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Article

Crop Cultivation Reshapes Soil Microbiomes to Drive Heavy Metal Mobilization in Restored Mining Areas

by
Xiaolong Lan
1,2,
Xinyin Liao
1,
Jiaxin Xiao
1,
Yanlong Jia
1,3,
Wenjie Lin
1,2,*,
Zhongwen Huang
1,2,
Zengping Ning
4,
Tangfu Xiao
5 and
Enzong Xiao
5,*
1
School of Chemistry and Environmental Engineering, Hanshan Normal University, Chaozhou 521041, China
2
Institute of Environmental Chemistry and Technology, Hanshan Normal University, Chaozhou 521041, China
3
School of Resources and Environmental Engineering, Guizhou Institute of Technology, Guiyang 550002, China
4
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
5
School of Environmental Science and Engineering, Guangzhou University, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Agriculture 2025, 15(8), 804; https://doi.org/10.3390/agriculture15080804
Submission received: 25 February 2025 / Revised: 27 March 2025 / Accepted: 1 April 2025 / Published: 8 April 2025
(This article belongs to the Special Issue Heavy Metal Pollution and Remediation in Agricultural Soils)
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Abstract

:
Mining activities cause substantial heavy metal release. Ecosystem restoration is considered one of the most effective ways to prevent heavy metal mobilization in mining areas. Previous studies have suggested that microorganisms play crucial roles in heavy metal transport in heavy metal-contaminated farmland soils. However, the interactions between the geochemical fractions of heavy metals and microbial communities under crop cultivation in restored mining areas are still unclear. In this study, we systematically collected farmland soil (FS) and grassland soil (GS) from a restored mining area to reveal the effects of crop cultivation on the composition of soil microbiomes and their potential roles in heavy metal mobilization. The results revealed that the exchangeable fractions of heavy metals (Cd, Zn, and As) in FS (11%, 11%, and 1.3% on average, respectively) were significantly greater than those in GS (30%, 19%, and 3.2% on average, respectively), indicating that agricultural activities promoted heavy metal mobilization in restored mining areas. In addition, we determined that microbial attributes, including microbial diversity, composition, and community structure, were significantly different in FS and GS. Furthermore, our results revealed that such differences were driven mainly by heavy metals and their exchangeable fractions in soils. Notably, the dominant genera enriched in FS were extensively involved in heavy metal mobilization, which is consistent with the fact that heavy metal metabolism-related genes were enriched in FS. Taken together, our findings suggest that soil microorganisms play an important role in heavy metal mobilization under crop cultivation in restored mining areas.

1. Introduction

Mining activities generate large amounts of waste rocks that accumulate in open-pit valleys [1]. These wastes generally contain high concentrations of heavy metals, which can be easily leached into water bodies and soils by wind and rain action [2,3]. Isolating mining waste is considered a key method for preventing the release and transformation of heavy metals [4,5]. Ecosystem restoration has been shown to not only restore mining ecosystems but also reduce the exposure of tailings to air, thereby decreasing heavy metal mobilization [2,6,7]. Although a series of ecosystem restoration efforts have been carried out in China [1], these efforts are frequently disrupted by natural factors (i.e., weather variations, plant invasions, and animal activities) and human activities (i.e., crop cultivation and construction activities) [8]. Among these factors, crop cultivation is the most common disturbance factor in restored mining areas because a shortage of farmland resources generally exists in mining areas [7,9,10]. However, it is still unclear whether crop cultivation induces changes in the geochemical fractions of heavy metals in restored mining areas.
Crop cultivation can not only alter vegetation types but also involve periodic activities such as pesticide and fertilizer use, irrigation, and tillage. Previous studies have indicated that crop cultivation generally alters soil conditions, such as pH, nutrient levels, and heavy metal speciation, via rhizospheric effects and agricultural activities [11]. For example, the application of nitrogen and phosphorus fertilizers can increase the dissolution of soil minerals, whereas irrigation and tillage increase contact between minerals and water or air, which can increase heavy metal mobility and bioavailability [12]. Moreover, rhizosphere exudates from crops are likely to lower soil pH and thus increase heavy metal mobilization [13]. Therefore, these studies highlight the importance of crop cultivation in promoting the transport of heavy metals in mining area soils [14]. Notably, microorganisms harbored in soils perform a variety of ecological functions, which include the biogeochemical cycling of nutrients and heavy metals [15,16,17]. However, little is known about whether changes in microbial assembly result in heavy metal mobilization under crop cultivation in restored mining areas. In fact, previous studies suggested that the enriched genus Nitrospira (N. defluvii) encodes As reduction genes (arsC), which can oxidize ammonium to nitrate and reduce As5+ to As3+ [18]. Interestingly, members of the genus Nitrospira are generally sensitive to agricultural practices, such as fertilization [19] and irrigation [20]. However, there is a lack of direct evidence for the microbially mediated transport of heavy metals induced by crop cultivation in restored mining areas. Additionally, owing to the high diversity and abundance of soil microorganisms, elucidating their interactions is challenging [21]. Recent studies have shown that keystone taxa can provide deeper insights into the soil microbial structure and community ecological functions [21]. The response of soil microorganisms in mining areas to environmental changes is driven by changes in keystone taxa [22,23]. Therefore, investigating whether crop cultivation alters keystone taxa and their potential ecological functions in restored mining area soils is crucial.
To address this, we systematically collected farmland soils (disturbed) and grassland soils (undisturbed) from a restored mining area. By analyzing physicochemical parameters and microbial composition, we aimed to investigate the following: (1) whether crop cultivation affects soil physicochemical parameters, particularly the geochemical fractions of heavy metals; (2) whether crop cultivation alters the composition and structure of microbial communities, and if so what the specific driving factors are; and (3) what environmental functions are performed by the microorganisms that are enriched in farmland soils. Our findings provide scientific evidence for the safe utilization of land in restored mining areas.

2. Materials and Methods

2.1. Site Description and Sampling

The Hezhang Pb/Zn mine is located in Hezhang County, Guizhou Province, Southwest China. Hezhang is situated on the Yunnan–Guizhou Plateau and has an elevation ranging from 1230 to 2900 m [24]. The primary soil types in the area are loamy soil and limestone soil. The main crop is maize, which covers 80% of the cultivated area [25]. The Hezhang mine is one of China’s most well-known Pb/Zn mines, with a mining history spanning several centuries [26,27]. Mining in Hezhang can be traced back to the 17th century. Owing to the indigenous zinc smelting process, severe heavy metal contamination has occurred in Hezhang [27]. The mining and smelting facilities are located primarily in the valleys and river catchment areas around Magu Town. In recent years, local environment departments have implemented a series of effective measures to restore the mining areas. However, local residents have no choice but to plant crops in restored mining areas to meet the demand for food [27].
In this study, we selected a restored mining area in Hezhang (104.8872° E, 27.1259° N). This area is located in Magu Town. During the field investigation, we found that local villagers had engaged in crop cultivation in the restored mining areas for five years. The crops included maize, peppers, potatoes, and vegetables. Since cornfields have the largest area, we have chosen them as the representative of farmland soil. We systematically collected 12 farmland soil samples and 12 grassland soil samples from this restored mining area. During the sampling process, the topsoil layers were removed with a plastic trowel, and then four subsamples were mixed into one composite sample. Visible stones and plant debris were removed before the samples were placed in plastic bags. Each sample was divided into two portions: one for physicochemical parameter analysis and the other for microbial testing. All subsamples were packed into sterile centrifuge tubes, respectively. All collected samples were temporarily stored in a mobile refrigerator. After transport to the laboratory, the samples for microbial sequencing were preserved in a deep-freezing refrigerator (−80 °C). All soil samples were collected on 16 July 2023.

