Selenium Alleviates Manganese Toxicity in ‘Red Fuji’ Apple: Insights From Combined Physiological and Transcriptomic Approaches

ABSTRACT

Selenium (Se), a beneficial element, plays a pivotal role in mitigating plant stress, yet its potential in alleviating manganese (Mn) toxicity in apple trees remains poorly understood. This study explores the potential of Se in alleviating Mn‐induced stress in Malus domestica Borkh. cv. Red Fuji, a cultivar known for its sensitivity to Mn. Through a combination of physiological analysis and transcriptomic profiling, we provide comprehensive insights into the mechanisms by which Se enhances Mn tolerance. The results demonstrated that selenite application balanced nutrient elements, stabilized the photosynthetic system, and reduced oxidative stress. Additionally, selenite promoted cell wall remodeling and modulated hormone levels. Transcriptome analysis further revealed that selenite regulated genes associated with polyunsaturated fatty acid hydrolase, sucrose hydrolase, and photosynthesis, indicating its role in alleviating Mn stress. GO and KEGG enrichment analyses highlighted that differentially expressed genes were enriched in pathways related to phenylpropanoid biosynthesis, alpha‐linolenic acid metabolism, and galactose metabolism, suggesting key metabolic pathways involved in Mn stress responses. Weighted gene co‐expression network analysis (WGCNA) identified core module genes strongly correlated with antioxidant enzymes and substances, chlorophyll content, and mineral element concentrations. In conclusion, Se effectively mitigates Mn stress in ‘Fuji’ apples through multi‐level regulatory mechanisms, providing a theoretical foundation for the application of Se‐enriched agriculture in apple cultivation.

1. Introduction

Manganese (Mn) is an essential trace element for plant growth, and its active form, Mn²⁺, plays an important role in the growth and development of plants (Hou et al. 2022). In acidic soil environments, excessive Mn can be toxic, affecting the photosynthetic system, causing oxidative damage, metabolic disorders, and a series of other changes (Zemunik et al. 2020). This leads to leaf chlorosis, wilting, and inhibition of root growth, further impairing plant growth (Pan et al. 2024). Therefore, how to effectively mitigate the negative effects of Mn stress has become one of the key issues in improving crop stress resistance.

Selenium (Se) is an essential beneficial nutrient for plants (Li et al. 2022), involved in various important biochemical reactions and serving as a catalyst and cofactor for enzymes. As a key abiotic stress‐alleviating element, Se enhances plant stress tolerance by eliminating excess reactive oxygen species, reducing oxidative membrane damage, protecting photosynthetic function, and maintaining mineral nutrient homeostasis. It also restricts the absorption and translocation of toxic heavy metals, thereby effectively improving plant adaptability to adverse environments (Akhter et al. 2026). Previous studies have shown that Se can alleviate various biotic and abiotic stresses in plants (Mushtaq et al. 2023), including drought stress (Han et al. 2022), salt stress (Subramanyam et al. 2019), and low‐temperature stress (Huang et al. 2018). Currently, research on Se in alleviating heavy metal stress mainly focuses on elements such as cadmium (Cd), lead (Pb), and mercury (Hg) by enhancing the antioxidant capacity, improving photosynthesis, regulating mineral element imbalances, and increasing glutathione (GSH) content. Although Se has been studied for its alleviating effect on Mn stress in wheat (Sieprawska et al. 2021), research on Se's role in alleviating Mn stress in other species, especially apple trees, is relatively scarce, and its mechanisms remain largely unexplored. Transcriptome analysis, an effective tool for elucidating plant stress response mechanisms, plays a crucial role in revealing gene expression regulatory networks. However, transcriptomic investigations into the role of Se in mitigating abiotic stress in apple remain scarce.

Apple is an important economic crop among fruit trees. ‘Fuji’, as a widely planted and popular variety, holds a significant position in the global market (Liu et al. 2024). However, the cultivation of ‘Fuji’ apples faces numerous challenges, particularly its sensitivity to Mn stress. This study aims to explore the role of Se in alleviating Mn stress in ‘Fuji’ apples, systematically assessing hormone content, antioxidant enzyme activity, non‐enzymatic antioxidant content, element content, and transcriptomic changes, with the goal of revealing the mechanisms underlying Se's alleviation of Mn stress. Additionally, this study seeks to provide novel theoretical insights into enhancing the resistance of ‘Fuji’ apples to Mn stress, while offering practical guidance on the application of selenium in apple cultivation.

2. Materials and Methods

2.1. Treatment of ‘Fuji’ Seedlings

In this study, ‘Fuji’ apple ( Malus domestica ‘Fuji’) tissue culture seedlings were used as the experimental material. All materials were cultured in a temperature‐controlled tissue culture room, with the growth conditions maintained at 25°C. For hydroponic experimentation, it is recommended to select healthy, uniformly developed tissue‐cultured seedlings with well‐established root systems. Four treatment groups were set up for the experiment: the control group used 1/2 MS basic nutrient solution; treatment groups included 1/2 MS nutrient solution supplemented with 0.5 mM MnSO₄ (Mn group), 0.05 μM Na₂SeO₃ (Se group), and 0.5 mM MnSO₄ + 0.05 μM Na₂SeO₃ (Se + Mn group). The nutrient solution was changed every 3 days to ensure stable nutrient supply. Seedlings were cultured in a controlled climate chamber with the following environmental conditions: light cycle of 12 h light/12 h dark, relative humidity 80%, and temperature maintained at 25°C ± 2°C. After 24 h of treatment, three seedlings from each group were collected as biological replicates, immediately frozen in liquid nitrogen, and stored in an ultra‐low temperature freezer at −80°C for subsequent transcriptomic sequencing analysis. When visible stress symptoms appeared (at day 7), three independent biological replicates were established per treatment. Each replicate was created by pooling 50 leaves of the same developmental stage, randomly selected from multiple plants within the same treatment group. The leaves from each biological replicate were ground separately into a fine powder under liquid nitrogen. Following this, a 0.5 g aliquot of this homogeneous powder was used for each physiological assay.

