Zinc and Manganese Impact on Cabbage (Brassica rapa) Cadmium Tolerance: Comparative Transcriptomic and Metabolomic Study

Zinc and Manganese Impact on Cabbage (Brassica rapa) Cadmium Tolerance: Comparative Transcriptomic and Metabolomic Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Zinc and Manganese Impact on Cabbage (Brassica rapa) Cadmium Tolerance: Comparative Transcriptomic and Metabolomic Study Wanyu Li, Fanxin Qin, Banglin Luo, Qiu Huang, Anqi Xu, Rui Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6088882/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Plant and Soil → Version 1 posted 5 You are reading this latest preprint version Abstract Background and Aims Zinc (Zn) and manganese (Mn), as essential micronutrients, exhibit competitive antagonism against cadmium (Cd) through cation transporter competition. The effects of Cd stress and Zn / Mn nutrition on plant growth, development, physiological characteristics, and gene expression in some crops have been widely studied, but the molecular mechanism by which Zn / Mn alleviates Cd toxicity in the roots of cabbage at the transcriptomic and metabolomic levels remains unclear. Methods The response of cabbage roots to Cd stress under Zn, Mn and ZnMn treatment were evaluated in an experiment with cabbage roots. The content of Cd was determined by ICP-MS. Roots transcriptome sequencing was performed on the Illumina platform, with differential expression genes (DEGs) analyzed using DESeq2. Root metabolites were analyzed via LC-MS, with metabolite data processed using MetaboAnalystR package. Results Zn treatment exhibited the strongest inhibition of Cd, primarily by up-regulating genes involved in cell wall synthesis, phenylpropanoid biosynthesis, and secondary metabolite production. Mn treatment had the weakest effect on Cd inhibition, mainly regulating hydrolase activity, tryptophan metabolism, and lipid metabolism to reduce Cd absorption in cabbage roots. ZnMn co-treatment showed a lower Cd inhibition rate than Zn, but it down-regulated numerous genes and disrupted amino acid metabolism, suggesting that while it reduces Cd content and may harms plant physiological functions. Conclusion This study highlights the potential of micronutrients resist Cd stress in crops, particularly in the leafy vegetables. Based on Cd reduction and plant physiological safety, we believe that Zn treatment is better than Mn treatment, and better than ZnMn co-treatment. Cd accumulation Plant roots Lipid Molecular mechanisms Multi-omics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The rapid expansion of global industrialization and intensive agricultural practices including mining operations, application of contaminated fertilizers, sewage irrigation, and fossil fuel combustion has led to severe cadmium (Cd) contamination in arable soils, with Cd emerging as the predominant heavy metal pollutant worldwide (Tan et al. 2022 ). As a non-essential element with high bioaccumulation potential, Cd poses significant health risks through dietary accumulation (Grant et al. 2008 ; Sasaki et al. 2009 ; Huang et al. 2016 ). While cereal crops such as wheat and rice exhibit notable Cd bioaccumulation (Sirot et al. 2008 ; Kim et al. 2018 ), leafy vegetables represent a critical exposure pathway due to their high consumption rates. Particularly concerning is cabbage ( Brassica rapa ), which demonstrates elevated Cd accumulation compared to other vegetables and serves as a dietary staple across China and East Asia (Yang et al. 2010 ; Huang et al. 2019b ). Therefore, urgent strategies are needed to reduce Cd contamination in agricultural products. Zinc (Zn) is one of the essential plant micronutrients,participates in multifaceted physiological processes ranging from phytohormone modulation and PSII complex repair to the maintenance of mesophyll CO 2 concentrations and oxidative stress mitigation (Yamasaki et al. 2007 ; Hänsch et al. 2009 ; Ismail 2010 ; Karen et al. 2010 ; Rizwan et al. 2019 ). Since Cd and Zn have similar characteristics in their atomic structures, they have some of the same chemical properties (Huang et al. 2019a ). As a result, when Cd 2+ and Zn 2+ coexist, their absorption and transport in the plant affect each other (Lin et al. 2012). Emerging evidence suggests that Zn supplementation at optimal concentrations can effectively mitigate the genotoxicity of soybean seedlings under Cd stress, alter subcellular distribution and regulate the composition of its chemical form, and enhance Cd tolerance mechanisms (Zare et al. 2018 ; Du et al. 2020 ). However, the precise molecular mechanisms governing Zn-mediated Cd detoxification remain poorly characterized. Manganese (Mn) is an essential microelement for plant growth that orchestrates fundamental metabolic pathways, including photosynthesis, signal transduction, antioxidant activity, and biosynthesis of primary metabolites (Millaleo et al. 2010 ; Sochaet al. 2014; Faria et al. 2020 ). Mn and Cd are transition elements and divalent metal cations that share similar biochemical properties (Liu et al. 2018 ). As a non-essential element for plants, Cd may share some of the Mn transporters (Ge et al. 2021 ). Field trials have demonstrated that Mn application significantly suppresses Cd accumulation in both staple crops (e.g., maize, soybean) and hyperaccumulator species (Baszynski et al. 1980 ; Hernandez et al. 1998 ), while concurrently enhancing the photosynthetic index of Pokeweed and Lupine (Zornoza et al. 2010 ). Nevertheless, the regulatory effects of Mn on Cd partitioning in leafy vegetables, particularly at the molecular levels, remain largely unexplored. The effects of Cd stress and Zn/Mn nutrition on plant growth, development, physiological characteristics, and gene expression have been widely studied in various crops. However, the molecular mechanisms by which Zn/Mn alleviate Cd toxicity in cabbage roots at the transcriptome and metabolome levels remain unknown. To address these challenges, we employed an integrated multi-omics approach to investigate the physiological and molecular mechanisms of Zn, Mn, and ZnMn co-treatments in reducing Cd uptake in cabbage. Using transcriptomic, metabolomic, and co-enrichment analyses, this study elucidates Zn, Mn, and ZnMn-mediated Cd transport regulation, identifies key metabolic pathways involved in Cd tolerance, and establishes a theoretical framework for developing phytoremediation strategies. Material and methods Experimental materials, hydroponic growth conditions and experimental treatments The cabbage variety, SuLv(SL) was used in this study, which is widely grown in Southwest China. The variety was selected from 7 cultivars in our previous study, and the variety has a high Cd accumulate ability when grown in Cd stress. Seeds were surface-sterilized with 10% H 2 O 2 for 15 min, thoroughly washed with deionized water. After sterilization, seeds were placed on sterile filter paper-lined Petri dishes moistened with deionized water and germinated in darkness at 28°C using an artificial climate incubator, after germination the seeds were sown into pots filled with vermiculite (3 plants per pot). During this period, the seedlings were irrigated with 1/10 strength Hoagland nutrient solution for 10 days, followed by 1/5 strength Hoagland nutrient solution for 20 days. Selecting the growth of the same seedlings in deionized water starvation treatment for 1 day, and then the complete nutrient solution containing Cd, Zn, Mn for stress, respectively. All treatments were maintained in controlled-environment chambers with 14/10 h, 65% relative humidity, and 28/22°C day/night temperatures. The nutrient solution was renewed every 72 h to maintain pH stability and oxygen saturation (> 85%). A total of 7 treatments (Table 1 ), each set of three replicates, harvested after 14 days. Table 1 Experimental treatments Treatment Treatment time 1-7d 8-14d CK 5 mg·kg − 1 Cd 5 mg·kg − 1 Cd Cd + Zn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Zn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Zn Zn + Cd 10 mg·kg − 1 Zn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Zn Cd + Mn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Mn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Mn Mn + Cd 10 mg·kg − 1 Mn 5 mg·kg − 1 Cd + 10 mg · kg − 1 Mn Cd + Zn + Mn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Zn + 10 mg·kg − 1 Mn 5 mg·kg − 1 Cd + 10 mg·kg − 1 Zn + 10 mg·kg − 1 Mn Zn + Mn + Cd 10 mg·kg − 1 Zn + 10 mg·kg − 1 Mn 5 mg·kg-1 Cd + 10 mg·kg-1 Zn + 10 mg·kg-1 Mn Determination of Cd concentration The roots were washed thoroughly with tap water and soaked in 20 mM Na2EDTA solution for 15 min to remove heavy metal ions from the root surface, and then removed and rinsed with deionized water. Part of the samples immediately flash-frozen in liquid nitrogen and stored at -80°C for subsequent transcriptomic and metabolomic profiling. In the other part samples, separated into root and shoot fractions, heat inactivated at 105°C for 30 min, then dried at 75℃ to constant weight, and milled for the determination of Cd content in cabbage. The dried samples were digested (HNO 3 ,5mL) and then analyzed by inductively coupled plasma mass spectroscopy (ICP-MS) (Thermo Scientific, USA). A certified reference material (CRM) of plant (GBW-10045a, provided by the National Research Center for CRM, China) was used to control the precision of the analytical procedures. RNA-Seq extraction, library construction and sequencing Cabbage root samples with Cd, Cd + Zn, Cd + Mn and Cd + Zn + Mn treatments (Treatment groups screened by Result 3.1) were selected and immediately snap-frozen in liquid nitrogen and stored at -80°C until nucleic acid extraction. First, the total RNA was extracted from the cabbage root samples by 1% agar-spectrophotometer gel electrophoresis, then the purity of the RNA was determined by NanoPhotometer spectrophotometer (IMPLEN, CA, USA). Then, the Agilent 2100 bioanalyzer system (Agilent Technologies, CA, USA) is used to accurately detect RNA integrity, and then the qualified total RNA samples are constructed and inspected. Finally, the high-throughput double-ended sequencing platform (Illumina) was used to collect 150bp double-ended read length data. Three biological replicates were analyzed to ensure metabolic profile repeatability. Transcriptome assembly and annotation Based on the fluorescence signal matrix obtained by Illumina platform, the light intensity distribution spectrum is decoded into nucleotide sequence by base recognition algorithm (CASAVA), and the standard fastq format file is generated, and the read sequence and corresponding Qphred quality scoring system are recorded completely. In order to ensure the quality and reliability of data analysis, it is necessary to filter the original data. Contaminated reads carrying primer connectors were eliminated by double-ended sequence alignment, abnormal sequences with fuzzy base (N) ratio of more than 1% were excluded, and low confidence read segments with lower sequencing quality score than Qphred20 with base coverage > 50% were filtered out. At the same time, Q20, Q30 and GC contents were calculated for clean data. All subsequent analyses are of high quality based on clean data. Differential expression gene screening In this experiment, DESeq2 was used to analyze the difference in expression of two genes in samples treated with different treatments (Wang et al. 2020a ). In the detection of Differentially expressed genes, |log2FC|≥1 and(FDR-adjusted P < 0.05)were used as screening criteria. The Fold Change represents the ratio of expression between two samples, and the False Discovery Rate (FDR) is obtained by correcting the P-value of the significance of the difference. Differential gene enrichment analysis GO and KEGG pathway enrichment analysis was performed on differential genes. GO and KEGG enrichment analysis of differentially expressed genes was performed through clusterProfiler (3.4.4) software. The FDR-adjusted P < 0.05 was selected as the threshold to identify significant enrichment of gene sets. Metabolite extraction, sequencing and analysis 100 mg frozen tissue was pulverized under liquid nitrogen using a prechilled mortar were placed in EP tubes. 500 µL of 80% methanol in water was added, vortexed and shaken, and the samples were allowed to stand on an ice bath for 5 min, then centrifuged at 15000 g for 20 min at 4°C. A certain amount of supernatant was diluted with mass spectrometry-grade water to 53% methanol, and then centrifuged for 20 min at 15000 g for 20 min at 4°C, and then the supernatant was collected. A quality control sample (QC) is prepared by mixing an equal portion the supernatant of the sample, and then the QC samples were inserted into the sample queue and injected into liquid chromatography-mass spectrometry (LC-MS) for analysis. The samples were separated on a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system and analysed by mass spectrometry on a Q Exactive mass spectrometer (Thermo), using electrospray ionisation (ESI) in the positive and negative ion modes, respectively. Six biological replicates were analyzed to ensure metabolic profile repeatability. Identification of differential metabolites Metabolomic data were compared using mzCloud, mzVault and Masslist databases. The intersex and intra-sex heterometabolites were screened by T-test and Foldchange (FC). The screening criteria were P 1. The value of log2FC indicates the degree to which the differential metabolite is up-regulated (log2FC > 0) or down-regulated (log2FC < 0). The putative identification was classified as level 2 (accurate mass matching and fragment pattern). This analytical workflow adheres to the quality control criteria outlined in the metabolomics reporting standards established by Sumner et al. ( 2007 ), specifically following their tiered classification framework for metabolite identification confidence. KEGG enrichment analysis of differential metabolites KEGG database was used for functional annotation of differential metabolites. The enrichment analysis of differential metabolites is based on the R language MetaboAnalystR package, and KEGG pathway enrichment was performed according to the annotation results set analysis. KEGG co-enrichment analysis of transcriptome and metabolome During KEGG annotation, transcriptome and metabolome were annotated to multiple metabolic pathways at the same time. The screening criteria were |log2FC| > 1, the transcriptome pathway P < 0.01 and the combination pathway P < 0.05 for analysis, and the relevant metabolic pathways were screened out. Statistical analysis Data statistical analyses including independent samples test and one-way ANOVA with the least significant difference (LSD) test were performed using excel 2010 and SPSS 22.0, Spearman method was used for correlation analysis, and graphical analysis was executed using Origin 2021 and CorelDRAW X8. The translocation factor of cabbage The translocation factor (TF) in cabbage were calculated based on Cd concentration under Cd stress in the respective organs as follows (An et al. 2022 ). $$\:TF=\frac{Cd\:content\:in\:leaves}{Cd\:content\:in\:roots}$$ Results Leaves and roots Cd accumulation and translocation regulation in cabbage Exogenous Zn / Mn / ZnMn supplementation significantly regulated Cd distributions in cabbage ( P < 0.05), with