Heavy Metal Accumulation Affects the Structure of Microorganisms and Increases Abundance of Resistance Genes in Rare Earth Mining Areas


 Background

Environmental pollution from rare earth mining areas is of great concern, but the impact on microbial ecology and genomics has received little attention. In this study, the relationship between heavy metals and soil microbial community in the northern rare earth mining area was explored.
Methods

In order to study the detoxification mechanisms of heavy metals by microorganisms in this typical rare earth mining area, the study area was divided into three parts (mining area, residential area and control area). Analysis of microbial community diversity, structure and functional abundance using high-throughput sequencing techniques. Analysis of the effect of heavy metal pollution on the abundance of heavy metal resistance genes in soils of different regions using real-time fluorescence quantitative PCR.
Results

The results showed that the heavy metal pollution rules: mining area > residential area > control area. Under the condition of long-term heavy metal pollution, the original microbial community composition was changed, and the species richness and evenness of soil in mining areas were higher than that in residential areas. The high-throughput sequencing analysis showed that existed metal-resistant microbial communities such as Actinobacteria, Proteobacteria, Korarchaeota and so on under the stress of heavy metal. High concentrations of heavy metals can inhibit the activities of catalase and sucrase. According to Tax4Fun function prediction analysis, heavy metal accumulation increased the ABC transporter protein in microbial function. The results of fluorescence quantification experiments also demonstrated that the abundance of heavy metal resistance genes, czcA, czcB, czcC and czcD, encoding ABC transporter proteins, increased with increasing heavy metal concentrations.
Conclusions

In conclusion, the accumulation of heavy metals not only changed the soil physicochemical properties and the microbial community structure, but also decreased soil enzyme activities and increased the abundance of resistance genes, which activated the detoxification mechanism of heavy metals. which provided a reference for future ecological remediation.


Conclusions
In conclusion, the accumulation of heavy metals not only changed the soil physicochemical properties and the microbial community structure, but also decreased soil enzyme activities and increased the abundance of resistance genes, which activated the detoxi cation mechanism of heavy metals. which provided a reference for future ecological remediation.