2.2. Measurements of Element Content

Freshly collected ‘Fuji’ apple seedling samples were thoroughly rinsed with deionized water and gently dried with sterile filter paper. The samples were then placed in a 60°C constant‐temperature oven for drying for 48 h until a constant weight was reached, to determine the dry weight of the samples. The dried plant material was ground, passed through a 2 mm nylon sieve, and homogenized into a fine powder. The elemental content of the samples was quantified using an Inductively Coupled Plasma Mass Spectrometer (ICP‐MS). Each sample was measured in triplicates to ensure the reliability of the experimental data.

2.3. Measurement of Photosynthetic Pigment Content and Fluorescence Parameters

The determination of chlorophyll and carotenoid content followed the method established by Bao and Leng (2005). Chlorophyll fluorescence parameters were measured on the same day the experiment was completed. Fresh leaf samples from each treatment group were collected, dark‐adapted for 30 min, and then fluorescence parameters were measured using a portable chlorophyll fluorescence meter (FluorCam system, Qingdao Ecological Technology Co. Ltd.). At least three biological replicates were measured per treatment group to ensure the reliability of the data.

2.4. Assessment of Antioxidant Biomarkers

Physiological indicators including glutathione reductase (GR; Catalog No. A062‐1‐1), oxidized glutathione (GSSG; Catalog No. A061‐2‐1), total soluble protein (TP; Catalog No. A045‐2‐2), total amino acids (T‐AA; Catalog No. A026‐1‐1), ascorbic acid (AsA; Catalog No. A009‐1‐1), and others were measured using reagent kits provided by the Nanjing Jiancheng Bioengineering Institute. Superoxide dismutase (SOD) activity was measured using the nitroblue tetrazolium (NBT) photoreduction method (Qu et al. 2024), peroxidase (POD) activity was determined by the method of Maehly (Maehly and Chance 1954), and catalase (CAT) activity was measured using the ammonium molybdate method (Weydert and Cullen 2009). The thiobarbituric acid (TBA) method was employed to determine the content of malondialdehyde (MDA) and soluble sugars (Tan et al. 2025). The relative conductivity method was utilized to assess the extent of cell membrane damage by immersing plant leaves in deionized water, disrupting the cells through boiling, and measuring the changes in conductivity before and after treatment (Tan et al. 2025). The hydroxylamine oxidation method was used to quantify the content of superoxide anions (O₂ ⁻; Tan et al. 2025).

2.5. Hormone Content Measurements

The hormone content is determined by the enzyme‐linked immunosorbent assay (ELISA). Briefly, a 0.5 g sample was homogenized in liquid nitrogen, and the hormones were extracted by 80% cold methanol and 1 mmol l⁻¹ butylated hydroxytoluene overnight at 4°C. The homogenate was centrifuged at 10000 g for 10 min at 4°C, after which the supernatant was passed through a C 18 Sep‐Pak cartridge (Waters). Then, the supernatant was dried in N₂ and re‐dissolved in PBS (0.01 mol l⁻¹, pH 7.4) for endogenous hormone analysis by ELISA. The coating antigens and mouse monoclonal antibodies against the hormones were produced by the Phytohormones Research Institute (China Agricultural University). The levels of endogenous BR, IAA, ZR, GA₃, GA₄, JA‐ME, ABA, DHZR and IPA were quantified using standard curves. All samples were evaluated in three biological replicates.

2.6. Measurement of Cell Wall Component Content

Cell wall extraction was performed according to the method described by Du et al. (2024). The components of the cell wall were determined using standard chemical analysis methods: pectin content was measured by the carbazole colorimetric method following anhydrous ethanol extraction (Melton and Smith 2001); lignin content was determined using the acetyl bromide method (Pan et al. 2012), with color development following neutralization with sodium hydroxide and reaction with hydroxylamine hydrochloride; cellulose content was assessed by the anthrone colorimetric method after derivatization with methyl trifluoroacetate (Chen et al. 2009); hemicellulose content was quantified by the ferric chloride–phloroglucinol colorimetric method after hydrolysis with concentrated sulfuric acid (Melton and Smith 2001). All measurements were conducted in triplicates.

2.7. Transcriptome Sequencing

Total RNA was extracted from frozen leaf powder using a plant RNA extraction kit (Vazyme) according to the manufacturer's instructions. RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer, with all samples showing an OD260/280 ratio between 1.8 and 2.2. RNA integrity was further validated by agarose gel electrophoresis, and the RNA Quality Number (RQN) was measured using an Agilent 5300 system. Only samples with an RQN greater than 6.5 were selected for further processing.

For library preparation, 1 μg of total RNA was used per sample. mRNA was enriched using oligo(dT) beads and fragmented to approximately 300 bp. First‐strand cDNA synthesis was carried out with random hexamer primers, followed by second‐strand synthesis. The double‐stranded cDNA was then end‐repaired, adenylated, and ligated with Illumina adapters. The ligated products were purified, size‐selected, and PCR‐amplified to generate the final libraries. These libraries were quantified and sequenced on an Illumina NovaSeq 6000 platform (Majorbio), generating 150 bp paired‐end reads.

The raw sequencing data were processed using SeqPrep and Sickle software to remove adapter sequences and low‐quality reads, yielding high‐quality clean reads. These clean reads were aligned to the apple reference genome (Malus × domestica HFTH1 Genome v1.0) using HISAT2 software. Transcript abundance was normalized and expressed as transcripts per million (TPM). To ensure the reliability and reproducibility of the data, each treatment group was sequenced with three independent biological replicates, where each replicate represented RNA isolated from a separate set of seedlings.