significant impacts on both tissue accumulation and translocation (Fig. 1 ). Under Cd stress (CK), the Cd content and the translocation factor (TF) in the leaves and roots of cabbage had different degrees of change. Compared with CK, the Zn + Cd treatment in the leaves and roots had the lowest Cd content, which decreased by 57.81% and 89.59%, respectively. In the second instance, Mn + Cd treatment reduced the Cd content of the leaves and roots by 55.87% and 61.44%, respectively. Finally, Zn + Mn + Cd treatment reduced the Cd content of the leaves and roots by 31.42% and 85.86%, respectively. Compared with CK, the Zn + Mn + Cd treatment resulted in the lowest Cd TF from the roots to the leaves, with a reduction of 79.37%. Additionally, under Zn + Cd treatment, the TF of Cd was low, decreasing by 75.32%. Finally, the Cd + Zn + Mn treatment led to a 64.85% reduction in the TF of Cd. Notably, Zn supplementation showed superior Cd mitigation capacity compared to combined ZnMn application (leaves: 57.81% vs 31.42%; roots: 89.59% vs 85.86%), while Mn supplementation showed intermediate efficacy. This classification pattern (Zn > ZnMn > Mn) suggests potential antagonistic interactions during multi-nutrient treatment. Transcriptome sequencing data quality control and screening of differential expressed genes (DEGs) We sequenced the transcriptome of cabbage roots from Cd stress alone (CK) and three comparisons (Zn-vs-CK, Mn-vs-CK, ZnMn-vs-CK), with a total of 12 samples (3 biological replicates), and the sequencing results are shown in Table 2 . Each sample generated 40430474–62739316 raw reads, and after quality filtering of the raw reads, each sample generated 40179032–62434486 clean reads. The clean bases ranged from 5.61G − 8.77G, Q30 ≥ 96.90% and GC ≥ 45.73%. RNA-Seq data showed significant changes in the gene expression profiles of cabbage roots after exposure to Zn, Mn, and ZnMn compared to CK. Venn analysis revealed that 209 DEGs were co-regulated in three treatments, along with 600, 1134, and 1611 unique DEGs in Zn, Mn, and ZnMn treatments, respectively (Fig. 2 A). Following rigorous quality control (FDR ≤ 0.05, |log 2 FC| ≥ 1), we identified three distinct differential expression patterns (Fig. 2 B). Among them, we observed Zn-mediated regulation of 1158 DEGs (578↑/580↓), Mn-mediated regulation of 2332 DEGs (784↑/1548↓), and ZnMn regulation of 2932 DEGs (829↑/2103↓). Notably, the ZnMn co-treatment induced the most DEGs, 153% more than Zn treatment and 26% more than Mn treatment. Table 2 RNA-seq results Sample Raw reads Raw bases Clean reads Clean bases Q20 Q30 GC − pct CK1 44471236 6.67G 44188812 6.21G 99.05% 96.90% 45.93% CK2 43544426 6.53G 43286202 6.07G 99.08% 96.99% 46.35% CK3 53626960 8.04G 53285418 7.43G 99.08% 96.98% 46.12% Mn1 58756664 8.81G 58437354 8.24G 99.08% 96.98% 46.10% Mn2 62739316 9.41G 62434486 8.77G 99.11% 97.05% 45.73% Mn3 53676566 8.05G 53373716 7.45G 99.10% 97.04% 46.67% Zn1 44253686 6.64G 43992354 6.09G 99.08% 96.98% 46.66% Zn2 55753668 8.36G 55403554 7.68G 99.08% 96.99% 46.49% Zn3 40430474 6.06G 40179032 5.61G 99.10% 97.03% 46.61% ZnMn1 57105138 8.57G 56833982 7.99G 99.12% 97.08% 46.42% ZnMn2 55412320 8.31G 55152068 7.73G 99.15% 97.13% 46.66% ZnMn3 60654002 9.10G 60334884 8.47G 99.13% 97.09% 46.56% Functional enrichment of DEGs in cabbage roots, GO enrichment analysis of DEGs To determine the functional significance of transcriptional changes in cabbage roots under Zn, Mn, and ZnMn treatments, GO classification was implemented for the DEGs (Figs. 3 A-C). Expression analysis revealed distinct regulatory patterns (Supplementary Figure S1 ). Zn treatment specifically up-regulated DEG categories among the top 20 GO terms, which included plant-type cell wall (GO:0009505), β-glucosidase activity (GO:0008422), response to toxic substances (GO:0009636), response to reactive oxygen species (GO:0000302), hydrolase activity hydrolyzing O-glycosyl compounds (GO:0004553), hydrolase activity acting on glycosyl bonds (GO:0016798), plant-type cell wall organization or biogenesis (GO:0071669), and response to high light intensity (GO:0009644). Mn treatment specifically up-regulated DEG categories among the top 20 GO terms, which included glucosinolate metabolic processes (GO:0019760) and hydrolase activity hydrolyzing O-glycosyl compounds (GO:0004553). Notably, ZnMn co-treatment exhibited a divergent enrichment profile, no significant up-regulation of DEGs was observed in the top 20 GO terms. Instead, the up-regulated DEGs were predominantly enriched in the integral component of the plasma membrane (GO:0005887) in all GO enrichment terms. The above results suggested that these biological processes serve as a core adaptive mechanism to alleviate Cd stress. Functional enrichment of DEGs in cabbage roots, KEGG pathway classification of DEGs. KEGG pathway enrichment analysis was also performed to further understand the biological meanings of all the DEGs (Figs. 3 D-F). The Zn-vs-CK comparison up-regulated DEGs in the top 14 KEGG pathways were enriched in phenylpropanoid biosynthesis (brp00940), tryptophan metabolism (brp00380), and protein processing in the endoplasmic reticulum (brp04141). The Mn-vs-CK comparison up-regulated DEGs in the top 14 KEGG pathways were enriched in tryptophan metabolism (brp00380) and photosynthesis (brp00195). The ZnMn-vs-CK comparison up-regulated DEGs in the top 20 KEGG pathways were enriched in circadian rhythm - plant (brp04712). In conclusion, Zn treatment induced the most substantial up-regulation of DEGs across metabolic pathways, followed by Mn treatment showing intermediate activation. Notably, ZnMn co-treatment demonstrated a progressive reduction in pathway enrichment magnitude, exhibiting the least transcriptional activation in KEGG-annotated biological systems. Quantitative distribution of DEGs across metabolic pathways was systematically presented in Supplementary Figure S2 . The up-regulated genes were involved in key reactions of these pathways, providing insight into the mechanisms of Cd tolerance under Zn, Mn, and ZnMn treatments in cabbage roots. Metabolome sequencing data quality control and screening of differential expressed metabolites (DAMs) Qualitative and quantitative analyses were conducted through positive (POS) and negative (NEG) ion modes to enhance analytical robustness. The overall distribution of differential metabolites in each comparison group is illustrated in Fig. 4 A-B. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) demonstrated distinct metabolic separation between treatment groups, with tight intra-group clustering and clear inter-group segregation (Figs. 4 C-D), confirming data repeatability. Differential abundance analysis identified 242 (124↑/118↓), 357 (168↑/189↓), and 316 (175↑/141↓) significantly altered metabolites in the Zn-vs-CK, Mn-vs-CK, and ZnMn-vs-CK comparisons, respectively (Figs. 5 A-C). Venn analysis revealed that 437 DAMs were co-regulated across the three treatments, along with 44, 63, and 54 unique DAMs in the Zn, Mn, and ZnMn treatments, respectively (Fig. 5 D). Functional enrichment of DAMs in cabbage roots, KEGG pathway classification of DAMs KEGG pathway enrichment analysis was performed to identify differentially enriched metabolic pathways among the three experimental treatments following the single Cd exposure (Figs. 6 A-C). In the Zn-vs-CK comparison, 49 metabolites mapped to 9 significantly enriched pathways ( P < 0.01), and the significantly up-regulated DAMs include ether lipid metabolism (ko00565) and biosynthesis of various plant secondary metabolites (ko00565). The Mn-vs-CK comparison exhibited 69 metabolites associated with 14 pathways (FDR < 0.01), and the significantly up-regulated DAMs include glycerophospholipid metabolism (ko00564), steroid hormone biosynthesis (ko00140), arachidonic acid metabolism (ko00590), and ether lipid metabolism (ko00565). ZnMn-vs-CK analysis identified 68 metabolites across 8 enriched pathways ( P < 0.01), and the significantly up-regulated DAMs include glycerophospholipid metabolism (ko00564), arginine and proline metabolism (ko00330), and ether lipid metabolism (ko00565). Notably, while the three comparisons exhibited distinct enriched pathway profiles, all shared significant enrichment in fundamental metabolic processes and secondary metabolite biosynthesis (Supplementary Table S1 ). A conserved pattern emerged in plant secondary metabolism and lipid metabolism-related pathways, showing consistent enrichment across all treatments. This observation prompted detailed investigation into plant secondary metabolism and lipid metabolism-related pathways and their associated differentially abundant metabolites. To sum up, Zn treatment regulated lipid metabolism and affected plant secondary metabolism, Mn treatment completely enhanced lipid metabolism, and ZnMn treatment activated lipid metabolism and affected amino acid metabolism. Pathway interrogation demonstrated that all treatments modulated lipid metabolism, which exhibited cascading effects on interconnected biological pathways (Supplementary Figure S3). Correlation analysis of transcriptome and metabolome Based on the expression levels of genes and metabolites, the Spearman correlation algorithm was used to generate a heat map illustrating their relationships. The integrated correlation analysis of transcriptome and metabolome revealed complex interaction networks, as shown in Figs. 7 A-C. The heatmaps demonstrated reciprocal multi-association patterns, such as where individual genes could coordinate with multiple metabolites and vice versa. The results indicated that Bra029761, Bra000964, and Bra018649 genes exhibited a strong positive correlation, whereas Bra038734 displayed a strong negative correlation with Bra018589 and Bra023377 genes. M757T111 exhibited a weak correlation with most other metabolites in the Zn-vs-CK comparison ( P < 0.05). Bra006793 and Bra040972 genes were in the same cluster, showing relatively consistent expression patterns, while M235T379 and M297T175 metabolites showed significant differences in expression levels between clusters in the Mn-vs-CK comparison ( P < 0.05). Bra031485, Bra031287, and Bra019824 showed a high positive correlation with other genes, while ENSRNA049459692 showed a weak correlation with other genes. M349T110, M160T423, and M457T28 were weakly correlated with most other metabolites in the ZnMn-vs-CK comparison ( P 0.80) systematically characterized these relationships across all comparisons (Figs. 7 D-F). Quadrants 3 and 7 contained positively correlated pairs (r > 0.80), while quadrants 1 and 9 displayed negative correlations (r < -0.80). Central quadrant 5 showed non-significant interactions, whereas quadrants 2 and 8 exhibited stable gene expression with differential metabolite accumulation ( P < 0.05). Conversely, quadrants 4 and 6 demonstrated transcriptional variations without corresponding metabolite-level changes. To identify core regulatory nodes, we established high-stringency correlation networks (|r| > 0.90). These networks integrated the top 10 metabolites with functionally significant genes (|log2FC| > 1, P 10) across experimental groups (Supplementary Figure S4; Table S2 ). The correlation analysis suggests that these genes may play a direct or indirect regulatory role in the metabolism of key metabolites throughout the entire developmental period of cabbage KEGG Co-Enrichment analysis of transcriptome and metabolome An integrated pathway analysis of transcriptomic and metabolomic profiles was conducted to elucidate the regulatory mechanisms underlying Zn, Mn, and ZnMn interactions under Cd stress in cabbage roots. After rigorous quality control (|log2FC| > 1), we identified 130 shared metabolic pathways across all comparisons of KEGG co-enrichment analyses (supplemental table S3). Notably, each treatment exhibited distinct pathway activation patterns with two significance thresholds (transcriptome P < 0.01; combination P < 0.05). The Zn-vs-CK comparison revealed five prioritized pathways: Fatty acid elongation (C00062), Cutin, suberine, and wax biosynthesis (C00073), Phenylpropanoid biosynthesis (C00940), Valine, leucine, and isoleucine degradation (C00280), and Pentose and glucuronate interconversions (C00040). Mn-vs-CK analysis identified six core pathways: Biosynthesis of nucleotide sugars (C01250), Tryptophan metabolism (C00380), Glyoxylate and dicarboxylate metabolism (C00630), Glutathione metabolism (C00480), Flavonoid biosynthesis (C00941), and Amino sugar and nucleotide sugar metabolism (C00520). The ZnMn-vs-CK group demonstrated enhanced metabolic complexity, with twelve significantly enriched pathways: Glutathione metabolism (C00480), Glucosinolate biosynthesis (C00966), Fatty acid elongation (C00062), Biosynthesis of various plant secondary metabolites (C00999), Stilbenoid, diarylheptanoid, and gingerol biosynthesis (C00945), Sulfur metabolism (C00920), Tryptophan metabolism (C00380), Cysteine and methionine metabolism (C00270), Phenylalanine, tyrosine, and tryptophan biosynthesis (C00400), Zeatin biosynthesis (C00908), Flavonoid biosynthesis (C00941), and Tropane, piperidine, and pyridine alkaloid biosynthesis (C00960). This multi-omics integration highlights the functions and regulatory mechanisms of biomolecules, particularly in exogenous detoxification and specialized metabolite synthesis pathways, suggesting coordinated molecular responses for Cd stress mitigation. Discussion The persistence of Cd in agricultural soils poses significant ecological risks, owing to its high mobility and tendency for phytoaccumulation. As the primary interface for heavy metal uptake, plant root systems mediate Cd absorption through shared transport pathways for divalent cations, particularly via Fe 2+ , Mn 2+ , and Zn 2+ transporters (Zhou et al. 2021 ). Our findings demonstrate that Zn/Mn supplementation effectively decreased Cd accumulation and translocation in cabbage, with the Zn + Cd treatment achieving the lowest root and leaf Cd concentrations, and the ZnMn + Cd combination showing minimal translocation efficiency (Fig. 1 ). This mitigation effect likely stems from competitive inhibition at shared cation uptake channels, where Zn 2+ / Mn 2+ ions interfere with Cd 2+ absorption through similarities in ionic radius and charge parity (Fang et al. 2021 ; Li et al. 2022a ; Lu et al. 2022 ). The observed root-to-leaf Cd translocation reduction aligns with the dual-phase metal exclusion mechanism: primary restriction at rhizospheric uptake followed by secondary limitation in xylem loading (Uraguchi et al. 2011 ). Our results corroborate previous findings across multiple species: foliar Zn application reduced rice grain Cd (Wang et al. 2018 ), while increasing Zn 2+ concentrations reduced Cd uptake in rice roots through OsZIP transporter competition (Fahad et al. 2015 ). Similarly, Mn supplementation decreased wheat grain Cd by 46% via enhanced Mn-SOD activity and vacuolar Cd sequestration (Huang et al. 2021 ). Notably, Zn showed better Cd fixation efficiency in plant tissues, mainly through enhancing the synthesis and crosslinking of cell wall pectin, thus significantly improving the binding ability of Cd 2+ , while Mn-induced chloroplast protection mechanisms in lettuce ( Lactuca sp. ) suggest that our observed effects may involve both transport competition and potentiation of the