Background
The over-exploitation of rare earth elements has caused serious deserti cation and environmental pollution, and in China, ecological restoration of mining areas is receiving increasing attention (Wei et al., 2019 ;Wang et al., 2020). In recent years, with the increase of rare earth mining and smelting production, heavy metal pollution of soil in the surrounding areas has become a growing concern and a worldwide problem (Rodriguez et al., 2009;Frossard et al., 2018). So far, mining activities, especially the mining of metal ores, is a major source of soil heavy metal pollution (Huang et al., 2018). Due to different mining activities and habitat speci cities, soil properties and heavy metal contents vary considerably over short spatial distances and elevation gradients (Zhao et al., 2019). Heavy metal pollutants caused by mining can be consumed by the human body through the food chain through soil, water, plants, etc., causing great harm to the human health (Liao and Xie 2007;Vinhal-Freitas et al., 2017). Soil contaminated with heavy metals causes changes in soil physicochemical properties and microbial activity, and microbial activity is more sensitive to heavy metals than animal or plant growth in the same soil, soil microbial biomass, soil enzyme activity and metabolic entropy are soil biological parameters (Liao and Xie, 2007), which are responsive to external conditions such as climate, human behavior and heavy metal pollution, and can re ect soil pollution status to some extent (Tang et al., 2019), It can be used as an effective index to evaluate the ecological impact of heavy metal pollution in soil (Ivshina et al., 2014). Heavy metal contaminants have been shown to be harmful to soil microorganisms, and soil heavy metal contamination may lead to signi cant changes in microbial diversity, structure and activity (Filip, 2002;Nacke et al., 2014). Microorganisms make an important contribution to the maintenance of terrestrial ecosystems and their biodiversity because the enzymatic activity of soil microorganisms and soil microbial biomass control the cycling and storage of nutrients in the soil (Li et al., 2017). However, the presence of heavy metals in the mining process brings great pressure to microorganisms, which must survive in a heavy metal contaminated environment, thus having a greater impact on microbially mediated soil nutrient cycling (Khan et al., 2010). Both the concentration of heavy metals in the soil and their biological effectiveness in uence the toxicity of heavy metals (Kenarova et al., 2014). Soil pH can in uence the sorption of metals by substances in the soil, such as organic matter, by altering the surface charge and dissociability of heavy metal sorbents, which in turn affects the bioeffectiveness and toxicity of heavy metals to microorganisms (Bang and Hesterberg, 2004). Heavy metal contamination reduces soil microbial biomass, diversity and biochemical activity due to negative selection of microorganisms sensitive to heavy metal pollutants and inhibition of microbial metabolic activity (Azarbad et al., 2016;He et al., 2016;Yao et al., 2017), hence, low microbial biomass and slower soil organic matter decomposition activity in heavy metal contaminated soils due to low microbial biomass, functional diversity and metabolic e ciency of heavy metal tolerant bacteria in soil microbial communities (Mergeay, 2000). Heavy metals, soil and bacteria interact in a complex manner, and soil microbial communities play an important role in determining soil quality and regulating soil physicochemical properties (Guo et al., 2017); Therefore, soil microbial activity is often considered as a sensitive and effective indicator of mine ecosystems (Liao and Xie, 2007;Liu et al., 2016).
Soil enzyme activity is often considered as a sensitive biological index to evaluate soil quality (Spohn and Kuzyakov, 2014). Studies have shown that redox enzymes and hydrolytic enzymes can be mainly used to evaluate heavy metal pollution. In general, high concentrations of heavy metals could degrade soil cells, destroy soil microbial communities, and inhibit soil enzyme activity (Ciarkowska et al., 2014). Also, catalase is able to break down H 2 O 2 and protect organisms from damage. In addition, catalase has been used as a bioindicator to detect the presence of various heavy metal contaminants (Xian et al., 2015;Hu et al., 2014). Sucrase activity re ects the ability of the soil to break down sucrose and free monosaccharides, which are the main source of energy for soil microorganisms (Frankenberger and Johanson, 1983). Thus, enzyme activity can be used to indicate improvements in the rehabilitation of soils after mining (Schimann et al., 2012). Enzymatic activities are also used for determining the effect of various pollutants including heavy metals on soil microbial quality (Shen et al., 2005;Khan et al., 2007). Studies have shown that heavy metals (Zn, Cu, Ni, V and Cd) in soil would reduce the activities of soil urease, alkaline phosphatase and xylanase (Spohn and Kuzyakov, 2014). It was also found that soil microorganisms polluted by lead-zinc tailing dams reduced urease activity. Enzyme activity varies with the presence of heavy metals, and it depends on different soil properties, heavy metal types and concentrations. Therefore, the integration of multiple enzymes broadly representing microbial metabolism into a comprehensive index is necessary to assess both the toxicity levels of heavy metals in soil microcrops and the ecological impact of heavy metal contamination in soil systems. Microorganisms in soil contaminated by heavy metals have strong adaptability and viability. The emergence of heavy metal resistance genes in complex microbial communities under heavy metal stress reveals the biological processes and strategies necessary for the survival of microorganisms in extreme environments (Xavier et al., 2019;Thomas et al., 2020). Many bacteria have evolved genetic adaptations to adapt to their environment and acquire metal resistance, multiple genes such as cadB, chrA, pbrA, MerA and NiCoT have reported systerms for bacterial resistance and detoxi cation, respectively for cadmium, chromium, lead, mercury and nickel, as well as in the involvement of transport of transition metals (Janssen et al., 2010;Das et al., 2016). In response to environmental pollution threatens the survival of microorganisms with various resistance mechanisms (Han et al., 2020), such as metal e ux pump mediated transport, metal produced by permeation barrier, the transformation of heavy metals by intracellular and extracellular enzymes and detoxi cation, this makes the microbes can by increasing the resistance mechanism of genes to expand their niche in heavy metal contaminated soil (Guo et al., 2018;Xi et al., 2021).
Due to mining activities, there are signi cant differences between heavy metal and physical and chemical properties in different regions (Kenarova et al., 2014), the microbial community structure will also be adjusted to adapt to different habitats. (Pérez-de-Mora et al., 2006; J Kozdroj, 2001). At present, the effects of tailing waste accumulation on the distribution of heavy metals and bacterial communities remain unclear. As a rare earth ore in the north, this region has a special habitat, it is of great signi cance to study the effects of rare earth mining on soil physical and chemical properties, heavy metals and soil bacterial communities. The present study aimed to (1) evaluate the effects of manganese (Mn), copper(Cu), lead (Pb) etc. on soil enzyme activities, microbial function, community diversity in rare earth mining areas; (2) assess whether these microbial characteristics can be used as possible indicators of soil pollution by heavy metals and (3) analyze the in uence of heavy metal pollution on the abundance of heavy metal resistance genes in soil in different regions.