2.8. RNA Extraction and RT‐qPCR Analysis

Total RNA was isolated from ‘Fuji’ apple leaves and reverse transcribed into first‐strand cDNA using commercial kits from Nanjing Novozan Biotech. Quantitative real‐time PCR (qPCR) was conducted on an ABI 7500 system (Applied Biosystems) with Taq Pro Universal SYBR qPCR Master Mix (Vazyme). Reactions (20 μL) were performed under the following program: 95°C for 30 s, followed by 40 cycles of 95°C for 10 s and 60°C for 30 s. Relative expression was determined using the 2^−ΔΔCt method, with MdEF‐α as the internal reference gene. Three biological replicates, each with three technical replicates, were analyzed to ensure reproducibility. Gene‐specific primers were designed using Primer3 Plus, synthesized by Tsingke Biotechnology, and are listed in Table S3.

2.9. Data Analysis

All experimental data were based on three independent biological replicates and are presented as mean ± standard error (SE). Prior to statistical analysis, data normality was verified using the Shapiro–Wilk test, and homoscedasticity (variance homogeneity) was assessed via Levene's test to ensure compliance with ANOVA assumptions. Data analysis was initially performed using Excel for descriptive statistics, followed by one‐way analysis of variance (ANOVA) using SPSS software. For post hoc multiple comparisons, the Tukey's HSD test was employed to determine specific pairwise differences, with statistical significance set at p < 0.05. To address concerns regarding statistical power with three replicates, effect sizes (r) were calculated to quantify the magnitude of treatment effects, where ω ² ≥ 0.14 was considered a strong effect. Additionally, post hoc power analyses were conducted using G*Power 3.1, which confirmed that statistical power exceeded 0.8 for all measured parameters, indicating sufficient reliability to detect treatment effects. Graphs were created using Prism and Origin software to ensure professional and accurate data visualization.

Differentially expressed genes (DEGs) were identified based on the following criteria: |log2FC| > 1 (FC, fold change) and p < 0.05, additionally, the Benjamini‐Hochberg (BH) method was employed for false discovery rate (FDR) correction to address the issue of multiple testing. GO enrichment analysis was performed using Blast2Go (version 2.5) software. A p‐value threshold of 0.05 was applied, and the results were corrected for multiple testing using the Benjamini‐Hochberg (BH) FDR correction method. KEGG pathway enrichment analysis was conducted using KOBAS (version 2.1.1) software. A p‐value threshold of 0.05 was applied, and results were also corrected for multiple testing using the Benjamini‐Hochberg (BH) FDR correction method.

Weighted Gene Co‐expression Network Analysis (WGCNA) was performed using the WGCNA package (v1.63). Briefly, gene expression data were preprocessed by filtering out genes with a TPM < 1 in all samples or a coefficient of variation < 0.1, ensuring both stable expression and sufficient variability for downstream analysis. A signed network was constructed with a soft power threshold (β = 10) to meet the scale‐free topology criterion (R ² > 0.85). Gene modules were identified using the dynamic tree cut method, with the following parameters: minModuleSize = 30, minKMEtoStay = 0.3, and the modules were merged with a mergeCutHeight = 0.25 to minimize redundancy. The association between modules and traits was assessed via Spearman correlation between the module eigengenes and phenotypic data, with statistical significance determined using two‐tailed Student's t‐tests.

3. Results

3.1. Se Ameliorates Element Imbalance and Repairs the Photosynthetic System Under Mn Stress

To investigate the impact of Se on element metabolism under Mn stress, we first examined the changes in element content within the plants (Figure 1A). The results showed that both Se and Se + Mn combined treatments elevated Se levels in ‘Fuji’ seedlings. Mn treatment alone significantly increased the Mn content to 3.90 times that of the control group. However, the Mn content decreased by 18.01% after Se application (r = 0.885, p < 0.001). Further analysis revealed that Mn treatment markedly reduced the contents of copper (Cu), while magnesium (Mg) and zinc (Zn) also exhibited a decrease, although this was not statistically significant. The Se + Mn combined treatment brought the levels of these elements toward the control values, with some exceeding the control group, suggesting that Se played a regulatory role in alleviating Mn‐induced disruptions of elemental balance. In addition, Mn stress resulted in a non‐significant increase in iron (Fe) content, while molybdenum (Mo) and calcium (Ca) contents remained unchanged. However, the combined treatment of Se and Mn significantly enhanced the contents of Fe, Mo, and Ca, with increases of 52.13%, 32.45%, and 19.72%, respectively, compared to the control group (r _Fe  = 0.835, p < 0.001; r _Mo  = 0.836, p < 0.001; r _Ca  = 0.648, p < 0.05). These values were also significantly higher than those observed under Mn stress alone.

FIGURE 1.

The effects of Se and Mn on the elements and photosynthesis of apple seedlings. (A) The concentrations of various elements (Mn, Se, Ca, Zn, Mg, Fe, Mo, Cu) under different treatments: Control, Se, Mn, and Se + Mn. (B) The photosynthetic pigment content, including chlorophyll (Chl), carotenoids (Car), and fluorescence parameters (Fo, Fm, Fp, Fv, Fv/fm), measured in fresh weight (FW). Data are presented as the mean ± standard error (n = 3). Different letters (a–d) indicate significant differences between treatments (p < 0.05).

Chloroplast pigment analysis revealed the reparative effect of exogenous Se on the photosynthetic system (Figure 1B). Compared to Mn‐only treatment, the combined Se + Mn treatment increased chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (Chl a + b), and carotenoids (Car) by 9.43%, 24.75%, 14.36%, and 4.85%, respectively, indicating that Se effectively counteracted the degradation of photosynthetic pigments induced by Mn stress. Further analysis of chlorophyll fluorescence kinetics revealed that Mn stress decreased maximum fluorescence (Fm), variable fluorescence (Fv), Peak Fluorescence (Fp), Initial fluorescence (Fo), and the maximum photochemical efficiency of photosystem II (Fv/fm) by 14.91%–43.31% compared to the control (r _Fp  = −0.763, p < 0.01; r _Fo  = −0.526, p < 0.05; r _Fv/Fm  = −0.825, p < 0.001). However, exogenous Se improved these parameters by 2.91%–39.89% relative to the Mn‐only treatment, suggesting that Se alleviates Mn toxicity by stabilizing photosystem II.