antioxidant system (Ramos et al. 2002 ; Zheng et al. 2024 ). These collective findings establish Zn / Mn application as a promising plant management strategy, leveraging essential micronutrient supplementation to disrupt Cd entry routes. Transcriptomic profiling demonstrated its effectiveness in elucidating the transcriptional regulatory networks underlying Cd stress adaptation in plants, while functional annotation analysis further delineated treatment-specific biological processes through differential GO enrichment patterns (Li et al. 2022b ). The GO annotation analysis revealed that Zn preferentially up-regulated cell wall biosynthesis genes, Mn induced hydrolase activity-related transcripts, and ZnMn co-treatment enhanced genes associated with plasma membrane integrity. Zn-induced cell wall reinforcement supports its role as the primary Cd exclusion barrier, with pectin methylesterases and lignin biosynthesis enzymes likely enhancing Cd immobilization through polysaccharide crosslinking and metal-ligand complexation (Loix et al. 2017 ). Mn-mediated hydrolase activation (e.g., proteases, lipases) may facilitate metabolic reprogramming, especially in seed germination-related pathways (e.g., α-amylase, β-glucosidase), supporting the role of hydrolases in heavy metal detoxification through protein turnover and toxic metabolite degradation (Zhang et al. 2008 ). ZnMn co-treatment induces lipid metabolism genes and alters phosphatidylcholine composition to achieve plasma membrane specialization and enhance membrane stability. This is a key adaptation for maintaining ion homeostasis under Cd stress (Yao et al. 2020 ). Specifically, the enhanced membrane stability resulting from ZnMn co-treatment is due to Zn-facilitated Cd 2+ sequestration in plant cell walls through enhanced pectin cross-linking and lipid membrane structure, thereby reducing Cd 2+ transmembrane absorption efficiency (Chande et al. 2010; Kučerka et al. 2017 ). Mn blocks the transmembrane transport of Cd by activating the cellular Mn transporter and enzymatic activity associated with lipid synthesis, thereby promoting the synthesis of phospholipids and cuticle in the cell membrane (Salomé et al. 2017; Wei et al. 2018 ). Overall, these transcriptional divergences suggest treatment-specific Cd tolerance mechanisms: Zn treatment results in the highest number of up-regulated DEGs in GO terms, particularly enhancing extracellular isolation and effectively reducing Cd uptake by cabbage roots. Mn treatment has relatively few up-regulated DEGs, primarily regulating intracellular protein balance and enhancing intracellular Cd differentiation. In contrast, ZnMn treatment down-regulates DEGs in the top 20 GO terms, suggesting that ZnMn co-treatment may negatively affect cabbage root growth. These findings advance our understanding of micronutrient-mediated Cd mitigation strategies. The KEGG annotation results demonstrated that the DEGs were involved in various biological pathways. In this paper, several pathways were selected for further analysis based on the number of enriched genes, aiming to understand potential Cd tolerance mechanisms in cabbage roots (Li et al. 2022b ). The results revealed that DEGs were predominantly enriched in three core pathways: phenylpropanoid metabolism (Zn treatment), tryptophan metabolism (Mn treatment), and circadian rhythm regulation (ZnMn co-treatment), with each pathway demonstrating distinct mechanisms of Cd tolerance. The phenylpropanoid metabolism pathway, a critical plant defense system, confers resistance to abiotic stresses, including salinity, drought, and heavy metal toxicity, through the biosynthesis of protective metabolites (Yadav et al. 2020 ). Downstream products such as lignins, flavonoids, and procyanidins enhance metal tolerance via chelation, antioxidant activity, and structural reinforcement (Ge et al. 2023 ). In this study, transcriptomic analysis of Zn-treated cabbage roots revealed pronounced up-regulation of peroxidase (POD) and lignin biosynthesis genes (Supplementary Figure S5A). POD is an oxidative enzyme widely found in plants; it mediates xylem lignification, redox homeostasis, and stress adaptation through the catalytic degradation of reactive oxygen species (Barceló et al. 2007 ). Lignins, dominant components of secondary cell walls, immobilize heavy metals via carboxyl and phenolic functional groups, effectively restricting cytoplasmic Cd entry (Li et al. 2018 ; Su et al. 2020 ; Yu et al. 2022 ). This aligns with our prior findings of Zn-induced cell wall-related gene activation, suggesting Zn stimulates endogenous antioxidant systems to mitigate oxidative damage by reducing ROS accumulation (Maleva et al., 2018 ). The observed lignin-mediated cell wall compartmentalization may be an important Cd detoxification mechanism in both the roots and leaves of cabbage. Tryptophan biosynthesis in plants is linked to growth and adaptive mechanisms, which synthesize indole-derived secondary metabolites essential for defense and environmental interaction. These include indole-3-acetic acid (IAA), serotonin, and melatonin, which have a wide range of functions in higher plants, including physiological processes such as seed germination, root growth and development, senescence, flowering, or fruit ripening, as well as in the response mechanisms against biotic and abiotic stresses (Corpas et al. 2021 ). In this study, Mn treatment specifically enriched IAA biosynthesis genes in cabbage roots (Supplementary Figure S5B), potentially elevating indolepyruvate, indoleacetate, and other related metabolites levels. IAA is considered a heavy metal bioadsorption regulator, capable of restoring plant growth. It enhances primary metabolite synthesis and antioxidant capacity while inhibiting Cd uptake, possibly through hemicellulose-1-mediated cell wall modification (Piotrowska-Niczyporuk et al. 2012 ). This mechanism may explain the reduced root Cd absorption observed in Mn-treated plants. Plants can integrate environmental signals, including temperature and light, and coordinate various physiological processes, such as photoperiodic flowering, hormone signaling, growth, metabolism, and responses to biological and abiotic stresses. Circadian rhythm is a biological rhythm influenced by many external factors, including abiotic stress (Venkat et al. 2022). In this study, ZnMn co-treatment significantly activated the circadian rhythm pathway, up-regulating CDF1 (cycling DOF factors), a unique DOF-family transcription factor that coordinates stress responses (Supplementary Figure S5C). CDF1 protein is a distinct transcription factor in the plant DOF family, playing an important role in plant growth and development. CDF transcription factors are involved in inducing the expression of stress-response genes, accumulating osmotic regulatory substances, and maintaining the carbon/nitrogen balance and endogenous hormone balance. They also play a crucial role in responding to abiotic stress and improving stress tolerance in crops (Fornara et al. 2009 ; Corrales et al. 2017 ; Renau-Morata et al. 2017 ). In summary, Zn treatment triggered phenylpropanoid biosynthesis, while Mn treatment enhanced tryptophan metabolism. ZnMn co-treatment activated the circadian rhythm-related detoxification metabolic response in cabbage under Cd stress. This may be because, under Cd stress, phenylpropanoids in plants indirectly influence the oscillatory expression of genes associated with the biological clock by modulating ROS homeostasis and hormone signaling. This, in turn, establishes a bidirectional regulatory network between metabolism and circadian rhythms (Lv et al. 2024 ). Additionally, indoles derived from tryptophan metabolism interact with circadian clock components via jasmonic acid signaling, promoting the night-specific activation of defense genes and, consequently, regulating the expression of photoperiodic response genes (Qin et al. 2024). These results indicate that the pathways enriched in KEGG may play a crucial role in maintaining normal conditions in plants under Cd stress. Plants produce structurally diverse specialized metabolites, including bioactive alkaloids and terpenoids, in response to biotic and abiotic environmental stresses (Shoji et al. 2021). In this study, Zn, Mn, and ZnMn co-treatments had significant effects on lipid-related metabolism, plant secondary metabolism, and the amino acid metabolism pathways. These results suggest that the above metabolites play an important role in the Cd stress response, which will be the focus of our research. Lipids are not only an important structural component of the cell membrane and the surface of plant tissues and organs, but also a physiologically active substance and signal molecule, playing a crucial role in plant growth, development, and stress response (Kim 2020 ; Zhao et al. 2021 ). In this study, the results of the KEGG enrichment analysis of DAMs revealed significant enrichment in the fatty acid elongation pathway in three comparisons, with notable effects on lipids and lipid-like molecules (C00157, C04230, C01233, C00670), organic nitrogen compounds (C00588), and nucleosides, nucleotides, and analogues (C00307). Some studies have pointed out that increased lipid metabolism directly contributed to maintaining the stability of the cell membrane and reducing cell damage under Cd stress (Feng et al. 2024 ). Organic nitrogen compounds, such as phosphocholine, can participate in the synthesis of phospholipids and may promote lipid metabolism in plants (Ulch et al. 2025 ). CDP-choline, a nucleoside, nucleotide, and analogue, is the precursor of phosphatidylcholine and acetylcholine, key components of the cell membrane. It participates in the formation of the phospholipid bilayer of the cell membrane, regulating membrane fluidity and protecting against oxidative damage to the membrane (Weiss, 1995 ). Therefore, we believe that Zn, Mn, and ZnMn treatments enhance Cd tolerance in cabbage by maintaining normal lipid synthesis and metabolism, thereby improving cabbage's Cd tolerance. In plants, the secondary metabolic pathways produce a diversity of compounds called plant secondary metabolites (PSMs); these compounds contain a large group of structurally diverse molecules originating from either primary metabolites or intermediates in the biosynthetic pathways of these primary metabolites (Piasecka et al. 2015 ; Pang et al. 2021 ). Plant secondary metabolites play a variety of functions, such as in plant growth and developmental processes, innate immunity (Piasecka et al. 2015 ), defense response signaling (Isah, 2019 ), and response to environmental stresses (Yang et al. 2018 ). The results of this study revealed that Zn treatment significantly up-regulated the biosynthesis of a variety of plant secondary metabolites, mainly phenylpropanoids, polyketides (C01864), and organic acids and derivatives (C00073). Studies have found that Zn can induce the up-regulation of key enzymes in the phenylpropanoid metabolic pathway and promote polymerization of lignin monomers. Meanwhile, Zn can increase the content of flavonoids in plant cells and enhance their complexation with Cd 2+ , thus enhancing the cell wall's fixation of Cd 2+ (Marreiro et al. 2017 ; Wang et al. 2023 ). Zn may reduce cytoplasmic free Cd 2+ concentration by promoting the synthesis of organic acids in plants, specifically chelating Cd 2+ and transporting it to the vacuole for isolation (Song et al. 2025 ). Therefore, we believed that plant secondary metabolites are involved in stabilizing cabbage roots during Cd stress and that maintaining normal plant secondary metabolism is an important way to improve the Cd tolerance of cabbage. Amino acids have a variety of prominent functions in plants, not only as precursors for protein biosynthesis but also as participants in a large number of cellular reactions, influencing many physiological processes, and playing a key role in signal transduction processes as well as plant stress responses (Hildebrandt et al. 2015 ). The results of this study revealed that ZnMn co-treatment significantly up-regulated the biosynthesis of a variety of organic acids and derivatives (C00791 and C02714) and organic nitrogen compounds (C00179). Some studies have indicated that plants, when exposed to metals, typically synthesize a variety of low-molecular-weight compounds, particularly specific free amino acids (FAAs), which are recognized as compatible solutes (Mahdavian et al. 2023). These FAAs have been shown to act as signaling molecules and play a crucial role in plant physiology (Dinkeloo et al. 2018 ). Notably, the levels of FAAs significantly increase in response to both abiotic and biotic stressors, positioning them as reliable biomarkers of stress (Zhu et al. 2018 ). From this, we inferred that the regulation of amino acid metabolism is considered the main strategy for plant protection and survival under adverse living conditions. Moreover, the ZnMn co-treatment promotes amino acid metabolism in cabbage roots, which suggests that the treatment may have adverse effects on the roots. Based on the above, Zn treatment regulated plant secondary metabolism to alleviate Cd toxicity, Mn treatment primarily improved lipid metabolism, which helped to decrease Cd stress, while ZnMn co-treatment may disturb amino acid metabolism in plants under Cd stress. The changes in the metabolism levels mentioned above play a crucial role in the mechanism by which plants adapt to Cd stress. Metabolomic and transcriptomic analyses were integrated to elucidate coordinated pathway interactions between DEGs and DAMs in cabbage under Cd stress. To investigate the associations between the metabolome and transcriptome, the KEGG pathways enriched by both were integrated (Du et al. 2024 ). In this study, DEGs and DAMs were mostly enriched in metabolic pathways, including amino acid metabolism, lipid metabolism, nucleotide metabolism, metabolism of terpenoids and polyketides, and metabolism of cofactors and vitamins. Based on KEGG co-enrichment analysis, the fatty acid elongation pathways were significantly enriched in the Zn-vs-CK and ZnMn-vs-CK comparisons. The tryptophan metabolism, glutathione metabolism, and flavonoid biosynthesis pathways were significantly enriched in the Mn-vs-CK and ZnMn-vs-CK comparisons. Lipid metabolism includes systemic biochemical reactions such as fat synthesis, decomposition, and transport. It is the core metabolic pathway for maintaining cell energy homeostasis. The fatty acid elongation pathway plays an important role in lipid metabolism and cell function, converting nutrients into metabolic intermediates for membrane biosynthesis, energy storage, and signaling molecule generation (Rohrig et al. 2016; Xie et al. 2020 ). Our results further suggest that the enrichment of key biosynthetic genes and metabolites involved in the lipid metabolism pathway under Zn and ZnMn co-treatments may play an important role in cabbage's response to Cd stress. Tryptophan, the sole indole-containing amino acid, modulates plant stress tolerance through its derivatives, including auxins and melatonin, which regulate oxidative balance and metabolic adaptation (Xue et al. 2023 ). The activation of the glutathione metabolism pathway serves as a conserved detoxification mechanism in plants under Cd stress. Glutathione effectively reduces the transport