2.2. Chemical Analysis

The samples for soil physicochemical parameter analysis were air-dried and then ground to 200 mesh via a ceramic grinder. A 10 g soil sample was weighed and placed in a 50 mL centrifuge tube, to which 20 mL of ultrapure water was added. The mixtures were shaken, vortexed, and allowed to settle before the pH was measured with a pH meter (PHS-3C, China). The soil organic matter (SOM) content was determined via a titration method [28]. In brief, a 1 g soil sample was placed in a 500 mL Erlenmeyer flask, which was followed by the addition of 10 mL of potassium dichromate solution. Then, 20 mL of sulfuric acid was quickly added, mixed thoroughly, and left undisturbed before being diluted to 250 mL. Three drops of the indicator o-phenanthroline were added, and the mixture was titrated with a standard solution of ferrous sulfate. The soil total carbon (TC), total nitrogen (TN), and total sulfur (TS) contents were measured according to methods in our previous study [29]. Additionally, a 100 mg soil sample was placed in a Teflon digestion vessel and subjected to high-temperature and high-pressure digestion using mixed concentrated acid (HF + HNO3). During the digestion process, standard samples (GBM908-10 and MRGeo08), blank samples, and parallel samples were used to evaluate the quality of the sample pretreatment process. The extraction of heavy metal fractions was conducted via a method proposed by the Institute for Reference Materials and Measurement (IRMM) [30]. We exclusively considered the exchangeable fraction of heavy metals in soil, as it directly reflects the portion of soil heavy metal absorbed by plants and microorganisms [29]. In brief, a 0.5 g soil powder sample was mixed with 20 mL of 0.11 M HOAc and shaken at room temperature for 16 h, which was followed by centrifugation and filtration to obtain the supernatant. The concentrations of major and trace elements in the digested and extracted solutes were determined via ICP‒MS (Agilent, 7900, Santa Clara, CA, USA). Standard solutions (from the Analysis and Testing Center of National Nonferrous Metal and Electronic Materials, China) were also used to ensure accuracy during the testing process. Meanwhile, blank, parallel, and certificated sample experiments were established during the testing process. Results indicated that the blank samples were all blow detections, the relative standard deviations of all parallel samples were all <10%, and the recovery rates for certified reference materials were between 90 and 110%, respectively.

2.3. High-Throughput Sequencing of the V4 Region of 16S rRNA Genes

According to the manufacturer’s instructions, 0.25 g of fresh soil sample was taken, and the soil genomic DNA was extracted via an MP fastDNA® Spin Kit (MP Bio, Santa Ana, CA, USA). The concentration and purity of the extracted DNA were determined via electrophoresis on a 1% agarose gel. All extracted DNA samples were then stored in a −80 °C freezer. The 16S rRNA gene from the extracted samples was amplified via PCR, which targeted the hypervariable regions V4-V5 via the primers via Gephi 515f/907r (515f: 5′-GTGYCAGCMGCCGCGGTAA-3′, 907r: 5′-CYCAATTCMTTTRAGTTT-3′) [31]. The purified PCR amplicons were sequenced on an Illumina MiSeq platform. During analysis, paired-end reads were merged via FLASH and further filtered via QIIME 2 (Quantitative Insights into Microbial Ecology) [32,33]. The raw data were first processed to remove chimeric sequences via UCHIME (http://www.drive5.com/usearch/manual/uchime_algo.html (accessed on 7 April 2024). The details of amplicon sequencing quality are listed in Table S2. Then, operational taxonomic units (OTUs) were selected via the UPARSE chimera filtering method at 97% similarity [34]. Each OTU was taxonomically classified via the RDP classifier (version 2.2, http://sourceforge.net/projects/rdp-classifier/ (accessed on 7 April 2024)) and the Greengenes database (http://greengenes.lbl.gov (accessed on 7 April 2024)). For predicting microbial functional genes, PICRUSt analysis was first performed on the imageGP web platform [35,36], which was followed by comparison with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify relevant metabolic functional genes.

2.4. Statistical Analysis

We used one-way analysis of variance (ANOVA) to observe whether there were significant differences in physicochemical parameters and microbial diversity between tilled and grassland soils. Principal coordinate analysis (PCoA) based on weighted UniFrac distances was used to analyze differences in microbial composition between the two sample groups. Using the rfPermute package (R statistical software, version 3.0.2), the random forest method was employed to identify which environmental factors were important predictors of microbial diversity, community structure, and keystone taxa. The tidyverse and DESeq2 packages were used to analyze significantly enriched OTUs in the two sample groups. Co-occurrence network analysis was used to examine the soil microbial community structure. Specifically, we first selected the top 1000 OTUs based on abundance and calculated the Spearman correlation coefficients between them (ρ). We only considered values with |ρ| > 0.7 and p < 0.05 to construct the co-occurrence networks [37]. The final network construction was completed via Gephi (0.9.5) software. In this study, we used nodes with low betweenness centralities and high degrees as criteria to identify keystone OTUs [22,38]. We also used the Stamp software platform to analyze differences in the distribution of the top 50 genera and related functional genes between the two sample groups.

3. Results

3.1. Geochemical Compositions in Farmland and Grassland Soils

The pH of the farmland and grassland soils from the restored mining area ranged from 6.13 to 7.35 (Figure 1 and Table S1). The contents of heavy metals As, Cd, Pb, and Zn in all the soil samples greatly exceeded the local background values (Table S1). The pH and the Zn content in farmland soil were significantly lower than those in grassland soil, whereas the contents of Mn, K, Mg, Na, P, SOM, TN, As, and Cd were significantly greater in farmland soil (Figure 1 and Figure S1). There were no significant differences in the remaining parameters between the two groups (Figure 1 and Figure S1). Additionally, the contents of the exchangeable fractions of As (Asexe) and Cd (Cdexe) in farmland soils were significantly greater than those in grassland soils, whereas the contents of the exchangeable fractions of Pb (Pbexe) and Zn (Znexe) were significantly lower (Figure 1). To better compare the mobility of heavy metals in the two soil groups, we further compared the percentages of exchangeable heavy metals. We found that the percentages of Asexe, Cdexe, and Znexe in farmland soil were significantly greater than those in grassland soil, with only Pbexe being significantly lower (Figure 1). Correlation analysis revealed that the pH, heavy metals, and their exchangeable fractions, S, SOM, and TC, were positively correlated with each other, whereas P and TN were mostly negatively correlated with the other parameters in farmland and grassland soils (Figure S2).

3.2. Microbial Diversity and Taxon Enrichment in the Farmland and Grassland Soils

After filtering and clustering analysis, a total of 2,588,212 reads were identified from the 24 samples. A total of 3975 OTUs were obtained according to these criteria. The maximum, minimum, and average OTU abundances for all samples were 117,564, 95,680, and 107,842, respectively. Microbial α-diversity (Shannon index) was significantly greater in the grassland soil than in the farmland soil (Figure 2A). PCoA revealed significant differences in β-diversity between the two sample groups (Figure 2B). Random forest analysis of α-diversity revealed that SOM and C were the main predictors in the grassland soil, whereas heavy metals and their related forms, such as As and Znexe, were the main predictors in the farmland soil (Figure 2C,D). We further used linear model analysis to quantify the OTUs enriched in each sample group. Compared with those in the grassland soil, 69 and 78 OTUs were significantly enriched and depleted, respectively, in the farmland soil (Figure 3). The total abundance of enriched OTUs in cultivated soil was substantially greater than that of depleted OTUs (Figure 3). Taxonomic assignments revealed that Proteobacteria, Acidobacteria, and Bacteroidetes were the major enriched phyla (Figure 3). Correlation analysis revealed that compared with those in the grassland soil environmental factors in the farmland soil had greater associations with the top 10 phyla (Figure S3).
After filtering and clustering analysis, a total of 2,588,212 reads were identified from the 24 samples. A total of 3975 OTUs were obtained according to these criteria. The maximum, minimum, and average OTU abundances for all samples were 117,564, 95,680, and 107,842, respectively. Microbial α-diversity (Shannon index) was significantly greater in the grassland soil than in the farmland soil (Figure 2A). PCoA revealed significant differences in β-diversity between the two sample groups (Figure 2B). Random forest analysis of α-diversity revealed that SOM and C were the main predictors in the grassland soil, whereas heavy metals and their related forms, such as As and Znexe, were the main predictors in the farmland soil (Figure 2C,D). We further used linear model analysis to quantify the OTUs enriched in each sample group. Compared with those in the grassland soil, 69 and 78 OTUs were significantly enriched and depleted, respectively, in the farmland soil (Figure 3). The total abundance of enriched OTUs in cultivated soil was substantially greater than that of depleted OTUs (Figure 3). Taxonomic assignments at the phylum level revealed that Proteobacteria, Acidobacteria, and Bacteroidetes were the major enriched phyla (Figure 3). Correlation analysis revealed that compared with those in the grassland soil environmental factors in the farmland soil had greater associations with the top 10 phyla (Figure S3).