3.2. Se Alleviates Oxidative Damage Induced by Mn Stress

Redox balance is crucial for the growth and development of fruit trees (Figure 2). Compared to the control, Mn treatment alone significantly induced oxidative stress in seedlings, as evidenced by the O₂ ⁻ content increasing from 21.28 ± 0.20 to 24.00 ± 0.45 nmol g⁻¹ (1.13‐fold, r = 0.595, p < 0.05), MDA levels being 1.56 times those of the control group, and relative electrical conductivity increasing from 86.02% ± 0.92% to 96.58% ± 3.00% (1.12‐fold). In contrast, the Se + Mn combined treatment led to reductions of 6.3%, 37.81%, and 6.3% in these parameters, respectively, compared to Mn treatment alone. Se treatment also alleviated oxidative damage by modulating the antioxidant system. In terms of antioxidant enzyme activity, the Se + Mn combination reduced SOD, POD, and CAT activities by 65.7%, 19.8%, and 24.0%, respectively, compared to Mn‐only treatment, suggesting that Se mitigates the excessive activation of antioxidant enzymes caused by Mn stress (r _CAT  = 0.633, p < 0.05). At the level of antioxidant metabolites, Mn stress increased the accumulation of TP, T‐AA, SS, AsA, GR, and GSSG (r _TP  = 0.729, p < 0.01; r _T‐AA  = 0.776, p < 0.01; r _AsA  = 0.588, p < 0.05; r _GR  = 0.866, p < 0.001; r _GSSG  = 0.883, p < 0.001). However, Se treatment significantly reduced these markers by 1.6%, 26.99%, 26.99%, 51.98%, 23.47%, 6.77%, and 5.03%, respectively. Overall, Se treatment helps restore the balance between reactive oxygen species (ROS) production and scavenging, thus alleviating oxidative stress induced by Mn.

FIGURE 2.

Effect of Se and Mn treatments on various physiological and biochemical parameters in apple seedlings. Parameters measured include: (A) O₂ ⁻ content, (B) MDA content, (C) relative conductivity, (D) SOD activity, (E) POD activity, (F) CAT activity, (G) TP content, (H) T‐AA content, (I) SS content, (J) ASA content, (K) GR content, (L) GSSG content. Data are presented as means ± standard error (SE) of three biological replicates. Different letters indicate significant differences between treatments (p < 0.05).

3.3. Se Modulates Hormone Levels and Cell Wall Remodeling Against Mn Stress

Hormonal analysis revealed differential regulatory effects of Se under Mn stress (Figure 3A). Growth‐promoting hormones, including zeatin (ZR; r = 0.708, p < 0.01), indole‐3‐acetic acid (IAA), brassinolide (BR), gibberellin GA₄, and jasmonic acid methyl ester (JA‐ME), were reduced under Mn stress compared to the control group. However, Se + Mn treatment increased the levels of these hormones by 0.89, 8.47, 0.39, 0.29, and 5.91 ng/g, respectively. In contrast, the levels of Gibberellic Acid 3(GA₃; r = −0.896, p < 0.001), indole‐3‐pyruvic acid (IPA), and dihydrozeatin riboside (DHZR; r = −0.988, p < 0.001) were decreased under Mn stress, and further reduced by 15.54%, 10.22%, and 27.26%, respectively, following Se application. Mn stress also led to a 15.43% reduction in abscisic acid (ABA) content relative to the control; however, exogenous Se did not significantly alter ABA levels (r = 0.963, p < 0.05).

FIGURE 3.

Effect of Se and Mn on plant hormone levels and cell wall components in apple seedlings. (A) Heatmap displaying the log10‐transformed relative levels of plant hormones, including BR, GA₄, ZR, IAA, JA‐ME, GA₃, DHZR, ABA, and IPA, under different treatments: Control, Se, Mn, and Se + Mn. (B) Heatmap showing the log10‐transformed relative contents of cell wall components, including cellulose, hemicellulose, lignin, and pectin, under the same treatments. The color scale indicates the relative intensity of each component, with warmer colors (red) representing higher values and cooler colors (blue) representing lower values. All data are based on three biological replicates.

To investigate whether Se mitigates cell wall damage induced by Mn stress, the contents of key cell wall components were measured (Figure 3B). Mn stress notably increased cellulose content while reducing hemicellulose. However, the Se + Mn treatment reversed these effects, restoring both cellulose and hemicellulose levels (r _Cellulose  = 0.985, p < 0.001). Mn treatment alone did not alter lignin or total pectin content, but the Se + Mn combination led to a 9.57% reduction in lignin and a substantial 1.35‐fold increase in total pectin relative to the control.

3.4. Analysis of Differentially Expressed Genes (DEGs) Alleviating Mn Stress in Apple Seedlings by se

High‐throughput sequencing was performed on 12 samples, which included the control group, Mn treatment group (Mn1, Mn2, Mn3), Se treatment group (Se1, Se2, Se3), and Se + Mn combined treatment group (Se + Mn1, Se + Mn2, Se + Mn3). A total of 54,182,522 to 59,610,492 raw reads were generated. Following the removal of low‐quality sequences, the clean reads ranged from 53,905,006 to 59,271,594. The Q20 base content for all samples exceeded 98.37%, and the Q30 base content was above 94.77%, confirming the high quality of the sequencing data (Table S1).