of Cd from the roots to the shoots and enhances the resistance of plants to Cd (Nakamura et al. 2020 ; Huang et al. 2022 ; Li et al. 2021 ; Wang et al. 2021 ). Flavonoids are major secondary metabolites derived from the phenylpropane pathway in plants and play an important role in plant development, affecting basic physiological metabolism, as well as stress and disease resistance (Wang et al. 2020b ). These findings suggest that Mn treatment and ZnMn co-treatment are closely related to Cd tolerance in Chinese cabbage. Tryptophan may alleviate Cd stress in cabbage roots by balancing IAA levels. Glutathione maintains intracellular homeostasis by scavenging reactive oxygen species in Chinese cabbage roots, while flavonoid accumulation enhances antioxidant capacity and directly removes Cd-induced ROS. Therefore, lipids, tryptophan, glutathione, and flavonols play a vital role in Zn, Mn, and ZnMn treatments under Cd stress. In future studies, the identification of key genes and the verification of potential up-regulated differential metabolites will be the focus of our research, which will contribute to a deeper understanding of Cd tolerance and accumulation mechanisms in cabbage. Conclusion In this study, we explored the regulatory mechanism of Cd accumulation in cabbage roots through the addition of Zn, Mn, and ZnMn co-treatment. The results showed significant differences in Cd enrichment levels among treatment groups, with the strength of intermetallic antagonism being Zn > ZnMn > Mn. Multi-omics analysis revealed that Zn treatment significantly up-regulated DEGs, activated cell wall and phenylpropanoid biosynthesis, and regulated secondary metabolism to alleviate Cd stress. Mn treatment regulated intracellular protein balance, enhanced tryptophan metabolism, and promoted lipid metabolism to improve metabolic detoxification efficiency. Notably, although ZnMn co-treatment effectively attenuated Cd accumulation in cabbage roots, it concurrently triggered adverse physiological consequences, including down-regulation of DEGs and disrupted amino acid metabolism. We postulate that this may be because ZnMn treatment specifically activates or inhibits the expression of certain ion transport-related genes, which need further investigation. This study may provide valuable insights into the mechanisms of Cd inhibition in agricultural systems. Declarations Funding This work was supported by Calcium-dependent mechanism for the formation and maintenance of karst biodiversity and its application basis, National Natural Science Foundation of China-Guizhou Provincial Government Joint Program, Grant No. U1812401. Data availability The datasets generated or analyzed during the current study are available from the corresponding author upon reasonable request. The authors declare no conflict of interest related to this study. Acknowledgements The authors acknowledge the support of NSFC to Guizhou Normal University, Department of Life science, funded by the National Natural Science of University and Research, Project U1812401. Authors contributions WYL contributed to the conceptualization, methodology, data reduction, prepared the figures, original manuscript writing, and writing, reviewing, and editing. FXQ contributed to conceptualize the experimentation, acquired funding and planning, reviewed and edited the writing. 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J Agric Food Chem 73(13):7645-7657. https://doi.org/10.1021/acs.jafc.4c12704 Uraguchi S, Kamiya T, Sakamoto T, Kasai K, Sato Y, Nagamura Y, Yoshida A, Kyozuka J, Ishikawa S, Fujiwara T (2011) Low-affinity cation transporter (OsLCT1) regulates cadmium transport into rice grains, Proc. Natl. Acad. Sci 108: 20959-20964. https://doi.org/10.1073/pnas.1116531109 Venkat A, Muneer S (2022) Role of Circadian Rhythms in Major Plant Metabolic and Signaling Pathways. Front Plant Sci 13:836244. https://doi.org/10.3389/fpls.2022.836244 Wang H, Li Y, Wang S, Kon, D, Sahu S K, Bai M, Li H, Li L, Xu Y, Liang H, Liu H, Wu H (2020a) Comparative transcriptomic analyses of chlorogenic acid and luteolosides biosynthesis pathways at different flowering stages of diploid and tetraploid Lonicera japonica. 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Cell Metab 35:1304-1326. https://doi.org/10.1016/j.cmet.2023.06.004 Yadav V, Wang Z, Wei C, Amo A, Ahmed B, Yang X, Zhang X (2020) Phenylpropanoid Pathway Engineering: An Emerging Approach towards Plant Defense. Pathogens 9:312. https://doi.org/10.3390/pathogens9040312 Yamasaki S, Sakata SK, Hasegawa A, Suzuki T, Kabu K, Sato E, Kurosaki T, Yamashita S, Tokunaga M, Nishida K, Hirano T (2007) Zinc is a novel intracellular second messenger. J Cell Biol 177:637-645. https://doi.org/10.1083/jcb.200702081 Yang J, Guo H, Ma Y, Wang L, Wei D, Hua L (2010) Genotypic variations in the accumulation of Cd exhibited by different vegetables. J. Environ. Sci, 22:1246-1252. https://doi.org/10.1016/s1001-0742(09)60245-x Yang L, Wen K S, Ruan X, Zhao Y X, Wei F, Wang Q (2018) Response of Plant Secondary Metabolites to Environmental Factors. Molecules 23(4):762. https://doi.org/10.3390/molecules23040762 Yao Y, Long D, Xun H (2020) Diverse Functions of Lipids and Lipid Metabolism in Development. Biochem. Med 4:1900564. https://doi.org/10.1002/smtd.201900564 Yu G, Wang LG, Han Y, He QY (2012) Cluster Profiler: an R Package for Comparing Biological Themes Among Gene Clusters. Integr. Biol. Camb 16:284-287. https://doi.org/10.1089/omi.2011.0118 Yu M, Zhuo R, Lu Z, Li S, Chen J, Wang Y, Li J, Han X (2022) Molecular insights into lignin biosynthesis on cadmium tolerance: Morphology, transcriptome and proteome profiling in Salix matsudana. J. Hazard. Mater 441:129909. https://doi.org/10.1016/j.jhazmat.2022.129909 Zare AA, Khoshgoftarmanesh AH, Malakouti MJ, Bahrami HA, Chaney RL (2018) Root uptake and shoot accumulation of cadmium by lettuce at various Cd:Zn ratios in nutrient solution. Ecotoxicol Environ Saf 148:441-446. https://doi.org/10.1016/j.ecoenv.2017.10.045 Zhang H, Hu LY, Hu KD, He YD, Wang SH, Luo JP (2008) Hydrogen sulfide promotes wheat seed germination and alleviates oxidative damage against copper stress. J Integr Plant Biol 50:1518-29. https://doi.org/10.1111/j.1744-7909.2008.00769.x Zhao X, Wei Y, Zhang J, Yang L, Liu X, Zhang H, Shao W, He L, Li Z, Zhang Y, Xu J (2021) Membrane Lipids' Metabolism and Transcriptional Regulation in Maize Roots Under Cold Stress. Front Plant Sci 12:639132. https://doi.org/10.3389/fpls.2021.639132 Zheng S, Xu C, Zhu H, Huang D, Wang H, Zhang Q, Li X, Zhu Q (2024) Foliar application of zinc and selenium regulates cell wall fixation, physiological and gene expression to reduce cadmium accumulation in rice grains. J Hazard Mater 480:136302. https://doi.org/10.1016/j.jhazmat.2024.136302 Zhou J, Zhang C, Du BY, Cui HB, Fan XJ, Zhou DM, Zhou J (2021) Soil and foliar applications of silicon and selenium effects on cadmium accumulation and plant growth by modulation of antioxidant system and Cd translocation: comparison of soft vs. durum wheat varieties. J. Hazard Mater 402:123546-123555. https://doi.org/10.1016/j.jhazmat.2020.123546 Zhu G, Xiao H, Guo Q, Zhang Z, Zhao J, Yang D (2018) Effects of cadmium stress on growth and amino acid metabolism in two Compositae plants. Ecotoxicology and environmental safety, 158:300-308. https://doi.org/10.1016/j.ecoenv.2018.04.045 Zornoza P, Beatriz SP, Ramón OC (2010) Interaction and accumulation of manganese and cadmium in the manganese accumulator Lupinus albus. J. Plant. Physiol 167:1027-1032. https://doi.org/10.1016/j.jplph.2010.02.011 Supplementary Files SupplementaryfileUpdatedversion.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Plant and Soil → Version 1 posted Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Editor assigned by journal 13 Apr, 2025 First submitted to journal 13 Apr, 2025 Editorial decision: Accept but incomplete 29 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6088882","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446544867,"identity":"979b57b2-3173-4796-a4b5-f1b027dab286","order_by":0,"name":"Wanyu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIie3PPwuCQBjH8UcEW65anxD0FQQXB5KLvZWToLGlxak/CDf5Ahx8F0HzgeB07q4uTQ76BqKgqclrC7rv/Hzg+QGYTD+YY9tS9gme5lBKPTKbiLjN1ZovLhXXIx5RjE1FwmmpqOZjyAOXOLiHSg1NB5G3PI+T3aIgeLCy+hoWsGWBHCcVdohWivXNJSDj2ziJBRKKlvC7uyYhpb0iHOMMlKNJJsJqc4kMoWJhQTW2+Om8l8Pj6G1k2TZdEnmj5KPXqG/O3+RbYTKZTH/RE/WAQxew4RZDAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-7091-0224","institution":"Guizhou Normal University School of Life Sciences","correspondingAuthor":true,"prefix":"","firstName":"Wanyu","middleName":"","lastName":"Li","suffix":""},{"id":446544868,"identity":"e99cb0cf-cd7a-48cb-b7ab-ab33dc704443","order_by":1,"name":"Fanxin Qin","email":"","orcid":"https://orcid.org/0000-0003-1296-1063","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fanxin","middleName":"","lastName":"Qin","suffix":""},{"id":446544869,"identity":"fcea4439-955b-4cb4-8b41-559ba16a7b82","order_by":2,"name":"Banglin Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Banglin","middleName":"","lastName":"Luo","suffix":""},{"id":446544870,"identity":"76e267cf-8130-46fd-b174-f16c5e811079","order_by":3,"name":"Qiu Huang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qiu","middleName":"","lastName":"Huang","suffix":""},{"id":446544871,"identity":"ef954b77-ec16-432c-9b44-8709745d971e","order_by":4,"name":"Anqi Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anqi","middleName":"","lastName":"Xu","suffix":""},{"id":446544872,"identity":"b4bd5977-5819-4ec9-930a-645a189fd040","order_by":5,"name":"Rui Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-02-23 08:15:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6088882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6088882/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-025-07594-1","type":"published","date":"2025-07-02T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81250507,"identity":"a22c15fe-bdd6-4cd0-89ba-790d2f6ffe00","added_by":"auto","created_at":"2025-04-24 03:08:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30932,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Cd content in leaves and roots of cabbage under different treatment. (B) Total Cd transport coefficient of cabbage leaves and roots under different treatment.\u003c/p\u003e\n\u003cp\u003eNote: Values in the figure are mean ± standard error (n=3), and different letters in the figure indicate significant differences between different treatments (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/14f2265b06d2e3d51b6ba90e.jpg"},{"id":81251077,"identity":"ea8b441c-9ab5-43e3-a31b-aa375b50e49f","added_by":"auto","created_at":"2025-04-24 03:16:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24636,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Venn diagram of DEGs in cabbage roots under three treatments. (B) Statistics of DEGs in cabbage roots under three treatments.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/1aab52062a0345c76ec4dd98.jpg"},{"id":81250510,"identity":"8ddf8851-0426-438a-8d95-b2999f24f12c","added_by":"auto","created_at":"2025-04-24 03:08:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":349515,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Number of DEGs of enriched GO terms in Zn treatment, (B) Number of DEGs of enriched GO terms in Mn treatment, (C) Number of DEGs of enriched GO terms in ZnMn co-treatment, (D) Enriched KEGG pathways of DEGs in Zn treatment, (E) Enriched KEGG pathways of DEGs in Mn treatment, (F) Enriched KEGG pathways of DEGs in ZnMn co-treatment.\u003c/p\u003e\n\u003cp\u003eNote: The x-axis denotes the rich factor, and the y-axis denotes Go pathway terms in figures A-C. The size of the bubble corresponds to the number of enriched genes, while the color of the bubble indicates the enrichment significance in this figure.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/3ce54c4c66c1273681b35c43.jpg"},{"id":81250513,"identity":"1b7f468c-2bbc-4da1-b327-1852f6a4ae92","added_by":"auto","created_at":"2025-04-24 03:08:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137164,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Intergroup PCA plot of cabbage roots in positive ion mode, (B) Intergroup PCA plot of cabbage roots in negative ion mode, (C) Intergroup OPLS-DA plot of cabbage roots in positive ion mode, (D) Intergroup OPLS-DA plot of cabbage roots in negative ion ion mode.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/8af4c078a98fc4a68ce6d4c3.jpg"},{"id":81251078,"identity":"ad69b27b-f11e-490c-aa82-5828abde7391","added_by":"auto","created_at":"2025-04-24 03:16:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202035,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Volcano plot of DAMs in cabbage roots under Zn treatment, (B) Volcano plot of DAMs in cabbage roots under Mn treatment, (C) Volcano plot of DAMs in cabbage roots under ZnMn co-treatment, (D) Venn plot of DAMs under three treatments.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/74ea1ca878620a7ea4c44365.jpg"},{"id":81252104,"identity":"698c313c-96a7-4680-b1a3-ea7829830d8b","added_by":"auto","created_at":"2025-04-24 03:32:56","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178821,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Enriched KEGG pathways of DAMs in Zn treatment, (B) Enriched KEGG pathways of DAMs in Mn treatment, (C) Enriched KEGG pathways of DAMs in ZnMn co-treatment.\u003c/p\u003e\n\u003cp\u003eNote: The size of the bubble corresponds to the number of enriched metabolites, while the color of the bubble indicates the enrichment significance in this figure.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/d24d89833577035eb48b2e5d.jpg"},{"id":81251080,"identity":"c67e2ee6-6efd-428e-ac5e-0a46cd249611","added_by":"auto","created_at":"2025-04-24 03:16:56","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":366259,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Heatmap of all identified DEGs and DAMs in Zn treatment, (B) Heatmap of all identified DEGs and DAMs in Mn treatment, (C) Heatmap of all identified DEGs and DAMs in ZnMn co-treatment, (D) Nine-quadrant diagrams in Zn treatment, (E) Nine-quadrant diagrams in Mn treatment, (F) Nine-quadrant diagrams in ZnMn co-treatment.\u003c/p\u003e\n\u003cp\u003eNote: In the correlation cluster heat map, the horizontal axis represents metabolites, while the vertical axis represents genes, the phylogenetic tree on the left represents the hierarchical clustering of genes, while the tree above displays the hierarchical clustering of metabolites. The color of each column indicates the correlation between genes and metabolites, with red indicating a stronger positive correlation and blue indicating a stronger negative correlation (figures A-C). The dashed line on the horizontal axis represents the fold change threshold for the transcriptome, while the dashed line on the vertical axis represents the fold change threshold for the metabolome. Genes and metabolites outside these threshold lines indicate significant differences, whereas those within the threshold lines indicate non-significant differences (figures D-F).