Sample collection
The sampling area for this study was in the northern rare earth mining area and its surrounding areas. According to the general layout of the rare earths, it was divided into three parts, namely the mining area(MA), the residential area(RA) and the control area(CA), as shown in the Fig. 1. Three 20×20 m plots were established at each functional area and were considered true replicates in July 2019. There were 27 mining areas, 6 residential areas and 3 control areas. In detailed, at each sampling site, surface sediments(0-20 cm) were sampled in 3 points, the topsoil was collected from three random points by shovel at each plot and was placed into sterile centrifuge tube, immediately preserved with dry ice before being transported to the laboratory. Each sample was divided into three parts: one for DNA extraction and high-throughput sequencing of the soil microbes and stored in a refrigerator at -80°C, the other for chemical analysis stored at 4°C in the refrigerator for determination of soil enzyme activity, and the rest was air-dried for subsequent physical and chemical analysis.

Physical and chemical analyses
The pH was measured using a pH meter (PHS-3C, Shanghai INESA Instrument Co., Ltd., China), in 1:2.5 (soil/water) H 2 O suspensions after 1 h of shaking. The moisture content (MC) of the soil was determined by drying to a constant weight at 105°C ± 2°C in a drying oven, then weighing. Cation exchange capacity (CEC) was determined by calcium acetate exchange method. Oxidation-reduction potential (ORP) was tested in accordance with the "Determination of soil redox potential Potentiometric method" (HJ 746-2015). The Electrical conductance (EC) of soil samples were determined using a conductivity meter (DDS-II A) [1:5.0 (w/v) soil/water ratio]. The soil organic matter (SOM)was investigated by the K 2 Cr 2 O 7 colorimetric method (Jakobsen, 1998  concentrated HClO 4 and HNO 3 (v/v)). Each sample had three replicates, and each tested sample was measured three times, in order to calculate the mean value.

Typical enzymes activity experiments
Soil enzyme activities were measured using the soil enzyme activity kit, which mainly measured ve soil enzyme activities, namely, soil urease (UE), soil catalase (CAT), soil sucrose (SC), soil neutral phosphatase (NP) and soil alkaline phosphatase (ALP). Each index was measured for three repetitions in the same treatment (Gianfreda et al., 2005), and the enzyme activity was measured according to the instructions of the kit purchased (the kit was purchased from Nanjing Jiancheng Bioengineering Research Institute). , and a large number of reads were generated. In order to ensure the quality level of these reads and ensure subsequent analysis, sequencing quality control was required. First, stitching according to overlap, the sequenced joints and primers should be taken out. The low-quality data are then ltered to obtain the sequencing sequence available for subsequent analysis. OTU was screened with 97% similarity level, and each sample OTU was compared with the Silva database (Dickey et al., 2014). In order to obtain the species classi cation information corresponding to each OTU, RDP classi er was used for the taxonomic analysis of the OTU representative sequence.