Gene expression profiling identified 525 differentially expressed genes (DEGs) between the Se treatment group and the control group, with 371 genes significantly upregulated and 154 genes significantly downregulated (Figure 4A). Among the upregulated genes, the coordinated activation of iron ion‐binding (HF13467, HF13464) and calcium ion‐binding genes (HF26306, HF42906) suggests that Se treatment may regulate metal ion absorption and transport by modulating Ca²⁺/Fe²⁺ homeostasis. The significant downregulation of carboxypeptidase genes (HF06067) implies that Se may mitigate protein degradation by inhibiting protease activity. The detailed differential expression information can be found in Appendix A. In the comparison between the Mn stress group and the Se + Mn combined treatment group, 191 DEGs were identified. Among these, 75 genes were specifically upregulated, including metal ion‐binding genes (HF09044; Figure 4B), key fatty acid biosynthesis genes (HF09876), lignin synthesis genes (HF13121), and pectin methylesterase inhibitor genes (HF28911). This expression pattern may reduce Mn bioavailability by enhancing cell wall modification, particularly through pectin methylation. Among the 116 downregulated genes, the inhibition of fatty acid hydrolase (HF04309), sucrose hydrolase (HF23741), and redox enzymes (HF08356) suggests that Se alleviates Mn‐induced carbohydrate and lipid hydrolysis, thereby contributing to the reduction of Mn toxicity. The detailed differential expression information is in Appendix B.

FIGURE 4.

Differential gene expression analysis between various treatment groups. (A) Volcano plot comparing Se treatment to the control group, with significant genes marked in blue (upregulated) and red (downregulated). (B) Volcano plot comparing Se + Mn treatment to Mn treatment, with significant genes similarly marked. (C) One‐way ANOVA of the top 10 most significantly expressed genes under different treatments, with p‐values indicating significance. (D) Venn diagram displaying the overlap of differentially expressed genes (DEGs) between Se_vs_Control and Se + Mn_vs_Mn comparisons, with 21 common DEGs identified.

Multi‐group comparative analysis revealed that Se + Mn treatment specifically activated genes related to the photosynthetic system (HF29915, HF23741, HF08356), suggesting that Se intervention effectively restores photosynthesis damaged by Mn stress. Furthermore, genes involved in oxidative stress and antioxidant responses were significantly downregulated in the Se + Mn treatment, and the physiological data showing reduced ROS content confirmed that Se alleviates Mn‐induced oxidative damage by inhibiting reactive oxygen species accumulation (Figure 4C).

Venn analysis revealed the interactive features of differentially expressed genes (DEGs) across treatment groups (Figure 4D). Notably, among the 21 shared genes between Se + Mn_vs_Mn_G (Se + Mn_vs_Mn DEGs) and Se_vs_Control_G (Se_vs_Control DEGs), KEGG enrichment analysis identified significant enrichment in nitrogen metabolism (map00910), phenylpropanoid biosynthesis (map00940), and flavonoid biosynthesis (map00941) pathways. These findings suggest that Se may mitigate Mn toxicity by sustaining the synthesis of phenylpropanoids and flavonoids. The preserved expression of these shared genes further supports the notion that Se‐regulated functions are maintained under Mn stress.

3.5. GO Analysis of se Alleviating Mn Stress in Apple Seedlings

GO functional annotation revealed the multidimensional regulatory role of Se in mitigating Mn stress (Figure 5A,B). In both the Se_vs_Control_G and Se + Mn_vs_Mn_G groups, molecular function analysis showed significant enrichment in catalytic activity and binding functions. Cellular component analysis indicated that Se specifically upregulated genes related to cellular and membrane components. In biological processes, co‐enrichment of cellular processes and metabolic processes underscored Se's critical role in regulating plant stress responses. GO enrichment analysis revealed that genes in the Se_vs_Control_G group were significantly enriched for O‐methyltransferase activity (GO: 0008171), DNA‐binding transcription factor activity (GO: 0003700), and heme binding (GO: 0020037). In the Se + Mn_vs_Mn_G group, genes were significantly enriched in FMN binding (GO: 0010181) and secondary active transmembrane transporter activity (GO: 0015291; Figure 5C,D).

FIGURE 5.

Gene Ontology (GO) analysis of differentially expressed genes (DEGs) from Se_vs_Control and Se + Mn_vs_Mn treatments. (A) GO annotations analysis for Se_vs_Control comparison, showing the distribution of genes across biological process (red), cellular component (blue), and molecular function (green) categories. (B) GO annotations analysis for Se + Mn_vs_Mn comparison. (C) GO enrichment analysis for Se_vs_Control, with significant enriched terms visualized as bubble plots, where the size of the bubbles indicates the number of genes, and color represents the significance level (adjusted p‐value). (D) GO enrichment analysis for Se + Mn_vs_Mn with similar representation.

3.6. KEGG Analysis of se Alleviating Mn Stress in Apple Seedlings

KEGG pathway enrichment analysis revealed the hierarchical regulatory roles of Se in plant metabolic networks. In the Se_vs_Control _G group, metabolic pathways predominated among the differentially expressed genes, with the phenylpropanoid biosynthesis pathway (map00940) significantly enriched by 23 genes, accounting for 95.83% of the pathway. In signal transduction, plant hormone signal transduction (map04075) and the MAPK signaling pathway (map04016) exhibited synergistic enrichments. Regarding environmental adaptation, genes were predominantly enriched in the plant‐pathogen interaction pathway (map04626; Figure 6A). Further KEGG functional enrichment analysis showed that differentially expressed genes were primarily enriched in phenylpropanoid biosynthesis, tryptophan metabolism, and plant‐pathogen interaction pathways (Figure 6B). A large number of structural genes and regulatory genes involved in phenylpropanoid biosynthesis were significantly upregulated. Meanwhile, multiple DEGs related to hormone synthesis and signal perception were differentially expressed in the plant hormone signal transduction and MAPK signaling pathways, participating in the regulation of plant stress responses. In the Se + Mn_vs_Mn_G group, metabolic pathways remained predominant, with a shift in regulatory focus toward lipid metabolism (Figure 6C). Notably, the α‐linolenic acid metabolism pathway (map00592), enriched with six genes, suggests that Se may mitigate Mn stress by modulating α‐linolenic acid metabolism. Further KEGG functional enrichment analysis identified significant enrichment in pathways such as α‐linolenic acid metabolism, phenylpropanoid biosynthesis, and galactose metabolism (Figure 6D). Key DEGs associated with lipid hydrolysis and metabolism were altered in the α‐linolenic acid metabolism pathway. In addition, numerous DEGs functioning in carbohydrate metabolism and cell wall modification were differentially expressed in galactose metabolism and phenylpropanoid biosynthesis pathways, which jointly mediated the mitigation effect of Se on Mn toxicity.