\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/f6f89130dff50fe59758b39f.jpg"},{"id":86178871,"identity":"84f343d8-19ad-40f4-a271-5c74f4724e68","added_by":"auto","created_at":"2025-07-07 16:04:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2533098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/10a501ed-21ca-451e-9e44-719c1596fce7.pdf"},{"id":81250543,"identity":"bcca5897-fef4-43dc-9225-5778d61f2475","added_by":"auto","created_at":"2025-04-24 03:08:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5694971,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileUpdatedversion.docx","url":"https://assets-eu.researchsquare.com/files/rs-6088882/v1/f90d5e87fabd05ee75bd7647.docx"}],"financialInterests":"","formattedTitle":"Zinc and Manganese Impact on Cabbage (Brassica rapa) Cadmium Tolerance: Comparative Transcriptomic and Metabolomic Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid expansion of global industrialization and intensive agricultural practices including mining operations, application of contaminated fertilizers, sewage irrigation, and fossil fuel combustion has led to severe cadmium (Cd) contamination in arable soils, with Cd emerging as the predominant heavy metal pollutant worldwide (Tan et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a non-essential element with high bioaccumulation potential, Cd poses significant health risks through dietary accumulation (Grant et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sasaki et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While cereal crops such as wheat and rice exhibit notable Cd bioaccumulation (Sirot et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), leafy vegetables represent a critical exposure pathway due to their high consumption rates. Particularly concerning is cabbage (\u003cem\u003eBrassica rapa\u003c/em\u003e), which demonstrates elevated Cd accumulation compared to other vegetables and serves as a dietary staple across China and East Asia (Yang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Therefore, urgent strategies are needed to reduce Cd contamination in agricultural products.\u003c/p\u003e \u003cp\u003eZinc (Zn) is one of the essential plant micronutrients,participates in multifaceted physiological processes ranging from phytohormone modulation and PSII complex repair to the maintenance of mesophyll CO\u003csub\u003e2\u003c/sub\u003e concentrations and oxidative stress mitigation (Yamasaki et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; H\u0026auml;nsch et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ismail \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Karen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rizwan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since Cd and Zn have similar characteristics in their atomic structures, they have some of the same chemical properties (Huang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). As a result, when Cd\u003csup\u003e2+\u003c/sup\u003e and Zn\u003csup\u003e2+\u003c/sup\u003e coexist, their absorption and transport in the plant affect each other (Lin et al. 2012). Emerging evidence suggests that Zn supplementation at optimal concentrations can effectively mitigate the genotoxicity of soybean seedlings under Cd stress, alter subcellular distribution and regulate the composition of its chemical form, and enhance Cd tolerance mechanisms (Zare et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Du et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the precise molecular mechanisms governing Zn-mediated Cd detoxification remain poorly characterized.\u003c/p\u003e \u003cp\u003eManganese (Mn) is an essential microelement for plant growth that orchestrates fundamental metabolic pathways, including photosynthesis, signal transduction, antioxidant activity, and biosynthesis of primary metabolites (Millaleo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sochaet al. 2014; Faria et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mn and Cd are transition elements and divalent metal cations that share similar biochemical properties (Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a non-essential element for plants, Cd may share some of the Mn transporters (Ge et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Field trials have demonstrated that Mn application significantly suppresses Cd accumulation in both staple crops (e.g., maize, soybean) and hyperaccumulator species (Baszynski et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Hernandez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), while concurrently enhancing the photosynthetic index of Pokeweed and Lupine (Zornoza et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Nevertheless, the regulatory effects of Mn on Cd partitioning in leafy vegetables, particularly at the molecular levels, remain largely unexplored.\u003c/p\u003e \u003cp\u003eThe effects of Cd stress and Zn/Mn nutrition on plant growth, development, physiological characteristics, and gene expression have been widely studied in various crops. However, the molecular mechanisms by which Zn/Mn alleviate Cd toxicity in cabbage roots at the transcriptome and metabolome levels remain unknown. To address these challenges, we employed an integrated multi-omics approach to investigate the physiological and molecular mechanisms of Zn, Mn, and ZnMn co-treatments in reducing Cd uptake in cabbage. Using transcriptomic, metabolomic, and co-enrichment analyses, this study elucidates Zn, Mn, and ZnMn-mediated Cd transport regulation, identifies key metabolic pathways involved in Cd tolerance, and establishes a theoretical framework for developing phytoremediation strategies.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental materials, hydroponic growth conditions and experimental treatments\u003c/h2\u003e \u003cp\u003eThe cabbage variety, SuLv(SL) was used in this study, which is widely grown in Southwest China. The variety was selected from 7 cultivars in our previous study, and the variety has a high Cd accumulate ability when grown in Cd stress. Seeds were surface-sterilized with 10% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 15 min, thoroughly washed with deionized water. After sterilization, seeds were placed on sterile filter paper-lined Petri dishes moistened with deionized water and germinated in darkness at 28\u0026deg;C using an artificial climate incubator, after germination the seeds were sown into pots filled with vermiculite (3 plants per pot). During this period, the seedlings were irrigated with 1/10 strength Hoagland nutrient solution for 10 days, followed by 1/5 strength Hoagland nutrient solution for 20 days. Selecting the growth of the same seedlings in deionized water starvation treatment for 1 day, and then the complete nutrient solution containing Cd, Zn, Mn for stress, respectively. All treatments were maintained in controlled-environment chambers with 14/10 h, 65% relative humidity, and 28/22\u0026deg;C day/night temperatures. The nutrient solution was renewed every 72 h to maintain pH stability and oxygen saturation (\u0026gt;\u0026thinsp;85%). A total of 7 treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), each set of three replicates, harvested after 14 days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental treatments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTreatment time\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-7d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8-14d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u0026thinsp;+\u0026thinsp;Zn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u0026thinsp;+\u0026thinsp;Cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u0026thinsp;+\u0026thinsp;Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u0026thinsp;+\u0026thinsp;Cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u003csup\u003e\u0026middot;\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u0026thinsp;+\u0026thinsp;Zn\u0026thinsp;+\u0026thinsp;Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u0026thinsp;+\u0026thinsp;Mn\u0026thinsp;+\u0026thinsp;Cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Zn\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Mn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 mg\u0026middot;kg-1 Cd\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg-1 Zn\u0026thinsp;+\u0026thinsp;10 mg\u0026middot;kg-1 Mn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDetermination of Cd concentration\u003c/h3\u003e\n\u003cp\u003eThe roots were washed thoroughly with tap water and soaked in 20 mM Na2EDTA solution for 15 min to remove heavy metal ions from the root surface, and then removed and rinsed with deionized water. Part of the samples immediately flash-frozen in liquid nitrogen and stored at -80\u0026deg;C for subsequent transcriptomic and metabolomic profiling. In the other part samples, separated into root and shoot fractions, heat inactivated at 105\u0026deg;C for 30 min, then dried at 75℃ to constant weight, and milled for the determination of Cd content in cabbage.\u003c/p\u003e \u003cp\u003eThe dried samples were digested (HNO\u003csub\u003e3\u003c/sub\u003e,5mL) and then analyzed by inductively coupled plasma mass spectroscopy (ICP-MS) (Thermo Scientific, USA). A certified reference material (CRM) of plant (GBW-10045a, provided by the National Research Center for CRM, China) was used to control the precision of the analytical procedures.\u003c/p\u003e\n\u003ch3\u003eRNA-Seq extraction, library construction and sequencing\u003c/h3\u003e\n\u003cp\u003eCabbage root samples with Cd, Cd\u0026thinsp;+\u0026thinsp;Zn, Cd\u0026thinsp;+\u0026thinsp;Mn and Cd\u0026thinsp;+\u0026thinsp;Zn\u0026thinsp;+\u0026thinsp;Mn treatments (Treatment groups screened by Result 3.1) were selected and immediately snap-frozen in liquid nitrogen and stored at -80\u0026deg;C until nucleic acid extraction. First, the total RNA was extracted from the cabbage root samples by 1% agar-spectrophotometer gel electrophoresis, then the purity of the RNA was determined by NanoPhotometer spectrophotometer (IMPLEN, CA, USA). Then, the Agilent 2100 bioanalyzer system (Agilent Technologies, CA, USA) is used to accurately detect RNA integrity, and then the qualified total RNA samples are constructed and inspected. Finally, the high-throughput double-ended sequencing platform (Illumina) was used to collect 150bp double-ended read length data. Three biological replicates were analyzed to ensure metabolic profile repeatability.\u003c/p\u003e\n\u003ch3\u003eTranscriptome assembly and annotation\u003c/h3\u003e\n\u003cp\u003eBased on the fluorescence signal matrix obtained by Illumina platform, the light intensity distribution spectrum is decoded into nucleotide sequence by base recognition algorithm (CASAVA), and the standard fastq format file is generated, and the read sequence and corresponding Qphred quality scoring system are recorded completely. In order to ensure the quality and reliability of data analysis, it is necessary to filter the original data. Contaminated reads carrying primer connectors were eliminated by double-ended sequence alignment, abnormal sequences with fuzzy base (N) ratio of more than 1% were excluded, and low confidence read segments with lower sequencing quality score than Qphred20 with base coverage\u0026thinsp;\u0026gt;\u0026thinsp;50% were filtered out. At the same time, Q20, Q30 and GC contents were calculated for clean data. All subsequent analyses are of high quality based on clean data.\u003c/p\u003e\n\u003ch3\u003eDifferential expression gene screening\u003c/h3\u003e\n\u003cp\u003eIn this experiment, DESeq2 was used to analyze the difference in expression of two genes in samples treated with different treatments (Wang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). In the detection of Differentially expressed genes, |log2FC|\u0026ge;1 and(FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)were used as screening criteria. The Fold Change represents the ratio of expression between two samples, and the False Discovery Rate (FDR) is obtained by correcting the P-value of the significance of the difference.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene enrichment analysis\u003c/h2\u003e \u003cp\u003eGO and KEGG pathway enrichment analysis was performed on differential genes. GO and KEGG enrichment analysis of differentially expressed genes was performed through clusterProfiler (3.4.4) software. The FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was selected as the threshold to identify significant enrichment of gene sets.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetabolite extraction, sequencing and analysis\u003c/h3\u003e\n\u003cp\u003e100 mg frozen tissue was pulverized under liquid nitrogen using a prechilled mortar were placed in EP tubes. 500 \u0026micro;L of 80% methanol in water was added, vortexed and shaken, and the samples were allowed to stand on an ice bath for 5 min, then centrifuged at 15000 g for 20 min at 4\u0026deg;C. A certain amount of supernatant was diluted with mass spectrometry-grade water to 53% methanol, and then centrifuged for 20 min at 15000 g for 20 min at 4\u0026deg;C, and then the supernatant was collected. A quality control sample (QC) is prepared by mixing an equal portion the supernatant of the sample, and then the QC samples were inserted into the sample queue and injected into liquid chromatography-mass spectrometry (LC-MS) for analysis. The samples were separated on a Vanquish LC ultra-high performance liquid chromatography (UHPLC) system and analysed by mass spectrometry on a Q Exactive mass spectrometer (Thermo), using electrospray ionisation (ESI) in the positive and negative ion modes, respectively. Six biological replicates were analyzed to ensure metabolic profile repeatability.\u003c/p\u003e\n\u003ch3\u003eIdentification of differential metabolites\u003c/h3\u003e\n\u003cp\u003eMetabolomic data were compared using mzCloud, mzVault and Masslist databases. The intersex and intra-sex heterometabolites were screened by T-test and Foldchange (FC). The screening criteria were \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (corrected by FDR), |log2FC| \u0026gt; 1. The value of log2FC indicates the degree to which the differential metabolite is up-regulated (log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0) or down-regulated (log2FC\u0026thinsp;\u0026lt;\u0026thinsp;0). The putative identification was classified as level 2 (accurate mass matching and fragment pattern). This analytical workflow adheres to the quality control criteria outlined in the metabolomics reporting standards established by Sumner et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), specifically following their tiered classification framework for metabolite identification confidence.