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Article

Ecological Responses of Mercury to Selenium in Farmland: Insights from Metal Transport in Crops, Soil Properties, Enzyme Activities, and Microbiome

by
Yuxin Li
1,
Shuyun Guan
2,
Guangpeng Pei
2,
Xiaorong Zhang
2,
Yongbing Zhang
1,
Junbao Huang
1,
Yingzhong Lv
1 and
Hua Li
3,*
1
Pomology Institute, Shanxi Agricultural University, Taiyuan 030031, China
2
College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, China
3
School of Environment Science and Resources, Shanxi University, Taiyuan 030006, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(16), 1753; https://doi.org/10.3390/agriculture15161753 (registering DOI)
Submission received: 7 July 2025 / Revised: 12 August 2025 / Accepted: 14 August 2025 / Published: 16 August 2025
(This article belongs to the Section Agricultural Soils)
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Versions Notes

Abstract

Selenium (Se) is a natural detoxifier of the heavy metal mercury (Hg), and the interaction between Se and Hg has been widely investigated. However, the ecological response of Hg to Se in Hg-contaminated farmland requires further study, especially the relationship between Se–Hg interactions and soil abiotic and biological properties. Through a field experiment, the effects of different levels of exogenous Se (0, 0.50, 0.75, 1.00, and 2.00 mg kg−1) on Hg and Se transport in maize, soil properties, enzyme activities, and the microbial community in Hg-contaminated farmland were systematically studied. The Se treatments significantly reduced the Hg concentration in maize roots, stems, leaves, and grains and significantly increased the Se concentration in maize tissues. Except for the 0.75 mg kg−1 Se treatment which significantly increased electrical conductivity compared to the control, other Se treatments hadnon-significant effect on soil physicochemical properties (pH, conductivity, organic matter content, and cation exchange capacity) and oxidoreductase activities (catalase, peroxidase, and ascorbate peroxide). The activities of soil invertase, urease, and alkaline phosphatase increased significantly after Se application, and the highest enzyme activities were observed with a 0.50 mg kg−1 Se treatment. The bacteria and fungi with the highest relative abundance in this study were Proteobacteria (>30.5%) and Ascomycota (>73.4%). The results of a redundancy analysis and predictions of the microbial community showed that there was a significant correlation between the soil nutrient cycle enzyme activity, microbial community composition, and microbial community function. Overall, exogenous Se application was found to be a viable strategy for mitigating the impact of Hg stress on ecosystems. Furthermore, the results provide new insights into the potential for the large-scale application of Se in the remediation of Hg-contaminated farmland.

1. Introduction

The global pollutant mercury (Hg) is of significant concern owing to the ecological risks posed by its high toxicity and potential for bioaccumulation [1,2,3]. Several anthropogenic activities (e.g., small-scale gold mining, non-ferrous metal production, and cement production) result in excessive releases of Hg into the environment [1]. Thus, during the Anthropocene epoch, soil ecosystems faced great challenges from Hg contamination [4]. While farmland provides essential crop nutrients, heightened Hg levels undermine soil ecological functions, reduce crop safety, and initiate food chain contamination with downstream health implications [5,6,7]. In soils and plants, Hg(II) and methyl-Hg are the most bioavailable forms of Hg, and the form and concentration of Hg in the soil directly determine the form and concentration of Hg in plants [8]. The availability of Hg is also influenced by the soil abiotic (e.g., pH, organic matter (OM), cation exchange capacity (CEC)) and biological properties (e.g., microbial community) [5].
Different physical and chemical remediation techniques have been developed and applied for soils contaminated with Hg, such as thermal desorption, soil washing, vitrification, electrical remediation, and immobilization [1,9]. However, considering the impact on soil properties, crop growth, the ecological environment, and cost, the large-scale application of traditional techniques is limited [1]. For example, thermal desorption can severely affect soil OM and microbial communities, destroying soil properties [6,10], while soil washing is limited by its high cost and loss of essential elements [11]. Therefore, to mitigate the stress and potential Hg risk in soil and plants, there is an urgent need to develop practical approaches that could balance soil properties, plant growth, and remediation costs.
As an essential trace element, selenium (Se) critically regulates biological functions and antagonizes heavy metal toxicity through metal–Se protein complexes formation during exposure [12,13,14]. Since Parizek and Ostadalova [15] first discovered the Se–Hg antagonism, these interactions have attracted the attention of environmental scientists. Mercury sulfide (HgS) is the main form of Hg in soil [16]. Sulfur (S) and Se are adjacent class VIA elements with similar atomic structures (e.g., comparable atomic radii and outermost electron structures). This enables Se to partially substitute for S to form the more insoluble precipitates of HgSe or an isomorphous series of HgS−HgSe [6]. This occurs because the binding affinity of HgSe (1045) is much higher than that of HgS (1039) [17], while the solubility product constant (Ksp) of HgSe (∼10−60) is markedly lower than that of HgS (∼10−52) [18]. Much of the existing research has focused on Se–Hg interactions in aquatic ecosystems [19,20,21]. However, soils contaminated with Hg due to human activities are also a key source of human exposure to Hg [22]. As a result, researchers have used the mechanisms of Se–Hg antagonism in aquatic systems to explore how to reduce Hg accumulation in terrestrial plants, including rice (Oryza sativa) [19,23], pak choi (Brassica chinensis) [24], and Chinese mustard (Brassica juncea) [25]. They found that the formation of insoluble HgSe complexes in soil or plant roots was a key factor in reducing the mobility of soil Hg [19,23]. Furthermore, McNear et al. [26] confirmed that the formation and enrichment of HgSe complexes on plant roots decreased the bioavailability of soil Hg. The study employed advanced molecular techniques, including capillary reversed-phase chromatography coupled with inductively coupled plasma mass spectrometry (capRPLC-ICPMS) and X-ray absorption near-edge structure (XANES) spectroscopy. These studies provide preliminary evidence for Se–Hg interactions in terrestrial ecosystems, but they mainly focused on rice-flooded soils without carefully considering alkaline soils [19,23,27,28,29]. Field data regarding the interaction between Se and Hg in alkaline agricultural soil are still relatively scarce.
Some studies have shown that the formation of HgSe complexes could constrain the biogeochemical cycles of Hg and Se in soils and plants [19,29,30,31,32]. The morphology of Hg and Se is also affected by the abiotic and biotic properties of the soil, such as the soil composition, redox potential, mineral element content, OM content, pH, CEC, enzyme activity, and microbial community structure [13,33,34,35]. For example, inorganic Hg could be reduced to Hg0 under the action of soil OM [36], which could be directly combined with Se0 to generate HgSe complexes. Selenium could increase the abundance of Arthrobacter in the soil and reduce the bioavailability of cadmium (Cd) [37]. Systematic research on soil factors influencing Se–Hg interactions is crucial to understand the environmental behavior of Hg and determine techniques for its remediation. How soil properties and microbial communities change under the mediation of Se and the relationships between soil properties (abiotic and biotic) and Hg transport in soil–plant systems are still poorly understood.
In this study, we hypothesized that exogenous Se could effectively inhibit the uptake of Hg and increase the uptake of Se in maize, which was related to soil physicochemical properties, enzyme activity, and microbial community structure and diversity. Field experiments with different levels of Se application to Hg-contaminated agricultural soil were conducted. The objectives were to evaluate whether exogenous Se was suitable for remediating Hg-contaminated agricultural soil. This study aims to provide a comprehensive understanding of Hg to Se in farmland.

