Integration of metabolomics and transcriptomics analyses reveals the effects of nano-selenium on pak choi | Scientific Reports

Scientific Reports volume 15, Article number: 11215 (2025) Cite this article Metrics details Selenium is an indispensable nutrient for plants, and optimizing selenium levels can enhance plant growth

Scientific Reports volume 15, Article number: 11215 (2025) Cite this article

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Selenium is an indispensable nutrient for plants, and optimizing selenium levels can enhance plant growth and metabolism, leading to improved yield and quality. In comparison to conventional inorganic or organic selenium fertilizers, nano-selenium demonstrates superior safety and enhanced biological activity, making it more suitable for crop production. Although nano-selenium fertilizer is extensively used in various crops, its application in pak choi remains limited. As a vital source of selenium, previous research on pak choi (Brassica chinensis var. pekinensis cv. 'Suzhouqing') has primarily focused on investigating physiological effects with limited exploration of the molecular mechanism. Therefore, this study aims to investigate the impact of nano-selenium on pak choi through an integrated analysis of transcriptome and metabolome. Specifically, we examined the effects of different concentrations of nano-selenium (0, 5, 10 and 20 mg L−1) on the growth and nutritional quality of Suzhouqing. The findings revealed that a low concentration (5 mg L−1) of nano-selenium significantly increased leaf weight and total selenium content, while modulating primary metabolites such as soluble amino acids, proteins, sugars and ascorbic acid. Additionally, it influenced secondary metabolites including glucosinolates, phenolic acids and flavonoids. Consequently, this enhancement in growth performance and nutritional quality was attributed to the regulation of pathways involved in selenocompound metabolism, phenylpropanoid biosynthesis, and flavonoid biosynthesis by key enzymes such as methionine S-methyltransferase, 5-methyltetrahydrofolate-homocysteine methyltransferase, kynurenine-oxoglutarate transaminase, thioredoxin reductase, phenylalanine ammonian-lyase, 4-coumarate-CoA ligase, flavonoid 3’, 5'-hydroxylase, naringenin 3-dioxygenase, flavonol synthase and bifunctional dihydroflavonol 4-reductase. These results provide comprehensive insights into the physiological and molecular mechanisms underlying the influence of nano-selenium on plant growth and nutritional quality. Therefore, they offer a solid theoretical basis and technical support for breeding and cultivation strategies aimed at producing selenium-rich pak choi.

Selenium (Se) is an essential element in living organisms, which has been scientifically proven to possess diverse biological functions such as anti-cancer, antioxidant, anti-aging properties, and immune system enhancement1. Additionally, selenium also plays a crucial role in promoting plant photosynthesis, enhancing antioxidant capacity, participating in protein metabolism, improving fruit quality and increasing crop yield2. Research studies have demonstrated that the application of selenium fertilizer to the roots significantly increases the net photosynthetic rate, intercellular CO2 concentration, transpiration rate and stomatal conductance of plant leaves3. Plants primarily acquire selenium from soil, however, approximately 72% of regions in China suffer from low selenium levels4. Therefore, exogenously adding selenium fertilizer is an important approach for cultivating selenium-enriched agricultural products1. Currently available forms of selenium fertilizer include inorganic selenium and organic selenium as well as nano-selenium. Compared to inorganic and organic forms, nano-selenium exists as a single substance with zero valence state exhibiting remarkable characteristics such as high activity level along with non-toxicity and easy absorption properties5, making it the optimal choice for biofortification purposes involving enhanced levels of dietary selenium intake.

Suzhouqing (Brassica chinensis ssp. pekinensis) is an autotetraploid non-tuberculating variety of pak choi, characterized by its short growth cycle and high germination rate. It is extensively cultivated in both southern and northern regions of China. Research has demonstrated that appropriate selenium supplementation through biological enrichment can effectively enhance yield and quality, thereby increasing the selenium levels in the human body. Currently, the majority of studies investigating the impact of selenium on pak choi growth and nutritional quality focus solely on physiological indicators, with limited attention given to the molecular mechanisms underlying selenium’s effects. This limitation impedes a comprehensive understanding of pak choi’s physiological functions and mechanisms, as well as hindering the utilization of selenium-rich plant resources and development of corresponding agricultural products. In recent years, there has been a gradual increase in the study of the effects of selenium on plants using transcriptomic and metabolomic techniques. Guo et al.6 conducted a molecular-level analysis of Pueraria lobata (willd.) Ohwi in response to selenium stimulation and identified structural genes, phosphate transporters and sulfate transporters that may be involved in selenium metabolism. Zheng et al.7 determined 9,161 differentially expressed genes through transcriptomic analysis of beet leaves and initially screened out genes related to the beet’s response to nano-selenium. Li et al.8 analyzed the mechanism by which nano-selenium (20 mg L−1) improves the nutritional value of capsicum using metabonomics. The results revealed that activation of phenylpropane and branch chain fatty acid pathways, as well as gene expression related to capsaicin synthase, led to enhanced synthesis of capsaicin, flavonoids and total phenols. However, single omics approaches have inherent limitations as they only provide insights from a singular perspective. Transcriptomics focuses on RNA while metabolomics examines metabolites; they cannot fully reveal an organism’s overall function. Additionally, single omics analyses overlook inter-omics relationships which may result in missing crucial biological information. To systematically explore and comprehend these complex mechanisms more comprehensively, it is necessary to employ multi-omics association analysis for integrating data from multiple omics levels. This approach allows for joint investigation into biological problems at both transcriptome and metabolome levels while identifying hub genes / metabolites that contribute towards explaining macro biological phenomena extensively thereby facilitating comprehensive understanding of complex biology at systemic levels.