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eKEGG enrichment analysis of differential metabolites\u003c/h2\u003e \u003cp\u003eKEGG database was used for functional annotation of differential metabolites. The enrichment analysis of differential metabolites is based on the R language MetaboAnalystR package, and KEGG pathway enrichment was performed according to the annotation results set analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKEGG co-enrichment analysis of transcriptome and metabolome\u003c/h2\u003e \u003cp\u003eDuring KEGG annotation, transcriptome and metabolome were annotated to multiple metabolic pathways at the same time. The screening criteria were |log2FC| \u0026gt; 1, the transcriptome pathway \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and the combination pathway \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for analysis, and the relevant metabolic pathways were screened out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData statistical analyses including independent samples test and one-way ANOVA with the least significant difference (LSD) test were performed using excel 2010 and SPSS 22.0, Spearman method was used for correlation analysis, and graphical analysis was executed using Origin 2021 and CorelDRAW X8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe translocation factor of cabbage\u003c/h2\u003e \u003cp\u003eThe translocation factor (TF) in cabbage were calculated based on Cd concentration under Cd stress in the respective organs as follows (An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:TF=\\frac{Cd\\:content\\:in\\:leaves}{Cd\\:content\\:in\\:roots}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLeaves and roots Cd accumulation and translocation regulation in cabbage\u003c/h2\u003e \u003cp\u003eExogenous Zn / Mn / ZnMn supplementation significantly regulated Cd distributions in cabbage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with significant impacts on both tissue accumulation and translocation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Under Cd stress (CK), the Cd content and the translocation factor (TF) in the leaves and roots of cabbage had different degrees of change. Compared with CK, the Zn\u0026thinsp;+\u0026thinsp;Cd treatment in the leaves and roots had the lowest Cd content, which decreased by 57.81% and 89.59%, respectively. In the second instance, Mn\u0026thinsp;+\u0026thinsp;Cd treatment reduced the Cd content of the leaves and roots by 55.87% and 61.44%, respectively. Finally, Zn\u0026thinsp;+\u0026thinsp;Mn\u0026thinsp;+\u0026thinsp;Cd treatment reduced the Cd content of the leaves and roots by 31.42% and 85.86%, respectively. Compared with CK, the Zn\u0026thinsp;+\u0026thinsp;Mn\u0026thinsp;+\u0026thinsp;Cd treatment resulted in the lowest Cd TF from the roots to the leaves, with a reduction of 79.37%. Additionally, under Zn\u0026thinsp;+\u0026thinsp;Cd treatment, the TF of Cd was low, decreasing by 75.32%. Finally, the Cd\u0026thinsp;+\u0026thinsp;Zn\u0026thinsp;+\u0026thinsp;Mn treatment led to a 64.85% reduction in the TF of Cd. Notably, Zn supplementation showed superior Cd mitigation capacity compared to combined ZnMn application (leaves: 57.81% vs 31.42%; roots: 89.59% vs 85.86%), while Mn supplementation showed intermediate efficacy. This classification pattern (Zn\u0026thinsp;\u0026gt;\u0026thinsp;ZnMn\u0026thinsp;\u0026gt;\u0026thinsp;Mn) suggests potential antagonistic interactions during multi-nutrient treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome sequencing data quality control and screening of differential expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eWe sequenced the transcriptome of cabbage roots from Cd stress alone (CK) and three comparisons (Zn-vs-CK, Mn-vs-CK, ZnMn-vs-CK), with a total of 12 samples (3 biological replicates), and the sequencing results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each sample generated 40430474\u0026ndash;62739316 raw reads, and after quality filtering of the raw reads, each sample generated 40179032\u0026ndash;62434486 clean reads. The clean bases ranged from 5.61G \u0026minus;\u0026thinsp;8.77G, Q30\u0026thinsp;\u0026ge;\u0026thinsp;96.90% and GC\u0026thinsp;\u0026ge;\u0026thinsp;45.73%. RNA-Seq data showed significant changes in the gene expression profiles of cabbage roots after exposure to Zn, Mn, and ZnMn compared to CK. Venn analysis revealed that 209 DEGs were co-regulated in three treatments, along with 600, 1134, and 1611 unique DEGs in Zn, Mn, and ZnMn treatments, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Following rigorous quality control (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1), we identified three distinct differential expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among them, we observed Zn-mediated regulation of 1158 DEGs (578\u0026uarr;/580\u0026darr;), Mn-mediated regulation of 2332 DEGs (784\u0026uarr;/1548\u0026darr;), and ZnMn regulation of 2932 DEGs (829\u0026uarr;/2103\u0026darr;). Notably, the ZnMn co-treatment induced the most DEGs, 153% more than Zn treatment and 26% more than Mn treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRNA-seq results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw bases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClean bases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGC\u003csub\u003e\u0026minus;\u003c/sub\u003epct\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44471236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44188812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.21G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43544426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.53G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43286202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.07G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53626960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.04G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53285418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.43G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58756664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.81G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58437354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.24G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62739316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.41G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62434486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.77G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e45.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53676566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.05G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53373716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.45G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44253686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.64G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43992354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.09G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55753668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.36G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55403554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.68G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40430474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.06G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40179032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.61G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZnMn1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57105138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.57G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56833982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.99G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZnMn2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55412320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.31G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55152068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.73G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZnMn3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60654002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.10G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60334884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.47G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment of DEGs in cabbage roots, GO enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo determine the functional significance of transcriptional changes in cabbage roots under Zn, Mn, and ZnMn treatments, GO classification was implemented for the DEGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Expression analysis revealed distinct regulatory patterns (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Zn treatment specifically up-regulated DEG categories among the top 20 GO terms, which included plant-type cell wall (GO:0009505), β-glucosidase activity (GO:0008422), response to toxic substances (GO:0009636), response to reactive oxygen species (GO:0000302), hydrolase activity hydrolyzing O-glycosyl compounds (GO:0004553), hydrolase activity acting on glycosyl bonds (GO:0016798), plant-type cell wall organization or biogenesis (GO:0071669), and response to high light intensity (GO:0009644). Mn treatment specifically up-regulated DEG categories among the top 20 GO terms, which included glucosinolate metabolic processes (GO:0019760) and hydrolase activity hydrolyzing O-glycosyl compounds (GO:0004553). Notably, ZnMn co-treatment exhibited a divergent enrichment profile, no significant up-regulation of DEGs was observed in the top 20 GO terms. Instead, the up-regulated DEGs were predominantly enriched in the integral component of the plasma membrane (GO:0005887) in all GO enrichment terms. The above results suggested that these biological processes serve as a core adaptive mechanism to alleviate Cd stress.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional enrichment of DEGs in cabbage roots, KEGG pathway classification of DEGs.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis was also performed to further understand the biological meanings of all the DEGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F). The Zn-vs-CK comparison up-regulated DEGs in the top 14 KEGG pathways were enriched in phenylpropanoid biosynthesis (brp00940), tryptophan metabolism (brp00380), and protein processing in the endoplasmic reticulum (brp04141). The Mn-vs-CK comparison up-regulated DEGs in the top 14 KEGG pathways were enriched in tryptophan metabolism (brp00380) and photosynthesis (brp00195). The ZnMn-vs-CK comparison up-regulated DEGs in the top 20 KEGG pathways were enriched in circadian rhythm - plant (brp04712). In conclusion, Zn treatment induced the most substantial up-regulation of DEGs across metabolic pathways, followed by Mn treatment showing intermediate activation. Notably, ZnMn co-treatment demonstrated a progressive reduction in pathway enrichment magnitude, exhibiting the least transcriptional activation in KEGG-annotated biological systems. Quantitative distribution of DEGs across metabolic pathways was systematically presented in Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The up-regulated genes were involved in key reactions of these pathways, providing insight into the mechanisms of Cd tolerance under Zn, Mn, and ZnMn treatments in cabbage roots.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMetabolome sequencing data quality control and screening of differential expressed metabolites (DAMs)\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003cp\u003eQualitative and quantitative analyses were conducted through positive (POS) and negative (NEG) ion modes to enhance analytical robustness. The overall distribution of differential metabolites in each comparison group is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) demonstrated distinct metabolic separation between treatment groups, with tight intra-group clustering and clear inter-group segregation (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D), confirming data repeatability. Differential abundance analysis identified 242 (124\u0026uarr;/118\u0026darr;), 357 (168\u0026uarr;/189\u0026darr;), and 316 (175\u0026uarr;/141\u0026darr;) significantly altered metabolites in the Zn-vs-CK, Mn-vs-CK, and ZnMn-vs-CK comparisons, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Venn analysis revealed that 437 DAMs were co-regulated across the three treatments, along with 44, 63, and 54 unique DAMs in the Zn, Mn, and ZnMn treatments, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment of DAMs in cabbage roots, KEGG pathway classification of DAMs\u003c/h2\u003e \u003cp\u003eKEGG pathway enrichment analysis was performed to identify differentially enriched metabolic pathways among the three experimental treatments following the single Cd exposure (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). In the Zn-vs-CK comparison, 49 metabolites mapped to 9 significantly enriched pathways (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the significantly up-regulated DAMs include ether lipid metabolism (ko00565) and biosynthesis of various plant secondary metabolites (ko00565). The Mn-vs-CK comparison exhibited 69 metabolites associated with 14 pathways (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the significantly up-regulated DAMs include glycerophospholipid metabolism (ko00564), steroid hormone biosynthesis (ko00140), arachidonic acid metabolism (ko00590), and ether lipid metabolism (ko00565). ZnMn-vs-CK analysis identified 68 metabolites across 8 enriched pathways (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the significantly up-regulated DAMs include glycerophospholipid metabolism (ko00564), arginine and proline metabolism (ko00330), and ether lipid metabolism (ko00565).