2. Materials and Methods

2.1. Site Description

The study area was a farmland in Xinzhou, Shanxi Province, China (located at 39°14′ N, 113°30′ E), which was surrounded by several small gold mines, all of which are now closed. The Hg concentration in the farmland soil was 5.38 mg kg−1. The soil Se concentration was 0.27 mg kg−1, indicating that the area was Se deficient [13]. Maize is planted year-round in the study area. The region has a continental climate and is located in the north temperate zone of China, with an average annual temperature of 6.3 °C and an annual precipitation of 860 mm. The agricultural soil had the following properties: texture (loam), type (brown soil), clay (5.14%), slit (41.78%), sand (53.08%), pH (7.70), electrical conductivity (EC) (269 μs cm−1), OM (19.41 g kg−1), and CEC (15.16 cmol kg−1).

2.2. Experimental Design

Field experiments were conducted using different Se applications as single-factor variables. Sodium selenite (Na2SeO3) was supplemented in soil as exogenous Se, which was purchased from Shandong West Asia Chemical Co. Ltd., China. Five distinct treatments were implemented: (1) no Se supplementation (CK); (2) Se supplementation at 0.50 mg kg−1 (i.e., 130 mg m−2); (3) Se supplementation at 0.75 mg kg−1 (i.e., 195 mg m−2); (4) Se supplementation at 1.00 mg kg−1 (i.e., 260 mg m−2); (5) Se supplementation at 2.00 mg kg−1 (i.e., 520 mg m−2). The soil bulk density (1300 kg m−3) was determined from undisturbed cores sampled at 0–20 cm depths (plough layer). Selenium supplementation amounts per unit area (mg m−2) were calculated as:
Searea = ρb × D × Semass
where ρb = bulk density (1300 kg m−3); D = soil depth (0.2 m); Semass = target Se concentration (mg kg−1).
Exogenous Se was added to the soil in the form of a Na2SeO3 solution, and it was evenly mixed with the surface soil (0−20 cm) by tillage. Each treatment was replicated in three different test areas (20 m2). The experimental plots of the parallel treatment were separated by ridges, and the ridges between different treatments were covered with plastic film below 30 cm of the surface to prevent interference between different treatments.
‘Luyu 1161’ maize (Zea mays L.) served as the test material. The seeds pretreated with commercial protectants were sown in early May and harvested in late September (approximately 140 days of growth). Field management practices, i.e., fertilization, irrigation, and weeding, were conducted according to local planting habits.

2.3. Sample Collection

The soil and plant samples were collected after maize was harvested. Each sample consisted of five subsamples collected randomly in a 20 m2 area. The harvested mature maize was separated into roots, stems, leaves, and grains. The roots were soaked in EDTA-Na2 (0.5 mM) for 20 min to remove the elements attached to the root surface, then they were washed with distilled water. The stems, leaves, and grains of maize were washed with tap water and distilled water. Then, different tissues were dried at 105 °C for 30 min and at 75 °C to constant weight. Dried samples were sieved to analyze the Hg and Se concentrations.
Rhizospheric soil samples were collected from 15 sites using a five-point sampling method. Rhizospheric soil collection involved excavating intact root–soil systems, manually dislodging loosely adhered soil. Root-attached soil samples (about 15 g) were transferred to 25 mL centrifuge tubes prefilled with 0.86% NaCl (isotonic salt solution), then subjected to a 30 min ice bath incubation with intermittent agitation (5 min intervals). The plant roots were then removed and centrifuged at 4000× g for 30 min at 4 °C. The sediment at the bottom of the tube was the rhizospheric soil sample. Some fresh samples were frozen in liquid nitrogen, stored in cryovials at −80 °C, and shipped on dry ice for DNA extraction and high-throughput determination. The remaining soil samples were air-dried and ground, and the soil physicochemical properties, enzyme activities, and elemental concentrations were determined.