3.3. Microbiota Co-Occurrence Networks and Keystone Taxa in the Farmland and Grassland Soils

In this study, we constructed co-occurrence networks for the microbial communities in the two sample groups (Figure 4). The relevant parameters of the two networks exhibited substantial differences. The farmland soil and grassland soil networks contained 76,622 and 68,266 edges (associations between taxa), respectively (Figure 4). The average degree and closeness centrality parameters were greater in the farmland soil network (Figure 4). We identified 10 keystone taxa in each of the two networks. The degree of keystone OTUs in cultivated soil was greater than 400, whereas in grassland soil it was less than 350 (Figure 4). There were also significant differences in species composition and abundance between the two groups (Figure 4). Specifically, the keystone taxa in farmland soil were mainly Proteobacteria and Acidobacteria, whereas those in grassland soil were primarily Proteobacteria and Bacteroidetes (Figure 4). Random forest analysis revealed that the main environmental factors associated with the six keystone OTUs in farmland soil were Pb, Cd, As, Asexe, Pbexe, and Cdexe (Figure 5).

3.4. Distribution Pattern of Enriched Genera in the Farmland and Grassland Soils

In this study, differential analysis of the top 50 genera revealed that Blastocatella, Chryseolinea, Gp3, Gp4, Gp6, Kofleria, Lacibacterium, Nitrospira, and Povalibacter were significantly enriched in farmland soil (Figure 6). In contrast, Altererythrobacter, Aridibacter, Arthrobacter, Blastococcus, Bradyrhizobium, Devosia, Gemmatimonas, Georgenia, Ohtaekwangia, Opitutus, Phenylobacterium, Saccharibacteria_genera_incertae_sedis, and Salinibacterium were significantly enriched in the grassland soil (Figure 6).

3.5. Predicted Metabolic Functions in the Farmland and Grassland Soils

The prediction results of microbial functional genes revealed significant differences in the abundance of microbially related functional genes between farmland soil and grassland soil. For example, functional genes related to metabolism, organismal systems, cellular processes, environmental information processing, and genetic information processing were significantly enriched in farmland soil, whereas only a few related genes were enriched in grassland soil (Figure S4). We further selected heavy metal-related functional metabolic genes for comparative analysis. The results revealed that the arsenic reduction-related metabolic gene (arsC) was significantly enriched in grassland soil (Figure 7). In contrast, As transport-related metabolic genes (arsA and ARC3) and Zn transport-related metabolic genes (znuC and ZnuB) were enriched in farmland soil (Figure 7).