Quantitative analysis of resistance genes
The normal PCR ampli cation of each functional gene was performed using the sample DNA as the template. Ampli cation products by agarose gel electrophoresis test, cut to strip, by DNA gel recovery kit back to collect pure, with cloning vector pGEM-T try agent box into the line of pure products of enzyme, and turned in sense the state cells Escherichia coli DH5α, blue white spot on the ampicillin at screen, choose positive clone (white spot) propagation as microbial sequencing analysis, further cloning identi cation results. Plasmids was extracted from the cultured liquid, its concentration was determined, and the copy number was calculated. Gradient dilution was performed by a tenfold gradient. Fluorescence constant PCR ampli cation was performed using the different concentration standard as the mold plate, and the standard curve was drawn. Different functional groups were quantitatively ampli ed by uorescence quantitative PCR. Quantitative PCR system: dye uorescence quantitative reagent(SYBR premix Ex Taq) 10 µl, each of the upstream and downstream primers at 20 µmol/L was 0.5 µl, DNA template 1.0 µl, and the double steamed water was supplemented to 20 µl(He et al., 2016).

Statistics and data analysis
All data were analyzed using SPSS version 16.0 and Origin2019b. According to the annotation results of all sample species, the structure of phylum and genus level microbial community was analyzed. Using the principal component analysis (PCA) to the original environment data matrix for dimension reduction, correspondence analysis (CCA) was discussed environmental factors signi cantly in uence microbial community structure (Magurran and Anne, 1988).
The differences in the microbial diversity index and enzymatic activity were compared using Analysis of Variance (ANOVA, IBM SPSS 24.0).

Results
Page 6/28 3.1 Heavy metal accumulation changes soil physicochemical properties in rare earth mining areas The accumulation of heavy metals in rare earth mining areas has affected the physicochemical properties of the soil, the differences of soil physical and chemical properties in each sample point are shown in Table 1. The soil pH ranged from 7.93 to 8.77 all was alkaline. Soil moisture content ranges from 3.97-18.93% Among all the sample points in the MA, the EC value of soil samples ranged from 86.67 µs/cm∼4723.33 µs/cm, the highest EC value was 5983.33 µs/cm in RA10, and the lowest EC value was 83.33 s/cm in control area DZ12. The ORP ranged from 282.67 mv to 331.67 mv, and there was no signi cant difference among the sampling points. The CEC of soil samples was 1.75 ~ 20.67 cmol/kg. The minimum value was 1.75 cmol/kg at MA8 in the MA and the maximum value was 20.67 cmol/kg at DZ12 in the CA. The content of SOM was signi cantly different, ranging from 3.33% to 22.65 %, the content of SOM in the sampling point MA7 was the highest, reaching 22.6 5%, and the content of organic matter in the comparison point DZ12 was the lowest, reaching 3.33 %. The TN content in the soil in the three regions ranged from the largest to the smallest as the CA > MA > RA, with a range of 0.35-1.87 g/kg.
The heavy metal contents of soil was determined at all sampling sites (Fig. 2

Heavy metals can affect soil enzyme activity
Soil enzyme activity varies with soil environment and structural categories, but the degree of response of each enzyme was also important for different soil environments (Fig. 3). In this study, catalase activity (Fig. 3A)  The range of alkaline phosphatase activity (Fig. 3D)

Correlation analysis between enzyme activity and environmental factors
The correlation analysis between soil enzyme activity and environmental factors was shown in Fig. 4 Among the heavy metals in the soil of the study area, Mn, Zn, Pb, and Cd had a great in uence on the enzyme activity. Catalase activity and sucrase activity were negatively correlated with Mn, Zn, Pb and Cd, with correlation coe cients between 0.68 and 0.8, respectively. Catalase and sucrase activities were signi cantly positively correlated with soil pH, moisture content, Eh, CEC, clay and content. The sucrase activity was negatively correlated with total nitrogen and granule. Urease activity had no signi cant correlation with soil heavy metal content, but had positive correlation with water content, pH and sand. Neutral phosphatase was positively correlated with total nitrogen and powder content, but negatively correlated with sand content. Alkaline phosphatase was positively correlated with silt content in soil and negatively correlated with sand content.