\u003c/p\u003e \u003cp\u003eNotably, while the three comparisons exhibited distinct enriched pathway profiles, all shared significant enrichment in fundamental metabolic processes and secondary metabolite biosynthesis (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A conserved pattern emerged in plant secondary metabolism and lipid metabolism-related pathways, showing consistent enrichment across all treatments. This observation prompted detailed investigation into plant secondary metabolism and lipid metabolism-related pathways and their associated differentially abundant metabolites. To sum up, Zn treatment regulated lipid metabolism and affected plant secondary metabolism, Mn treatment completely enhanced lipid metabolism, and ZnMn treatment activated lipid metabolism and affected amino acid metabolism. Pathway interrogation demonstrated that all treatments modulated lipid metabolism, which exhibited cascading effects on interconnected biological pathways (Supplementary Figure S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of transcriptome and metabolome\u003c/h2\u003e \u003cp\u003eBased on the expression levels of genes and metabolites, the Spearman correlation algorithm was used to generate a heat map illustrating their relationships. The integrated correlation analysis of transcriptome and metabolome revealed complex interaction networks, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C. The heatmaps demonstrated reciprocal multi-association patterns, such as where individual genes could coordinate with multiple metabolites and vice versa. The results indicated that Bra029761, Bra000964, and Bra018649 genes exhibited a strong positive correlation, whereas Bra038734 displayed a strong negative correlation with Bra018589 and Bra023377 genes. M757T111 exhibited a weak correlation with most other metabolites in the Zn-vs-CK comparison (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Bra006793 and Bra040972 genes were in the same cluster, showing relatively consistent expression patterns, while M235T379 and M297T175 metabolites showed significant differences in expression levels between clusters in the Mn-vs-CK comparison (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Bra031485, Bra031287, and Bra019824 showed a high positive correlation with other genes, while ENSRNA049459692 showed a weak correlation with other genes. M349T110, M160T423, and M457T28 were weakly correlated with most other metabolites in the ZnMn-vs-CK comparison (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eNine-quadrant analytical plots (|r| \u0026gt; 0.80) systematically characterized these relationships across all comparisons (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F). Quadrants 3 and 7 contained positively correlated pairs (r\u0026thinsp;\u0026gt;\u0026thinsp;0.80), while quadrants 1 and 9 displayed negative correlations (r \u0026lt; -0.80). Central quadrant 5 showed non-significant interactions, whereas quadrants 2 and 8 exhibited stable gene expression with differential metabolite accumulation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, quadrants 4 and 6 demonstrated transcriptional variations without corresponding metabolite-level changes. To identify core regulatory nodes, we established high-stringency correlation networks (|r| \u0026gt; 0.90). These networks integrated the top 10 metabolites with functionally significant genes (|log2FC| \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FPKM\u0026thinsp;\u0026gt;\u0026thinsp;10) across experimental groups (Supplementary Figure S4; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The correlation analysis suggests that these genes may play a direct or indirect regulatory role in the metabolism of key metabolites throughout the entire developmental period of cabbage\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eKEGG Co-Enrichment analysis of transcriptome and metabolome\u003c/h2\u003e \u003cp\u003eAn integrated pathway analysis of transcriptomic and metabolomic profiles was conducted to elucidate the regulatory mechanisms underlying Zn, Mn, and ZnMn interactions under Cd stress in cabbage roots. After rigorous quality control (|log2FC| \u0026gt; 1), we identified 130 shared metabolic pathways across all comparisons of KEGG co-enrichment analyses (supplemental table S3). Notably, each treatment exhibited distinct pathway activation patterns with two significance thresholds (transcriptome \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; combination \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Zn-vs-CK comparison revealed five prioritized pathways: Fatty acid elongation (C00062), Cutin, suberine, and wax biosynthesis (C00073), Phenylpropanoid biosynthesis (C00940), Valine, leucine, and isoleucine degradation (C00280), and Pentose and glucuronate interconversions (C00040). Mn-vs-CK analysis identified six core pathways: Biosynthesis of nucleotide sugars (C01250), Tryptophan metabolism (C00380), Glyoxylate and dicarboxylate metabolism (C00630), Glutathione metabolism (C00480), Flavonoid biosynthesis (C00941), and Amino sugar and nucleotide sugar metabolism (C00520). The ZnMn-vs-CK group demonstrated enhanced metabolic complexity, with twelve significantly enriched pathways: Glutathione metabolism (C00480), Glucosinolate biosynthesis (C00966), Fatty acid elongation (C00062), Biosynthesis of various plant secondary metabolites (C00999), Stilbenoid, diarylheptanoid, and gingerol biosynthesis (C00945), Sulfur metabolism (C00920), Tryptophan metabolism (C00380), Cysteine and methionine metabolism (C00270), Phenylalanine, tyrosine, and tryptophan biosynthesis (C00400), Zeatin biosynthesis (C00908), Flavonoid biosynthesis (C00941), and Tropane, piperidine, and pyridine alkaloid biosynthesis (C00960). This multi-omics integration highlights the functions and regulatory mechanisms of biomolecules, particularly in exogenous detoxification and specialized metabolite synthesis pathways, suggesting coordinated molecular responses for Cd stress mitigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe persistence of Cd in agricultural soils poses significant ecological risks, owing to its high mobility and tendency for phytoaccumulation. As the primary interface for heavy metal uptake, plant root systems mediate Cd absorption through shared transport pathways for divalent cations, particularly via Fe\u003csup\u003e2+\u003c/sup\u003e, Mn\u003csup\u003e2+\u003c/sup\u003e, and Zn\u003csup\u003e2+\u003c/sup\u003e transporters (Zhou et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our findings demonstrate that Zn/Mn supplementation effectively decreased Cd accumulation and translocation in cabbage, with the Zn\u0026thinsp;+\u0026thinsp;Cd treatment achieving the lowest root and leaf Cd concentrations, and the ZnMn\u0026thinsp;+\u0026thinsp;Cd combination showing minimal translocation efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This mitigation effect likely stems from competitive inhibition at shared cation uptake channels, where Zn\u003csup\u003e2+\u003c/sup\u003e / Mn\u003csup\u003e2+\u003c/sup\u003e ions interfere with Cd\u003csup\u003e2+\u003c/sup\u003e absorption through similarities in ionic radius and charge parity (Fang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The observed root-to-leaf Cd translocation reduction aligns with the dual-phase metal exclusion mechanism: primary restriction at rhizospheric uptake followed by secondary limitation in xylem loading (Uraguchi et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our results corroborate previous findings across multiple species: foliar Zn application reduced rice grain Cd (Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while increasing Zn\u003csup\u003e2+\u003c/sup\u003e concentrations reduced Cd uptake in rice roots through OsZIP transporter competition (Fahad et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, Mn supplementation decreased wheat grain Cd by 46% via enhanced Mn-SOD activity and vacuolar Cd sequestration (Huang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, Zn showed better Cd fixation efficiency in plant tissues, mainly through enhancing the synthesis and crosslinking of cell wall pectin, thus significantly improving the binding ability of Cd\u003csup\u003e2+\u003c/sup\u003e, while Mn-induced chloroplast protection mechanisms in lettuce (\u003cem\u003eLactuca sp.\u003c/em\u003e) suggest that our observed effects may involve both transport competition and potentiation of the antioxidant system (Ramos et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These collective findings establish Zn / Mn application as a promising plant management strategy, leveraging essential micronutrient supplementation to disrupt Cd entry routes.\u003c/p\u003e \u003cp\u003eTranscriptomic profiling demonstrated its effectiveness in elucidating the transcriptional regulatory networks underlying Cd stress adaptation in plants, while functional annotation analysis further delineated treatment-specific biological processes through differential GO enrichment patterns (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The GO annotation analysis revealed that Zn preferentially up-regulated cell wall biosynthesis genes, Mn induced hydrolase activity-related transcripts, and ZnMn co-treatment enhanced genes associated with plasma membrane integrity.\u003c/p\u003e \u003cp\u003eZn-induced cell wall reinforcement supports its role as the primary Cd exclusion barrier, with pectin methylesterases and lignin biosynthesis enzymes likely enhancing Cd immobilization through polysaccharide crosslinking and metal-ligand complexation (Loix et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mn-mediated hydrolase activation (e.g., proteases, lipases) may facilitate metabolic reprogramming, especially in seed germination-related pathways (e.g., α-amylase, β-glucosidase), supporting the role of hydrolases in heavy metal detoxification through protein turnover and toxic metabolite degradation (Zhang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). ZnMn co-treatment induces lipid metabolism genes and alters phosphatidylcholine composition to achieve plasma membrane specialization and enhance membrane stability. This is a key adaptation for maintaining ion homeostasis under Cd stress (Yao et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, the enhanced membrane stability resulting from ZnMn co-treatment is due to Zn-facilitated Cd\u003csup\u003e2+\u003c/sup\u003e sequestration in plant cell walls through enhanced pectin cross-linking and lipid membrane structure, thereby reducing Cd\u003csup\u003e2+\u003c/sup\u003e transmembrane absorption efficiency (Chande et al. 2010; Kučerka et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mn blocks the transmembrane transport of Cd by activating the cellular Mn transporter and enzymatic activity associated with lipid synthesis, thereby promoting the synthesis of phospholipids and cuticle in the cell membrane (Salom\u0026eacute; et al. 2017; Wei et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, these transcriptional divergences suggest treatment-specific Cd tolerance mechanisms: Zn treatment results in the highest number of up-regulated DEGs in GO terms, particularly enhancing extracellular isolation and effectively reducing Cd uptake by cabbage roots. Mn treatment has relatively few up-regulated DEGs, primarily regulating intracellular protein balance and enhancing intracellular Cd differentiation. In contrast, ZnMn treatment down-regulates DEGs in the top 20 GO terms, suggesting that ZnMn co-treatment may negatively affect cabbage root growth. These findings advance our understanding of micronutrient-mediated Cd mitigation strategies.\u003c/p\u003e \u003cp\u003eThe KEGG annotation results demonstrated that the DEGs were involved in various biological pathways. In this paper, several pathways were selected for further analysis based on the number of enriched genes, aiming to understand potential Cd tolerance mechanisms in cabbage roots (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The results revealed that DEGs were predominantly enriched in three core pathways: phenylpropanoid metabolism (Zn treatment), tryptophan metabolism (Mn treatment), and circadian rhythm regulation (ZnMn co-treatment), with each pathway demonstrating distinct mechanisms of Cd tolerance.\u003c/p\u003e \u003cp\u003eThe phenylpropanoid metabolism pathway, a critical plant defense system, confers resistance to abiotic stresses, including salinity, drought, and heavy metal toxicity, through the biosynthesis of protective metabolites (Yadav et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Downstream products such as lignins, flavonoids, and procyanidins enhance metal tolerance via chelation, antioxidant activity, and structural reinforcement (Ge et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, transcriptomic analysis of Zn-treated cabbage roots revealed pronounced up-regulation of peroxidase (POD) and lignin biosynthesis genes (Supplementary Figure S5A). POD is an oxidative enzyme widely found in plants; it mediates xylem lignification, redox homeostasis, and stress adaptation through the catalytic degradation of reactive oxygen species (Barcel\u0026oacute; et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Lignins, dominant components of secondary cell walls, immobilize heavy metals via carboxyl and phenolic functional groups, effectively restricting cytoplasmic Cd entry (Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This aligns with our prior findings of Zn-induced cell wall-related gene activation, suggesting Zn stimulates endogenous antioxidant systems to mitigate oxidative damage by reducing ROS accumulation (Maleva et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The observed lignin-mediated cell wall compartmentalization may be an important Cd detoxification mechanism in both the roots and leaves of cabbage.\u003c/p\u003e \u003cp\u003eTryptophan biosynthesis in plants is linked to growth and adaptive mechanisms, which synthesize indole-derived secondary metabolites essential for defense and environmental interaction. These include indole-3-acetic acid (IAA), serotonin, and melatonin, which have a wide range of functions in higher plants, including physiological processes such as seed germination, root growth and development, senescence, flowering, or fruit ripening, as well as in the response mechanisms against biotic and abiotic stresses (Corpas et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, Mn treatment specifically enriched IAA biosynthesis genes in cabbage roots (Supplementary Figure S5B), potentially elevating indolepyruvate, indoleacetate, and other related metabolites levels. IAA is considered a heavy metal bioadsorption regulator, capable of restoring plant growth. It enhances primary metabolite synthesis and antioxidant capacity while inhibiting Cd uptake, possibly through hemicellulose-1-mediated cell wall modification (Piotrowska-Niczyporuk et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This mechanism may explain the reduced root Cd absorption observed in Mn-treated plants.\u003c/p\u003e \u003cp\u003ePlants can integrate environmental signals, including temperature and light, and coordinate various physiological processes, such as photoperiodic flowering, hormone signaling, growth, metabolism, and responses to biological and abiotic stresses. Circadian rhythm is a biological rhythm influenced by many external factors, including abiotic stress (Venkat et al. 2022). In this study, ZnMn co-treatment significantly activated the circadian rhythm pathway, up-regulating CDF1 (cycling DOF factors), a unique DOF-family transcription factor that coordinates stress responses (Supplementary Figure S5C). CDF1 protein is a distinct transcription factor in the plant DOF family, playing an important role in plant growth and development. CDF transcription factors are involved in inducing the expression of stress-response genes, accumulating osmotic regulatory substances, and maintaining the carbon/nitrogen balance and endogenous hormone balance. They also play a crucial role in responding to abiotic stress and improving stress tolerance in crops (Fornara et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Corrales et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Renau-Morata et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, Zn treatment triggered phenylpropanoid biosynthesis, while Mn treatment enhanced tryptophan metabolism. ZnMn co-treatment activated the circadian rhythm-related detoxification metabolic response in cabbage under Cd stress. This may be because, under Cd stress, phenylpropanoids in plants indirectly influence the oscillatory expression of genes associated with the biological clock by modulating ROS homeostasis and hormone signaling. This, in turn, establishes a bidirectional regulatory network between metabolism and circadian rhythms (Lv et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, indoles derived from tryptophan metabolism interact with circadian clock components via jasmonic acid signaling, promoting the night-specific activation of defense genes and, consequently, regulating the expression of photoperiodic response genes (Qin et al. 2024). These results indicate that the pathways enriched in KEGG may play a crucial role in maintaining normal conditions in plants under Cd stress.