Therefore, in order to increase the selenium content of Suzhouqing leaves, this study sprayed different concentrations of nano-selenium (N0: 0, N1: 5 mg L−1; N2: 10 mg L−1; N3: 20 mg L−1) fertilizer on it. Through physiological index determination, metabonomics and transcriptomic analysis, the physiological and molecular mechanisms of the effects of nano-selenium on the growth and nutritional quality of Suzhouqing were investigated, thus providing theoretical basis and technical support for breeding and cultivation of selenium-rich pak choi.

As depicted in Fig. 1a, it demonstrates that the experimental group treated with nano-selenium spray exhibited an initial increase followed by a subsequent decrease in response to increasing nano-selenium concentration. Specifically, experimental groups N1 (5 mg L−1) and N2 (10 mg L−1) experienced increases of 29.00% and 38.00%, respectively, while group N3 (20 mg L−1) witnessed a slight decrease of 1.00%. The total selenium content is presented in Fig. 1b. In comparison to the control group (N0), N1, N2 and N3 showed increases. Carbohydrates are vital constituents of plant organisms, serving as essential substrates for metabolism and storage materials. As depicted in Fig. 1c, the application of nano-selenium via spraying resulted in reductions of soluble sugar content by 7.38%, 40.52%, and 3.95%, respectively, when compared to the control group.

Leaf weight (a), total selenium content (b), soluble sugar content (c), soluble protein content (d), amino acid (AA) content (e), total phenols (TP) content (f), flavonoid content (g) of Suzhouqing treated with different concentrations of nano-selenium. Note: Lowercase letters denote a significant difference between treatments (p < 0.05). FW is fresh weight.

Soluble protein plays a vital role as an osmotic regulator and nutrient, enhancing cell water retention ability while safeguarding cellular life matter and biofilm integrity. Consequently, it is frequently employed as one of the indicators for resistance screening purposes. As illustrated in Fig. 1d, N1 and N2 exhibited decreases in soluble protein content by fractions of 5.65% and 36.62% respectively, whereas N3 demonstrated an increase of 2.91%. The determination of amino acid (AA) content within plants holds significant importance when studying variations in nitrogen metabolism, absorption, transport, assimilation, as well as overall nutrient status under diverse conditions throughout different growth stages. As shown in Fig. 1e, a low concentration of nano-selenium (N1) led to a reduction in amino acid content by approximately 7.20%. Conversely, N2 experienced an increase by 12.28%, while N3 displayed a substantial rise of 84.37%. The total phenols (TP) possess the capacity to scavenge free radicals, catechol, tannic acid, and other phenolic substances present in plants, exhibiting inhibitory or toxic effects on insects and other organisms. As depicted in Fig. 1f, the application of nano-selenium resulted in a significant alteration in the total phenol content of plants: an increase of 21.38% under N1 treatment, while a decrease of 5.81% and 21.15% under N2 treatment and N3 treatment respectively. Flavonoids are secondary metabolites found in vegetables, fruits, herbs, and medicinal plants that exist either as free molecules or glycosides combined with sugars. As illustrated in Fig. 1g, N1 treatment led to an increase in flavonoid content by 14.15%, whereas N2 and N3 treatments exhibited decreases by 27.38% and 41.52% respectively.

The quality distribution of sequencing data for each sample in this study is presented in Table 1. The majority of the sequencing data exhibits a high quality, with over 80% distributed above Q30, indicating excellent quality and ensuring the smooth progress of subsequent analysis.

The expression of a gene is considered differentially expressed if it differs by more than twofold between two groups of samples. To determine whether the expression difference between the two samples is attributable to errors or essential differences, we conducted a hypothesis test on the expression data of all genes in both samples. In order to control the proportion of false positives in this hypothesis test, padj was introduced to correct the p value. In the volcano plot (Fig. 2), log10(padj) was used as the vertical axis to represent the significance level of expression differences, while the horizontal axis represented log2(Fold Change), indicating changes in gene expression multiples across different samples and visually displaying the overall distribution of genes with significant expression differences. A total of 122,425 genes exhibited significant differences between the nano-Se treatment group (N1) and N0, with 4,078 up-regulated and 4,462 down-regulated genes identified. In the N2 and N0 control groups, we detected 3,116 up-regulated genes and 4,005 down-regulated genes. Similarly, in the control group of N3 and N0, we identified 7,970 up-regulated genes and 9,934 down-regulated genes.

Volcano map of differentially expressed genes. Note: the horizontal coordinate is log2(Fold Change), that is, the logarithm of the fold change value; The ordinate is -log10(padj), which is the inverse of the logarithm of the corrected p value. Up-regulated genes are represented by red dots and down-regulated genes by blue dots.