The richness and diversity of soil bacterial community changed in different regions
The composition of soil microbial community was analyzed by non-metric multidimensional scale analysis (NMDS) as shown in Fig. 5. The microorganisms in the RA10, RA11 show a discrete state with other samples, indicating that the two sites were less similar to the species in the remaining mining areas. The distance between the control point and the sampling point in the mining area was relatively close, indicating that the soil sample taken from the control point and the sample taken from the mining area have a closer species composition, which was less similar to the samples taken from the RA.
In order to investigate the diversity and structure of the microbial communities, we used Illumina high-throughput sequencing technology to sequence the 16S rRNA. The alpha diversity indices of the bacterial communities were shown in Table 2. From the perspective of community abundance, according to the analysis of the Chao index, the greater the Chao index, the greater the number of OTUs and the greater the number of sample species. In this study, the Chao index of the MA was greater than the RA and the CA, indicating that the MA has a higher species richness. Secondly, it can be seen from Shannon index and Simpson index that the diversity of microbial communities in MA was relatively high, which was quite different from RA. As far as the points in the MA were concerned, the heavy metals at points MA1, MA2, and MA3 in the MA were generally higher than those at points MA4, MA5, and MA6 in the MA. In terms of microbial diversity and abundance, the less polluted MA4, MA5, and MA6 points Shannon index and Simpson index are higher than MA1, MA2, MA3. This also proves that as the content of heavy metals increases, the microbial diversity in the soil will gradually decrease.

Heavy metals can change the composition of soil bacterial community
After quality ltering, high quality sequences of bacteria 16S rRNA V3∼V4 were obtained from 12 soil samples in the mine area. Subsequently, a total of 266167 bacterial operational taxonomic units (OTUs) were assembled at a 97% con dence interval. In total, we extracted 79 identi ed phyla from all soil samples (Fig. 6). At the phylum level, Actinobacteria, Proteobacteria, Chloro exi and Thaumarchaeota were the most abundant phyla in mine soil. In addition, Actinobacteria were the most dominant phyla in the soils of MA and CA, the RA signi cantly enhanced the relative abundances of Proteobacteria compared to mine. Proteobacteria have become the most dominant group of bacteria in the RA of RA11 and RA12, The proportion of Actinomycetes was the largest in other mining areas and control points. In heavy metal highly polluted sediments, the abundance of Proteobacteria was relatively low. Secondly, some ora such as Korarchaeota, LCP_89, RsaHF231, etc. were not detected in RA and CA, but only in the MA, it might be due to that the long-term heavy metal pollution, speci c resistant bacteria appeared, changed the original microbial community composition.
The genus level analysis of microorganisms in the mine area was shown in Fig. 7, Thiobacillus and Luteimonas had the highest abundance in RA11 of the RA, with an average abundance of 22.43% and 23.54%, respectively, which were signi cantly different from the microbial communities in the MA and CA. The highest abundance of Solirubrobacter, Nocardia and Rubrobacter were found in the soil samples from the MA.

Relationship between microbial abundance and environmental parameters
Spearman order correlation was used to analyze correlations between environmental parameters and microbial abundance (Fig. 8). According to the analysis of the correlation between heavy metals and soil microbial community (phylum), it was found that Cyanobacteria and Chloro exi were positively correlated with Mn (P < 0.05). Chloro exi has a signi cantly positive relationship with Zn (P < 0.05), Zn was negatively correlated with Entotheonellaeota, thus inhibiting the growth of Entotheonelleota. Pb, Cd were positively correlated with Cyanobacteria Patescibacteria, Chloro exi (P < 0.05), signi cantly negative correlated with Entotheonellaeota (P < 0.05), inhibited the growth of the Entotheonellaeota phylum. There are two bacteria that are negatively associated with Hg, including Unclassi ed and Armatimonadetes.