2.4. Chemical Analysis

2.4.1. Plant Elemental Analysis

The Hg concentration in maize was determined following sample digestion by HNO3 in a pressure bomb, followed by a liquid chromatography–atomic fluorescence spectrometry analysis (LC-AFS 6500, Beijing Haiguang Instruments Co. Ltd., Beijing, China), according to China GB 5009.17-2014 [38]. The Se concentration in maize was determined following sample digestion by a HNO3–HClO4 mixture, followed by an analysis using the LC-AFS 6500 device according to China GB 5009.93-2017 [39]. The bioconcentration factor (BCF) and transfer factor (TF) of Hg and Se were calculated as follows:
BCF = concentration of Hg/Se in plant ÷ concentration of Hg/Se in soil
TF = concentration of Hg/Se in aboveground tissues ÷ concentration of Hg/Se in roots

2.4.2. Analysis of Soil Properties

Soil pH was measured using a pH meter (ST3100, Ohaus Instrument Co. Ltd., Shanghai, China) [40]. Soil EC was determined by a conductivity meter (Rex DDSJ-308A, Shanghai INESA Scientific Instrument Co. Ltd., Shanghai, China) (China HJ 802-2016 [41]). Soil OM was determined by the external heating potassium dichromate volumetric method [42]. Soil CEC was measured using the hexamminecobalt trichloride solution spectrophotometric method (China HJ 889-2017 [43]).
The methods used to determine enzyme activity were as follows: 3,5-dinitrosalicylic acid colorimetric method for invertase; sodium phenolate–sodium hypochlorite colorimetric method for urease; phenyl disodium phosphate colorimetric method for alkaline phosphatase; permanganate titration method for catalase; iodometry for peroxidase; 2,6-dichlorophenol indophenol titration method for ascorbate oxidase [44].

2.5. Microbial Community Structure and Diversity Analysis

Soil DNA of microorganisms was extracted with Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s instructions. Polymerase chain reaction (PCR) amplification of the bacterial 16S rDNA genes V3–V4 and fungi internal transcribed spacer (ITS) 1 region was achieved using the universal primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′)/806R (5′-GGACTACHVGGGTWTCTAAT-3′) and ITS5 (5′-GGAAGTAAAAGTCGTAACAAGG-3′)/ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′). Sequencing was then performed using the Illumina platform (Shanghai Personal Biotechnology Co., Ltd., Shanghai, China) in combination with the MiSeq Reagent Kit v3 (Illumina (China) Scientific Equipment Co., Ltd., San Diego, CA, USA).
The sequencing bioinformatics analysis used the QIIME2 2019.4 software platform [45]. The analysis process was slightly modified from the official tutorial (https://docs.qiime2.org/2019.4/tutorials/) (accessed on 10 November 2021). Briefly, the raw sequence data was digested by the Demux plugin and the primers were cleaved using the Cutadapt plugin [46]. The DADA2 plugin was used to quality filter, denoise, merge, and remove chimeras from sequences [47]. Non-single amplicon sequence variants (ASVs) (100% similarity) were aligned using multiple alignment using fast Fourier transform (MAFFT) [48]. The databases of SILVA Release 132 (bacteria) and UNITE Realease 8.0 (fungal) were used to classify ASVs using the feature-classifier plug-in in the classify-sklearn naïve Bayes taxonomy classifier [49].

2.6. Data Analysis

The experimental data were expressed as the mean ± standard error. A one-way analysis of variance and Tukey LSD test were used to analyze the significance of independent samples (SPSS 21.0). The microbial sequence analysis was mainly conducted using QIIME2 and R language (v3.2.0) at the ASV level. The microbial community relative abundance and diversity were assessed using alpha-diversity indices and the unweighted pair group method with an arithmetic mean (UPGMA) cluster analysis, which were calculated by QIIME2. The linear discriminant analysis effect size (LEFSe), principal co-ordinates analysis (PCoA), redundancy analysis (RDA), and predicted functions of the microbial community were performed using the Genescloud Platform (https://www.genescloud.cn) (accessed on 6 January 2022).

3. Results

3.1. The Effectiveness of Exogenous Se on the Uptake of Hg and Se in Maize

3.1.1. Changes in the Hg Concentration in Maize

The Hg concentrations in maize under the different treatments are shown in Figure 1a–d. Compared with the CK, Se application resulted in a significant decrease in the Hg concentrations in roots (12.80–20.52%), stems (47.14–67.14), leaves (29.70–47.12%), and grains (67.85–100.00%). However, non-significant variation of the Hg concentration in maize roots and leaves was found among the different Se treatments. As shown in Table S1, the variations of the Hg-BCF and Hg-TF values were consistent with that of the Hg concentration in maize tissues.

3.1.2. Changes in the Se Concentration in Maize

The Se concentration in maize tissues significantly increased as the Se applications increased (Figure 1e–h). Compared with the CK, the Se concentrations increased by 0.76–5.48 mg kg−1 in roots, 0.06–0.63 mg kg−1 in stems, 0.11–1.67 mg kg−1 in leaves, and 0.0023–0.0223 mg kg−1 in grains. A significant increase in the Se-BCF values in maize tissues was observed as the Se application increased (Table S1). The Se-TF values in stems and leaves did not change significantly under the different Se applications.

3.2. The Effectiveness of Exogenous Se on the Soil Properties

3.2.1. Changes in the Physicochemical Properties of Soil

As shown in Figure 2a,c,d, it was observed that the increase in Se supplementation in soil had no significant effect on the soil pH, OM, or CEC. A pH range of 7.74–7.86, OM content range of 17.82–18.39 g kg−1, and CEC range of 14.8–16.1 cmol kg−1 were recorded under the different treatments. Compared with the CK, the EC of the soil in the 0.75 mg kg−1 Se treatment increased by 0.18 units (Figure 2b). The other Se treatments had no significant effect on soil EC.

3.2.2. Changes in the Activities of Soil Enzymes

The activities of the soil enzymes are presented in Figure 3. Compared with the CK, increases in the activity levels of invertase (31.20–86.24%), urease (189.46–219.03%), and alkaline phosphatase (−6.22–86.09%) were observed with Se application. The maximum invertase, urease, and alkaline phosphatase activities were recorded with the 0.50 mg kg−1 Se treatment. Invertase and alkaline phosphatase activities exhibited biphasic responses to Se supplementation, peaking before declining with higher concentrations. Additionally, Se application resulted in no significant change in oxidoreductase activity (Figure 3d–f).