The results of enrichment analysis encompassed all sets of differential genes, up-regulated sets of differential genes, and down-regulated sets of differential genes for each combination in comparative analyses. The Gene Ontology (GO) enrichment analysis results were mainly divided into three parts: biological process and cellular component molecular function. In this study, the enrichment degree of each gene was calculated, and the results of the interaction of these genes in the leaves of pak choi were shown in Fig. 3. In the N1 vs N0 group, the predominant categories encompassed the extrinsic component of endosome membrane and hydrolase activity. While in the N2 vs N0 group, prominent categories included chaperone-mediated protein complex assembly, defense response to oomycetes, fucose biosynthetic process, and monocarboxylic acid binding. In the N3 vs N0 group, significant categories comprised enoyl-[acyl-carrier-protein] reductase activity, fatty acid synthase complex, jasmonic acid and ethylene-dependent systemic resistance, as well as ethylene-mediated signaling pathway.

Cluster heat map of differentially expressed genes. Note: The light yellow nodes in the figure are the GO term annotated by differentially expressed genes, and the gray nodes are differentially expressed genes. A line between a gene and a GO term indicates that the gene is annotated to the GO term. Different colored lines between nodes represent lines emanating from different GO terms.

The findings from the KEGG enrichment bubble diagram are presented in Fig. 4. It displays the top 20 pathway entries with the most notable enrichment. In the N1 vs N0 group, the major KEGG pathways encompass carbon metabolism, cofactor biosynthesis, amino acid biosynthesis, spliceosome regulation, mRNA surveillance pathway activation, and amino sugar and nucleotide sugar metabolism. In the N2 vs N0 group, additional pathways such as cysteine and methionine metabolism, glycolysis/gluconeogenesis regulation, and selenocompound metabolism are observed. Furthermore, in the N3 vs N0 group, fatty acid metabolism along with sulfur metabolism and arginine biosynthesis are also implicated.

Scatter plot of KEGG pathway enrichment for differentially expressed genes. Note: The vertical axis represents the pathway name, and the horizontal axis represents the GeneRatio corresponding to the pathway. The size of p.adjust is represented by the color of the dots. The smaller p.adjust is, the closer the color is to red.

For Weighted Gene Co-expression Network Analysis (WGCNA), we constructed a dendrogram based on the correlation of inter-gene expression levels and subsequently partitioned the identified modules. The corresponding results are presented in Fig. 5. Each color in Fig. 5 represents a specific module of genes within the cluster tree, indicating their functional relationship based on similar expression changes observed in physiological processes or different tissues. The vertical distance between nodes (genes) in the upper part of the tree signifies their dissimilarity, while the horizontal distance holds no significance.

Module level clustering tree.

Figure 6 depicts the expression patterns of module eigenvalues in different samples, with sample names displayed along the horizontal axis. Additionally, through analysis of a bar chart below, it becomes evident which samples generally exhibit high gene expression within this particular module. Since there are 3 replicates per treatment, there are 12 samples in total. As you can see from the figure, N23, N02, N32, and N13 have a higher eigengene expression. The key gene contained methionine S-methyltransferase, thioredoxin reductase, phenylalanine and histidine ammonia-lyase, 4-coumarate-CoA ligase and bifunctional dihydroflavonol 4-reductase.

Modular gene expression patterns. Note: The three repetitions of the first set of experiments are represented by N11, N12 and N13, while the three repetitions of the second set of experiments are represented by N21, N22 and N23. Similarly, the three repetitions of the third set of experiments are represented by N31, N32 and N33.

In order to assess the impact of metabolite changes between groups on Suzhouqing, we quantified the magnitude of these changes by calculating the Fold Change (FC) in conjunction with p values and identified key metabolites. The grouping information table utilizes the previous group as a reference to determine the fold change relative to both the mean of another group and the mean of the reference group. Positive values indicate an increase in fold change, while negative values indicate a decrease. As depicted in Fig. 7, metabolites within the yellow region exhibit a p value less than 0.05 and an absolute fold change greater than 2. These metabolites demonstrate significant differences among groups and substantial alterations, warranting further attention: physodic acid, arenobifagin, leupeptin and mundulone acetate.

Volcano map of multiple changes. Note: Each point represents a metabolite, the horizontal coordinate is the change multiple, the vertical coordinate is the T-test p value, the larger the change multiple, the smaller the p value (log10(p) is higher), the larger the point.

The metabolic pathways exhibited notable enrichment of differential metabolites: biosynthesis of amino acids, carbon metabolism, amino sugar and nucleotide sugar metabolism, selenocompound metabolism and pyruvate metabolism, suggesting their potential importance in the studied biological processes.

Heat maps can aggregate large amounts of data and visually show correlations between genes and metabolites. The heat map results of transcriptome and metabolome correlation are shown in Fig. 8.