Heavy metals can alter the function of microorganisms
Functional contributions of bacteria in soil samples from different regions were predicted based on OTU levels through the Tax4Fun database. From Fig. 9, it can be seen that ABC transport proteins and two-component regulatory systems accounted for the largest proportion of the predicted metabolic functions. The points MA7 and MA8, which were the most contaminated with Pb and Cd among all points, compared with the control points. It's worth noting that ABC transport proteins, a class of transporter protein, was higher in MA7 and MA8 than in the control site DZ12 in the heavily contaminated mine.

Heavy metals can increase the resistance of resistance genes.
Under the stress of heavy metals, the emergence of heavy metal resistance genes in complex microbial communities, microorganisms have a strong ability to adapt and survive in heavy metal contaminated soils. From the previous predictions of microbial metabolic functions, it was found that the ABC transporter proteins and two-component regulatory systems accounted for the largest proportion. Therefore, we selected czcA, czcB, czcC, and czcD genes belonging to the ABC transporter proteins to investigate the abundance of soil heavy metal resistance genes. The gene abundances of czcA, czcB, czcC and czcD were shown in Fig. 10. By comparing the three points of MA7, MA8 and RA11, the sample sites MA7 and MA8 had higher gene abundance of czcA, czcB, czcC and czcD than RA11 which were high concentration of heavy metals by heavy metals Zn and Cd, the gene abundances of czcA, czcB, czcC and czcD are higher than those of RA11. The gene abundance of czcA at the MA8 site was 0.4 times that of RA11 in residential areas, and the gene abundance of czcB at the most polluted MA8 site was 0.3 times of RA11 in residential areas. Gene abundance increases with increasing heavy metal concentrations, and through the outside to prevent czcA metal cation to enter the cell and was located in the outer membrane of regulon czcC relative expression of genes in the residential area were greater than the heavy metal pollution of mine, It was speculated that the studied area was a rare earth mining area, rare earth elements in the soil accumulation amount was larger, It interfered with the expression of czcA and czcC genes. The results showed that in the presence of a large number of Zn and Cd ions, the strains could adjust the expression of a large number of resistance genes in order to cope with the environmental changes.

Signi cant differences in soil physicochemical properties and heavy metal distribution between regions
Due to the frequent mining activities, heavy metal pollution of the mine soils is severe (Shu et al., 2003). Once these toxins enter agricultural soils, they could affect food production and security and pose a major threat to human health by entering the food chain (Guo et al., 2017). We assessed the ecological risks caused by these heavy metals, and investigated the structure and diversity of bacterial community under heavy metal pollution in this environment (Ma et al., 2020). Due to mining activity, the soil around rare earth mining area has been severely polluted not only with Mn and Cu, but also with Zn, Pb, Cd and Hg. The content of Mn, Pb and Cd of heavy metals reached the highest in MA8 of the mining area, which might be caused by the low terrain of the sampling site. The main wind direction of rare earth mining area was the northwest wind, and the pollutants in the mining area migrate and accumulate along the wind direction. The pollution rules of the six heavy metals were all: MA > RA > CA. The heavy metal content in the MA was signi cantly higher than that in the residential area and the control area due to the in uence of arti cial mining activities, mining industry tra c and so on. In the accumulation of heavy metal elements in the soil, Mn, Zn content was the highest. The soil in the rare earth mining area was polluted by elements Cd, Pb and Mn to different degrees, and showed a de nite accumulation of pollution, which was consistent with the previous investigation on the pollution status of the mining area (Fu et al., 2016).
The correlations between heavy metals and some soil properties, such as CEC and SOM, were signi cant, which may due to the soil substances limiting the transfer of heavy metals. Organic matter content was an important part of soil, which provided nutrients and energy for microbial life activities (Aikpokpodion, 2010). The content of soil organic matter can re ect the level of soil fertility. The content of organic matter in the sampling point MA7 in the mining area was the highest, reaching 22.65%. The high content of organic matter in the soil would affect the absorption of heavy metals in the soil. The high content of organic matter was conducive to the adsorption of heavy metals in soil. The content of organic matter in the control point DZ12 was the lowest of 3.33%. This sampling point was less polluted by mining, less polluted in soil, has higher microbial activity in the soil, and more able to decompose organic matter, so the content of organic matter in the soil was relatively low. Soil structure, moisture and electrical conductivity also have essential effects on the migration of heavy metals in soil. Cation exchange capacity was the main source of soil buffering performance and the important basis for soil improvement and rational fertilization (Kelly et al., 1998;Wucheng, 2008). The smallest cation exchange capacity values for the soil samples were at sample site MA8, which is largely devoid of vegetation and heavily contaminated with heavy metals. The soil fertility at this site is poor. The maximum value was at sample point DZ12 in the control area, which was less affected by mining activities and less polluted by heavy metals. Therefore, the soil has a strong ability to retain fertilizer.