3.3. The Effectiveness of Exogenous Se on the Diversity and Community of Soil Microorganisms

3.3.1. Sequencing Statistics

The 16S rRNA sequencing in 15 soil samples retrieved a total of 282,992 high-quality sequences and 6774 ASVs for bacteria. The ITS genes in all samples retrieved 537,613 high-quality sequences and 1710 ASVs for fungi.
The rarefaction curve indicated that the current sample’s sequencing depth was sufficient to reflect the microbial diversity in the community sample (Figure S1).

3.3.2. Changes in the Microbial Diversity

Based on the ASVs of bacteria and fungi, the alpha diversity indices were calculated to study the effects of Se applications on microbial diversity (Table 1). Compared with the CK, Se had no significant effect on the indices of Chao1, Shannon, and Pielou’s evenness of bacteria, indicating that Se had non-significant impacts on the richness, diversity, and uniformity of bacteria. Compared with the CK, the Good’s coverage index (i.e., coverage) of bacteria under the 0.75 mg kg−1 Se treatment was significantly increased, while with the 2.00 mg kg−1 Se treatment there was a significant decrease.
The Pielou’s evenness index and Good’s coverage index of the fungi showed that Se had no significant effect on fungal uniformity and coverage (Table 1). Compared with the CK, the Chao1 index and Shannon index of fungi, which characterize the richness and diversity of communities, were significantly reduced by 13.8% and 2.3%, respectively, under the 0.50 mg kg−1 Se treatment.
The beta diversity analysis of microorganisms was performed using a PCoA analysis (Figure 4). The PCoA results revealed that approximately 25.6% of the variance in bacterial community composition was explained (14.3% by the PCo1 and 11.3% by the PCo2). Approximately 60.9% of the variance in fungal community composition was explained (45.0% by the PCo1 and 15.9% by the PCo2). The differences in fungal beta diversity along the PCo axes, as well as the total explained variance, were significantly greater than those observed in the bacterial community. This indicated that the beta diversity of the fungal community was more sensitive to the treatment response in this study than that of the bacterial community.

3.3.3. Changes in the Microbial Community

Taxonomic classification of the ASVs of each sample identified 5–10 phyla for bacteria and 19–26 phyla for fungi. As shown in Figure 5a, the predominant bacterial phylum was Proteobacteria (30.5–34.3%), followed by Actinobacteria (10.8–19.3%), Acidobacteria (13.8–16.7%), Chloroflexi (12.7–15.4%), and Gemmatimonadetes (9.7–10.7%). The fungal phylum with the highest richness was Ascomycota, accounting for 73.4–84.5% of all fungal communities, followed by Mortierellomycota (4.6–8.9%) and Basidiomycota (1.8–6.8%) (Figure 5b).
A LEfSe was used to simultaneously analyze the differences among the sample microorganisms at all taxonomic levels to determine the marker species between groups, with the results shown in Figure 6. The bacterial marker species at the family level was Hyphomonadaceae in the CK. In the 0.75 mg kg−1 Se treatment, the bacterial marker species were Actinomycetes at the phylum level, Actinomycetes at the class level, Geodermatophilaceae at the family level, and Nonomuraea at the genus level. In the 1.00 mg kg−1 Se treatment, the bacterial marker species were Enterobacteriales at the order level, Parachlamydiaceae and Enterobacteriaceae at the family level, and Neochlamydia at the genus level. In the 2.00 mg kg−1 Se treatment, the bacterial marker species were Microgenomatia at the class level, Woesebacteria at the order level, Woesebacteria at the family level, and Woesebacteria at the genus level.
Figure 6b shows the marker species of fungi between different treatments. In the 0.50 mg kg−1 Se treatment, the fungal marker species were Malasseziomycetes at the class level, Malasseziales at the order level, Trichomeriaeae and Malasseziaceae at the family level, and Bradymyces and Malassezia at the genus level. In the 0.75 mg kg−1 Se treatment, the fungal marker species was Cercophora at the genus level. In the 2.00 mg kg−1 Se treatment, the fungal marker species were Filobasidiales at the order level; Piskurozymaceae, Didymosphaeriaceae, and Chaetosphaeriaceae at the family level; and Schizothecium, Solicoccozyma, Paraphaeosphaeria, Bjerkandera, Pyrenochaetopsis, Dinemasporium, Sagenomella, Scytalidium, Ramophalophor, and Sclerostagonospora at the genus level.

3.4. Relative Influences of Soil Properties on the Microbial Community

A RDA was conducted to evaluate the relative effect of soil factors on the microbial communities (genus level) in Hg-contaminated soils. The RDA1 and RDA2 axes explained 57.28% and 20.11% of the variance for bacteria and 58.70% and 11.09% of the variance for fungi, respectively. An integrated analysis of Figure 7 and Table S2 revealed non-significant associations (p ≥ 0.05) between soil property variations and microbial community restructuring. This absence of statistical linkage corresponds with Se’s negligible impact on soil properties, such as pH, EC, OM, CEC, and oxidoreductase activities.