Heat map of transcriptome and metabolome correlation. rows represent metabolites and columns represent genes. By default, Spearman method was used to calculate the correlation of each gene and metabolite, and 30 genes and metabolites were selected by default and the genes were represented by rows and the metabolites were represented by columns. In the heat map, different colors represent the phase relationship value R, red represents the positive correlation between gene expression and metabolite abundance, and blue represents the negative phase. Saturation represents the strength of the correlation, and the more saturated the color, the stronger the correlation. Associations with statistically significant differences are marked with an asterisk. If p ≤ 0.05, it is *; if p ≤ 0.01, it is **. Note: Cluster-12275.43424: Oligosaccharyltransferase, gamma subunit, Cluster-12275.44333: Phosphatidylinositol-4-phosphate 5-kinase and related FYVE finger-containing proteins, Cluster-12275.47312: Poly(A)-specific exoribonuclease PARN, Cluster-12275.83936: Serine/threonine protein kinase, Cluster-12275.50078: Calmodulin and related proteins (EF-Hand superfamily), Cluster-12275.42980: Translation initiation factor 1 (eIF-1/SUI1), Cluster-12275.67018: Serine/threonine protein kinase, Cluster-12275.45617: RRM motif-containing protein, Cluster-12275.45140: Acetylglutamate kinase/acetylglutamate synthase, Cluster-12275.42117: FKBP-type peptidyl-prolyl cis–trans isomerase, Cluster-12275.46179: 1,4-benzoquinone reductase-like; Trp repressor binding protein-like/protoplast-secreted protein, Cluster-12275.43416: Molecular chaperones HSP70/HSC70, HSP70 superfamily, Cluster-12275.49236: Molecular chaperones HSP70/HSC70, HSP70 superfamily, Cluster-12275.42540: Molecular chaperones HSP70/HSC70, HSP70 superfamily, Cluster-12275.48707: Molecular chaperones HSP70/HSC70, HSP70 superfamily, Cluster-12275.70079: Molecular chaperones HSP70/HSC70, HSP70 superfamily, Cluster-12275.43723: Predicted hydrolase related to dienelactone hydrolase, Cluster-12275.49152: Aquaporin (major intrinsic protein family), Cluster-12275.43755: START domain-containing proteins involved in steroidogenesis/phosphatidylcholine transfer, Cluster-12275.44580: WD40 repeat-containing protein, Cluster-12275.47844: DRIM (Down-regulated in metastasis)-like proteins, Cluster-12275.43469: S-adenosylmethionine synthetase, Cluster-12275.42615: Glyceraldehyde 3-phosphate dehydrogenase, Cluster-12275.49789: Plasma membrane H + -transporting ATPase, Cluster-12275.40121: Plasma membrane H + -transporting ATPase, POS_2319: (2E,4E)-N-(2-Methylpropyl)dodeca-2,4-dienamide; POS_3307: (2R)-1-(5-Hydroxy-3-methylpentyl)-2,5,5,8a-tetramethyldecahydro-2-naphthalenol; POS_5261: (2R,3S,4S,5R,6S)-2-(hydroxymethyl)-6-[4-(hydroxymethyl)-1-propan-2-ylcyclohex-3-en-1-yl]oxyoxane-3,4,5-triol; POS_6817: 6,7-Dimethoxy-8-(beta-D-glucopyranosyloxy)-2H-1-benzopyran-2-one; POS_7332: Nobiletin; POS_7099: 6-[3-[(3,4-Dimethoxyphenyl)methyl]-4-methoxy-2-(methoxymethyl)butyl]-4-methoxy-1,3-benzodioxole; POS_3539: 2-Monomyristin; POS_5431: 5,7,3'-Trihydroxy-6,4',5'-trimethoxyflavone; POS_7923: Oxaline; POS_5940: Digitoxigenin; POS_3415: Roquefortine A; POS_4203: Gelsenicine; POS_7646: (5-Benzoyloxy-4,6-dihydroxy-3-methoxycyclohexen-1-yl)methyl benzoate; POS_8708: 1-Pentadecanoyl-sn-glycero-3-phosphocholine; POS_9067: Mundulone acetate; POS_3069: Octadecanamide; POS_1011: Pyridoxine; POS_1992: 4-(2-Ethyl-6-methylphenyl)-5-methylmorpholin-3-one; POS_3750: Avocadene 2-acetate; POS_15346: Isobutyryl CoA; POS_639: Dioctyl phthalate.

The co-enrichment analysis in transcriptome and metabolome association integrates gene expression data with metabolite change data to identify gene sets and metabolic pathways that are co-enriched in specific biological processes, thereby elucidating the intrinsic relationship between gene expression and metabolic regulation, and providing valuable insights into the complexity and functionality of biological systems.

The KEGG pathways with significant co-enrichment analysis are shown in Fig. 9. In the N1 vs N0 group, the comparison between water spray and nano-selenium resulted in differential genes and metabolites that shared nine common metabolic pathways, including cofactor biosynthesis, amino acid biosynthesis, microbial metabolism in diverse environments, secondary metabolite biosynthesis, flavone and flavonol biosynthesis, selenocompound metabolism, phenylpropanoid biosynthesis, and aminoacyl-tRNA biosynthesis (Fig. 9a). In the N2 vs N0 group, the differential genes exhibited five common metabolic pathways (Fig. 9b), which included carbon metabolism, amino acid biosynthesis, amino sugar and nucleotide sugar metabolism, selenocompound metabolism, and pyruvate metabolism. The N3 vs N0 group showed seven common metabolic pathways for both differential genes and metabolites (Fig. 9c), including biosynthesis of amino acids, purine metabolism, carbon metabolism, selenocompound metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis and pyruvate metabolism.

KEGG enrichment histogram of differential genes and metabolites in Suzhouqing.