FIGURE 6.

KEGG pathway analysis of differentially expressed genes (DEGs) from Se_vs_Control and Se + Mn_vs_Mn treatments. (A) KEGG annotations analysis for Se_vs_Control, showing the distribution of genes across various KEGG pathways, categorized into metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems. (B) KEGG enrichment analysis for Se_vs_Control, visualized as a bubble plot where the size of the bubble represents the number of genes and color reflects the significance (adjusted p‐value). (C) KEGG annotations analysis for Se + Mn_vs_Mn comparison. (D) KEGG enrichment analysis for Se + Mn_vs_Mn, with similar representation.

3.7. Validation of the DEGs by RT − qPCR

We selected three genes from each of the three main KEGG‐enriched pathways for RT‐qPCR validation: alpha‐Linolenic acid metabolism, Phenylpropanoid biosynthesis, and Galactose metabolism. The RT‐qPCR results showed a consistent trend with the RNA‐seq data, and correlation analysis (R = 0.7181) confirmed the reliability of our transcriptome data (Figure 7). With the validated transcriptome data, we further analyzed the overall expression patterns of genes involved in antioxidant defense, hormone metabolism, and cell wall remodeling, and correlated their transcriptional changes with the corresponding physiological traits.

FIGURE 7.

Gene expression levels from RNA‐seq and RT‐qPCR and correlation analysis. RNA‐seq expression shown as log2 fold change (log2FC). (A) MdOPR2, (B) MdOPR11, (C) MdAOS, (D) BGLU47, (E) HCT, (F) MdPRXA2, (G) MdGH32, (H) MdUGE, (I) MdSIP1, (J) Correlation of log₂ fold changes between RNA‐seq and RT‐qPCR.

3.8. Correlation Analysis Between Transcriptomic Data and Physiological Responses

This study explores the role of Se in mitigating Mn stress through a comprehensive analysis of transcriptomic and physiological data (Figure 8A,B). The heatmap analysis revealed significant correlations between various gene modules and key physiological traits, particularly those related to photosynthesis, elemental accumulation, antioxidant enzymes and compounds, and hormone content. In terms of element accumulation, the genes in the modules MEskyblue3, MEbisque4, MEturquoise, MEdarkmagenta, Mered, MEdarkturquoise, and MEgrey exhibited significant negative correlations with Mn content, while showing positive correlations with the accumulation of other elements such as Fe, Ca, and Mg. These results suggest that the genes in these modules may regulate the transport and accumulation of Mn, reducing its harmful effects and thereby alleviating Mn toxicity to the plants. Regarding photosynthesis, the modules MEsalmon, MEdarkred, MEorange, MEfloralwhite, MEmediumpurple3, MEmidnightblue, MEmagenta, MEdarkorange2, MEpaleturquoise, MEthistle2, MEbrown, MEdarkgreen, and MElightcyan showed positive correlations with photosynthetic pigment content and chlorophyll fluorescence parameters (such as Fv/fm), suggesting that the genes within these modules may enhance photosynthetic efficiency to mitigate Mn stress. Furthermore, the modules MEdarkgrey, MEdarkslateblue, MEsteelblue, MEplum1, MEsaddlebrown, MEivory, MEblack, MEorangered4, MEpink, MEbrown4, and MEgreen exhibited positive correlations with antioxidant enzymes (such as SOD and POD) and antioxidant compounds (such as TP, T‐AA, and ASA), indicating that Se may alleviate oxidative damage induced by Mn by enhancing antioxidant capacity. Based on the GO, KEGG enrichment and WGCNA results shown above, transcriptome analysis revealed that a large set of antioxidant and redox‐related genes were differentially expressed under Mn stress, and Se application reversed their expression trends, which corresponded to the changes of antioxidant enzyme activities and antioxidant contents in physiological measurements. Regarding hormone content, the modules MEsalmon, MEorange, MEfloralwhite, MEdarkolivegreen, MEwhite, MEmagenta, MEdarkorange2, MEbrown, and MEdarkgreen were positively correlated with plant hormones (such as IAA and GA), suggesting that Se may regulate hormone levels to promote plant growth and enhance adaptation to Mn stress. Based on the GO, KEGG enrichment and WGCNA results shown above, genes related to plant hormone synthesis and signal transduction exhibited obvious expression alterations after different treatments, which led to the dynamic changes of endogenous hormone levels detected in this study. Additionally, the modules MEsteelblue, MEplum2, and MEskyblue displayed positive correlations with the accumulation of cell wall components, suggesting that Se may improve cell wall structure by regulating these modules, thereby strengthening the plant's ability to withstand external stressors. Based on the GO, KEGG enrichment and WGCNA results shown above, the expression of cell wall metabolism‐related genes was regulated by Se under Mn toxicity, which drove the remodeling of cell wall components observed in physiological experiments. Collectively, these results highlight the distinct roles that various gene modules play in regulating specific physiological responses, shedding light on the intricate relationships between gene expression and plant traits and further elucidating the potential mechanisms through which Se alleviates Mn stress in plants.