Heavy metals changed the activity of enzymes in the soil
Soil enzymes are considered as potential indicators of bacterial function and are closely related to soil biology and soil characteristics (García-Ruiz et al., 2008;Huang et al., 2019). In the soil of the study area, the heavy metals Cu, Zn, Cd and Pb had a great in uence on the enzyme activity. Sucrase and catalase activities were lower in areas with high concentrations of heavy metals, while these two enzymes were signi cantly and negatively correlated with Mn, Zn, Pb and Cd in the soil, indicating that high concentrations of heavy metals inhibit enzyme activity (Borowik et al., 2014). Soil pollution levels and human activities will affect the migration and transformation of Pb in soil, thus affecting its bioavailability and biotoxicity. The activity of catalase and sucrase was similar, and will decrease with the increase of heavy metal concentration. These results were consistent with those reported by Belyaeva et al (Belyaeva et al., 2005).
When the concentration of heavy metal exceeds a certain concentration, Pb, Cu and Zn ions can directly bind to the enzyme or substrate in the soil, inhibiting the enzyme activity in the soil. In some sites, the content of organic matter in the soil was low, and the adsorption of gold ions was weak. Pb 2+ was easy to bind to the enzyme free in the soil, and react with the active protein, such as to combine with mercapto to form metal sul des, or to combine with the substrate to form complexes, thus to mask the binding site of the enzyme, and nally to inactivate or inhibit the enzyme activity (Dick, 1997;Roscoe et al., 2000).

High concentration of heavy metals affected soil microbial community structure
In order to study the richness, uniformity, diversity and sequencing coverage of each sample, diversity analysis was performed on the samples ( Jennifer L et al., 2004). Long-term heavy metal pollution results in the change of soil microbial community structure, as well as the decrease of diversity and abundance (Xi et al., 2021). Soil microorganisms have a weak response to low concentration of heavy metal pollution, and that exceeding the tolerance concentration of microorganisms will lead to the decrease of microbial population (Vig et al., 2004). In this study, the microbial abundance and multisampling of RA11 samples in the RA were both small, but the microbial abundance and diversity of MA4, MA5 and MA6 samples were relatively high. This may be due to the presence of certain resistant bacteria in the soil under the stress of long-term heavy metal pollution, which improved the microbial diversity (Choi and Journal, 2009). In addition, the stress of heavy metal Cd can lead to the disappearance of part of the ora and the emergence of speci c resistant strains, which changes the composition of the original microbial community (Bartolomé et al., 2016). The heavy metals at MA1, MA2 and MA3 in the MA were generally higher than those at MA4, MA5 and MA6 in the MA. In terms of microbial diversity and abundance, the Shannon index and Simpson index of the lightly polluted MA4, MA5 and MA6 samples were all higher than those at MA1, MA2 and MA3. This also proves that the microbial diversity in soil decreases gradually with the increase of heavy metal content. Analysis at the microbial phylum level and genus level revealed that the abundance of the Actinobacteria was higher in the MA, while the abundance of the Proteobacteria was higher in the RA, and the corresponding abundance of the Solirubrobacter, Nocardia and Rubrobacter were higher in the MA than in the RA, and it can be concluded that heavy metals changed the microbial community structure in the area.Through the correlation analysis the negative effects of heavy metals on microbial community structure have been widely con rmed (Åkerblom et al., 2007;Zhang et al., 2015). Heavy metals may change the microbial community structure to a certain extent by destroying the cell structure, such as destroying chromosome replication and DNA synthesis, and then affecting nucleic acid metabolism (Zhang et al., 2015;Yan, 2020). Obviously, copper concentration is related to Methylococcales (phylum: Proteobacteria;Class:Gammaproteobacteria), This may be because Cu 2+ plays a key role in the nature and expression level of methane monooxygenase, and therefore plays a key role in the bacterial structure of methane-oxidizing bacteria (Semrau et al., 2010;Sara et al., 2016). Through the above understanding, aiming at the negative correlation of heavy metals to microorganisms and changed in microbial species in this study, it was proved that heavy metals have certain in uence on the microbial community structure.