4. Discussion

The Hezhang Pb/Zn mine in Guizhou has a long history of mining and smelting lead and zinc [27]. We found that many polluted areas in Hezhang have undergone ecosystem restoration. However, owing to the scarcity of agricultural land many restored areas have been cultivated with crops such as corn, peppers, and vegetables [27]. This study revealed severe heavy metal pollution in the soils of restored mining areas (Table S1). Our findings showed that agricultural practices can increase soil nutrients, such as SOM, TN, and TP (Figure 1), which is consistent with the findings of a previous study [39]. However, we also found that agricultural activities significantly reduced soil pH and increased the mobility of Cd, As, and Zn in the soil (Figure 1). A previous study has suggested that periodic irrigation and tillage activities improve sulfide oxidation, leading to a decrease in soil pH but an increase in heavy metal transport [40]. Another study also reported that the application of fertilizers could increase the dissolution of adsorbed heavy metals via ion exchange reactions [12]. These results indicated that agricultural activities significantly altered the soil physicochemical properties and increased the mobility of heavy metals in restored mining areas, rendering these soils unsuitable for crop cultivation. Notably, numerous studies have shown that changes in the transport of heavy metals are closely related to microbial processes in soils [41,42]. Therefore, we further compared and explored the differences in microbial diversity, composition, and community structure between farmland soils and grassland soils, as well as related environmental factors, and evaluated whether the microbial genera enriched in farmland soils were related to the transport of heavy metals.
In this study, we found significant differences in microbial diversity between farmland soils and grassland soils in the restored mining area (Figure 2A,B). Additionally, there were distinct OTUs enriched in the two groups of samples (Figure 3). These findings indicate that agricultural activities significantly altered microbial diversity and assembly in the restored mining area soils. These results are consistent with previous findings showing that differences in microbial diversity and composition are closely related to vegetation type and agricultural activities. Typically, different plants release different types and quantities of root exudates, which leads to the recruitment of different microbial communities [43]. Other studies have shown that fertilizer application can increase the level of soil nutrients (such as TOC, N, and P), which can promote the evolution, reproduction, and formation of complex microbial communities [41], whereas pesticide application can inhibit microbial activity [44]. Furthermore, tillage and irrigation can alter soil temperature, pH, redox potential, moisture content, and oxygen content, thereby affecting microorganisms [45,46]. Additionally, random forest analysis revealed that heavy metals and their exchangeable fractions in cultivated soils were the most critical factors influencing microbial diversity (Figure 2C). This finding is consistent with previous studies in which microorganisms were shown to be highly tolerant of metal-contaminated environments [47,48]. For example, phylum-level taxonomic classification of the major OTUs indicated that Acidobacteria and Proteobacteria were significantly enriched in farmland soils (Figure 3). Acidobacteria and Proteobacteria are commonly found in metal-contaminated soils and are widely involved in the biogeochemical cycling of heavy metals and nutrients. For example, members of Acidobacteria can facilitate the dissolution of nutrients present in the soil mineral phase, such as K, P, and S [41,49]; Acidobacteria also promotes the decomposition of high-molecular-weight organic matter and the fixation of C and N, helping crops adapt to heavy metal-contaminated soil environments [50]. Additionally, members of Proteobacteria can promote the dissolution of absorbed heavy metals through their cellular exudates or rhizosphere exudates, reduce heavy metals through microbial metabolism, or bind them with organic matter to form complexes, thereby immobilizing heavy metals [41,51,52]. Therefore, the microorganisms significantly enriched in farmland soils are likely involved in the biogeochemical cycling of heavy metals and nutrients.
Elucidating interactions within complex ecological networks of soil microorganisms is crucial for understanding the microbial community structure and responses to environmental factors [53]. Given the significant differences in edaphic factors between farmland and grassland soils in the restored mining area, we can reasonably infer that crop cultivation likely had a significant impact on the microbial community structure. In fact, we observed that farmland soils had taxa (nodes) with greater connectivity than did grassland soils, indicating that farmland soils harbored a more complex microbial network (Figure 4). There is evidence that the more complex the microbial community structure is, the better it can resist external environmental disturbances [54]. Therefore, we can infer that the microbial community structure in farmland soils is more complex and that the community has a stronger ability to resist environmental disturbances than that in grassland soils in restored mining areas. This inference is reasonable because farmland soils have relatively high nutrient levels and loosely bound fractions of heavy metals (Figure 1). In general, the greater the nutrient content in soil is, the more favorable it is for microbial reproduction [55,56]. Additionally, increased mobility of heavy metals is advantageous for the selection, adaptation, and growth of certain microbes [40].
Keystone taxa are considered to be closely associated with other taxa and play a key role in maintaining the network structure of microorganisms in environmental systems [57]. We detected significant differences in keystone taxa between farmland and grassland soils in the restored mining area (Figure 4). Therefore, we can reasonably infer that those agricultural activities altered microbial assembly in the soil ecosystem. This finding is consistent with those of a previous study, which revealed significant differences in the composition of keystone taxa across different land use types [41]. More importantly, heavy metals and their exchangeable fractions in farmland soils were identified as the main predictors of most keystone OTUs (Figure 5), indicating that they are also key factors in controlling network stability. Given the significant differences in heavy metals and their geochemical forms between the two soil types, we can reasonably infer that the activation of heavy metals induced by crop cultivation drives the formation of microbial community structures. Additionally, since the degree of keystone OTUs in grassland soils was much lower than that in farmland soils (Figure 4), we can speculate that the increase in heavy metal mobilization induced by agricultural activities shaped the microbial network and improved its stability. This finding is consistent with those of a previous study, which revealed that microorganisms adapt to heavy metal-contaminated environments and evolve metabolic genes encoding heavy metal detoxification [45]. Microorganisms may derive energy through changes in the valence state of heavy metals via the decomposition of TOC or nitrogen compounds in farmland soils, thereby reducing heavy metal toxicity [58,59]. Interestingly, OTU_177 in farmland soils had the highest degree and belongs to the family Gemmatimonadaceae. Members of the Gemmatimonadaceae family are commonly found to constitute a part of the core microbiome in As- and Sb-contaminated soils, which can oxidize As3+ to As5+ to obtain energy and thus increase As mobility [60]. Additionally, Gemmatimonadaceae is enriched in V-contaminated soils and plays an important role in the migration and transformation of V [61,62]. Zhang et al. (2021) further demonstrated that Gemmatimonadaceae can promote the transfer of V into cells for reduction by dissolving phosphorus [49]. Moreover, Gemmatimonadaceae can enhance nitrification processes (such as aerobic ammonia oxidation and aerobic nitrite oxidation) in soils and reduce Cd accumulation but increase Zn absorption in wheat grains [63]. These findings suggest that the dominant keystone taxon Gemmatimonadaceae plays a crucial role in the biogeochemical cycling of heavy metals and nutrients, which explains its core position in the microbial network in the farmland soils of the restored mining area.
We further found that many genera within the top 50 abundant taxa were enriched in either the farmland soils or the grassland soils of the mining area (Figure 6). Notably, the microorganisms enriched in the farmland soils in this study were also found to be enriched in other heavy metal-contaminated farmland soils. Our functional prediction results also revealed that most microbe-related functional genes were significantly enriched in farmland soils (Figure S4), indicating that crop cultivation played a crucial role in driving soil microbial functions. Specifically, the genus Blastocatella enriched in farmland soils is also highly tolerant to Cd-, Pb-, Zn-, and Hg-contaminated soils [64,65,66]. Liu et al. (2023) also reported that Blastocatella was significantly enriched in Zn-contaminated soils and had a significant positive correlation with the biomass and Zn accumulation of Lolium mutiflorum Lam., suggesting that Blastocatella may play an important role in the mobilization and bioavailability of Zn [66]. The genus Chryseolinea enriched in farmland soils has been reported to be closely associated with the degradation of lignin, carbohydrates, and some organic acids, as well as with phosphorus solubilization in soil [67,68]. Liang et al. (2023) reported that Chryseolinea was significantly enriched in Cd-contaminated soils and promoted the absorption of Cd by Cd-hyperaccumulating plants, indicating that Chryseolinea plays an important role in the mobilization and translocation of Cd [68]. The genera Gp3, Gp4, and Gp6 belong to the phylum Acidobacteria and are commonly found in acidic soils from mining areas [47]. A previous study has shown that Gp3 is enriched on coated As5+-Lp oxide slides and likely contains As-reducing metabolic genes that play crucial roles in As reduction and detoxification [69]. Gp4 has been found to be enriched in Cd-contaminated environments [42]. Gp6 is generally involved in the C and N nutrient cycles [70] and is enriched in soils and plants contaminated with heavy metals in mining areas [71]. A previous study has indicated that environmental factors such as the C/N ratio, Zn concentration, and cation exchange capacity regulate Gp6 distribution [72]. Nitrospira belongs to the phylum Nitrospirae and is a nitrifying bacterium with arsC-type arsenate reductase, which can reduce As5+ to As3+ and thus increase As mobility [73]. Povalibacter is closely associated with the various Pb, As, Hg, and Sb fractions in mining areas and is likely involved in the cycling of toxic elements [74]. Our functional gene prediction results also revealed that the functional genes related to As and Zn transport were significantly enriched in farmland soils (Figure 7), indicating that microbial activity promotes heavy metal mobilization in farmland soils. Further metagenomic characterization is imperative to corroborate the functional gene abundance linked to heavy metal biotransformation pathways in enriched soil microbiomes of FS.
Furthermore, many studies have shown that microorganisms enriched in grassland soils are closely related to reduced heavy metal mobility and bioavailability. For example, in remediated heavy metal-contaminated soils Altererythrobacter was significantly enriched in the rhizosphere of Festuca elata (F.elata) and likely played a key role in reducing soil heavy metal bioavailability and decreasing plant transfer rates in peat- and bentonite-amended soils [75]. Altererythrobacter has also been found to encode genes that can adapt to soils highly contaminated with heavy metals, indicating its potential for heavy metal pollution remediation [76]. Another study reported that Altererythrobacter could reduce Cr mobility and thus decrease Cr toxicity [77]. Members of Bradyrhizobium (sp. 750) are heavy metal-resistant plant growth-promoting rhizobacteria (PGPRs) and play a role in nitrogen fixation and the inhibition of heavy metal bioavailability in soils [78,79]. Although Bradyrhizobium is strongly resistant to As, its ability to reduce As5+ to As3+ and thus decrease As3+ mobilization has been reported [80]. This finding is consistent with our findings, as As-related reductase genes were significantly enriched in grassland soils (Figure 7), suggesting that microbial metabolic functions likely contributed to a reduction in As mobility. Taken together, our results confirmed that crop cultivation altered microbial assembly and promoted the microbially meditated mobilization of heavy metals in restored mining areas.

5. Conclusions

In this study, we investigated the interactions between the microbial community structure and the geochemical fractions of heavy metals associated with crop cultivation in restored mining areas. Our results indicated that crop cultivation enhanced heavy metal mobilization and altered the microbial assemblages in restored mining areas. Random forest analysis revealed that heavy metals and their exchangeable fractions associated with crop cultivation are the most important factors influencing microbial diversity, composition, and community structure in farmland soils. Keystone taxa and enriched genera are likely to increase heavy metal mobilization in farmland soils. Therefore, we recommended that local residents avoid crop cultivation in restored mining areas. Our results provide a scientific basis for ensuring the safety of land use in restored mining areas.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture15080804/s1. Figure S1: Geochemical profiles (Mn, Al, and Fe) of farmland soil (FS) and grassland soil (GS) in the restored mining area. Figure S2: Correlation heatmap showing the relationships between edaphic factors in farmland soil (FS) and grassland soil (GS) in the restored mining area. Figure S3: Correlation heatmaps of geochemical parameters with the top 10 phyla in farmland soil (FS) and grassland soil (GS) from the restored mining area. Figure S4: Distribution of microbial functions in farmland soil (FS) and grassland soil (GS) in the restored mining area. Table S1: Geochemical parameters and microbial α-diversity indices in farmland soil (FS) and grassland soil (GS) from the restored mining area [81].