The KEGG pathway of selenocompound metabolism in plants is a complex process encompassing selenium absorption, conversion, utilization and excretion. Selenium primarily enters the plant through its roots and subsequently undergoes cellular conversion into selenoproteins or other active forms to engage in physiological activities such as antioxidant defense and regulation of plant growth. These selenium compounds are metabolically transformed and distributed within plants via specific pathways, with some selenium potentially being released into the atmosphere as volatile selenium or excreted through root secretions. The detailed depiction provided by the KEGG pathway map serves as an essential reference for further investigation into the mechanisms underlying selenium metabolism in plants (Fig. 10). Phenylpropanoid biosynthesis serves as a preliminary step in flavonoid biosynthesis, with both pathways interconnected through shared intermediates. Specifically, the phenylpropane pathway commences with phenylalanine and undergoes a series of enzymatic reactions to generate crucial intermediates, including coumaryl coenzyme A. These intermediates subsequently enter the flavonoid pathway as precursors for synthesizing various flavonoid compounds such as flavonoids, flavonols, isoflavones, and anthocyanins. Henceforth, phenylpropane biosynthesis not only supplies essential raw materials for flavonoid biosynthesis but also exerts diverse physiological functions in plants via its metabolites by enhancing stress resistance and promoting growth and development. Simultaneously, as an extension of the phenylpropane pathway, flavonoid biosynthesis further diversifies plant secondary metabolites’ species and functionalities which significantly contribute to plant adaptation to environmental changes and human health.

The main metabolic pathways in the leaves of Suzhouqing leaf treated with different concentrations of nano-selenium. Note: The heat map visualizes the magnitude of values through varying color intensities or shades, thereby providing an intuitive representation of gene and metabolite expression levels.

In summary, the metabolites of the phenylpropanoid biosynthesis pathway, including lignin, flavonoids, and phenolic compounds, exert significant effects on the overall physiological state of plant leaves and indirectly regulate the levels of soluble sugars, soluble proteins, and amino acids. The phenylpropane biosynthesis pathway is one of the fundamental pathways for synthesizing phenolic substances by converting phenylalanine into coumaric acid through a series of enzymatic reactions. Thus, the activity of this pathway directly determines the content of phenolic substances in plant leaves. Moreover, precise regulation of key enzymes and genes in the flavonoid metabolic pathway can significantly alter the flavonoid content in plant leaves. Additionally, selenocompound metabolism directly influences selenium levels in leaves and further enhances chemical composition diversity as shown in Fig. 1.

The cruciferous vegetable Suzhouqing, widely cultivated in China, has previously demonstrated significant potential for alleviating selenium deficiency in humans. This study reveals that the application of an appropriate concentration of nano-selenium on the leaf surface can significantly enhance both the yield and selenium content of it. The data presented in Fig. 1 demonstrated that the concentration of the spray solution significantly influenced the physiological and biochemical parameters of pak choi. The results not only indicated the potential for promoting growth and enhancing quality but also provided a foundation for optimizing the spraying strategy. Specifically, at concentrations of 10 mg L−1 and 20 mg L−1, there was a significant increase in leaf weight, suggesting that the spray promoted plant growth. However, growth decelerated at 20 mg L−1, indicating that higher concentrations may induce stress. Moreover, the successful elevation of total selenium content in the spray solution is critical for developing selenium-enriched agricultural products. Notably, the increase in selenium content is non-linear, necessitating the identification of an optimal concentration to balance selenium enrichment with plant health. Additionally, the spray solution affected the levels of soluble sugar, soluble protein, amino acids, total phenols, and flavonoids, thereby regulating plant metabolism and improving overall quality. These changes reflect the plant’s response to varying spray concentrations and provide valuable insights for further research. These findings align with previous research conducted on tomato9, Hang cabbage10, ginger11 and broccoli12. The consistent positive outcomes observed across various crops, collectively support the notion that nano-selenium represents an effective approach to improving crop productivity and nutritional value. The absorption of nano-selenium by pak choi surpasses that of inorganic selenium and organic selenium, resulting in a higher selenium content in pak choi13. The metabolic processes and regulatory mechanisms of selenium in plants constitute a complex network of gene expression. Various intermediates produced during the selenium metabolism pathway may play a role in modulating plant growth and development. The metabolism of selenium compounds initiates with the conversion of selenocysteine by cystathionine gamma-synthase (CGS), leading to the liberation of selenocysteine. The resultant compound undergoes further metabolism by kynurenine-oxoglutarate transaminase (CBL), culminating in the release of selenium homocysteine. This intermediate then reacts with 5-methyltetrahydrofolate-homocysteine methyltransferase (MET) to yield selenium-methionine. Selenomethionine can subsequently be metabolized into either selenomethionine-tRNA or methylselenol. Additionally, methylselenol may also arise from the reaction between methylselenic acid and thioredoxin reductase or through the CTH-mediated transformation of selenomethylselenocysteine. In selenocompound metabolism, the following genes are up-regulated: methionine S-methyltransferase, MET, CBL, thioredoxin reductase (NADPH), 3'-phosphoadenosine 5’-phosphosulfate synthase (PAPSS).