\u003c/p\u003e \u003cp\u003ePlants produce structurally diverse specialized metabolites, including bioactive alkaloids and terpenoids, in response to biotic and abiotic environmental stresses (Shoji et al. 2021). In this study, Zn, Mn, and ZnMn co-treatments had significant effects on lipid-related metabolism, plant secondary metabolism, and the amino acid metabolism pathways. These results suggest that the above metabolites play an important role in the Cd stress response, which will be the focus of our research.\u003c/p\u003e \u003cp\u003eLipids are not only an important structural component of the cell membrane and the surface of plant tissues and organs, but also a physiologically active substance and signal molecule, playing a crucial role in plant growth, development, and stress response (Kim \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the results of the KEGG enrichment analysis of DAMs revealed significant enrichment in the fatty acid elongation pathway in three comparisons, with notable effects on lipids and lipid-like molecules (C00157, C04230, C01233, C00670), organic nitrogen compounds (C00588), and nucleosides, nucleotides, and analogues (C00307). Some studies have pointed out that increased lipid metabolism directly contributed to maintaining the stability of the cell membrane and reducing cell damage under Cd stress (Feng et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Organic nitrogen compounds, such as phosphocholine, can participate in the synthesis of phospholipids and may promote lipid metabolism in plants (Ulch et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). CDP-choline, a nucleoside, nucleotide, and analogue, is the precursor of phosphatidylcholine and acetylcholine, key components of the cell membrane. It participates in the formation of the phospholipid bilayer of the cell membrane, regulating membrane fluidity and protecting against oxidative damage to the membrane (Weiss, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Therefore, we believe that Zn, Mn, and ZnMn treatments enhance Cd tolerance in cabbage by maintaining normal lipid synthesis and metabolism, thereby improving cabbage's Cd tolerance.\u003c/p\u003e \u003cp\u003eIn plants, the secondary metabolic pathways produce a diversity of compounds called plant secondary metabolites (PSMs); these compounds contain a large group of structurally diverse molecules originating from either primary metabolites or intermediates in the biosynthetic pathways of these primary metabolites (Piasecka et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Plant secondary metabolites play a variety of functions, such as in plant growth and developmental processes, innate immunity (Piasecka et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), defense response signaling (Isah, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and response to environmental stresses (Yang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The results of this study revealed that Zn treatment significantly up-regulated the biosynthesis of a variety of plant secondary metabolites, mainly phenylpropanoids, polyketides (C01864), and organic acids and derivatives (C00073). Studies have found that Zn can induce the up-regulation of key enzymes in the phenylpropanoid metabolic pathway and promote polymerization of lignin monomers. Meanwhile, Zn can increase the content of flavonoids in plant cells and enhance their complexation with Cd\u003csup\u003e2+\u003c/sup\u003e, thus enhancing the cell wall's fixation of Cd\u003csup\u003e2+\u003c/sup\u003e (Marreiro et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zn may reduce cytoplasmic free Cd\u003csup\u003e2+\u003c/sup\u003e concentration by promoting the synthesis of organic acids in plants, specifically chelating Cd\u003csup\u003e2+\u003c/sup\u003e and transporting it to the vacuole for isolation (Song et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, we believed that plant secondary metabolites are involved in stabilizing cabbage roots during Cd stress and that maintaining normal plant secondary metabolism is an important way to improve the Cd tolerance of cabbage.\u003c/p\u003e \u003cp\u003eAmino acids have a variety of prominent functions in plants, not only as precursors for protein biosynthesis but also as participants in a large number of cellular reactions, influencing many physiological processes, and playing a key role in signal transduction processes as well as plant stress responses (Hildebrandt et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The results of this study revealed that ZnMn co-treatment significantly up-regulated the biosynthesis of a variety of organic acids and derivatives (C00791 and C02714) and organic nitrogen compounds (C00179). Some studies have indicated that plants, when exposed to metals, typically synthesize a variety of low-molecular-weight compounds, particularly specific free amino acids (FAAs), which are recognized as compatible solutes (Mahdavian et al. 2023). These FAAs have been shown to act as signaling molecules and play a crucial role in plant physiology (Dinkeloo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notably, the levels of FAAs significantly increase in response to both abiotic and biotic stressors, positioning them as reliable biomarkers of stress (Zhu et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). From this, we inferred that the regulation of amino acid metabolism is considered the main strategy for plant protection and survival under adverse living conditions. Moreover, the ZnMn co-treatment promotes amino acid metabolism in cabbage roots, which suggests that the treatment may have adverse effects on the roots.\u003c/p\u003e \u003cp\u003eBased on the above, Zn treatment regulated plant secondary metabolism to alleviate Cd toxicity, Mn treatment primarily improved lipid metabolism, which helped to decrease Cd stress, while ZnMn co-treatment may disturb amino acid metabolism in plants under Cd stress. The changes in the metabolism levels mentioned above play a crucial role in the mechanism by which plants adapt to Cd stress.\u003c/p\u003e \u003cp\u003eMetabolomic and transcriptomic analyses were integrated to elucidate coordinated pathway interactions between DEGs and DAMs in cabbage under Cd stress.\u003c/p\u003e \u003cp\u003eTo investigate the associations between the metabolome and transcriptome, the KEGG pathways enriched by both were integrated (Du et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, DEGs and DAMs were mostly enriched in metabolic pathways, including amino acid metabolism, lipid metabolism, nucleotide metabolism, metabolism of terpenoids and polyketides, and metabolism of cofactors and vitamins. Based on KEGG co-enrichment analysis, the fatty acid elongation pathways were significantly enriched in the Zn-vs-CK and ZnMn-vs-CK comparisons. The tryptophan metabolism, glutathione metabolism, and flavonoid biosynthesis pathways were significantly enriched in the Mn-vs-CK and ZnMn-vs-CK comparisons.\u003c/p\u003e \u003cp\u003eLipid metabolism includes systemic biochemical reactions such as fat synthesis, decomposition, and transport. It is the core metabolic pathway for maintaining cell energy homeostasis. The fatty acid elongation pathway plays an important role in lipid metabolism and cell function, converting nutrients into metabolic intermediates for membrane biosynthesis, energy storage, and signaling molecule generation (Rohrig et al. 2016; Xie et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our results further suggest that the enrichment of key biosynthetic genes and metabolites involved in the lipid metabolism pathway under Zn and ZnMn co-treatments may play an important role in cabbage's response to Cd stress.\u003c/p\u003e \u003cp\u003eTryptophan, the sole indole-containing amino acid, modulates plant stress tolerance through its derivatives, including auxins and melatonin, which regulate oxidative balance and metabolic adaptation (Xue et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The activation of the glutathione metabolism pathway serves as a conserved detoxification mechanism in plants under Cd stress. Glutathione effectively reduces the transport of Cd from the roots to the shoots and enhances the resistance of plants to Cd (Nakamura et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Flavonoids are major secondary metabolites derived from the phenylpropane pathway in plants and play an important role in plant development, affecting basic physiological metabolism, as well as stress and disease resistance (Wang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). These findings suggest that Mn treatment and ZnMn co-treatment are closely related to Cd tolerance in Chinese cabbage. Tryptophan may alleviate Cd stress in cabbage roots by balancing IAA levels. Glutathione maintains intracellular homeostasis by scavenging reactive oxygen species in Chinese cabbage roots, while flavonoid accumulation enhances antioxidant capacity and directly removes Cd-induced ROS.\u003c/p\u003e \u003cp\u003eTherefore, lipids, tryptophan, glutathione, and flavonols play a vital role in Zn, Mn, and ZnMn treatments under Cd stress. In future studies, the identification of key genes and the verification of potential up-regulated differential metabolites will be the focus of our research, which will contribute to a deeper understanding of Cd tolerance and accumulation mechanisms in cabbage.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we explored the regulatory mechanism of Cd accumulation in cabbage roots through the addition of Zn, Mn, and ZnMn co-treatment. The results showed significant differences in Cd enrichment levels among treatment groups, with the strength of intermetallic antagonism being Zn\u0026thinsp;\u0026gt;\u0026thinsp;ZnMn\u0026thinsp;\u0026gt;\u0026thinsp;Mn. Multi-omics analysis revealed that Zn treatment significantly up-regulated DEGs, activated cell wall and phenylpropanoid biosynthesis, and regulated secondary metabolism to alleviate Cd stress. Mn treatment regulated intracellular protein balance, enhanced tryptophan metabolism, and promoted lipid metabolism to improve metabolic detoxification efficiency. Notably, although ZnMn co-treatment effectively attenuated Cd accumulation in cabbage roots, it concurrently triggered adverse physiological consequences, including down-regulation of DEGs and disrupted amino acid metabolism. We postulate that this may be because ZnMn treatment specifically activates or inhibits the expression of certain ion transport-related genes, which need further investigation. This study may provide valuable insights into the mechanisms of Cd inhibition in agricultural systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Calcium-dependent mechanism for the formation and maintenance of karst biodiversity and its application basis, National Natural Science Foundation of China-Guizhou Provincial Government Joint Program, Grant No. U1812401.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData availability\u003c/b\u003e The datasets generated or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e \u003cp\u003e The authors declare no conflict of interest related to this study.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the support of NSFC to Guizhou Normal University, Department of Life science, funded by the National Natural Science of University and Research, Project U1812401.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAuthors contributions\u003c/b\u003e WYL contributed to the conceptualization, methodology, data reduction, prepared the figures, original manuscript writing, and writing, reviewing, and editing. 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Physiol 167:1027-1032. https://doi.org/10.1016/j.jplph.2010.02.011\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":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cd accumulation, Plant roots, Lipid, Molecular mechanisms, Multi-omics","lastPublishedDoi":"10.21203/rs.3.rs-6088882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6088882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and Aims Zinc (Zn) and manganese (Mn), as essential micronutrients, exhibit competitive antagonism against cadmium (Cd) through cation transporter competition. The effects of Cd stress and Zn / Mn nutrition on plant growth, development, physiological characteristics, and gene expression in some crops have been widely studied, but the molecular mechanism by which Zn / Mn alleviates Cd toxicity in the roots of cabbage at the transcriptomic and metabolomic levels remains unclear.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMethods\u003c/em\u003e The response of cabbage roots to Cd stress under Zn, Mn and ZnMn treatment were evaluated in an experiment with cabbage roots. The content of Cd was determined by ICP-MS. Roots transcriptome sequencing was performed on the Illumina platform, with differential expression genes (DEGs) analyzed using DESeq2. Root metabolites were analyzed via LC-MS, with metabolite data processed using MetaboAnalystR package.\u003c/p\u003e \u003cp\u003e \u003cem\u003eResults\u003c/em\u003e Zn treatment exhibited the strongest inhibition of Cd, primarily by up-regulating genes involved in cell wall synthesis, phenylpropanoid biosynthesis, and secondary metabolite production. Mn treatment had the weakest effect on Cd inhibition, mainly regulating hydrolase activity, tryptophan metabolism, and lipid metabolism to reduce Cd absorption in cabbage roots. ZnMn co-treatment showed a lower Cd inhibition rate than Zn, but it down-regulated numerous genes and disrupted amino acid metabolism, suggesting that while it reduces Cd content and may harms plant physiological functions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eConclusion\u003c/em\u003e This study highlights the potential of micronutrients resist Cd stress in crops, particularly in the leafy vegetables. Based on Cd reduction and plant physiological safety, we believe that Zn treatment is better than Mn treatment, and better than ZnMn co-treatment.\u003c/p\u003e","manuscriptTitle":"Zinc and Manganese Impact on Cabbage (Brassica rapa) Cadmium Tolerance: Comparative Transcriptomic and Metabolomic Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 03:08:51","doi":"10.21203/rs.3.rs-6088882/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-23T03:19:41+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T20:20:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T03:26:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-04-13T04:53:46+00:00","index":"","fulltext":""},{"type":"decision","content":"Accept but incomplete","date":"2025-03-29T07:55:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0ac61c11-0afd-49cc-9c1d-390c031ee9ca","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T15:59:28+00:00","versionOfRecord":{"articleIdentity":"rs-6088882","link":"https://doi.org/10.1007/s11104-025-07594-1","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2025-07-02 15:56:57","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-24 03:08:51","video":"","vorDoi":"10.1007/s11104-025-07594-1","vorDoiUrl":"https://doi.org/10.1007/s11104-025-07594-1","workflowStages":[]},"version":"v1","identity":"rs-6088882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6088882","identity":"rs-6088882","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}