Author Contributions

Conceptualization, X.L. (Xiaolong Lan), W.L., Z.N. and E.X.; methodology, X.L. (Xiaolong Lan), X.L. (Xinyin Liao), J.X., Y.J. and E.X.; software, X.L. (Xiaolong Lan), X.L. (Xinyin Liao) and J.X.; validation, Y.J., W.L. and E.X.; formal analysis, X.L. (Xiaolong Lan), X.L. (Xinyin Liao), J.X. and Y.J.; investigation, X.L. (Xiaolong Lan), X.L. (Xinyin Liao), J.X. and Y.J.; resources, T.X.; data curation, Y.J., W.L. and E.X.; writing—original draft preparation, X.L. (Xiaolong Lan); writing—review and editing, X.L. (Xiaolong Lan), Y.J., W.L., Z.H., Z.N., T.X. and E.X.; visualization, X.L. (Xiaolong Lan), X.L. (Xinyin Liao) and J.X.; supervision, W.L. and T.X.; project administration, X.L. (Xiaolong Lan), Y.J., W.L. and T.X.; funding acquisition, X.L. (Xiaolong Lan), Y.J., W.L. and T.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Guizhou Provincial Key Technology R&D Program (Project No. [2021]492), National Natural Science Foundation of China (42307345), and Project of Educational Commission of Guangdong Province of China (2023KSYS007, 2023KTSCX078, 2023KCXTD023, 2020ZDZX1032).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in the Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, T.; Wen, X.; Zhou, J.; Lu, Z.; Li, X.; Yan, B. A critical review on the migration and transformation processes of heavy metal contamination in lead-zinc tailings of China. Environ. Pollut. 2023, 338, 122667. [Google Scholar] [PubMed]
  2. Mahar, A.; Wang, P.; Ali, A.; Awasthi, M.K.; Lahori, A.H.; Wang, Q.; Li, R.; Zhang, Z. Challenges and opportunities in the phytoremediation of heavy metals contaminated soils: A review. Ecotoxicol. Environ. Saf. 2016, 126, 111–121. [Google Scholar] [CrossRef]
  3. Sánchez-Castro, I.; Molina, L.; Prieto-Fernández, M.-Á.; Segura, A. Past, present and future trends in the remediation of heavy-metal contaminated soil—Remediation techniques applied in real soil-contamination events. Heliyon 2023, 9, e16692. [Google Scholar]
  4. Liu, X.; Bai, Z.; Shi, H.; Zhou, W.; Liu, X. Heavy metal pollution of soils from coal mines in China. Nat. Hazards 2019, 99, 1163–1177. [Google Scholar]
  5. Xiao, T.; Yang, F.; Li, S.; Zheng, B.; Ning, Z. Thallium pollution in China: A geo-environmental perspective. Sci. Total Environ. 2012, 421–422, 51–58. [Google Scholar]
  6. Wong, M.H. Ecological restoration of mine degraded soils, with emphasis on metal contaminated soils. Chemosphere 2003, 50, 775–780. [Google Scholar] [CrossRef] [PubMed]
  7. Dhal, B.; Thatoi, H.N.; Das, N.N.; Pandey, B.D. Chemical and microbial remediation of hexavalent chromium from contaminated soil and mining/metallurgical solid waste: A review. J. Hazard. Mater. 2013, 250–251, 272–291. [Google Scholar]
  8. Zhuang, P.; McBride, M.B.; Xia, H.; Li, N.; Li, Z. Health risk from heavy metals via consumption of food crops in the vicinity of Dabaoshan mine, South China. Sci. Total Environ. 2009, 407, 1551–1561. [Google Scholar] [CrossRef]
  9. Qin, G.; Niu, Z.; Yu, J.; Li, Z.; Ma, J.; Xiang, P. Soil heavy metal pollution and food safety in China: Effects, sources and removing technology. Chemosphere 2021, 267, 129205. [Google Scholar] [CrossRef]
  10. Wu, Y.; Li, X.; Yu, L.; Wang, T.; Wang, J.; Liu, T. Review of soil heavy metal pollution in China: Spatial distribution, primary sources, and remediation alternatives. Resour. Conserv. Recycl. 2022, 181, 106261. [Google Scholar] [CrossRef]
  11. Yang, L.; Li, S.; Wen, T.; Meng, F.; Chen, G.; Qian, X. Influence of ferrous-metal production on mercury contamination and fractionation in farmland soil around five typical iron and steel enterprises of Tangshan, China. Ecotoxicol. Environ. Saf. 2020, 188, 109774. [Google Scholar] [CrossRef]
  12. Tang, G.; Zhang, X.; Qi, L.; Li, L.; Guo, J.; Zhong, H.; Liu, J.; Huang, J. Nitrogen and Phosphorus Fertilizer Increases the Uptake of Soil Heavy Metal Pollutants by Plant Community. Bull. Environ. Contam. Toxicol. 2022, 109, 1059–1066. [Google Scholar] [CrossRef] [PubMed]
  13. Fu, Y.; Li, M.; Li, J.; Chen, D.; Ye, T.; Wang, C.; Yan, F.; Qiu, Z. Soil microbial community variation in vanadium-enriched farmland surrounding vanadium titanomagnetite tailing in Southwest China. Appl. Soil. Ecol. 2024, 198, 105318. [Google Scholar] [CrossRef]
  14. Lu, Y.; Xu, Y.; Guo, Z.; Shi, C.; Wang, J. A Study of the Differences in Heavy Metal Distributions in Different Types of Farmland in a Mining Area. Bull. Environ. Contam. Toxicol. 2022, 109, 788–798. [Google Scholar] [CrossRef]
  15. Sun, W.; Sun, X.; Li, B.; Haggblom, M.M.; Han, F.; Xiao, E.; Zhang, M.; Wang, Q.; Li, F. Bacterial response to antimony and arsenic contamination in rice paddies during different flooding conditions. Sci. Total Environ. 2019, 675, 273–285. [Google Scholar] [CrossRef] [PubMed]
  16. Trivedi, P.; Leach, J.E.; Tringe, S.G.; Sa, T.; Singh, B.K. Plant–microbiome interactions: From community assembly to plant health. Nat. Rev. Microbiol. 2020, 18, 607–621. [Google Scholar] [CrossRef]
  17. Bolan, S.; Sharma, S.; Mukherjee, S.; Kumar, M.; Rao, C.S.; Nataraj, K.C.; Singh, G.; Vinu, A.; Bhowmik, A.; Sharma, H.; et al. Biochar modulating soil biological health: A review. Sci. Total Environ. 2024, 914, 169585. [Google Scholar] [CrossRef] [PubMed]
  18. Lücker, S.; Wagner, M.; Maixner, F.; Pelletier, E.; Koch, H.; Vacherie, B.; Rattei, T.; Damsté, J.S.S.; Spieck, E.; Le Paslier, D.; et al. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. Proc. Natl. Acad. Sci. USA 2010, 107, 13479–13484. [Google Scholar] [CrossRef]
  19. Han, S.; Huang, Q.; Chen, W. Partitioning Nitrospira community structure and co-occurrence patterns in a long-term inorganic and organic fertilization soil. J. Soils Sediments 2021, 21, 1099–1108. [Google Scholar] [CrossRef]
  20. Wu, J.; Ma, C.; Li, F. Microbial community structure and function in paddy soil as affected by water-saving irrigation mode. Eur. J. Soil. Biol. 2022, 113, 103450. [Google Scholar] [CrossRef]
  21. de Menezes, A.B.; Prendergast-Miller, M.T.; Richardson, A.E.; Toscas, P.; Farrell, M.; Macdonald, L.M.; Baker, G.; Wark, T.; Thrall, P.H. Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters. Environ. Microbiol. 2015, 17, 2677–2689. [Google Scholar] [PubMed]
  22. Li, Y.; Yang, R.; Häggblom, M.M.; Li, M.; Guo, L.; Li, B.; Kolton, M.; Cao, Z.; Soleimani, M.; Chen, Z.; et al. Characterization of diazotrophic root endophytes in Chinese silvergrass (Miscanthus sinensis). Microbiome 2022, 10, 186. [Google Scholar]
  23. Agler, M.T.; Ruhe, J.; Kroll, S.; Morhenn, C.; Kim, S.-T.; Weigel, D.; Kemen, E.M. Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation. PLoS Biol. 2016, 14, e1002352. [Google Scholar]
  24. Bi, X.; Feng, X.; Yang, Y.; Li, X.; Sin, G.P.Y.; Qiu, G.; Qian, X.; Li, F.; He, T.; Li, P.; et al. Heavy metals in an impacted wetland system: A typical case from southwestern China. Sci. Total Environ. 2007, 387, 257–268. [Google Scholar]
  25. Bi, X.; Feng, X.; Yang, Y.; Qiu, G.; Li, G.; Li, F.; Liu, T.; Fu, Z.; Jin, Z. Environmental contamination of heavy metals from zinc smelting areas in Hezhang County, western Guizhou, China. Environ. Int. 2006, 32, 883–890. [Google Scholar] [CrossRef] [PubMed]
  26. Zhu, Z.; Li, Z.; Bi, X.; Han, Z.; Yu, G. Response of magnetic properties to heavy metal pollution in dust from three industrial cities in China. J. Hazard. Mater. 2013, 246–247, 189–198. [Google Scholar]
  27. Han, J.; Liang, L.; Zhu, Y.; Xu, X.; Wang, L.; Shang, L.; Wu, P.; Wu, Q.; Qian, X.; Qiu, G.; et al. Heavy metal(loid)s in farmland soils on the Karst Plateau, Southwest China: An integrated analysis of geochemical baselines, source apportionment, and associated health risk. Land Degrad. Dev. 2022, 33, 1689–1703. [Google Scholar]
  28. Liu, Y.; Xiao, T.; Ning, Z.; Li, H.; Tang, J.; Zhou, G. High cadmium concentration in soil in the Three Gorges region: Geogenic source and potential bioavailability. Appl. Geochem. 2013, 37, 149–156. [Google Scholar]
  29. Xiao, E.; Krumins, V.; Dong, Y.; Xiao, T.; Ning, Z.; Xiao, Q.; Sun, W. Microbial diversity and community structure in an antimony-rich tailings dump. Appl. Microbiol. Biot. 2016, 100, 7751–7763. [Google Scholar]
  30. Rauret, G.; López-Sánchez, J.F.; Sahuquillo, A.; Rubio, R.; Davidson, C.; Ure, A.; Quevauviller, P. Improvement of the BCR three step sequential extraction procedure prior to the certification of new sediment and soil reference materials. J. Environ. Monit. 1999, 1, 57–61. [Google Scholar]