3.5. The Effectiveness of Exogenous Se on the Predicted Functions of the Microbial Community

Metabolic pathways in the microbial communities with significant differences between different treatments were determined using the metagenomeSeq method (Figure 8). Compared with the CK, the metabolic pathways in the 0.50 mg kg−1 Se treatment for the pyruvate fermentation to butanoate and the super pathway of Clostridium acetobutylicum acidogenic fermentation were increased at p < 0.01, and the metabolic pathways of L-glutamic acid degradation V, crotonic acid fermentation, glutaryl-CoA degradation, lactose and galactose degradation I, and benzoyl-CoA degradation II were increased at p < 0.001 (Figure 8a). Compared with the CK, in the 0.75 mg kg−1 Se treatment, the metabolic pathways, such as teichoic acid biosynthesis, creatinine degradation I, and methanol oxidation to CO2, were increased at p < 0.01, and the metabolic pathways, such as sucrose degradation III, methyl ketone biosynthesis, and mono-trans, poly-cis decaprenyl phosphate biosynthesis, were increased at p < 0.05. Additionally, the metabolic pathway of urate biosynthesis–inosine 5′-phosphate degradation was reduced at p < 0.001, and the metabolic pathways, such as UDP-N-acetyl-D-glucosamine biosynthesis I, L-histidine biosynthesis, and superpathway of L-threonine biosynthesis, were reduced at p < 0.01; other the metabolic pathways, such as adenine and adenosine salvage III, superpathway of purine deoxyribonucleosides degradation, and purine ribonucleosides degradation, were reduced at p < 0.05 (Figure 8b). Compared with the CK, in the 1.00 mg kg−1 Se treatment, the metabolic pathways of the superpathway of ornithine degradation, superpathway of L-threonine metabolism, and enterobacterial common antigen biosynthesis were increased at p < 0.001, and the superpathway of L-tryptophan biosynthesis pathway was increased at p < 0.05 (Figure 8c). Compared with the CK, in the 2.00 mg kg−1 Se treatment, the metabolic pathways of the L-lysine biosynthesis II, thiazole biosynthesis II, and superpathway of thiamin diphosphate biosynthesis II were increased at p < 0.001, and the metabolic pathways of the S-methyl-5-thio and alpha-D-ribose 1-phosphate degradation, formaldehyde assimilation II, and formaldehyde oxidation I were increased at p < 0.01, while the L-methionine salvage cycle III pathway was increased at p < 0.05. The metabolic pathway of Entner–Doudoroff pathway III was reduced at p < 0.001, and the metabolic pathways of mandelate degradation I and glycogen degradation II were reduced at p < 0.05 (Figure 8d).
The prediction of fungal community functional potential revealed non-significant variation in metabolic pathways in the 0.50 and 0.75 mg kg−1 treatments compared with the CK. Compared with the CK, in the 1.00 mg kg−1 Se treatment, the sulfate reduction I pathway was increased at p < 0.01, and the metabolic pathways, such as L-tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde, glucose and glucose-1-phosphate degradation, and NAD/NADP-NADH/NADPH mitochondrial interconversion, were increased at p < 0.05, while the 5-aminoimidazole ribonucleotide biosynthesis and metabolism I pathway was reduced at p < 0.05 (Figure 8e). Compared with the CK, in the 2.00 mg kg−1 Se treatment, the metabolic pathways of the superpathway of ubiquinol-6 biosynthesis, ubiquinol-9 biosynthesis, and ubiquinol-7 biosynthesis were increased at p < 0.001, and the sulfate reduction I pathway was increased at p < 0.01 (Figure 8f).

4. Discussion

4.1. Selenium Applications Reduce the Uptake of Hg and Increase the Uptake of Se by Maize

In this study, the effect of Se on Hg enrichment and transport in plants in the alkaline Hg-contaminated soil–plant system was consistent with the results of the acidic Hg-contaminated soil–plant system, in which Se significantly inhibited the uptake and transport of Hg by plants [19,28]. With Se supplementation, the Hg concentration and Hg-BCF in maize roots were significantly reduced. This was because insoluble HgSe complexes were formed, and then the availability of soil Hg and the Hg uptake and accumulation of plant roots were reduced [6,50]. The application of advanced analytical techniques (e.g., capRPLC-ICPMS, XANES) by McNear et al. further confirmed the presence of HgSe complexes on the surface of plant roots [26]. Additionally, reduced Hg accumulation in maize tissues and attenuated Hg-TF primarily stemmed from root-level HgSe complexation, a mechanism that has been demonstrated in analogous systems [25,51]. This process effectively limits Hg migration in maize bodies. Beyond plant-internal Hg sequestration, the optimal Se efficacy might result from dual phytomicrobial synergism [52,53]: (1) microbial reduction of Se to Se0/Se2− in the rhizosphere could form insoluble HgSe precipitates, reducing Hg bioavailability; (2) residual Se entering roots is rapidly assimilated into organic Se (e.g., selenomethionine), which could mitigate Hg phytotoxicity through mechanisms other than direct Hg binding. However, no statistically detectable divergence was observed in the Hg concentrations in maize tissues across most Se treatments. Notably, while the total Hg/Se molar ratios varied 8-fold (0.93–7.85), the Hg concentration in grains plateaued above 0.5 mg kg−1 Se treatments, suggesting bioavailability constraints overrode stoichiometric relationships. Qian et al. [54] and Xu et al. [29] both documented analogous phenomena. Selenium has been shown to promote the conversion of available soil Hg to a stable form [55]. However, the proportion of bioavailable Hg in alkaline soils is lower than in acidic soils [56]. The Se/Hg molar ratio has a significant dose-dependent effect on the reduction in Hg concentration in plants [50]. Once the threshold for antagonism was reached, further increases in the amount of exogenous Se did not further reduce the Hg concentration in plants.
Soil Se levels constitute the primary determinant of plant Se accumulation [57]. Our data demonstrate exogenous Se amendments dose-dependently elevated both the tissue Se content and Se-BCF values in maize. This dose–response relationship aligns with findings by Li et al. [27] and Feudis et al. [58]. Notably, the plant Se concentration serves as an important indicator for assessing phytostress resilience [35]. Consequently, enhanced plant Se assimilation reflects both edaphic Se enrichment and physiological demand for Hg stress mitigation [59,60]. Furthermore, the increase in Se accumulation in maize tissues was also indirectly affected by other soil characteristics, such as enzyme activity and microbial communities. These characteristics were also the key factors influencing the uptake of Se and Hg in plants [33,34,35].
Although the current study was conducted in a Se-deficient region, it is crucial to address the potential safety implications of Se accumulation in maize for consumers. The maximum Se concentration observed was 0.02 mg kg−1 in maize grain. According to the Chinese safety standard GH/T 1135-2024 [61], the Se concentrations in Se-enriched grains and their by-products range from 0.15 to 0.50 mg kg−1. Our observed levels were well below this safety threshold. Assuming a typical daily consumption range of 150–300 g of maize flour (or maize-based food) by a healthy adult in this region, the estimated daily Se intake from maize containing 0.02 mg kg−1 Se would be approximately 3–6 μg. This represents 5.45–10.91% of the recommended daily intake (55 μg) by the World Health Organization (WHO). Based on these estimates and assuming typical consumption patterns in the region, the Se levels in the enriched maize from this study appear unlikely to pose a significant toxicological risk under normal consumption. Therefore, careful optimization of Se application rates is critical to ensure that enrichment targets remain well within safe limits. Future studies should include monitoring the actual Se intake and biomarkers (e.g., blood Se) in target populations consuming biofortified maize to confirm long-term safety.