Some studies have demonstrated that the enhanced application of selenium fertilizer can effectively enhance crop quality. For instance, Zhang et al.14 revealed that foliar spraying of selenium fertilizer could augment the levels of soluble solids, soluble sugars, soluble proteins, vitamin C, and flesh firmness in grape fruits while reducing titratable acidity and tannin content, thereby improving fruit quality. Song et al.15 discovered that leaf surface application of nano-selenium fertilizer could elevate the levels of soluble solids and vitamin C content in Jujube jujube. Fan et al.16 demonstrated a significant increase in total organic acid content, total soluble sugar content, vitamin C content, and sugar-acid ratio in tomato fruits following the application of 0.5 ~ 2 mg kg−1 sodium selenite and sodium selenate to the soil. Wu and Zhang17 exhibited an elevation in organic acid content and vitamin C with the utilization of selenium fertilizer. In this study, low concentrations of nano-selenium were found to enhance the accumulation of soluble sugars, soluble proteins, free amino acids and ascorbic acid in Suzhouqing. Soluble sugars act as cell osmotic regulators while ascorbic acid serves as a potent antioxidant, both playing crucial roles in maintaining plant tolerance to environmental stress. Soluble proteins and free amino acids are essential components of numerous enzymes involved in various metabolic processes. Moreover, the accumulation of primary metabolites also provides necessary precursors for secondary metabolite synthesis. Additionally, compared to the control group, spraying nano-selenium fertilizer increased flavonoid content in this study. Flavonoids are widely accumulated secondary metabolites in vascular plants that play significant functions in plant physiology, ecology, growth, and development18. The main steps involved in flavonoid synthesis within the phenylpropane biosynthesis pathway include hydroxylation by chalcone synthase (CHS), isomerization by chalcone isomerase (CHI), and hydroxylation by naringenin 3-dioxygenase (F3H) from p-coumaryl-CoA and malonyl-CoA; bifunctional dihydroflavonol 4-reductase (DFR) catalyzes their conversion into colorless anthocyanidins; leucoanthocyanidin dioxygenase/anthocyanidin synthase (LDOX/ANS) converts colorless dihydroflavonols into colored anthocyanidins; UDP glucoside-3-O-glycosyltransferase (UFGT) facilitates the formation of stable anthocyanins from unstable ones.

In this study, as illustrated in Fig. 10, the expression of key enzyme genes involved in the synthetic pathway was influenced by nano-selenium. Specifically, genes such as phenylalanine ammonia-lyase (PAL), flavonoid 3’,5'-hydroxylase (CYP73A), 4-coumarate-CoA ligase (4CL), flavanone 3-hydroxylase (F3H), flavonol synthase (FLS), and dihydroflavonol 4-reductase (DFR) exhibited altered expression levels. Following treatment with exogenous nano-selenium, a significant increase in transcription levels was observed compared to the control group (Fig. 2). Additionally, metabolomics analysis, as shown in Fig. 7, revealed a substantial accumulation of important phenolic acids and flavonoid metabolites, including coniferin, eugenin, kaempferol, and quercetin. Overall, selenium’s metabolic mechanism appears to be closely associated with phenylpropanoid metabolic pathways. According to Xu et al.19 research in 2019 on viticulture leaf spraying with a selenium concentration of 50 mg L−1 can enhance antioxidant contents like proanthocyanidins, total flavonoids, total phenols and vitamin C, resulting in an improved antioxidant capacity. It has been reported that a low concentration of Na2SeO3 (≤ 8 μM) can enhance the growth of purple lettuce, elevate the levels of anthocyanin, ascorbic acid, and soluble sugar, and stimulate photosynthesis in purple lettuce. Further investigations have revealed that the F3H and UFGT genes play a pivotal role in the process of anthocyanin accumulation in purple lettuce, with their up-regulated expression levels potentially being the primary reason for selenium-induced enhancement of anthocyanin accumulation. Selenium inhibits anthocyanin degradation by positively regulating the F3H and UFGT genes, thereby maintaining stable levels of anthocyanins. Deng et al.20 treated ginkgo biloba leaves with biologically synthesized nanometer-scale selenium and observed that 1.6 mmol L−1 biological nanometer selenium significantly promotes primary metabolite synthesis in ginkgo biloba leaves while further enhancing flavonoid metabolite synthesis. According to the reported research findings, selenium can modulate key enzyme genes and transcription factors in the flavonoid synthesis pathway, thereby influencing the biosynthesis of flavonoid metabolites. The optimal concentration of selenium plays a crucial role in promoting the synthesis of flavonoids.

In this study, the effects of various concentrations of nano-selenium on the growth and nutritional quality of Suzhouqing were investigated. The findings indicated that a low concentration (5 mg L−1) of nano-selenium increased leaf weight and total selenium content while regulating primary metabolites (soluble amino acids, soluble proteins, soluble sugars and ascorbic acid) and secondary metabolites (glucosinolate, phenolic acids and flavonoids), thereby enhancing plant growth and nutritional quality. Furthermore, combined metabolomic and transcriptomic analysis identified key pathways involved in selenium absorption/assimilation (methionine S-methyltransferase, MET, CBL and NADPH), biosynthesis amino acids, as well as flavonoid/phenolic acid biosynthesis (PAL, 4CL, CYP73A, F3H, FLS and DFR). The findings elucidate the physiological and molecular mechanisms underlying the impact of nano-selenium on the growth and nutritional quality of Suzhouqing. They systematically unveil the intricate interplay between selenocompound metabolism, biosynthesis of amino acids and flavonoid biosynthesis in response to selenium, thereby offering a novel avenue for comprehending the gene regulatory network.