FIGURE 8.

WGCNA analysis of gene modules and their correlation with traits. (A) Gene dendrogram showing hierarchical clustering of genes, with module colors indicated at the bottom. (B) Heatmap depicting the correlation between gene modules and traits. Red indicates positive correlations, while blue represents negative correlations.

4. Discussion

Selenium functions as a cofactor of antioxidant enzymes and widely relieves various plant abiotic stresses (Liu et al. 2022). This suggests that plants may enhance their resistance mechanisms to adapt to stressful environments (Luo et al. 2024). Previous research has demonstrated the critical role of Se in alleviating stress in plants caused by salinity, drought, and Cd exposure (Han et al. 2022; Nie et al. 2023). In the present study, the reduced Mn content following Se treatment indicates that Se may mitigate oxidative damage by limiting Mn accumulation.

Mn stress can disrupt the balance of elemental content within plants. Consistent with lettuce under Cd stress (Matraszek et al. 2016), Mn treatment decreased Mg, Cu and Zn contents in ‘Fuji’ seedlings. In contrast, these elements were restored under the combined Se + Mn treatment, helping recover nutrient balance. Mo content further increased with Se application, showing a synergistic effect to improve Mn tolerance. Ca maintains cell wall integrity and cellular signaling, while Fe acts as a cofactor for antioxidant enzymes (Wdowiak et al. 2024). The increased Ca and Fe after Se treatment aided plant adaptation to Mn stress. Transcriptomic analysis revealed Se upregulated Ca and Fe binding genes, consistent with elemental changes (Figure 1A), highlighting selenium's function in alleviating Mn‐induced elemental imbalance.

Previous studies confirmed excess Mn triggers phytotoxicity (Zemunik et al. 2020). In this study, Mn stress degraded chlorophyll, carotenoids, and reduced chlorophyll fluorescence parameters (Fo, Fm, Fv, Fp, Fv/fm). However, Se effectively recovered these indicators, consistent with studies Se's effect on salt‐stressed Proso millet, Cd‐stressed tomato, and high‐temperature stressed peony (Li et al. 2016; Ji et al. 2022). Collectively, these results indicated that Se can effectively protect the photosynthetic system from damage induced by Mn stress (Figure 1B).

Heavy metals induce ROS accumulation and membrane lipid peroxidation (Nagajyoti et al. 2010). In the present study, Se + Mn treatment reduced O₂ ⁻ and MDA levels, consistent with previous reports (Hasanuzzaman et al. 2022; Sieprawska et al. 2021; Zhang et al. 2023), indicating relieved oxidative damage. Lower relative conductivity further supported this conclusion. The activities of SOD, POD, CAT, and GR also declined, reflecting reduced oxidative stress and recovered plant metabolism. TP and GSSG showed no obvious changes, meaning Se barely affected protein metabolism and the glutathione cycle. Decreased total amino acids and soluble sugars indicated alleviated osmotic pressure. The drop in AsA content resulted from reduced ROS demand (Figure 2). Overall, Se effectively relieves Mn‐induced oxidative stress and maintains metabolic homeostasis.

Plant hormones play a critical role in regulating plant growth and stress responses (Verma et al. 2016). Similar to Mn‐stressed soybean (Liu et al. 2023), growth‐promoting hormones (ZR, IAA, BR, GA₄, JA‐ME) decreased under Mn stress, and Se significantly recovered their levels. By contrast, GA₃, IPA, and DHZR were further reduced after Se treatment, suggesting Se's growth‐promoting effect is independent of these pathways. Although GA₃ is critical for plant growth, cell division, and expansion, and IPA, DHZR, and ABA are involved in stress tolerance and water balance (Nelissen et al. 2017), our results suggest that Se alleviates Mn stress via alternative mechanisms rather than restoring these specific hormones (Figure 3A).

The cell wall, as the first line of defense against stress, plays a critical role in binding heavy metals (Guo et al. 2024). Mn stress reduced hemicellulose content and damaged cell structure, as observed in Mn‐treated cucumber leaves (Eskandari and Gineau 2020). Se restored hemicellulose content, suggesting that Se effectively mitigates the degradation of hemicellulose Meanwhile, Mn did not alter lignin obviously, and Se + Mn treatment reduced lignin to improve cell wall flexibility. The observed increase in pectin content suggests that Se + Mn treatment may regulate pectin synthesis to bind excess Mn, thereby reducing its toxicity to the plant (Ling et al. 2022). The increase in lignin content under Mn stress, as observed in wheat (Dziwornu et al. 2018), suggests that lignin contributes to enhanced cell wall rigidity, Under combined Se + Mn treatment, lignin content continued to increase, likely due to Se's role in promoting lignin biosynthesis, thereby improving the Mn tolerance of ‘Fuji’ apple trees.

Transcriptomics helps reveal plant heavy metal stress mechanisms (Xu et al. 2024). We identified 525 DEGs between Se and Control, including 371 upregulated genes (Figure 4A). Se upregulated Ca/Fe binding genes, consistent with elevated mineral contents and related studies (Matraszek and Hawrylak‐Nowak 2010). It also downregulated carboxypeptidase genes to inhibit excessive protein degradation and maintain cellular protein homeostasis (Akhzari et al., Akhzari and Pessarakli 2015). The reduced total amino acids in the Se + Mn group also proved that Se prevents abnormal protein breakdown. Seventy‐five genes were specifically upregulated in Se + Mn versus Mn groups, including genes for metal ion binding, fatty acid biosynthesis, and strigolactone synthesis (Zhang et al. 2024). Strigolactones may enhance nutrient uptake and antioxidant capacity to improve Mn tolerance (Kapoor et al. 2023). Upregulated pectinesterase inhibitor genes modify cell wall structure and reduce Mn bioavailability. Se recovered hemicellulose and increased cellulose and pectin, consistent with previous studies (Yang et al. 2022; Zhang et al. 2021). These results confirm Se improves cell wall stability to resist Mn toxicity. Photosynthesis is a critical process for plant growth, serving as the cornerstone for energy metabolism and maintaining normal physiological functions (Tanaka and Makino 2009). Se upregulated photosynthesis‐related genes and recovered photosynthetic function, which is supported by studies on multiple fruit crops (Feng et al. 2015; Xu et al. 2020). Meanwhile, Se downregulated oxidative stress‐related genes and reduced ROS accumulation (Figure 4C). It also inhibited fatty acid and sucrose hydrolysis, maintaining normal energy metabolism and membrane stability (Wei et al. 2022).