4.2. The Impact of Se on Soil Enzyme Activities

As rapid responders to environmental perturbations, enzymes governing soil nutrient cycling and oxidoreductase processes serve as sensitive bioindicators of soil health [35,62,63]. Notably, Se supplementation demonstrates significant capacity to counteract heavy metal inhibition of these key enzymes [35,64], a phenomenon corroborated by our data. Soil invertase and alkaline phosphatase activities exhibited stimulation followed by suppression under escalating Se dosages—mirroring trends documented by Hu et al. [65]. Enzymatic responsiveness to Se gradients correlated with the microbial community and functions (Figure 7 and Figure 8), mechanistically explained through Se’s competitive binding at enzyme catalytic sites and substrates, thereby modulating microbially derived enzyme production [66]. Low Se concentrations promote the growth and reproduction of microorganisms, because they can be decomposed and utilized by microorganisms, resulting in increased soil enzyme synthesis, secretion, and activity [67]. With the continuous supplementation of Se, selenide was produced under the action of microorganisms, and selenide could be related to the active site of the protease molecule to form a stable compound that competed with substrates and inhibited enzyme activity [55,68]. Furthermore, elevated Se levels additionally suppressed bacterial enzymatic biosynthesis and secretion [69].

4.3. The Regulatory Effect of Se on Soil Microorganisms

Soil microorganisms are sensitive to environmental changes and can effectively reflect soil quality; therefore, they are often used as indicators of soil change [70,71]. The diversity of bacteria directly affects the health and ecological function of soils, and responds notably to changes in the external environment [72,73]. In this study, no statistically detectable divergence was observed in different Se treatments on the richness, diversity, and uniformity of the bacterial community, indicating that the soil bacterial community could effectively resist external disturbances and maintain the soil ecological function, with Se causing minimal disruption [74]. Additionally, microbial diversity often exhibits high inertia to short-term perturbations. Consistent outcomes emerged in studies by Kang et al. [75], who found that Se application had non-significant impacts on microbial richness and diversity in the rhizospheric soil. The coverage of bacteria significantly decreased in the 2 mg kg−1 Se treatment. The reason for this was the release of persistent free radicals during the transfer and transformation of high Se concentrations, which had a toxic effect on microorganisms [74].
Fungi have a greater ability to decompose than bacteria and are also able to connect the different root systems, facilitating the exchange of nutrients and water [76,77]. The richness and diversity of fungal communities were significantly decreased under the 0.50 mg kg−1 Se treatment. This could be due to the changes in the structure and diversity of soil microbial communities after Se application, with the dominant bacteria and fungi inhibiting the growth and reproduction of other fungi, resulting in a decrease in fungal richness and diversity [78,79].
As in previous studies [80,81,82], the dominant phyla of soil bacteria in this study were Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes. Among them, Proteobacteria and Acidobacteria have a strong complexation and adsorption capacity for heavy metals, and they have the potential to remediate heavy metal-contaminated soils [83,84]. The results of this study were similar to those of Ma et al. [37], in which the relative abundance of Acidobacteria significantly increased under most Se treatments, indicating that Se could reduce the availability of soil Hg. Furthermore, Acidobacteria also play a key role in the biogeochemical carbon cycle [78]. Therefore, the trends of soil invertase activity (Figure 3a) and bacterial carbon metabolism (Figure 8) under Se mediation were consistent with the trends of the relative abundance of Acidobacteria.
The highest relative abundance of soil fungi in this study was observed for Ascomycota. Trude et al. [85] also found that Ascomycota was the dominant fungus in heavy metal-contaminated soil. Ascomycota is highly resistant to heavy metals and has important effects on the function and stability of soil ecosystems, through its interactions with other microorganisms [86,87]. The second most abundant fungi in this study were Mortierellomycota. These fungi can decompose OM and promote nutrient cycling, which in turn enhances plant growth and disease resistance [88].
A series of soil environmental factors have an impact on the structure and diversity of microbial communities [70]. The RDA analysis (Figure 7) and correlation analysis (Table S2) showed that no statistically detectable divergence was observed in soil physicochemical properties and microbial communities. This could be attributed to the statistical analysis revealing non-significant variation in the physicochemical properties of the soil after Se supplementation (Figure 2).
Based on the metagenonmeSeq analysis, the microbial communities between different Se treatments had significantly different metabolic pathways, which was consistent with the findings of Nie et al. [89], indicating that Se had a regulatory effect on soil microbial community function. Wang et al. [90] reported that bacterial chemotaxis and ABC transporters, degradation of chlorocyclohexane and chlorobenzene, and other secondary metabolites were significantly upregulated in rhizospheric microorganisms after Se application. In this study, Se regulated the functions of nitrogen metabolism, carbon metabolism, energy metabolism, membrane transport, signal transduction, and the signaling molecules of soil bacteria. These functions had a positive impact on improving the heavy metal resistance in soil–plant system and plant growth [89].
In summary, the accumulation of Hg and Se in maize tissues was used to assess the ability of Se to reduce Hg uptake and increase Se uptake in crops. The soil physicochemical properties, enzyme activities, and the structure and diversity of bacteria and fungi were assessed to determine the effect of Se on the soil ecological environment. The results revealed that appropriate Se supplementation was an effective measure for the remediation of Hg-contaminated agricultural soils. This study provided valuable insights into the comprehensive effects of Se in the remediation of Hg-contaminated farmland and its impact on ecosystems.

