene and sample were analyzed with three biological replicates and three technical replicates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1\u0026nbsp;qRT-PCR primers used in the study.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eName of primers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eSequence of forward primers (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eSequence of reverse primers(5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaA06G0124500ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0124500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eCTTCGATCTGTACGATCAGGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eCCTCGAAATTAACATTCCCGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaC05G0211300ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0211300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eGTCAAACGCTACTCTTTCTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eGCCAAATTGTTTTGTGGCTCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaA08G0218600ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0218600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eTATGAAGCTTCCTCTGTCCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eGTTGTTGCTGTTGTTCTCATCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaA03G0308600ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0308600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eGTACAACATCAGCAGCTACAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eAAATCACGATCAGATTACCGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaA08G0316300ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0316300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eGCAAGAGTAGAGCTTGAAGTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eCCACAATTAACACTCATCAATGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaA02G0384700ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0384700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eAGCCTTTATACGTCATCGAGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eCGTAGAGCACATGAAACCAAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaC07G0420700ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0420700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eCCAATTCCCTTCATCCTTCTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eGAAGGAGTAGGAAGATGGATCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaC07G0494200ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0494200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eGTTTTTGGTTTCGTTCTTTCGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eTCGATCAGAGGAACTTCTGATG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.154761904761905%\" valign=\"top\"\u003e\n \u003cp\u003eBnaC09G0529900ZS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.922619047619047%\" valign=\"top\"\u003e\n \u003cp\u003eq0529900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.696428571428573%\" valign=\"top\"\u003e\n \u003cp\u003eCGAGAAGCAGGGATTTGAAAAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.226190476190474%\" valign=\"top\"\u003e\n \u003cp\u003eCATCCTCCATATGTGCACCTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was\u0026nbsp;supported\u0026nbsp;by Hunan Provincial Natural Science Foundation of China\u0026nbsp;(2023JJ50083, 2023JJ50475), the\u0026nbsp;National Natural Science Foundation of China\u0026nbsp;(32371589), Research Foundation of Education Bureau of Hunan Province, China (23B0809,\u0026nbsp;22B0844) and the construct program of plant protection applied characteristic discipline in Hunan Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianjun Liu: Writing\u0026mdash;original draft, Writing\u0026mdash;review and editing, Resources. Liang You: Writing\u0026mdash;review and editing,\u0026nbsp;Methodology. Wencong Yu: Methodology,\u0026nbsp;Data curation, Formal analysis.\u0026nbsp;\u0026nbsp;Yuhui Yuan: Data curation, Formal analysis. Wei Zhang: Material provision. Mingli Yan: Data curation. Yu Zheng: Data curation. Renyan Duan: Writing\u0026mdash;review and editing. Guiyuan Meng: Writing\u0026mdash;review and editing.\u0026nbsp;Yong Chen:Writing\u0026mdash;review and editing.\u0026nbsp;Zhongsong Liu: Writing\u0026mdash;review and editing, Fund support. Guohong Xiang: Writing\u0026mdash;review and editing, Fund support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq data from the 12 samples used in this study have been uploaded to the NCBI SRA database. The accession numbers are as follows: SRR29906309 (RCK1), SRR29906308 (RCK2), SRR29906305 (RCK3), SRR29906304 (RTr1), SRR29906303 (RTr2), SRR29906302 (RTr3), SRR29906301 (LCK1), SRR29906300 (LCK2), SRR29906299 (LCK3), SRR29906298 (LTr1), SRR29906307 (LTr2), and SRR29906306 (LTr3). RCK (1, 2, 3), RTr (1, 2, 3), LCK (1, 2, 3), and LTr (1, 2, 3) represent three biological replicates for the control and treated samples of roots and leaves, respectively.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNaja, G. M. \u0026amp; Volesky, B. Toxicity and Sources of Pb, Cd, Hg, Cr, As, and Radionuclides in the Environment. (Kluwer Academic Publishers, 2017).\u003c/li\u003e\n\u003cli\u003eZhang, Y., O\u0026apos;Loughlin, E. J. \u0026amp; Kwon, M. J. Antimony redox processes in the environment: A critical review of associated oxidants and reductants. \u003cem\u003eJ. Hazard. 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Biochem.\u003c/em\u003e \u003cstrong\u003e405\u003c/strong\u003e, 138-140, doi:10.1016/j.ab.2010.05.032 (2010).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Brassica napus L., Sb stress, Physiological indexes, Transcriptome sequencing","lastPublishedDoi":"10.21203/rs.3.rs-4850929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4850929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Antimony (Sb), a non-essential heavy metal, exerts severe toxic effects on the growth and development of plants. This study investigated the response of Brassica napus to 75 mg/L Sb(III) stress under hydroponic conditions, focusing on Sb accumulation, physiological indexes, and transcriptome sequencing. Sb accumulation in six B. napus varieties ranged from 199.73 to 561.42 mg/kg. Enzymatic activities (SOD, POD, CAT) and MDA content showed initial increases followed by declines under varying Sb treatments. Transcriptomic analysis identified 8,802 genes in root tissues and 13,612 genes in leaf tissues responsive to Sb stress, predominantly involved in oxidative stress responses, glutathione metabolism, plant hormone signaling, ABC transporters, and MAPK pathways. Upregulation of antioxidant-related genes like GPX2, APX2, PER34, and GSTU4 in root tissues correlated with physiological index changes, while photosynthesis-related genes were largely downregulated in leaf tissues. This study provides crucial insights into B. napus's response mechanisms to Sb stress and highlights its potential for phytoremediation efforts.","manuscriptTitle":"Transcriptome profiles of leaves and roots of Brassica napus L. in response to antimony stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-06 15:12:10","doi":"10.21203/rs.3.rs-4850929/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-02T04:48:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T07:17:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-19T01:49:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337046112207660576790325667885568415620","date":"2024-11-05T06:44:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153702571185686774304349971702640266744","date":"2024-11-02T13:47:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T20:44:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150232718571795254739670471991939224554","date":"2024-09-05T07:33:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-30T12:26:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-30T12:24:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-12T08:29:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-10T12:27:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-03T01:53:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afdbc112-01f8-45f2-8d5f-fdcaa431ecc5","owner":[],"postedDate":"September 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37134261,"name":"Biological sciences/Plant sciences/Plant stress responses"},{"id":37134262,"name":"Biological sciences/Genetics/Sequencing/Rna sequencing"}],"tags":[],"updatedAt":"2025-03-24T16:02:03+00:00","versionOfRecord":{"articleIdentity":"rs-4850929","link":"https://doi.org/10.1038/s41598-025-88521-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-19 15:57:45","publishedOnDateReadable":"March 19th, 2025"},"versionCreatedAt":"2024-09-06 15:12:10","video":"","vorDoi":"10.1038/s41598-025-88521-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-88521-3","workflowStages":[]},"version":"v1","identity":"rs-4850929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4850929","identity":"rs-4850929","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}