  31. Kuczynski, J.; Stombaugh, J.; Walters, W.A.; González, A.; Caporaso, J.G.; Knight, R. Using QIIME to Analyze 16S rRNA Gene Sequences from Microbial Communities. Curr. Protoc. Bioinform. 2011, 36, 10.17.1–10.17.20. [Google Scholar]
  32. Bokulich, N.A.; Subramanian, S.; Faith, J.J.; Gevers, D.; Gordon, J.I.; Knight, R.; Mills, D.A.; Caporaso, J.G. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 2013, 10, 57–59. [Google Scholar]
  33. Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [PubMed]
  34. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar]
  35. Langille, M.G.I.; Zaneveld, J.; Caporaso, J.G.; McDonald, D.; Knights, D.; Reyes, J.A.; Clemente, J.C.; Burkepile, D.E.; Vega Thurber, R.L.; Knight, R.; et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 2013, 31, 814–821. [Google Scholar] [CrossRef]
  36. Chen, T.; Liu, Y.-X.; Huang, L. ImageGP: An easy-to-use data visualization web server for scientific researchers. iMeta 2022, 1, e5. [Google Scholar]
  37. Sun, X.; Li, B.; Han, F.; Xiao, E.; Wang, Q.; Xiao, T.; Sun, W. Vegetation type impacts microbial interaction with antimony contaminants in a mining-contaminated soil environment. Environ. Pollut. 2019, 252, 1872–1881. [Google Scholar] [CrossRef] [PubMed]
  38. Hu, A.; Ju, F.; Hou, L.; Li, J.; Yang, X.; Wang, H.; Mulla, S.I.; Sun, Q.; Bürgmann, H.; Yu, C.-P. Strong impact of anthropogenic contamination on the co-occurrence patterns of a riverine microbial community. Environ. Microbiol. 2017, 19, 4993–5009. [Google Scholar] [CrossRef]
  39. Wang, W.; Yang, Y.; Li, J.; Bu, P.; Lu, A.; Wang, H.; He, W.; Santos Bermudez, R.; Feng, J. Consecutive Fertilization-Promoted Soil Nutrient Availability and Altered Rhizosphere Bacterial and Bulk Fungal Community Composition. Forests 2024, 15, 514. [Google Scholar] [CrossRef]
  40. Sun, W.; Xiao, E.; Xiao, T.; Krumins, V.; Wang, Q.; Häggblom, M.; Dong, Y.; Tang, S.; Hu, M.; Li, B.; et al. Response of Soil Microbial Communities to Elevated Antimony and Arsenic Contamination Indicates the Relationship between the Innate Microbiota and Contaminant Fractions. Environ. Sci. Technol. 2017, 51, 9165–9175. [Google Scholar]
  41. Xiao, E.; Wang, Y.; Xiao, T.; Sun, W.; Deng, J.; Jiang, S.; Fan, W.; Tang, J.; Ning, Z. Microbial community responses to land-use types and its ecological roles in mining area. Sci. Total Environ. 2021, 775, 145753. [Google Scholar] [CrossRef]
  42. Haque, S.; Srivastava, N.; Pal, D.B.; Alkhanani, M.F.; Almalki, A.H.; Areeshi, M.Y.; Naidu, R.; Gupta, V.K. Functional microbiome strategies for the bioremediation of petroleum-hydrocarbon and heavy metal contaminated soils: A review. Sci. Total Environ. 2022, 833, 155222. [Google Scholar] [CrossRef]
  43. Wang, L.; Zhang, B.; Fang, Y.; Yin, H.; Fu, S.; Chang, S.X.; Cai, Y. Distinct effects of canopy vs understory and organic vs inorganic N deposition on root resource acquisition strategies of subtropical Moso bamboo plants. Sci. Total Environ. 2024, 927, 172424. [Google Scholar] [CrossRef]
  44. Alvarez, A.; Saez, J.M.; Davila Costa, J.S.; Colin, V.L.; Fuentes, M.S.; Cuozzo, S.A.; Benimeli, C.S.; Polti, M.A.; Amoroso, M.J. Actinobacteria: Current research and perspectives for bioremediation of pesticides and heavy metals. Chemosphere 2017, 166, 41–62. [Google Scholar] [CrossRef]
  45. Sun, W.; Xiao, E.; Pu, Z.; Krumins, V.; Dong, Y.; Li, B.; Hu, M. Paddy soil microbial communities driven by environment- and microbe-microbe interactions: A case study of elevation-resolved microbial communities in a rice terrace. Sci. Total Environ. 2018, 612, 884–893. [Google Scholar] [CrossRef] [PubMed]
  46. Guo, Q.; Zhai, W.; Li, P.; Xiong, Y.; Li, H.; Liu, X.; Zhou, Z.; Li, B.; Wang, P.; Liu, D. Nitrogen fertiliser-domesticated microbes change the persistence and metabolic profile of atrazine in soil. J. Hazard. Mater. 2024, 469, 133974. [Google Scholar] [CrossRef]
  47. Xu, R.; Sun, X.; Häggblom, M.M.; Dong, Y.; Zhang, M.; Yang, Z.; Xiao, E.; Xiao, T.; Gao, P.; Li, B.; et al. Metabolic potentials of members of the class Acidobacteriia in metal-contaminated soils revealed by metagenomic analysis. Environ. Microbiol. 2022, 24, 803–818. [Google Scholar] [CrossRef]
  48. Lan, X.; Lin, W.; Ning, Z.; Su, X.; Chen, Y.; Jia, Y.; Xiao, E. Arsenic shapes the microbial community structures in tungsten mine waste rocks. Environ. Res. 2023, 216, 114573. [Google Scholar] [CrossRef] [PubMed]
  49. Zhang, M.; Lu, G.; Li, Z.; Xu, F.; Yang, N.; Sun, X.; Xu, R.; Sun, W. Effects of antimony on anaerobic methane oxidization and microbial community in an antimony-contaminated paddy soil: A microcosm study. Sci. Total Environ. 2021, 784, 147239. [Google Scholar] [CrossRef]
  50. Xu, R.; Li, B.; Xiao, E.; Young, L.Y.; Sun, X.; Kong, T.; Dong, Y.; Wang, Q.; Yang, Z.; Chen, L.; et al. Uncovering microbial responses to sharp geochemical gradients in a terrace contaminated by acid mine drainage. Environ. Pollut. 2020, 261, 114226. [Google Scholar] [CrossRef]
  51. Wang, M.; Zhang, B.; Zhang, H.; Geng, R.; Huang, Y.; Yang, J.-Y.; Teng, Y. Ecological and Health Risks of Vanadium in the Biogeosphere. Rev. Environ. Contam. Toxicol. 2024, 262, 13. [Google Scholar]
  52. Gu, J.; Guo, F.; Lin, L.; Zhang, J.; Sun, W.; Muhammad, R.; Liang, H.; Duan, D.; Deng, X.; Lin, Z.; et al. Microbiological mechanism for “production while remediating” in Cd-contaminated paddy fields: A field experiment. Sci. Total Environ. 2023, 885, 163896. [Google Scholar] [CrossRef] [PubMed]
  53. Li, Y.; Zhang, M.; Xu, R.; Lin, H.; Sun, X.; Xu, F.; Gao, P.; Kong, T.; Xiao, E.; Yang, N.; et al. Arsenic and antimony co-contamination influences on soil microbial community composition and functions: Relevance to arsenic resistance and carbon, nitrogen, and sulfur cycling. Environ. Int. 2021, 153, 106522. [Google Scholar] [PubMed]
  54. Guseva, K.; Darcy, S.; Simon, E.; Alteio, L.V.; Montesinos-Navarro, A.; Kaiser, C. From diversity to complexity: Microbial networks in soils. Soil Biol. Biochem. 2022, 169, 108604. [Google Scholar]
  55. Jing, X.; Chen, X.; Fang, J.; Ji, C.; Shen, H.; Zheng, C.; Zhu, B. Soil microbial carbon and nutrient constraints are driven more by climate and soil physicochemical properties than by nutrient addition in forest ecosystems. Soil Biol. Biochem. 2020, 141, 107657. [Google Scholar]
  56. Lan, X.; Ning, Z.; Jia, Y.; Lin, W.; Xiao, E.; Cheng, Q.; Cai, Q.; Xiao, T. The rhizosphere microbiome reduces the uptake of arsenic and tungsten by Blechnum orientale by increasing nutrient cycling in historical tungsten mining area soils. Sci. Total Environ. 2024, 924, 171429. [Google Scholar]
  57. Wang, X.; Teng, Y.; Wang, X.; Li, X.; Luo, Y. Microbial diversity drives pyrene dissipation in soil. Sci. Total Environ. 2022, 819, 153082. [Google Scholar]
  58. Tang, J.; Zhang, J.; Ren, L.; Zhou, Y.; Gao, J.; Luo, L.; Yang, Y.; Peng, Q.; Huang, H.; Chen, A. Diagnosis of soil contamination using microbiological indices: A review on heavy metal pollution. J. Environ. Manag. 2019, 242, 121–130. [Google Scholar]
  59. Zhang, B.; Zhang, H.; He, J.; Zhou, S.; Dong, H.; Rinklebe, J.; Ok, Y.S. Vanadium in the Environment: Biogeochemistry and Bioremediation. Environ. Sci. Technol. 2023, 57, 14770–14786. [Google Scholar]
  60. Li, B.; Xu, R.; Sun, X.; Han, F.; Xiao, E.; Chen, L.; Qiu, L.; Sun, W. Microbiome–environment interactions in antimony-contaminated rice paddies and the correlation of core microbiome with arsenic and antimony contamination. Chemosphere 2021, 263, 128227. [Google Scholar] [CrossRef]
  61. Fei, Y.; Zhang, B.; He, J.; Chen, C.; Liu, H. Dynamics of vertical vanadium migration in soil and interactions with indigenous microorganisms adjacent to tailing reservoir. J. Hazard. Mater. 2022, 424, 127608. [Google Scholar] [CrossRef]
  62. Zhang, H.; Zhang, B.; Gao, Y.; Wang, Y.; Lu, J.; Chen, J.; Chen, D.; Deng, Q. The role of available phosphorous in vanadate decontamination by soil indigenous microbial consortia. Environ. Pollut. 2021, 289, 117839. [Google Scholar] [CrossRef]
  63. Liu, N.; Liu, Q.; Min, J.; Zhang, S.; Li, S.; Chen, Y.; Dai, J. Specific bacterial communities in the rhizosphere of low-cadmium and high-zinc wheat (Triticum aestivum L.). Sci. Total Environ. 2022, 838, 156484. [Google Scholar] [CrossRef] [PubMed]