5. Conclusions

This study was a comprehensive and detailed investigation of the response of Hg-contaminated agricultural soil to exogenous Se, focusing on Hg and Se transport in maize, soil properties, enzyme activities, and the microbiome. The results revealed that Se significantly reduced the absorption and accumulation of Hg in maize tissues, although no difference in the Hg concentration in maize was observed under 0.50−2.00 mg kg−1 Se treatments. The Se concentrations in maize tissues significantly increased with increasing Se application. While Se supplementation in soil did not change the soil properties and activities of oxidoreductase, it significantly activated the soil enzyme activities involved in nutrient cycling, which are closely related to the soil microbial communities and bacterial functions. Proteobacteria and Ascomycota were the dominant bacterial and fungal phyla, respectively. Moreover, Se significantly affected microbial communities and diversity and regulated the bacterial community functions. The results of this study provide strong data support for the large-scale application of Se in alkaline, Hg-contaminated, Se-deficient farmland to produce crops with low Hg and high Se contents. Future studies of Se application in soil remediation should focus on the quantitative analysis of extracellular HgSe nanoparticles and tracking Se speciation dynamics during plant microbial transfer. Additionally, it is important to investigate the effective duration of Se supplementation and monitor the actual Se intake and biomarkers (e.g., blood Se) in target populations consuming biofortified plants to ensure their long-term safety, as well as the ecological risk to the microbial community structure and activity.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture15161753/s1: Figure S1. Rarefaction curves based on the microbial alpha diversity indices. Table S1. Effects of Se on the bioconcentration factor (BCF) and translocation factor (TF) of Hg and Se in maize. Table S2. Correlations between environmental variables and microbial community (genera).

Author Contributions

Conceptualization, Y.L. (Yuxin Li), Y.Z., J.H. and H.L.; methodology, Y.L. (Yuxin Li) and G.P.; software, S.G. and X.Z.; validation, Y.L. (Yuxin Li) and H.L.; formal analysis, Y.L. (Yuxin Li) and S.G.; investigation, Y.L. (Yuxin Li) and G.P.; resources, Y.L. (Yingzhong Lv) and H.L.; data curation, Y.L. (Yuxin Li) and G.P.; writing—original draft preparation, Y.L. (Yuxin Li) and S.G.; writing—review and editing, Y.L. (Yuxin Li) and H.L.; visualization, X.Z. and J.H.; project administration, Y.L. (Yingzhong Lv), Y.Z. and H.L.; funding acquisition, G.P. and Y.L. (Yingzhong Lv). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Program of Shanxi Province, China (No. 202303021212103, No. 202303021221057); the Research Award Fund for Outstanding Doctor Working in Shanxi, China (No. SXBYKY2023019); and the Scientific Research Starting Project for the Doctor of Shanxi Agricultural University, China (No. 2023BQ14).