The experimental material was Suzhouqing, and the seeds were purchased from Jiangxi Ruibao Seed Co., LTD. (Jiujiang, Jiangxi Province, China). The breeding process of Suzhouqing is as follows: The maternal Ogu-CMS0173 is a male cytoplasmic sterile small rice line cultivated from light leaf germplasm of Suzhouqing. The male parent 9,960 is a dark green-leaved inbred line with large tree shape obtained through extensive breeding in Suzhouqing. Nano-selenium refers to tiny red selenium nanoparticles ranging in size from 20 to 60 nm developed by the Zhongnong Institute of Selenium-Enriched Agricultural Technology in Beijing, China. The growth medium used in this study was organotrophic soil whose physicochemical properties were described in detail by Wang et al.21,22. Experimental seedlings were planted on February 6th, 2023 in a solar greenhouse at Jiangxi Agricultural University. On February 22nd, the plants were transplanted into plastic culture pots measuring 33 × 24.4 × 13.5 cm with two plants per pot. Three selenium concentrations (N1: 5 mg L−1; N2: 10 mg L−1; N3: 20 mg L−1) and a water control group (N0) were randomly assigned and repeated three times. On March 1st, a total of 20 mL of nano-selenium solution or an equal amount of water was evenly sprayed on both sides of the leaves for the first application without dripping off the blade surface. The second application took place on March 11th when the seedlings had grown six leaves. On March 21st, the third fertilization was carried out. Finally, on March 31st, samples were collected, bagged, labeled and stored at – 80 °C for subsequent determination of physiological indicators.

The soluble sugar content was determined using the method described by Buysse and Merckx23. Briefly, 0.10 g of leaf sample was mixed with 1 mL of 80% ethanol and heated in a water bath at 80 °C for 30 min. This process was repeated twice, and the combined supernatants were used as the extraction solution. Then, a volume of 50 μL of extract was diluted by a factor of 40 and mixed with 5 mL of anthrone sulfuric acid reagent. After boiling for 10 min, the mixture was cooled to room temperature before measuring its absorbance at a wavelength of 620 nm. Finally, the total soluble sugar content was determined based on a standard curve.

The BCA assay, involving two reactions, was used to determine the soluble protein content24. In the first reaction, copper ion complex formed amide bonds which reduced copper in an alkaline solution. Subsequently, the BCA reagent primarily reduced by copper–amide bond complex as well as tyrosine and tryptophan residues.

The amino acid (AA) content determination followed Chen et al.'s25 method. Leaves (0.10 g) were extracted with distilled water (10 mL) in boiling water bath for 45 min, and resulting supernatant (1 mL) mixed with ninhydrin reagent (2%, 0.5 mL) pH = 8 phosphate buffer solution (0.5 mL). After heating reaction mixture in boiling-water bath for15 min cooling it to room temperature additional volume distilled water added before measuring absorbance at wavelength 562 nm using MetashUV-5200 UV–vis spectrophotometer.

The total selenium content was determined by hydride generation atomic fluorescence spectrometry (HG-AFS) as follows26: 0.1 g of leaves were weighed and placed in a digestion vessel made of polytetrafluoroethylene. They were soaked overnight with 5 mL of nitric acid. The vessel was then covered with the inner lid and placed in a constant temperature drying oven at 80 °C for 1–2 h, followed by another 1–2 h at 120 °C. Subsequently, the temperature was raised to and maintained at 160 °C for 4 h before naturally cooling to room temperature. After opening, the sample was heated on an electric heating plate while adding a solution of hydrochloric acid (6 mol L−1) containing potassium ferricyanide (100 g L−1). The resulting mixture was transferred into a volumetric bottle with a capacity of 25 mL and diluted with pure water until reaching volume.

We utilized a plant flavonoid assay kit (Suzhou Michy Biomedical Technology Co., Ltd) to determine the flavonoid content in the leaves. The method of Xia et al.27 and Jia et al.28 was also used in this experiment to determine flavonoid content. The method for quantifying flavonoids was as follows: the sample was dried until a constant weight is achieved, crushed, and sieved through a 40 mesh sieve. Approximately 0.05 g of the sample was then weighed and mixed with 1 mL of 60% ethanol at room temperature. The mixture was homogenized and subsequently extracted by shaking at 60 °C for 2 h. After centrifugation at 10,000g and 25 °C for 10 min, the supernatant was collected. Prior to measurement, the ELISA instrument was preheated for 30 min and the wavelength adjusted to 510 nm. In a 96-well plate, we combined an aliquot of the supernatant (80 µL) with an equal volume of 60% ethanol (80 µL), mixed thoroughly, allowed it to stand at room temperature for 15 min, and measured the absorbance value at 510 nm. Total phenol content in leaves was measured with a plant total phenol (TP) kit (Suzhou Michy Biomedical Technology Co., Ltd). The determination method for total phenols (TP) content remained consistent with that described by Song et al.29. The same procedure used for measuring flavonoids in preparing supernatant could be applied when measuring total phenols. Before measurement, preheat the ELISA instrument for 30 min and adjust the wavelength to 765 nm. Then, we added 10 µL of supernatant and 10 µL of 60% ethanol into a 96-wellplate, mixed well, and allowed it to stand at room temperature for 10 min. Finally, we determined the absorbance value at 765 nm.