  64. Chun, S.-J.; Kim, Y.-J.; Cui, Y.; Nam, K.-H. Ecological network analysis reveals distinctive microbial modules associated with heavy metal contamination of abandoned mine soils in Korea. Environ. Pollut. 2021, 289, 117851. [Google Scholar] [CrossRef] [PubMed]
  65. Guo, H.; Nasir, M.; Lv, J.; Dai, Y.; Gao, J. Understanding the variation of microbial community in heavy metals contaminated soil using high throughput sequencing. Ecotoxicol. Environ. Saf. 2017, 144, 300–306. [Google Scholar] [CrossRef] [PubMed]
  66. Liu, T.; Wang, S.; Chen, Y.; Luo, J.; Hao, B.; Zhang, Z.; Yang, B.; Guo, W. Bio-organic fertilizer promoted phytoremediation using native plant leymus chinensis in heavy Metal(loid)s contaminated saline soil. Environ. Pollut. 2023, 327, 121599. [Google Scholar] [CrossRef]
  67. Gong, X.; Shi, W.; Zhang, Z.; Luo, M.; Zhang, B.; Wan, S.; Huang, J. Exploring the effects of zeolite, biochar, and diatomite as additives for enhancing heavy metals passivation and eliminating antibiotic resistance genes in composts during vermicomposting of dewatered sludge and green waste. J. Environ. Chem. Eng. 2024, 12, 112201. [Google Scholar] [CrossRef]
  68. Liang, Y.; Yin, Q.; Jiang, Z.; Yan, H.; Nian, Y. Pollution characteristics and microbial community succession of a rural informal landfill in an arid climate. Ecotoxicol. Environ. Saf. 2023, 262, 115295. [Google Scholar] [CrossRef]
  69. Dia, A.; Lauga, B.; Davranche, M.; Fahy, A.; Duran, R.; Nowack, B.; Petitjean, P.; Henin, O.; Martin, S.; Marsac, R.; et al. Bacteria-mediated reduction of As(V)-doped lepidocrocite in a flooded soil sample. Chem. Geol. 2015, 406, 34–44. [Google Scholar] [CrossRef]
  70. Guo, J.; Zhang, Y.; Huang, H.; Yang, F. Deciphering soil bacterial community structure in subsidence area caused by underground coal mining in arid and semiarid area. Appl. Soil Ecol. 2021, 163, 103916. [Google Scholar] [CrossRef]
  71. Sun, X.; Song, B.; Xu, R.; Zhang, M.; Gao, P.; Lin, H.; Sun, W. Root-associated (rhizosphere and endosphere) microbiomes of the Miscanthus sinensis and their response to the heavy metal contamination. J. Environ. Sci. 2021, 104, 387–398. [Google Scholar] [CrossRef] [PubMed]
  72. de Chaves, M.G.; Silva, G.G.Z.; Rossetto, R.; Edwards, R.A.; Tsai, S.M.; Navarrete, A.A. Acidobacteria Subgroups and Their Metabolic Potential for Carbon Degradation in Sugarcane Soil Amended With Vinasse and Nitrogen Fertilizers. Front. Microbiol. 2019, 10, 1680. [Google Scholar] [CrossRef]
  73. Ke, T.; Zhang, D.; Guo, H.; Xiu, W.; Zhao, Y. Geogenic arsenic and arsenotrophic microbiome in groundwater from the Hetao Basin. Sci. Total Environ. 2022, 852, 158549. [Google Scholar] [CrossRef] [PubMed]
  74. Deng, R.; Tang, Z.; Hou, B.; Ren, B.; Wang, Z.; Zhu, C.; Kelly, S.; Hursthouse, A. Microbial diversity in soils from antimony mining sites: Geochemical control promotes species enrichment. Environ. Chem. Lett. 2020, 18, 911–922. [Google Scholar] [CrossRef]
  75. Liu, C.; Lin, H.; He, P.; Li, X.; Geng, Y.; Tuerhong, A.; Dong, Y. Peat and bentonite amendments assisted soilless revegetation of oligotrophic and heavy metal contaminated nonferrous metallic tailing. Chemosphere 2022, 287, 132101. [Google Scholar] [CrossRef]
  76. Wu, Y.-H.; Cheng, H.; Zhou, P.; Huo, Y.-Y.; Wang, C.-S.; Xu, X.-W. Complete genome sequence of the heavy metal resistant bacterium Altererythrobacter atlanticus 26DY36T, isolated from deep-sea sediment of the North Atlantic Mid-ocean ridge. Mar. Genom. 2015, 24, 289–292. [Google Scholar] [CrossRef]
  77. Liu, H.; Yuan, R.; Sarkodie, E.K.; Tang, J.; Jiang, L.; Miao, B.; Liu, X.; Zhang, S. Insight into functional microorganisms in wet–dry conversion to alleviate the toxicity of chromium fractions in red soil. Front. Microbiol. 2022, 13, 977171. [Google Scholar] [CrossRef]
  78. Dary, M.; Chamber-Pérez, M.A.; Palomares, A.J.; Pajuelo, E. “In situ” phytostabilisation of heavy metal polluted soils using Lupinus luteus inoculated with metal resistant plant-growth promoting rhizobacteria. J. Hazard. Mater. 2010, 177, 323–330. [Google Scholar] [CrossRef]
  79. Sujkowska-Rybkowska, M.; Banasiewicz, J.; Rekosz-Burlaga, H.; Stępkowski, T. Anthyllis vulneraria and Lotus corniculatus on calamine heaps form nodules with Bradyrhizobium liaoningense-related strains harboring novel in Europe symbiotic nifD haplotypes. Appl. Soil Ecol. 2020, 151, 103539. [Google Scholar] [CrossRef]
  80. Armendariz, A.L.; Talano, M.A.; Wevar Oller, A.L.; Medina, M.I.; Agostini, E. Effect of arsenic on tolerance mechanisms of two plant growth-promoting bacteria used as biological inoculants. J. Environ. Sci. 2015, 33, 203–210. [Google Scholar] [CrossRef]
  81. CNEMC (the Chinese Environmental Monitoring Centre). The Background Values of Soil Elements in China; China Environmental Science Press: Beijing, China, 1990. (In Chinese) [Google Scholar]
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Figure 1. Geochemical profiles and percentages of the exchangeable fractions of As (Asexe), Cd (Cdexe), Pb (Pbexe), and Zn (Znexe) in farmland soil (FS) and grassland soil (GS) from the restored mining area. The letters “a” and “b” are used to mark the significance of the difference between the different groups.
Figure 1. Geochemical profiles and percentages of the exchangeable fractions of As (Asexe), Cd (Cdexe), Pb (Pbexe), and Zn (Znexe) in farmland soil (FS) and grassland soil (GS) from the restored mining area. The letters “a” and “b” are used to mark the significance of the difference between the different groups.
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Figure 2. (A) Distributions of the microbial α-diversity index (Shannon index) in farmland soil (FS) and grassland soil (GS). The letters “a” and “b” are used to mark the significance of the difference between the different groups; (B) PCoA plot illustrating the Bray–Curtis distance for community beta diversity; (C) Random forest analysis was employed to predict the Shannon index in FS; (D) Random forest analysis was employed to predict the Shannon index in GS.
Figure 2. (A) Distributions of the microbial α-diversity index (Shannon index) in farmland soil (FS) and grassland soil (GS). The letters “a” and “b” are used to mark the significance of the difference between the different groups; (B) PCoA plot illustrating the Bray–Curtis distance for community beta diversity; (C) Random forest analysis was employed to predict the Shannon index in FS; (D) Random forest analysis was employed to predict the Shannon index in GS.
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Figure 3. The volcanic plots illustrate significantly enriched (purple circles) and depleted (green circles) OTUs, along with the distributions of enriched species (at the phylum level) in FS and GS.
Figure 3. The volcanic plots illustrate significantly enriched (purple circles) and depleted (green circles) OTUs, along with the distributions of enriched species (at the phylum level) in FS and GS.
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Figure 4. Microbial co-occurrence network in farmland soil and grassland soil. The network structural parameters (edges, average degree, closeness centrality, betweenness centrality, and clustering) in the two soil types are shown. Keystone OTUs and their absolute abundances in the two soil types are shown (the different OTU colors correspond to different phyla).
Figure 4. Microbial co-occurrence network in farmland soil and grassland soil. The network structural parameters (edges, average degree, closeness centrality, betweenness centrality, and clustering) in the two soil types are shown. Keystone OTUs and their absolute abundances in the two soil types are shown (the different OTU colors correspond to different phyla).
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Figure 5. Random forest analysis of keystone OTUs in FS as predicted by edaphic factors.
Figure 5. Random forest analysis of keystone OTUs in FS as predicted by edaphic factors.
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Figure 6. Distributions of enriched genera (top 50) in FS and GS (p < 0.05).
Figure 6. Distributions of enriched genera (top 50) in FS and GS (p < 0.05).
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Figure 7. Distributions of predicted microbial functions related to heavy metal metabolisms in FS and GS (p < 0.05).
Figure 7. Distributions of predicted microbial functions related to heavy metal metabolisms in FS and GS (p < 0.05).
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MDPI and ACS Style