GO and KEGG enrichment analyses highlighted the multifaceted regulatory roles of Se in alleviating Mn stress. O‐methyltransferase genes were significantly upregulated, previously shown under UV‐B stress in E. lathyris (Zhao et al. 2024), suggesting that Se enhances flavonoid production to boost antioxidant capacity and reduce Mn‐induced oxidative damage. Concurrently, ten WRKY genes were upregulated in the DNA‐binding transcription factor activity category, have been implicated in responses to various metal stresses, including Cd, As, Al, and Cu (Li et al. 2024). They were induced, indicating that Se may coopt WRKY‐mediated transcriptional programmes to improve stress adaptation (Jiang et al. 2017). KEGG pathway enrichment further resolves the core metabolic basis of Se‐induced tolerance: the phenylpropanoid biosynthesis pathway was most significantly enriched, with 23 genes upregulated. Although this pathway is canonically associated with lignin and flavonoid synthesis, physiological data showed reduced lignin content in Se + Mn‐treated plants, indicating Se redirects phenylpropanoid metabolic flux away from lignification towards flavonoid accumulation for antioxidant defence. This re‐routing is coupled with Se‐induced upregulation of the galactose metabolism pathway, which supplies UDP‐galactose for pectin biosynthesis: pectin's abundant carboxyl groups chelate apoplastic Mn²⁺ to limit cytosolic influx and toxicity (Abdelsalam et al. 2025), while L‐galactose derived from the same pathway serves as a precursor for AsA biosynthesis to expand the antioxidant pool for Mn‐induced ROS scavenging (Zhang et al. 2015). Thus, Se reprograms cell wall composition to prioritise chelation‐based Mn tolerance over barrier reinforcement via lignification. Additional pathway enrichments further support Se‐mediated growth maintenance under Mn stress: upregulation of tryptophan metabolism likely boosts auxin (IAA) synthesis from tryptophan to support root development and photomorphogenesis (Jiang et al. 2017), while enrichment of the fatty acid elongation pathway suggests Se modulates membrane fatty acid composition to maintain membrane fluidity and stability for environmental interactions.

WGCNA analysis was employed to explore the biological relationships between gene co‐expression networks and plant phenotypes, enabling the identification of key genes that are strongly correlated with specific traits (Tang et al. 2023). The results indicate that Se significantly enhances the plant's tolerance to Mn stress by modulating multiple gene modules, thereby improving photosynthesis, antioxidant capacity, elemental accumulation, hormone levels, and cell wall component content. Specifically, several gene modules are negatively correlated with Mn content, suggesting that Se may reduce the harmful effects of Mn by regulating its transport and accumulation, thus mitigating its toxicity to plants. Moreover, Se enhances photosynthetic and antioxidant efficiency by modulating the expression of genes associated with photosynthesis and ROS removal, which improves the plant's ability to counteract oxidative damage. Additionally, Se influences hormone‐related genes, promoting plant growth and facilitating better adaptation to stress. Furthermore, Se's regulatory effects on the cell wall enhance the structural stability of plant cells, further improving their resistance to stress (Figure 8).

5. Conclusions

In conclusion, both physiological and transcriptomic analyses underscore the efficacy of Se in mitigating Mn stress in ‘Fuji’ apples. Se alleviates the detrimental effects of Mn stress through multiple mechanisms, including the regulation of nutrient element balance, improvement of photosynthetic performance, alleviation of oxidative damage, modulation of hormone levels, and promotion of cell wall remodeling, thereby enhancing physiological stability. Transcriptomic analysis further revealed that Se modulates key genes associated with metal ion transport, lipid metabolism, and carbohydrate metabolism, providing a molecular foundation for its role in alleviating Mn stress. WGCNA highlighted that Se regulates antioxidant responses, mineral element homeostasis, and photosynthetic processes through specific gene modules, thereby improving plant adaptability under Mn stress. These findings offer novel insights into the physiological and molecular mechanisms by which Se mitigates Mn stress in ‘Fuji’ apples and provide compelling evidence for the application of Se‐based agricultural practices in apple cultivation.

Author Contributions

X.Q. conceived and supervised the project. W.X. and X.Q. designed the experiments. W.X., L.W., Y.T., J.S., Y.N., and J.Y. performed the experiments. W.X. and X.Q. analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.

Funding

This work was jointly supported by the following grants: the Natural Science Foundation of Shandong Province (ZR2022MC124), Project of Undergraduate Teaching Reform Research of Shandong Province (M2023061), and the Youth Fund of Shandong Natural Science Foundation (ZR2024QC145).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: The specific description of the genes mentioned in Figure 4C.

Table S2: Sequencing data for different samples.

Table S3: qRT‐PCR Primers.

Appendix A: Differentially expressed genes between Se and Control.

Appendix B: Differentially expressed genes between Se + Mn and Mn.

Xie, W. , Wei L., Tian Y., et al. 2026. “Selenium Alleviates Manganese Toxicity in ‘Red Fuji’ Apple: Insights From Combined Physiological and Transcriptomic Approaches.” Physiologia Plantarum 178, no. 4: e71054. 10.1111/ppl.71054.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.