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agriculture 15 01753 g001
Figure 1. Changes in the Hg and Se concentrations in maize under different treatments: (a) Hg concentration in roots; (b) Hg concentration in stems; (c) Hg concentration in leaves; (d) Hg concentration in grains; (e) Se concentration in roots; (f) Se concentration in stems; (g) Se concentration in leaves; (h) Se concentration in grains. Different letters represent significant differences between treatments at p < 0.05.
Figure 1. Changes in the Hg and Se concentrations in maize under different treatments: (a) Hg concentration in roots; (b) Hg concentration in stems; (c) Hg concentration in leaves; (d) Hg concentration in grains; (e) Se concentration in roots; (f) Se concentration in stems; (g) Se concentration in leaves; (h) Se concentration in grains. Different letters represent significant differences between treatments at p < 0.05.
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Figure 2. Changes in soil physicochemical properties under the different treatments: (a) pH; (b) electrical conductivity; (c) organic matter; (d) cation exchange capacity. Different letters represent significant differences between treatments at p < 0.05.
Figure 2. Changes in soil physicochemical properties under the different treatments: (a) pH; (b) electrical conductivity; (c) organic matter; (d) cation exchange capacity. Different letters represent significant differences between treatments at p < 0.05.
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Figure 3. The changes in soil enzyme activities with different treatments: (a) soil invertase activity; (b) soil urease activity; (c) soil alkaline phosphatase activity; (d) soil catalase activity; (e) soil peroxidase activity; (f) soil ascorbate oxidase activity. Different letters represent significant differences between treatments at p < 0.05.
Figure 3. The changes in soil enzyme activities with different treatments: (a) soil invertase activity; (b) soil urease activity; (c) soil alkaline phosphatase activity; (d) soil catalase activity; (e) soil peroxidase activity; (f) soil ascorbate oxidase activity. Different letters represent significant differences between treatments at p < 0.05.
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Figure 4. The PCoA results for the microbial community: (a) PCoA results for bacteria; (b) PCoA results for fungi. Se0.5, Se supplementation of 0.50 mg kg−1; Se0.7, Se supplementation of 0.75 mg kg−1; Se1, Se supplementation of 1.00 mg kg−1; Se2, Se supplementation of 2.00 mg kg−1.
Figure 4. The PCoA results for the microbial community: (a) PCoA results for bacteria; (b) PCoA results for fungi. Se0.5, Se supplementation of 0.50 mg kg−1; Se0.7, Se supplementation of 0.75 mg kg−1; Se1, Se supplementation of 1.00 mg kg−1; Se2, Se supplementation of 2.00 mg kg−1.
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Figure 5. Relative abundance of microbial communities at the phylum level: (a) relative abundance of bacteria; (b) relative abundance of fungi.
Figure 5. Relative abundance of microbial communities at the phylum level: (a) relative abundance of bacteria; (b) relative abundance of fungi.
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Figure 6. The linear discriminant analysis effect size (LEfSe) results for soil microbial community structure: (a) marker species of bacteria; (b) marker species of fungi. Se0.5, Se supplementation of 0.50 mg kg−1; Se0.7, Se supplementation of 0.75 mg kg−1; Se1, Se supplementation of 1.00 mg kg−1; Se2, Se supplementation of 2.00 mg kg−1. LDA, linear discriminant analysis.
Figure 6. The linear discriminant analysis effect size (LEfSe) results for soil microbial community structure: (a) marker species of bacteria; (b) marker species of fungi. Se0.5, Se supplementation of 0.50 mg kg−1; Se0.7, Se supplementation of 0.75 mg kg−1; Se1, Se supplementation of 1.00 mg kg−1; Se2, Se supplementation of 2.00 mg kg−1. LDA, linear discriminant analysis.
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Figure 7. The results of a redundancy analysis (RDA) of environmental variables on soil microbial community structure (genera): (a) RDA of environmental variables on bacteria; (b) RDA of environmental variables on fungi. Se0.5, Se supplementation of 0.50 mg kg−1; Se0.7, Se supplementation of 0.75 mg kg−1; Se1, Se supplementation of 1.00 mg kg−1; Se2, Se supplementation of 2.00 mg kg−1. LDA, linear discriminant analysis.
Figure 7. The results of a redundancy analysis (RDA) of environmental variables on soil microbial community structure (genera): (a) RDA of environmental variables on bacteria; (b) RDA of environmental variables on fungi. Se0.5, Se supplementation of 0.50 mg kg−1; Se0.7, Se supplementation of 0.75 mg kg−1; Se1, Se supplementation of 1.00 mg kg−1; Se2, Se supplementation of 2.00 mg kg−1. LDA, linear discriminant analysis.
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Figure 8. The metabolic pathways analysis with significant differences in soil microbial communities: (a) CK vs. 0.5 mg kg−1 Se for bacteria; (b) CK vs. 0.75 mg kg−1 Se for bacteria; (c) CK vs. 1.00 mg kg−1 Se for bacteria; (d) CK vs. 2.00 mg kg−1 Se for bacteria; (e) CK vs. 1.00 mg kg−1 Se for fungi; (f) CK vs. 2.00 mg kg−1 Se for fungi.
Figure 8. The metabolic pathways analysis with significant differences in soil microbial communities: (a) CK vs. 0.5 mg kg−1 Se for bacteria; (b) CK vs. 0.75 mg kg−1 Se for bacteria; (c) CK vs. 1.00 mg kg−1 Se for bacteria; (d) CK vs. 2.00 mg kg−1 Se for bacteria; (e) CK vs. 1.00 mg kg−1 Se for fungi; (f) CK vs. 2.00 mg kg−1 Se for fungi.
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Table 1. Alpha diversity of soil microorganisms.
Table 1. Alpha diversity of soil microorganisms.
MicroorganismSe Supplementation (mg kg−1)Alpha Diversity Indices
Chao1ShannonPielou’s EvennessGood’s Coverage
BacterialControl292 ± 14 a5.33 ± 0.44 a0.6516 ± 0.0490 a0.9994 ± 0.0001 b
0.50319 ± 71 a5.05 ± 0.76 a0.6100 ± 0.0775 a0.9995 ± 0.0002 ab
0.75263 ± 37 a4.73 ± 0.94 a0.5856 ± 0.1022 a0.9998 ± 0.0000 a
1.00250 ± 38 a4.09 ± 0.78 a0.5117 ± 0.0828 a0.9998 ± 0.0001 ab
2.00375 ± 18 a5.99 ± 0.23 a0.7032 ± 0.0040 a0.9990 ± 0.0002 c
FungiControl1584 ± 33 a9.72 ± 0.03 a0.9248 ± 0.0032 a0.9869 ± 0.0008 a
0.501366 ± 43 b9.50 ± 0.03 b0.9223 ± 0.0003 a0.9893 ± 0.0007 a
0.751420 ± 59 ab9.60 ± 0.05 ab0.9253 ± 0.0016 a0.9890 ± 0.0008 a
1.001591 ± 25 a9.68 ± 0.04 ab0.9195 ± 0.0020 a0.9865 ± 0.0004 a
2.001563 ± 112 a9.71 ± 0.10 a0.9249 ± 0.0009 a0.9873 ± 0.0015 a
Note: Different letters represent significant differences between treatments at p < 0.05.
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Li, Y.; Guan, S.; Pei, G.; Zhang, X.; Zhang, Y.; Huang, J.; Lv, Y.; Li, H. Ecological Responses of Mercury to Selenium in Farmland: Insights from Metal Transport in Crops, Soil Properties, Enzyme Activities, and Microbiome. Agriculture 2025, 15, 1753. https://doi.org/10.3390/agriculture15161753

AMA Style

Li Y, Guan S, Pei G, Zhang X, Zhang Y, Huang J, Lv Y, Li H. Ecological Responses of Mercury to Selenium in Farmland: Insights from Metal Transport in Crops, Soil Properties, Enzyme Activities, and Microbiome. Agriculture. 2025; 15(16):1753. https://doi.org/10.3390/agriculture15161753

Chicago/Turabian Style

Li, Yuxin, Shuyun Guan, Guangpeng Pei, Xiaorong Zhang, Yongbing Zhang, Junbao Huang, Yingzhong Lv, and Hua Li. 2025. "Ecological Responses of Mercury to Selenium in Farmland: Insights from Metal Transport in Crops, Soil Properties, Enzyme Activities, and Microbiome" Agriculture 15, no. 16: 1753. https://doi.org/10.3390/agriculture15161753

APA Style

Li, Y., Guan, S., Pei, G., Zhang, X., Zhang, Y., Huang, J., Lv, Y., & Li, H. (2025). Ecological Responses of Mercury to Selenium in Farmland: Insights from Metal Transport in Crops, Soil Properties, Enzyme Activities, and Microbiome. Agriculture, 15(16), 1753. https://doi.org/10.3390/agriculture15161753

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