Heavy metals promote certain functions of microorganisms
Heavy metals have an important in uence on microbial function (Liu et al., 2018;Singh et al., 2019). ABC transporter and two-component regulatory system accounted for the largest proportion in metabolic function prediction. MA7 and MA8 points with the heaviest Pb and Cd pollution among all the points were selected for comparison with the control points. It was found that MA7 and MA8 of ABC transporters were greater than the control point DZ12 in the heavily polluted mining area, indicating that under the stress of high concentration of heavy metal ions, ABC transporters and the two-component regulatory system responded simultaneously and made corresponding adjustments to make a large number of ABC proteins expressed, which together eliminated the harmful substances such as heavy metals from the body (Liu et al., 2018). The two-component regulatory system enables bacteria to sense, respond to, and adapt to a wide range of environments, stressors, and growth strips. Therefore, when the concentration of heavy metal ions in the outside world increases, the proportion of the corresponding bicomponent system of microorganisms increases, thus further adapting to the complex environment(Wu et al., 2019).

Heavy metals affected the expression of resistance gene
Through quantitative analysis of heavy metal resistance genes, it was found that the abundance of resistance genes was higher in the sites with heavy metal pollution. In order to further study the in uence of heavy metal resistance genes, the correlation analysis between heavy metal resistance genes and environmental factors showed that czcD gene was signi cantly correlated with Zn, and signi cantly positively correlated with Cu. Therefore, when the content of zinc and copper in the environment increased, czcD abundance, the regulatory gene of resistance gene in soil, was signi cantly promoted. The relative expression of czcD gene abundance was greater in the MA than in the RA, indicating that under the stress of heavy metal ions at high concentrations, czcD resistance gene was abundantly expressed to regulate czcA and czcB, and the metal ions were transported through the metal transport mediated by the e ux pump. to the outside of the cell to complete the detoxi cation process of the soil. CzcD was involved in the regulation of the CZC system (Powell et al., 1994). It was a membrane-bound protein with at least four transmembraneheliums and was a subfamily member of the CDF protein family (Veglió et al., 1997). The role of czcD gene was mainly to regulate the heavy metal resistance of czcA and czcB (Fisher, 1985). Therefore, czcD would increase under high concentration of Zn and Cu stress to resist environmental changes.

Conclusions
After a long period of mining activity, this rare earth mine has a special habitat and the environment around the mine has been heavily contaminated with heavy metals. The content of heavy metals in the soil around the mining area was increasing and the ecological risk will be higher and higher. It was found that high concentrations of heavy metals inhibited catalase and sucrase activity, but promote the activity of phosphatase. Under the condition of long-term heavy metal pollution, major microbial communities such as Actinobacteria and Proteobacteria have appeared in the MA, and only Korarchaeota, LCP_89, RsaHF231 and other resistant strains have been found in the MA. It was found that heavy metal accumulation could increase ABC transporter proteins in microbial functions by high-throughput sequencing. czcA, czcB, czcC, and czcD genes belonging to ABC class of transporter proteins were selected for soil heavy metal resistance gene abundance survey, and it was found that czcA, czcB, czcC, and czcD had higher gene abundance in heavy metal contaminated sites. Therefore, the method of analyzing the microbial community to evaluate the toxicity of heavy metals is very promising. However, we need to account for the direct impact of the soil properties on microorganisms before this method can be applied widely.

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Competing interests