The transcriptome sequencing was described in detail by Wang et al.21,22. The general analysis process is as follows: Firstly, the sequenced sequences were spliced into transcripts. Subsequently, hierarchical clustering was performed on the transcripts using the Corset program. Further analysis was conducted based on the clustered sequences, encompassing quality control analysis, database annotation, gene expression level analysis, differential significance analysis, and functional enrichment through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analysis of gene co-expression network. We studied the metabolome of the sample using an LC–MS/MS-based approach. Leaf tissue (100 mg) was ground in liquid nitrogen, and the resulting homogenate was re-suspended by vigorous vortexing in pre-cooled 80% methanol. The samples were incubated on ice for 5 min, followed by centrifugation at 4 °C for 20 min at 15,000×g. A portion of the supernatant was diluted with LC–MS grade water to achieve a final concentration of 53% methanol. The samples were then transferred to fresh Eppendorf tubes and subjected to another centrifugation at 4 °C for 20 min at 15,000×g. The supernatant was subsequently injected into the LC–MS/MS system for analysis. The UHPLC-MS/MS analysis was conducted using the Vanquish UHPLC system (ThermoFisher Scientific, Germany) coupled with either the Orbitrap Q ExactiveTM HF or Orbitrap Q ExactiveTM HF-X mass spectrometers (ThermoFisher Scientific, Germany). Samples were injected onto a Hypersil Gold column (100 × 2.1 mm, 1.9 μm particle size) at a flow rate of 0.2 mL/min with a linear gradient over 12 min. In both positive and negative polarity modes, the mobile phases consisted of eluent A (0.1% formic acid in water) and eluent B (methanol). The solvent gradient was set as follows: 2% B for 1.5 min, increasing to 85% B over 3 min. The QExactiveTM HF mass spectrometer operated in both positive and negative polarity modes with the following settings: spray voltage 3.5 kV, capillary temperature 320 °C, sheath gas flow 35 psi, auxiliary gas flow 10 L/min, S-lens RF level 60, and auxiliary gas heater temperature 350 °C. Proteowizard software (v3.0.8789) was used to convert the obtained raw data into mzXML format. The XCMS package of R (v3.1.3) was used for peak identification, filtration, and alignment. The data matrix including mass-to-charge ratio, retention time, and peak area (intensity) was obtained. Then the precursor molecules in positive and negative ion modes were acquired, and the data were exported to Excel for subsequent analysis. The analysis process is based on the R language MetaboAnalystR package30. The process of transcriptome and metabolome association analysis includes enrichment analysis using KEGG, co-enrichment analysis utilizing p value combination methods, as well as visualization through KEGG coloring path diagrams.

Data is available on BIG Sub website (https://ngdc.cncb.ac.cn/gsub/submit/bioproject/list). This is the information in detail about it. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA021253) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

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The authors would like to thank all the reviewers who participated in the review. This study was supported by the Fund project of the Natural Science Foundation of China (No. 42167045), Natural Science Foundation of Jiangxi Province (No. 20224BAB215033), Jiangxi Province "science and technology + water conservancy" joint plan project (No. 2023KSG01004), Jiangxi Provincial Department of Education Science and echnology research project (No. GJJ210402 and GJJ210452).

Fund project of the Natural Science Foundation of China (No. 42167045), Natural Science Foundation of Jiangxi Province (No. 20224BAB215033), Jiangxi Province "science and technology + water conservancy" joint plan project (No. 2023KSG01004), Jiangxi Provincial Department of Education Science and echnology research project (No. GJJ210402 and GJJ210452).

Jiangxi Agricultural University, 1101 Zhimin Road, Economic and Technological Development Zone, Nanchang, 330045, Jiangxi, China

Yanyan Wang, Peiheng Sun, Mingying Nie, Jianyun Zhan, Liu Huang, Junda Wu, Xiaowu He, Na Li, Longsong Hu, Shiyu Liu, Chengfu Yuan, Changming Zhou, Guangjie Chen, Jialong Huang & Xiaofei Li

Academy of Water Science and Engineering, Nanchang, 330045, Jiangxi, China

Jie Zhang

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(1) Conceptualization, He, X.W.; Zhou, C.M. ; Chen, G.J; (2) Methodology, Wang, Y.Y.; Huang, J.L.; Li X.F.; (3) Software, Liu, S.Y.; (4) Validation, He, X.W.; (5) Formal analysis, Wu, J.D.; Huang, L.; Sun, P.H.; Zhan, J.Y.; (6) Investigation, Zhang, J.; Yuan, C.F.; Hu, L.S.; and Li, N.; (7) Resources, He, X.W.; (8) Data curation, Wu, J.D.; Huang, L.; Sun, P.H.; Zhan, J.Y.; (9) Writing—original draft ,Wang, Y.Y. ; Nie, M.Y.; (10) Writing—review and editing, Liu, S.Y. ; He, X.W.

Correspondence to Xiaowu He.

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Wang, Y., Sun, P., Nie, M. et al. Integration of metabolomics and transcriptomics analyses reveals the effects of nano-selenium on pak choi. Sci Rep 15, 11215 (2025). https://doi.org/10.1038/s41598-025-95165-w

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Received: 26 September 2024

Accepted: 19 March 2025

Published: 02 April 2025

DOI: https://doi.org/10.1038/s41598-025-95165-w

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