Lan, X.; Liao, X.; Xiao, J.; Jia, Y.; Lin, W.; Huang, Z.; Ning, Z.; Xiao, T.; Xiao, E. Crop Cultivation Reshapes Soil Microbiomes to Drive Heavy Metal Mobilization in Restored Mining Areas. Agriculture 2025, 15, 804. https://doi.org/10.3390/agriculture15080804

AMA Style

Lan X, Liao X, Xiao J, Jia Y, Lin W, Huang Z, Ning Z, Xiao T, Xiao E. Crop Cultivation Reshapes Soil Microbiomes to Drive Heavy Metal Mobilization in Restored Mining Areas. Agriculture. 2025; 15(8):804. https://doi.org/10.3390/agriculture15080804

Chicago/Turabian Style

Lan, Xiaolong, Xinyin Liao, Jiaxin Xiao, Yanlong Jia, Wenjie Lin, Zhongwen Huang, Zengping Ning, Tangfu Xiao, and Enzong Xiao. 2025. "Crop Cultivation Reshapes Soil Microbiomes to Drive Heavy Metal Mobilization in Restored Mining Areas" Agriculture 15, no. 8: 804. https://doi.org/10.3390/agriculture15080804

APA Style

Lan, X., Liao, X., Xiao, J., Jia, Y., Lin, W., Huang, Z., Ning, Z., Xiao, T., & Xiao, E. (2025). Crop Cultivation Reshapes Soil Microbiomes to Drive Heavy Metal Mobilization in Restored Mining Areas. Agriculture, 15(8), 804. https://doi.org/10.3390/agriculture15080804

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